152 results about "Tensor decomposition" patented technology

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

The invention discloses a lanyard data line interaction control method based on artificial intelligence, and the method comprises the following steps: S1, collecting interaction data of a lanyard data line, and carrying out the preprocessing of the interaction data; s2, segmenting according to a set window, performing singular value decomposition on each segment, and constructing a main feature set; s3, performing hypersphere embedding on the principal component vector, performing linear projection on the residual vector, and generating a characteristic spectrum by adopting Laplacian mapping; s4, performing tensor decomposition on the characteristic spectrum, and constructing a multi-order structure; s5, executing bidirectional loop iteration on the tensor interaction sequence and controlling information transmission; s6, analyzing the prediction action, matching the prediction action with an instruction mapping table, generating a control instruction, and sending the control instruction to the terminal equipment; and S7, counting execution feedback, updating a tensor interaction sequence weight, and optimizing an interaction control strategy. According to the invention, high-precision identification and stable adaptive control of the lanyard data line are realized, and the interaction precision, the response speed and the use convenience are effectively improved.

The invention provides a cable three-dimensional imaging method based on multi-modal features and physical constraints, and the method comprises the steps: generating a three-dimensional frequency domain-spatial domain coupling field according to a terahertz image and an X-ray image which are used for detecting a cable, carrying out the tensor decomposition of the three-dimensional frequency domain-spatial domain coupling field, and taking a core tensor as a cross-modal feature; after a fusion weight is calculated through the cross-modal features, the terahertz image and the X-ray data image are subjected to weighted fusion through the fusion weight, and a fusion image is generated; determining a target function for physical constraint, and performing iterative optimization on the fused image by adopting the fusion weight based on the target function until convergence to obtain a target image; target image reconstruction is accelerated in parallel through quantum derivation, and a three-dimensional image of the cable is obtained. Therefore, deep integration of structure information and material attributes in different modal images is realized, and the imaging quality of the three-dimensional image is improved.

The invention discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator, relates to the technical field of vector hydrophone array orientation estimation, and discloses a vector hydrophone array orientation estimation method for performing tensor decomposition by using a propagation operator. The method comprises the following steps: firstly, constructing a three-dimensional array manifold tensor composed of an array direction matrix and a vector hydrophone output matrix; respectively expanding received signal tensors according to three modes, solving a propagation operator based on a column block covariance matrix, constructing a normalized signal subspace, establishing a spatial spectrum function with a noise subspace, and obtaining a pitch angle and an azimuth angle of a sound source through spectrum peak search; according to the method, high-order singular value decomposition is avoided, the operand is greatly reduced, meanwhile, high resolution and low sidelobe direction finding performance are kept, and the method is suitable for a real-time underwater acoustic direction finding system of a ship-borne platform, a buoy platform and an unmanned platform.

The invention relates to the technical field of geophysical exploration, and discloses a three-dimensional fine electromagnetic detection method for ore control fracture of a deep gold ore. The method comprises the following steps: arranging a measuring point array to emit a coding artificial electromagnetic pulse signal, and synchronously receiving a plurality of induced magnetic field responses to form an original data volume; estimating a noise baseline based on statistical distribution and adaptively calibrating data; constructing a spatial correlation network by using the time sequence correlation of adjacent measuring points, and identifying a high-connectivity cluster as a suspected fracture region; extracting a regional time sequence to perform multi-level clustering, and screening a typical fracture response mode; based on the mode set global matching enhancement response, generating an enhancement data volume; multi-scale three-dimensional tensor decomposition is adopted to extract a tensor kernel component, and an electrical structure is reconstructed according to the spatial compactness and energy attenuation characteristics of the tensor kernel component; and finally, iteratively fusing with a geological constraint model to output a three-dimensional ore control fracture fine detection result. According to the method, intelligent identification and high-precision imaging of the deep fracture are realized.

The invention relates to a DAS signal positioning method based on adaptive tensor decomposition and dynamic correction. The method comprises the steps of converting phase difference data into strain rate data, constructing a three-dimensional tensor based on a time domain signal of a space point, solving an optimization problem after constraint modeling, and extracting a de-noised DAS signal. Calculating an initial fault position, and calculating an actual optical path and an apparent position after temperature change based on a thermal expansion effect and a thermo-optic effect; and a dynamic correction algorithm is set, a system coordinate reference is adaptively calibrated, and a final positioning result is calculated. According to the method, original signals are separated into a low-rank background field, a sparse event field and a structured noise field through unsupervised tensor decomposition, and the defects of dependence on labeled data and insufficient complex noise separation are overcome; by establishing a temperature-optical path coupling physical model and a dynamic correction algorithm for real-time cross-correlation calibration, positioning drift caused by environmental factors is accurately compensated, and high-fidelity denoising and accurate positioning of cable line events in a complex environment are realized.

The invention relates to the technical field of vehicle safety monitoring, and discloses a safety detection early warning method and system based on vehicle OBD data and a medium, and the safety detection early warning method based on the vehicle OBD data comprises the steps: collecting vehicle OBD real-time data, GPS positioning track data and environment data, and carrying out the preprocessing to form a time-space aligned multi-dimensional data set; oBD time position three-dimensional correlation features are extracted through a tensor decomposition technology; learning a mapping relation between OBD parameter change modes and safety risks in different driving scenes; constructing a space-time semantic association graph, and performing high-risk region identification through a graph convolutional network; according to the current driving situation, dynamic weighted fusion of the OBD data and the environment information is achieved, and graded early warning information is generated; a risk assessment method with context adaptability is provided, three-dimensional correlation features are extracted through a tensor decomposition technology, and complex interaction among parameters is effectively captured.

The invention relates to the technical field of data processing, and discloses a multi-source data difference attribution system and method based on tensor decomposition, and the method comprises the steps: obtaining an original engineering quantity list file, and then carrying out decoupling to generate a standardized list item text sequence and an initial value set, constructing unit prices to form a tetrahedral geometric model set, mapping the tetrahedral geometric model set to a holographic feature vector space, and generating a multi-dimensional list feature vector set; constructing a standard cost reference vector field, identifying a difference vector set and generating a visual cost difference topological data model; a natural language query instruction is responded, a target difference attribution tensor is locked, a logic verification chain is constructed, and an intelligent diagnosis report is output; according to the method, a computable logic distance is established in multi-source heterogeneous data by reversely reconstructing a high-dimensional price-rate feature space, so that the problem of attribution blocking caused by dimension collapse in traditional cost auditing is effectively solved, and accurate traceability and compliance automatic diagnosis of cost differences are realized.

The invention discloses a zero-carbon comprehensive energy digital management method and equipment, belongs to the field of energy management, and is used for solving the problem of inaccurate energy digital management regulation and control. Obtaining a dynamic carbon flow model of carbon emission space-time distribution and a dynamic carbon sink model of carbon absorption space-time change; performing coupling calculation on the dynamic carbon flow model and the dynamic carbon sink model on a unified spatial-temporal scale to fit and generate a net carbon spatial-temporal tensor of the park in a scheduling period; the net carbon space-time tensor is used for quantifying the net carbon emission of each region in each time slice in the park; tensor decomposition is carried out on the net carbon space-time tensor, and target areas which are adjacent in space but opposite in function are selected; and regulating and controlling energy supply and load demands among the target regions based on the carbon balance levels and the time dynamic modes of the target regions so as to generate a collaborative optimization strategy.

The invention discloses a hierarchical tensor decomposition and compression method for a radial sensor network. The hierarchical tensor decomposition and compression method comprises the following steps: (1) acquiring basic data; (2) carrying out hierarchical tensor modeling; (3) decomposing in branches; (4) fusing the branches; (5) performing adaptive quantization; (6) multi-hop collaborative coding; and (7) outputting compressed data. In order to solve the three core problems of multi-hop error accumulation, insufficient redundancy utilization among branches and poor dynamic adaptability in the radial sensor network, the method provided by the invention reduces multi-hop transmission error accumulation reduction by establishing a hierarchical tensor model, graph convolution fusion among the branches and adaptive multi-hop quantization, and improves the dynamic adaptability of the radial sensor network. And the transmission efficiency and the life cycle of the radial network are obviously improved.

The invention discloses a Tucker tensor decomposition-based missing value completion prediction method, and relates to the field of second-hand electronic product recovery evaluation result missing value research and application. The method comprises the following steps: 1) removing abnormal data based on a box plot; 2) removing the feature attributes with relatively high feature attribute missing rate; 3) searching feature attributes highly related to the recovery evaluation result value based on a mutual information mode; and 4) constructing a Tucker tensor decomposition completion model based on the training set, verifying the accuracy of the model by the test set, and finally realizing completion prediction of the missing value of the second-hand electronic product recovery evaluation result. The specific construction prediction process is divided into four small steps: initializing a sparse tensor and a factor matrix, calculating a complemented prediction tensor, calculating the difference Loss between a real tensor and the prediction tensor, descending according to a gradient, and simultaneously circulating the last three small steps until convergence. After model training is completed, the model can be directly provided for related employees to use, and therefore efficient recycling is achieved.

This invention discloses a privacy-preserving method for classifying and grading customer management data, specifically relating to the fields of data processing and information security. First, it establishes a mapping relationship between sensitive attributes and observable features, and weakens the impact of sensitive attributes on features through path-level decoupling. Then, it performs subspace projection on the decoupled features, extracts distribution perturbation features, and merges them with the original features to generate an implicit risk representation tensor. A tensor decomposition method with path consistency constraints is introduced to extract consistency risk factors across subspaces. Further, a graded decision path tree is constructed based on these risk factors, and the classification process is corrected through the constraint of unidentifiable sensitive attributes. Privacy leakage metrics are generated through counterfactual reconstruction analysis, and the decision path tree is structurally backtracked and optimized to obtain the final graded classification result. This invention effectively reduces the risk of sensitive attribute leakage while ensuring classification accuracy, thus improving the privacy protection capability of the data grading process.

The invention discloses a large model operating environment adaptive deployment method based on strategy optimization, and the method specifically comprises the steps: S1, collecting the computing power capacity, the video memory capacity, the communication bandwidth and the communication time delay of a computing node, and constructing an operating environment diagram structure; s2, constructing a node set and an edge set, configuring state feature vectors, and forming a topological calculation graph; s3, performing multi-scale filtering and persistent coherence calculation on the topology calculation graph to generate a topology gating vector; s4, generating a candidate calculation structure set under the limitation of the topology gating vector; s5, configuring different rank parameters for the weight tensor according to topology complexity distribution, and executing tensor decomposition; s6, performing tensor re-parameterization on the candidate calculation structure to form a large model calculation structure; and S7, mapping the large model calculation structure to a calculation node to complete deployment execution. According to the method, topological gating and heterogeneous rank tensor decomposition are introduced, and self-adaptive deployment of the structure constrained by the environment is achieved.

This invention discloses a method for assessing the weathering of sandstone artifacts based on the fusion of multi-source heterogeneous data driven by physical-manifold collaboration, belonging to the field of cultural relic protection technology. Addressing the contradiction that surface spectral data of sandstone artifacts is dense but cannot probe the interior, while internal physical data is accurate but extremely sparse and lossy, this invention proposes a "surface-to-interior" fusion strategy. First, using a physical information deep learning model, physical partial differential equations are introduced as prior constraints to extrapolate sparse point data into a continuous deep physical tensor across the entire field, achieving a "penetrating" effect. Second, based on Riemannian manifold geometry, spectral-physical enhancement features are mapped to the tangent space to extract noise-resistant surface manifold features. Furthermore, through coupled tensor decomposition, deep mechanisms and surface properties are forcibly aligned in the latent feature space. Finally, a high-order Laplacian hypergraph model is used to achieve pixel-level classification of weathering degree. This method effectively solves the problems of spatial scale mismatch and missing physical mechanisms in multi-source data, achieving a non-destructive, full-field, and accurate quantitative assessment of the weathering status of cultural relics.

The invention relates to the technical field of image communication transmission, in particular to a monitoring video compression storage method. The method comprises the following steps: acquiring a frame sequence of a monitoring video and constructing a three-dimensional video tensor; for any coordinate position in any frame of image, calculating the time sequence disorder degree of the coordinate position and the structural consistency of the coordinate position; the adaptive weight of each coordinate position is calculated based on the local structure consistency, an adaptive weight matrix is constructed, and each weight value is inversely proportional to the local structure consistency of the corresponding coordinate position; a weighted low-rank tensor decomposition algorithm is adopted to process the three-dimensional video tensor, a low-rank background tensor and a sparse foreground tensor are obtained, and the adaptive weight matrix is used for applying spatially variable sparsity constraint to the sparse foreground tensor; and compressing and storing the low-rank background tensor and the sparse foreground tensor respectively. The method has the effect of improving the image compression efficiency and fidelity.

This invention relates to the field of bioinformatics processing and medical laboratory data analysis, specifically a method for classifying hepatitis B antibody patterns based on plasma protein profiles. The method includes: acquiring host hardware information of the execution environment and extracting central processing unit (CPU) cache parameters; acquiring a high-dimensional sparse one-dimensional array and extracting non-zero feature indices; performing mapping calculations using a locality-sensitive hashing (LSH) algorithm to reconstruct the data into a locally dense two-dimensional matrix; dynamically segmenting the data into independent sub-blocks according to cache parameters and initial segmentation dimensions; performing low-rank tensor decomposition on the independent sub-blocks to extract local latent feature vectors; concatenating the sub-blocks, weighting them through a single-layer attention network, and inputting the result into a classifier function to output a target classification pattern vector; and generating dimension update instructions based on a preset dynamic adjustment mechanism to adjust subsequent segmentation dimensions. This invention achieves lower memory peaks, higher cache hit rates, and stable multi-label pattern output capabilities.

The invention provides an old city pattern recognition and updating method and system based on multi-source data fusion, and the method comprises the steps: collecting a multi-source data set, analyzing n-hour activity cycle characteristics, and generating a time period thermodynamic diagram; identifying a community structure by using a graph neural network, outputting social topology, and quantifying a texture evolution track by using a dynamic attitude model; quantizing the traditional spatial pattern by using a landscape sight line algorithm, and outputting a dynamic pattern recognition result; and configuring aging facilities by using a population adaptation algorithm, optimizing industrial layout by using a location entropy algorithm, and generating a micro-update scheme. According to the method, a space-time-society-ecology four-dimensional dynamic pattern recognition system is constructed; spatial pattern quantization and weight adjustment are realized by innovatively using a landscape sight line algorithm, and the traditional limitation is broken through; and finally, a tensor decomposition algorithm is used for fusing the configuration and optimization scheme, a micro-update scheme is generated, an update strategy is formed through iterative optimization, complex requirements are accurately responded, and the old city pattern recognition scientificity and the update scheme implementation efficiency are improved.

This invention relates to the field of vehicle control technology, specifically to a vehicle limit control method based on hybrid-order Koopman tensor decomposition and CBF fusion. The method involves acquiring the motion state of the target vehicle and estimating its motion state at the next moment. Based on the motion state at the next moment, the tire forces of the target vehicle are determined. The state vector of the target vehicle is determined according to the motion state at the next moment and the tire forces, and a control input vector is defined. Based on the state vector and the control input vector, a first-order linear Koopman model, a second-order bilinear Koopman model, and a third-order higher-order Koopman model are constructed. The weights of each Koopman model are determined based on a comprehensive index of the severity of the target vehicle's operating conditions. The prediction results of each Koopman model are calculated, and a fused prediction result is determined based on the weights of each Koopman model. A nominal controller is constructed based on the affine form of the fused prediction result. The nominal controller is solved to determine the control input vector at the current moment for controlling the target vehicle.

The invention provides an agent anthropomorphic interaction method and system based on cognitive graph tensor decomposition, and the method comprises the steps: receiving multi-modal input data issued by a user, converting the multi-modal input data into structured cognitive elements, and quantifying the cognitive elements into mathematical expressions, so as to obtain a three-dimensional cognitive graph tensor; decomposing the three-dimensional cognitive schema tensor to obtain a core tensor and a factor matrix; performing double-graph mapping based on the core tensor and the factor matrix to obtain a deep intention recognition result and a topic strategy; the anthropomorphic natural language response is generated and output based on the deep intention recognition result and the topic strategy, smooth transition of topics can be achieved, the embarrassment of'rigid turning 'of a traditional dialogue robot is avoided, and anthropomorphic experience is improved.

The invention relates to an IPTV video quality enhancement method and system, and relates to the technical field of video quality, and the method comprises the following steps: carrying out the inter-frame separation of an IPTV video stream, and obtaining a video frame sequence and inter-frame time sequence features; performing adaptive color gamut mapping on the video frame sequence based on the inter-frame time sequence characteristics to obtain color reconstruction sequence data; performing tensor decomposition and recombination on the color reconstruction sequence data to obtain a multi-dimensional texture feature map; performing dynamic bit depth optimization on the multi-dimensional texture feature map to obtain a high bit depth video data stream; and performing inter-frame dynamic compensation synthesis based on the high-bit-depth video data stream to obtain a quality-enhanced video output stream, thereby solving the technical problem that the enhanced video flickers, jumps or is distorted in detail in the motion process due to the fact that the temporal correlation between frames is often ignored in the prior art.

The invention discloses an image reconstruction method and device based on collaborative block term tensor decomposition, equipment and a medium, and relates to the technical field of signal processing. The method comprises the steps that an original tensor is decomposed into the sum of a plurality of low-rank terms, each low-rank term is decomposed based on a collaborative block term tensor, a low-rank decomposition model of the original tensor is obtained, and N mode factor matrixes of each low-rank term are expressed through a shared factor matrix and an independent factor matrix; constructing a reconstruction task model based on the low-rank decomposition model, and solving the reconstruction task model through an optimization algorithm to obtain a solution of each factor matrix; and substituting the solution of each factor matrix into the low-rank decomposition model to obtain a reconstructed image. According to the method, different low-dimensional items interact through a sharing factor mechanism, so that the reconstruction effect is improved; and meanwhile, computing resources are greatly reduced.

The application discloses a remote sensing ground object knowledge graph construction method based on cooperation of geographical object geometric features and graph enhancement retrieval, and comprises the following steps: data set preparation; basic triple extraction and feature analysis; LLM-assisted semantic and relationship enhancement; flexible quantization and output of ground object knowledge graph knowledge; knowledge conflict correction and final version graph construction. The application utilizes a tensor decomposition model to train a ground object knowledge graph to obtain a knowledge graph embedding vector, and through an unsupervised / semi-supervised mode, utilizes a low-dimensional vector representation of a ground object category learned from a knowledge graph triple to embed the semantic relationship among categories in the knowledge graph embedding vector, so as to provide global semantic prior knowledge for a downstream model, and effectively solve the semantic confusion problem in a remote sensing image.

The invention relates to the technical field of artificial intelligence, in particular to a three-dimensional scene atlas room classification method and system based on a multi-modal feature tensor, and the method comprises the steps: carrying out the feature extraction of scene observation data through a plurality of different pre-training models, and organizing a feature matrix set; tensor decomposition is carried out on the set, a common observation-category incidence matrix and a feature-category factor matrix corresponding to each model are synchronously obtained, and the decomposed rank is automatically determined as the number of room categories. And fusing the prototype features in each factor matrix to form category prototype representation, and distributing the most matched semantic tag for each potential category by calculating the semantic similarity between the category prototype representation and a predefined room type tag. And determining a room category to which each observation belongs according to the observation-category incidence matrix, and endowing a corresponding type label to a room layer node in the three-dimensional scene map, thereby completing automatic and interpretable construction of room layer semantic information.

The invention discloses a remote sensing ground object knowledge graph construction method based on geographic object geometric features and graph enhancement retrieval collaboration. The method comprises the following steps: preparing a data set; basic triple extraction and feature analysis are carried out; semantic and relation enhancement assisted by LLM; flexible quantification and output of knowledge of the ground object knowledge graph; and correcting knowledge conflicts and constructing a final edition map. According to the method, a tensor decomposition model is used for training a ground feature knowledge graph to obtain a knowledge graph embedded vector, through an unsupervised / semi-supervised mode, low-dimensional vector representation of ground feature categories is learned from a knowledge graph triple, the knowledge graph embedded vector is used for embedding a semantic relationship between categories, global semantic priori knowledge is provided for a downstream model, and the global semantic priori knowledge is provided for the downstream model. And the semantic confusion problem in the remote sensing image is effectively solved.

The invention provides a visual SLAM (Simultaneous Localization and Mapping) algorithm based on multi-scale neural tensor representation, which belongs to the technical field of indoor scene real-time reconstruction, and can efficiently capture the global structure and fine details of a scene by utilizing a tensor decomposition technology, and reduce the memory usage amount and the calculation overhead at the same time. In addition, the invention further introduces an efficient pixel-based color query method, and each pixel only needs neural network forward regression once in the method, so that the real-time performance of the neural radiation field-based SLAM (Simultaneous Localization and Mapping) is further improved. Experiments on a synthetic data set and a real data set show that the method is superior to the most advanced method in the aspects of reconstruction quality and camera tracking precision, and the efficiency in the aspects of running time and memory use is very high.

The application relates to the technical field of nondestructive testing, and discloses an aircraft defect identification method and system based on tensor decomposition and an attention mechanism. The aircraft defect identification method based on tensor decomposition and the attention mechanism comprises the following steps: acquiring multi-modal data of an aircraft; constructing the multi-modal data into a four-order space-time-modal tensor, and generating a dynamic graph structure based on the modal features of the tensor; applying a mixed constraint when decomposing the four-order tensor to obtain a core tensor and a factor matrix; extracting features through multi-scale pooling, combining topological persistent homology and a gated attention mechanism to distribute weights, and realizing feature fusion; identifying a defect type based on the fused features, and locating a defect area by using a factor matrix gradient amplitude and a dynamic threshold. Through multi-modal data fusion, dynamic graph regularization constraint and mixed tensor decomposition technology, the application improves the detection sensitivity and positioning accuracy of small defects on the surface of the aircraft, and enhances the physical interpretability of features and the robustness of the algorithm to complex working conditions.

The application relates to the technical field of power distribution network protection, and provides a waveform self-similarity fault type identification method and system based on a Tanimoto coefficient. The method comprises the following steps: acquiring multi-channel time sequence waveform data, pre-processing the waveform data and performing multi-scale division, and generating a multi-scale waveform feature primitive set containing time domain, frequency domain and time-frequency domain feature vectors; based on the multi-scale waveform feature primitive set, a generalized Tanimoto coefficient between different physical channels and different analysis scales is calculated, and a cross-scale and cross-channel self-similarity tensor is constructed; the self-similarity tensor is subjected to tensor decomposition, a time intensity vector of an atomic self-similarity mode corresponding to a fault mode is extracted, and is fused into a decoupled self-similarity feature vector; the decoupled self-similarity feature vector and a pre-stored standard fault feature template are subjected to weighted distance calculation and hierarchical comparison, and a fault type identification conclusion is output according to a comparison result. The method is helpful to improve the accuracy of fault type identification under complex working conditions.

The application relates to the technical field of data compression storage, and discloses a time series database driven monitoring data compression storage method, which comprises the following steps: S1, preprocessing multi-source monitoring data, and constructing a dynamic tensor containing a timestamp, a numerical index and a multi-dimensional label; S2, performing dynamic dimension reduction processing on the dynamic tensor to generate a core tensor and a multi-dimensional factor matrix; S3, based on the core tensor and the multi-dimensional factor matrix, determining compression parameters, decompression parallelism and index granularity through a joint optimization model; and S4, performing hierarchical coding on residual data generated by the dynamic dimension reduction processing to generate a light residual coding result. Through dynamic tensor decomposition and incremental updating technology, low storage overhead and real-time dimension expansion capability of streaming monitoring data are realized, the problems of calculation redundancy and storage expansion caused by the incapability of the streaming monitoring data to adapt to dynamic newly-added labels are solved, and the frequent reconstruction cost caused by dynamic expansion of the data is avoided.

The present disclosure belongs to the field of geomagnetic station earthquake anomaly extraction, and is an earthquake magnetic field anomaly extraction method, system and electronic equipment, comprising: constructing a three-dimensional non-negative tensor data body; adopting a spatial weighted non-negative tensor decomposition method to decompose the three-dimensional non-negative tensor data body, extracting R characteristic components, each characteristic component containing a frequency factor matrix, a time factor matrix and a station contribution factor matrix; calculating the proportion of the total energy of a target frequency band in the frequency factor matrix of each characteristic component in the total energy in the entire frequency range, and selecting the characteristic component with the largest proportion as the earthquake-related characteristic component; and based on the time factor matrix of the earthquake-related characteristic component, adopting an over-limit threshold method to extract an earthquake anomaly point. The present disclosure can retain and utilize all measured data to study earthquakes, and effectively detect earthquake anomalies by obtaining more relevant components of earthquake activity.

The invention provides a sluice water flow field prediction method and device based on brain-like inspiration and a server, and relates to the technical field of flow field prediction.The sluice water flow field prediction method comprises the steps that a calculation area of a sluice and boundary conditions, initial conditions and a control equation of the calculation area are obtained, and training data are collected; respectively processing each dimension in the coordinate value of the training data through mutually independent modular sub-network sets to obtain a feature vector set, and carrying out tensor decomposition type feature fusion processing on the feature vector set to obtain a predicted value of the flow field variable; and constructing a total loss function by using the predicted value of the flow field variable, the boundary condition, the initial condition and the control equation, and carrying out brain-like heuristic training processing on the modular sub-network set based on the total loss function to obtain a brain-like heuristic separated physical information neural network so as to carry out prediction processing on the flow field of the sluice under different working conditions. And obtaining a target prediction result. According to the invention, prediction efficiency and prediction accuracy can be significantly improved.