763 results about "Sparse matrix" patented technology

The invention discloses a big language model reasoning optimization method and system based on dynamic video memory compression and memory isomerism, and intelligent management of video memory resources is realized by integrating a dynamic compression strategy of KV Cache and a memory parallel architecture. The method comprises the following steps: 1) analyzing the spatial-temporal characteristics of the KV Cache in real time, adaptively selecting a quantization compression algorithm, a rarefaction algorithm or a low-rank decomposition algorithm, performing hierarchical storage based on attention head importance scores, keeping high precision of a core head, and implementing low-bit quantization on a secondary head; (2) the compressed inactive data are divided into a plurality of data blocks to be stored in a system memory, a parallel data channel group is established according to the number of physical channels, the compressed blocks are concurrently read through multiple channels during loading, and parallel decompression of a sparse matrix is accelerated through a GPU tensor core; and 3) constructing a KV Cache multiplexing mechanism and a parallel channel, and parallelizing a compression / decompression process and model calculation by adopting a hardware acceleration compression and asynchronous pipeline mechanism.

The invention discloses an APT attack path reconstruction method based on time sequence diagram comparison clustering and a medium. A security event standardized data set is obtained; mapping each security event into a multi-modal node through a heterogeneous time sequence diagram set construction method, and generating a directed edge to construct and complete a heterogeneous time sequence diagram set; a stage embedding time sequence diagram set is obtained through the joint attack stage set; outputting a time sequence diagram similarity matrix by adopting a multi-scale diagram similarity algorithm of time alignment perception; generating an event semantic sparse matrix based on the threat intelligence knowledge graph; obtaining an image clustering result set; uncertain samples in the graph clustering result set are obtained and processed, and an APT attack path reconstruction result is obtained. The problem that an APT attack path reconstruction method mainly depends on rule matching, single-dimensional feature comparison and manual analysis of security logs or alarm streams is solved, the interpretability of APT traceability is greatly enhanced, and subjective errors of manual research and judgment are reduced.

The invention relates to the technical field of audio processing, and discloses an audio processing method and system for speech spectrum reconstruction, and the method comprises the steps: carrying out the preprocessing of an original speech signal collected by a vehicle-mounted microphone, and obtaining a speech signal time-frequency matrix; performing singular value decomposition on the voice signal time-frequency matrix to obtain a sparse matrix representing voice components and a low-rank matrix representing noise components, and establishing a frequency band characteristic incidence matrix through a mutual information entropy calculation method; inputting the sparse matrix into a selection forgetting extreme learning machine for voice feature prediction to obtain predicted voice feature data; and based on the predicted voice feature data and the frequency band feature incidence matrix, performing voice probability calculation and feature interpolation on each time frequency point by adopting a logarithmic frequency spectrum amplitude calculation method to obtain reconstructed frequency spectrum data, thereby improving the accuracy of voice recognition and meeting the requirement of real-time processing in a vehicle-mounted environment at the same time.

The invention relates to a matrix double linked list compression storage computing device which comprises a definition module used for defining a plurality of register variables to represent double linked list compression storage of a matrix; the matrix data element processing module is used for deleting, inserting and / or covering matrix data elements in the Jacobian matrix; the matrix operation module is used for executing multiplication calculation of the Jacobian matrix and the column vector when the Jacobian matrix is a sparse matrix; and the matrix block resource allocation calculation module is used for calculating a product result component based on the matrix blocks of the Jacobian matrix when the Jacobian matrix is a non-sparse matrix, and the product result component is a result obtained by dividing a product result of the Jacobian matrix and the column vector according to row blocks. According to the method, the matrix operation speed during load flow calculation can be increased, consumption of storage resources on a chip is reduced, and the matrix calculation efficiency and the resource utilization rate are improved.

The invention relates to the technical field of performance simulation analysis, in particular to a performance simulation method of a power module. The method comprises the following steps: acquiring initial physical characteristic data and initial electrical characteristic data of a power module; the initial physical characteristic data and the initial electrical characteristic data are preprocessed and simulated, and physical field distribution characteristics in the power module are obtained; performing multi-physical field simulation analysis on the power module based on physical field distribution characteristics in the power module by using an adaptive grid generation algorithm and sparse matrix solution to obtain simulation result data; performing parameter adjustment and optimization on a simulation model of the power module according to the simulation result data to generate optimized simulation model parameter data; and according to the optimization simulation model parameter data, performing simplification processing on the design of the power module to establish a simplified model. Through multi-stage simulation and optimization, the performance and the control precision of the power module can be remarkably improved, and the stability and the efficiency of the system are improved.

The invention belongs to the technical field of geophysical exploration. The invention provides a transient electromagnetic data three-dimensional inversion method, a transient electromagnetic data three-dimensional inversion system, a medium, a product and transient electromagnetic data three-dimensional inversion equipment. Three-dimensional regularization inversion is carried out by taking a minimum sum of a product of a model constraint term and a regularization parameter and a data fitting term as an objective function. The step of obtaining the sensitivity matrix in the gradient of the target function comprises the steps of solving a three-dimensional forward modeling electromagnetic field full-discrete equation to obtain a forward modeling field, constructing an adjoint forward modeling electromagnetic field full-discrete equation with a coefficient matrix being a low-order three-diagonal matrix, solving the adjoint forward modeling electromagnetic field full-discrete equation to obtain a calculated adjoint field, and calculating the sensitivity matrix in the gradient of the target function. And obtaining a sensitivity matrix according to the forward modeling field and the adjoint field. According to the method, the coupling electromagnetic field is decomposed by using a direct splitting strategy, and the large sparse matrix is reconstructed into a series of low-order tridiagonal matrixes, so that only the low-order tridiagonal matrixes need to be solved in a forward modeling process or sensitivity calculation, and the three-dimensional inversion speed is remarkably increased.

The invention provides an all-silicon carbide double-sided heat dissipation module packaging method based on gradient thermal resistance optimization, and belongs to the technical field of electronic component packaging. Establishing a thermal resistance characteristic curve, and calculating a gradient thermal resistance vector to determine a heat flow path; constructing a heat dissipation temperature distribution matrix; obtaining thermal diffusion data; heat dissipation path thermal resistance is calculated by combining the thermal diffusion matrix and the temperature distribution matrix, and a thermal resistance sparse matrix is established and is dimensionally reduced into a thermal resistance low-dimensional matrix; calculating an optimal heat dissipation path parameter set; inputting the parameter set into a thermal resistance prediction deep attention network model for verification and fine tuning, and generating a final thermal resistance distribution scheme; a module structure and a material ratio are designed according to the scheme to form a sandwich structure; a silver paste sintering process is adopted to realize low thermal resistance connection and complete packaging, and the technical problems of non-uniform thermal resistance distribution and difficulty in optimizing a heat flow path in a double-sided heat dissipation structure of a silicon carbide power module in the prior art are solved.

The invention relates to an accelerator and an acceleration method based on Top-K sparse moment vector multiplication. The accelerator comprises a first acceleration unit and a second acceleration unit. The first acceleration unit is used for data preprocessing and sparse matrix coding, and the second acceleration unit is used for executing Top-K sparse matrix vector multiplication so as to solve the process of the first K results with the maximum values in the operation results of multiplication of a sparse matrix and a dense vector; wherein the first acceleration unit comprises a preprocessing module and a coding module; the data preprocessing step of the preprocessing module comprises quantization, sparse reconstruction and shuffling; the broadband demand of data transmission is reduced based on the quantization step of the sparse matrix; pruning non-zero elements which have small influence on sorting based on a sparse reconstruction algorithm; carrying out shuffling operation on the sparse matrix after sparse reconstruction so as to avoid a Top-K aggregation phenomenon; and the coding module is used for recoding the preprocessed sparse matrix. The method has the remarkable effects that the end-to-end performance is improved by 153.3 times, 2.5 times and 3.4 times respectively.

The invention provides a process window optimization method and device, a medium, a program product and a terminal, and solves the problems of low light intensity contrast ratio, limited process window and high finished product defect rate of weak point and dead pixel areas through an objective function optimization and numerical programming solution method. And establishing a reasonable optimization model by taking the sum of squares of light intensity logarithmic slopes of weak and dead pixel areas as an optimization target and combining an edge section moving range, an edge position error and mutual influence factors. And meanwhile, a plurality of matrix methods such as a sparse matrix and a dense matrix are introduced, so that the optimization efficiency and precision are improved, the accurate optimization of the mask pattern is realized, the process window is expanded, the defect rate is reduced, the process stability and adaptability are enhanced, and the production efficiency and the product quality are remarkably improved.

The invention relates to the technical field of geophysical exploration, and particularly provides a three-dimensional finite element transient electromagnetic forward modeling calculation method based on an iterative solution method.The method comprises the steps that tetrahedron space discretization is carried out based on a vector finite element method, and an electric field is given to six edges of a space tetrahedron; constructing a back-pushing Euler finite element equation set, and assembling a linear system formed by a large sparse matrix based on the equation set; performing iterative solution on the linear system based on an FGMRES algorithm to obtain an electric field value corresponding to a spatial tetrahedron unit edge; optimizing a spatial tetrahedral mesh based on a quadratic nesting method, and optimizing a time step length based on a third-order backward Euler difference format; a grid pre-encryption method is adopted, the calculation error of iterative solution is reduced, a region decomposition method is used for achieving parallel calculation, and the calculation efficiency is greatly improved. According to the method, the requirements of high precision and high efficiency in three-dimensional transient electromagnetic forward modeling calculation can be met, and an advanced calculation tool is provided for resource exploration in a complex geological environment.

The invention provides an efficient privacy set intersection method and device based on bucket coding, and the method comprises the steps: obtaining an input set and a target value set, the input set comprises a private set of a sender and a private set of a receiver, and the target value set is generated based on the private set of the receiver; encoding the private set of the receiver into a sparse matrix composed of a plurality of buckets through a hash function; determining a linear system of the sparse matrix; performing ascending sorting and re-labeling on the linear system according to the initial bit position set to obtain a target linear system; according to a bucket-based Gaussian elimination algorithm, based on a target linear system, through exclusive-or operation, determining oblivious key value pairs for storage; an intersection of the private set of the sender and the private set of the receiver is determined based on the oblivious key-value pair storage according to an oblivious linear evaluation protocol. According to the invention, communication resources required by privacy set intersection in an actual production environment can be further reduced.

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.

The disclosure describes hardware compression of sparse matrix content. One embodiment provides a graphics processor, the graphics processor comprising: a base die, the base die comprising a plurality of chiplet slots; and a plurality of chiplets, the plurality of chiplets being coupled with the plurality of chiplet slots. At least one chiplet of the plurality of chiplets comprises: a graphical core cluster comprising a plurality of processing elements; a shared local memory coupled with the plurality of processing elements; a plurality of matrix engines coupled to the shared local memory; and codec circuitry coupled with the shared local memory and the plurality of matrix engines. Codec circuitry is configured to decode matrix data stored in a first format in a shared local memory into a second format for consumption by a plurality of matrix engines.

The invention discloses a sparse matrix processing method and device in an image recognition process, equipment and a medium, and relates to the field of computer image processing, and the method comprises the steps: inputting a feature image into a convolutional neural network; if the convolution kernel meets a preset sparse matrix condition, performing structured sparseness on the convolution kernel to obtain a sparse matrix, compressing the sparse matrix by using an improved compression sparse row format to obtain a compression matrix, and constructing an index matrix of the compression matrix; storing the feature image, the compression matrix, the index matrix and the preset bias matrix in a global memory, activating a target thread bundle in the computing core, and distributing the target thread bundle to a target data processing channel; loading the compression matrix and the index matrix to a shared memory corresponding to the target data processing channel by using the target thread bundle, and loading the feature image and a preset bias matrix to a register file; and executing the operation instruction to read the current matrix operation data from the shared memory and the register file to the matrix calculation unit to complete the matrix multiplication and addition operation.

A systolic array can implement an architecture tailored to perform matrix multiplications on sparse matrices. Each processing element in the systolic array may include a register configured to store a value, and a multiplexor configured to select an input element from multiple input data buses based on metadata associated with the value. Each processing element may also include a multiplier configured to multiply the selected input element with the value to generate a multiplication result, and an adder configured to add the multiplication result to a partial sum input to generate a partial sum output.

The invention discloses a document comparison method based on an improved LCS algorithm, and relates to the field of data processing, and the method comprises the steps: receiving N to-be-compared documents uploaded by a user; dividing the N documents into N groups, wherein each group comprises a main document and N-1 auxiliary documents; setting a minimum common subsequence length threshold value L0; for each group, constructing a virtual two-dimensional array, taking the character number of the main document as the row number, taking the character number of the auxiliary document as the column number, adopting a sparse matrix for storage, and only recording matched character positions; traversing the elements of the first row and the first column row by row from the elements of the last row and the last column of the virtual two-dimensional array along the main diagonal direction of the two-dimensional array, and recording the content of continuous matching characters and the starting positions and the ending positions of the continuous matching characters in the main document and the auxiliary document; in view of low comparison efficiency of tobacco purchasing files in the prior art, optimization strategies such as document grouping, multi-thread parallel comparison and clustering combination are introduced, so that the comparison efficiency is improved.

The invention relates to the technical field of computing resource scheduling, and discloses a computing resource allocation method, device and equipment and a medium, and the method comprises the steps: constructing a global data set in a long-tail scene; a high-dimensional sparse matrix is constructed based on the global data set, the high-dimensional sparse matrix comprises a plurality of data dimensions, feature dimension reduction is performed based on the data dimensions to obtain a feature matrix after dimension reduction, and a feature sub-matrix is generated based on the feature matrix after dimension reduction; evaluating the feature sub-matrix, and determining the priority of the feature sub-matrix in the current scene based on an evaluation result; allocation of computing resources is adjusted based on the priority. According to the method, the vehicle-mounted system can preferentially process high-priority long-tail scenes under the condition that resources are limited, the processing efficiency is high, resource waste and processing delay are avoided, and the efficiency of the whole data processing chain can be optimized, so that the vehicle-mounted system has certain flexibility and coping capacity when facing complex and rare long-tail scenes.

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 multi-mark image classification method and device based on saliency features and a readable medium, and the method comprises the steps: constructing a regression frame based on a feature selection matrix, a feature space matrix and a pseudo-mark matrix, constructing a fitting model in combination with a traction matrix, a non-negative mark sparse matrix, a mark space matrix and the pseudo-mark matrix, constructing a mark correlation model through the pseudo mark matrix; constructing a nuclear norm model of the feature selection matrix to construct an objective function, and solving the objective function to obtain a final optimized feature selection matrix; determining an image feature subset based on the finally optimized feature selection matrix, and training an image classification model based on the image feature subset to obtain a trained image classification model; and the features corresponding to the image feature subsets in the image are input into the trained image classification model to obtain an image classification result, so that the correlation between category marks is learned, and the influence of low-density marks on feature selection is reduced.

The invention relates to a hierarchical optimization pruning method and device for electric power multi-modal defect detection and a medium, and the method comprises the steps: collecting multi-modal data in the defect detection of electric power equipment, inputting the multi-modal data into a pre-training defect detection model, carrying out the feature vector extraction of each modal, and carrying out the perception strategy based on a Hessian matrix, and obtaining the weight importance of each modal; calculating the L1 norm of each channel weight in the pre-trained defect detection model, evaluating the channel importance of the defect detection model, determining a redundant channel according to the L1 norm and cutting off the redundant channel, comparing the weight importance with a preset threshold, marking the weight importance lower than the preset threshold as a pruning object, generating a pruning mask matrix, and performing pruning on the pruning object; performing pruning operation on the pre-trained defect detection model through the pruning mask matrix; and sparse matrix representation optimization is carried out on the pruned model, and retraining is carried out. Compared with the prior art, the method has the advantages of enhanced adaptability, enhanced stability, high resource utilization rate and the like.

The invention provides a multi-LoRA large language model deployment system based on a cloud computing platform. A cloud computing AI platform layer trains a LoRA adapter matched with a basic large language model for a reasoning process; the multi-LoRA dynamic loading layer dynamically switches LoRA adapters needing to be mounted according to the request parameters, GPU optimization configuration is carried out according to service priorities in the request parameters, a basic large language model is loaded, and a plurality of LoRA adapters stacked in a sparse matrix form are mounted; and the resource scheduling optimization layer responds to the resource scheduling application, and outputs the request parameters subjected to priority management and hardware sensing optimization to the LoRA dynamic loading layer based on the container arrangement platform. According to the method, LoRA and container arrangement are deeply fused, and an automatic assembly line of training and reasoning is achieved. A video memory sharing mechanism enables a plurality of service scenes to share the same basic large language model, a LoRA adapter is loaded as required, and video memory occupation is greatly reduced. Full-life-cycle management from data preparation to model service is supported, and the large model deployment cost is remarkably reduced.

Embodiments are provided for converting a bit mask from a compressed format to an extended format. The bit mask is stored in a storage register in a compressed format. The compressed format does not include placeholder bits. The compressed format includes a single bit defining each element of a vector. The sizes of the elements can vary according to data types, for example, each element has 4, 8 and 16 bytes. In a single microoperation, the bitmask of the compressed format is converted to an extended format. The extended bit mask may be written to a predicate register. The extended bit mask includes a plurality of placeholder bits (the placeholder bits are not used in computation). The size of the extended bit mask corresponds to the size of the definition vector, including a plurality of elements of the data type. The extended bit mask may be used to execute an instruction. For example, a vector processor multiplies a sparse matrix with the vector using the extended bit mask.

The invention provides a sparse triangle solution parallel optimization method and system based on a GPU, and relates to the technical field of high-performance parallel computing. Obtaining a to-be-solved linear equation set; decomposing a sparse coefficient matrix in the system of linear equations to obtain a triangular sparse matrix; based on the triangular sparse matrix, sparse triangular parallel solving is carried out, and a final solution is obtained; wherein the sparse triangle parallel solution is characterized in that the hierarchy of each non-zero element is determined according to the dependency relationship among the non-zero elements, sub-tasks executed in sequence are divided based on the hierarchy, parallel solution is carried out by adopting the non-zero elements in the same hierarchy in a single sub-task, and synchronous solution is carried out among blocks among different hierarchies; obtaining a final solution through the kernel of the GPU; according to the method, the multi-thread parallelism of sparse triangle solution on the GPU can be further improved, and the problems of load imbalance and low resource utilization rate can be effectively avoided.

The invention discloses a millimeter-wave radar respiration and heart rate synchronous monitoring method based on sparse space-time constraint, which comprises the following steps: acquiring an echo pulse signal of a millimeter-wave radar, obtaining a respiration signal and a heart rate signal, and obtaining a sampling echo signal matrix; decomposing the signal matrix into a low-rank matrix and a sparse matrix, introducing a total variation regularization item, and establishing a joint optimization objective function; and performing iterative optimization on the target function to obtain a low-rank matrix, a respiratory signal sparse matrix and a heart rate signal sparse matrix, thereby realizing synchronous monitoring of respiration and heart rate. Synchronous detection of the apnea event and the heart rate abnormity event is realized. And the definition of total variation regularization is introduced in the establishment of the optimization model, so that the decoupling and reconstruction accuracy of the apnea and heart rate abnormality events is improved. In the process of optimizing a solution algorithm, a feature selection process is added, input data are pre-corrected, low-correlation features are screened out, and the robustness of judgment of the apnea and heart rate anomaly events is improved.

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.

A method of processing radar data includes detecting a received signal including one or more targets signals and interference, converting the received signal to a matrix signal, and decomposing the matrix signal into a low-rank matrix and a sparse matrix using a group sparsity based low-rank and sparse decomposition method. The method also includes computing an inverse transform of the low-rank matrix and outputting the one or more target signals.

Provided are a distributed parallel computing based graph computing method, a distributed parallel computing system and a computer readable medium. A graph computing method comprises: obtaining data of a graph to be computed, the graph comprising a plurality of vertices and edges; storing the graph according to a compressed sparse row format, and storing the graph in a sparse matrix form, wherein all adjacent edges having a same vertex are continuously stored, and an offset array is used to support an index function thereof; and for subgraphs in which the degrees of both source vertices and target vertices are greater than a predetermined threshold, in forward graphs, performing segmentation according to target vertices, i.e., the column of a matrix, and in reverse graphs, performing segmentation according to source vertices, i.e., the row of the matrix, and further dividing the subgraph into a plurality of segmented subgraph (SSG) components, so that a target vertex in each SSG is limited within a preset range. According to the SSG method of the embodiment for a high-degree vertex, the conventional problem that the space-time locality of accessing data of an adjacent vertex is poor due to a large vertex range of a single large graph is avoided or relieved.

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.