1072 results about "Similarity matrix" patented technology

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 a hydrological flow prediction method and system based on multi-site time-space association. The prediction method comprises the following steps: carrying out dimension reduction and feature reconstruction on original multi-site hydrological data through an auto-encoder; space-time correlation modeling and adjacency matrix dynamic construction are carried out, a multi-dimensional Euclidean distance matrix between stations is calculated based on a multivariable dynamic time warping (MDTW) algorithm, a similarity matrix is generated in combination with dynamic programming, and a dynamic adjacency matrix is constructed by fusing a geographic space adjacency relation; extracting spatial features of a GCN (Graphics Convolutional Network); carrying out adaptive time sequence decomposition and trend-period modeling; and carrying out multi-stage fusion prediction and result output, and generating a final prediction result through a decoder in combination with the decomposed trend item and periodic item. According to the method, accurate extraction and dynamic correlation modeling of spatial-temporal characteristics of multi-site hydrological data are realized, the accuracy and robustness of single-site flow prediction are improved, and the problems that multi-site spatial-temporal correlation modeling is insufficient, non-linear time sequence alignment is difficult, and single-site prediction precision is limited are solved.

The invention provides a large model training data synthesis method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of original military corpus data, generating a semantic vector through a pre-training language model, dividing the semantic vector into a plurality of topic clusters through clustering analysis, extracting the keyword and representative sample of each topic cluster, forming a theme ontology library; selecting associated topic pairs based on the topic similarity matrix, and generating a synthetic sample through a predefined template; controlling the language style of the synthetic sample through the cue word instruction, and generating a stylized sample; dividing difficulty grades of the stylized samples to obtain graded samples; performing multi-dimensional quality evaluation and screening on the graded samples to obtain screened samples; the screened samples are used for training a large language model, and according to the performance of the large language model in each theme task, theme weights are adjusted, a synthesis strategy is updated, and a sample structure is optimized. According to the method, high-quality and diversified training data can be provided for large model training.

The invention relates to an experimental data processing method and device, an AI analysis module and computer equipment, and belongs to the field of data processing.The method comprises the steps that multi-dimensional original data are partitioned according to types, and formats are unified; generating a similarity matrix based on time and space neighborhood information, and marking abnormal fluctuation points; effective signals are separated through a time-frequency feature matching noise library; extracting multi-layer features of basic statistics, time sequence correlation and trend change; and dynamically screening core features to update the tracking type experimental model. The matched AI analysis module integrates hardware circuits of data partitioning, similarity calculation, anomaly marking, noise matching, feature extraction and model updating, and whole-process acceleration is achieved. According to the method, through multi-dimensional data compatibility processing, accurate anomaly detection, multilayer feature fusion and model adaptive optimization, the experimental data processing efficiency and conclusion reliability are remarkably improved, and the method is suitable for real-time analysis of multiple scenes such as scientific research and industry.

The invention provides a conference activity execution task decomposition, arrangement and management method based on an AI technology, and relates to the technical field of conference management, and the method comprises the steps: obtaining conference demand information, converting the conference demand information into digital representation data, employing a bidirectional recursive decomposition strategy to carry out task decomposition, and generating subtask data; calculating a task similarity matrix and a conflict matrix to construct a task execution directed graph to generate initial arrangement data; collecting an execution state to generate feedback data, calculating a risk assessment score, and adjusting a task execution scheme when the risk assessment score exceeds a threshold value. According to the invention, the execution efficiency of conference activities can be improved, the resource conflict risk is reduced, and dynamic optimization adjustment is realized.

The invention discloses an intelligent processing method and system for judicial batch filing, and the method specifically comprises the steps: carrying out the cross verification of an original case element through a cross-modal information complementation mechanism, and generating a first case element; constructing a case similarity matrix according to the first case elements, calculating a fact relevance weight between cases by adopting an improved BERT-Siamese network based on the case similarity matrix, synchronously identifying jurisdiction competition and proof conflicts through a legal program conflict detection algorithm, and generating a dynamic grouping scheme; matching a classification document template library according to the dynamic grouping scheme, and calling a Transform-XL enhanced generation model to generate legal documents in batches; and performing full-process intelligent verification on batches of legal instruments through the association map, and detecting material integrity, logic consistency and legal compliance. According to the method, intelligence, high efficiency and precision of judicial batch filing are realized, and the case processing efficiency and the document generation quality are improved.

The invention provides an underground pipe network defect intelligent identification and early warning method and system based on deep learning, and relates to the technical field of pipe network detection, and the method comprises the steps: employing a pipeline detection robot to obtain multi-source data, and carrying out the preprocessing; mapping the features to a manifold space, constructing a similarity matrix, and carrying out geometric difference weighted fusion; constructing an incidence matrix to calculate a spatial distance and mechanical strength, and carrying out iterative propagation to generate a defect characteristic spectrum; and acquiring an evolution sequence by adopting a self-adaptive sliding window, determining influence factors based on anti-fact intervention, and calculating a state transition probability to determine an optimal maintenance scheme. According to the method, the pipe network defect identification accuracy is improved, and pipeline state prediction and maintenance decision intelligence are realized.

The invention relates to an article sequence recommendation method and system based on collaborative filtering and an LLM perspective, and the method comprises the steps: obtaining a user historical data set and an article data set, and carrying out the preprocessing, and obtaining an article title similarity matrix, a user behavior sequence, and historical collaborative filtering interaction information; historical collaborative filtering interaction information is enhanced through a large language model; constructing a sequence recommendation model, inputting the user behavior sequence into the sequence recommendation model for training, and correcting the deviation of the user behavior sequence through comparative learning according to the item title similarity matrix and the enhanced historical collaborative filtering interaction information to obtain a trained sequence recommendation model; and inputting the user behavior sequence of the user into the trained sequence recommendation model for prediction, calculating prediction scores, and generating a recommendation list according to score sorting, thereby completing article sequence recommendation. According to the method, the performance of the sequence recommendation system is greatly improved by solving the cold start problem.

The invention discloses a multi-stage spatial-temporal clustering method and system based on a fused mahalanobis distance. The method comprises the following steps: acquiring a spatio-temporal data set, and determining a spatio-temporal neighbor relation of samples in the data set; calculating a space communication distance and a time decay distance of the sample; fusing the space communication distance and the time decay distance by using a mahalanobis distance to obtain a relative distance of the sample; selecting a class cluster center from the data set according to the local density of the sample and the relative distance; adopting a multi-stage distribution strategy to distribute non-class-cluster center samples to corresponding class clusters; wherein the multi-stage allocation strategy comprises an inevitable allocation stage based on space-time shared neighbor and a similarity allocation stage based on a weighted similarity matrix. According to the method, the key problems that an existing space-time clustering algorithm is insufficient in space-time attribute coupling processing and sensitive to distribution errors are solved, and the clustering accuracy, robustness and practicability in the fields of intelligent traffic analysis, seismic sequence recognition and the like are remarkably improved.

The invention discloses an intelligent storage system supporting multi-source heterogeneous data fusion management, which relates to the technical field of multi-source heterogeneous data fusion, and comprises a data source adaptation module, an intelligent data source adapter is configured in a DataWorks data integration module, multi-source heterogeneous data is accessed through the intelligent data source adapter, and the data source adaptation module is connected with the DataWorks data integration module; extracting a semantic feature vector and a technical feature set; the neural symbol hybrid inference module inputs the semantic feature vector and the technical feature set into a neural symbol hybrid inference engine, calculates a similarity matrix between multi-source heterogeneous data fields through a BERT-based neural network, and imports the technical feature set into a field knowledge graph constructed by Neo4j for symbol logic verification to generate a unified metadata model; according to the method, a semantic similarity matrix calculated by a BERT-based neural network is combined with symbol logic verification of a Neo4j knowledge graph through a neural symbol hybrid inference engine, so that automatic semantic alignment and logic consistency verification of multi-source heterogeneous data are realized.

The invention relates to the technical field of automation and artificial intelligence crossing, in particular to an industrial monitoring and early warning method and system based on a multi-modal large model, and the method comprises the steps: obtaining multi-modal industrial data, carrying out the initial feature processing of the obtained multi-modal industrial data, and carrying out the initial feature processing of the obtained multi-modal industrial data; and performing deep feature extraction fusion based on the extracted preliminary features, taking a multi-modal fusion feature matrix as input to construct a multi-modal large model, performing model training on the constructed multi-modal large model, and performing real-time monitoring and abnormity early warning by using the trained multi-modal large model. According to the method, space-time alignment and feature association are realized through a defect feature matrix of a visual mode, a trajectory similarity matrix of a motion mode and a time sequence reconstruction matrix of a sensing mode and through dynamic time warping and cross-modal parameter sharing; and associated abnormalities of product quality, equipment actions and running states can be captured at the same time, so that the problem that the collaborative analysis capability of a traditional method on multi-source heterogeneous data is insufficient is solved.

The invention provides a generative adversarial-driven intelligent security defense method and system, and solves the problem of dynamic network security defense through three-layer architecture innovation: 1, data fusion layer reconstruction: employing a multi-modal feature extraction engine driven by an MoE architecture, dynamically allocating computing power resources to a plurality of expert models, and improving the heterogeneous data distillation efficiency; an LLM for fine adjustment in the security field is introduced, a cross-modal semantic similarity matrix is constructed, and the accuracy of unstructured threat intelligence analysis is improved; a second dynamic attack and defense layer is constructed, a GPT-4 architecture attack generator is deployed, and generation of a multi-stage APT attack chain is simulated; a double-agent reinforcement learning framework is designed, and the confrontation training efficiency is improved; upgrading a three-cognitive decision-making layer, constructing a dynamic threat map based on a time sequence diagram neural network, and updating an adjacent matrix in real time; a plurality of agent clusters are deployed, the capabilities of encrypted traffic analysis and attack blocking are improved, and the problems of data layer defects, attack and defense confrontation limitation and decision-making layer bottleneck in the prior art are solved.

The invention discloses a tensor depth semi-supervised learning method for high-dimensional small sample data classification. The tensor depth semi-supervised learning method comprises the steps of preprocessing original high-dimensional small sample data; constructing a deep neural network comprising a feature extraction module and a classifier module; constructing a second-order similarity matrix and a third-order similarity tensor based on the low-dimensional embedding representation obtained by pre-training; combining the second-order similarity matrix and the third-order similarity tensor to construct an objective function containing multi-order smooth constraints; then, taking the low-dimensional embedded representation obtained by pre-training as input, performing iterative optimization on the target function by adopting a gradient descent algorithm through a label propagation network formed by a full connection layer, and generating a pseudo label; and inputting the original high-dimensional small sample data, the low-dimensional embedded features and the pseudo labels into the deep neural network, iteratively updating the network in a semi-supervised mode until convergence, and outputting a final prediction result. According to the method, more accurate label propagation is realized, and the semi-supervised classification precision is improved.

The invention discloses a multi-modal offshore wind power ultra-short-term prediction method in the field of offshore wind power plant cluster power prediction, and aims to solve the technical problems of spatial-temporal feature splitting and insufficient dynamic dependency relationship modeling. The method comprises the steps of performing anomaly detection and restoration on fan data, and generating a corrected wind power cluster data set; extracting a mean value, a standard deviation and a latest value of core operation data of each fan through a dynamic time window, and constructing a multi-dimensional node feature; a static geographic similarity matrix is generated based on geographic coordinates, a basic wake effect matrix is generated in combination with real-time wind direction data, correction is carried out through the maximum mutual information quantization time-delay effect, and then a dynamic adjacency matrix is obtained through self-adaptive fusion; and integrating the multi-dimensional node features and the dynamic adjacency matrix into a space-time diagram sequence data architecture, inputting the space-time diagram sequence data architecture into a multi-scale wake flow perception diagram space-time prediction model, and outputting a multi-fan power prediction value. According to the invention, high-precision multi-fan power prediction can be realized.

The invention provides a self-adaptive semantic-driven data set field matching method and system, and the system comprises a data preprocessing module which is used for carrying out the cleaning, standardization and preliminary analysis of an input data set, and extracting a field name, a data type, a field description and a data sample; the deep semantic representation modeling module is used for constructing a field-level semantic representation vector; the multi-level similarity calculation module is used for comprehensively calculating the grammatical similarity, the semantic similarity and the statistical similarity among the fields, dynamically adjusting the weight of the similarity of each level by adopting a weighted fusion algorithm, and generating a comprehensive similarity matrix; and the matching result management and application module is used for generating a field matching mapping table and a fusion suggestion according to the comprehensive similarity matrix. According to the method, high-precision automatic matching of data set fields is realized by fusing deep semantic understanding, multi-dimensional similarity calculation and incremental adaptive learning, and the efficiency and accuracy of data set fusion are remarkably improved.

The invention provides a project cost big data management and analysis system, and relates to the technical field of data management, and the system comprises a data collection and preprocessing module which is used for collecting original cost data from a heterogeneous data source, and carrying out the preprocessing of the original cost data, and obtaining the preprocessed cost data; the semantic feature extraction module is used for converting the preprocessed cost data into a multi-dimensional feature vector based on a multi-level feature extraction system; the similarity calculation module is used for calculating similarities among different cost data based on the multi-dimensional feature vectors to obtain a similarity matrix; and the data storage and management module is used for storing the cost data, the multi-dimensional feature vector and the similarity matrix by adopting a mixed storage architecture, and providing retrieval and recommendation functions of cost projects based on a multi-level feature space index structure. According to the method, the limitation that a traditional method only depends on keyword matching is solved, and the system can recognize the deep incidence relation between the items.

The invention relates to the technical field of multi-view clustering, in particular to a multi-view clustering method and device, and can solve the problem that the overall effect of an existing method in a large-scale clustering task is limited due to the fact that the existing method has problems in the aspects of calculation efficiency, robustness and multi-view information integration to a certain extent. The method comprises the following steps: dynamically learning an anchor matrix and a projection matrix for each view, and constructing a bipartite graph to generate a similarity matrix; calculating a graph Laplacian matrix based on the similarity matrix of each view, and extracting spectrum embedding; the spectrums of multiple views are embedded and stacked into a third-order tensor, and cross-view shared information is extracted by using a low-rank tensor constraint; multi-view atlas embedding is aligned through a spectrum rotation technology, and a discrete clustering indication matrix is directly output.

A method for delineating an ecological corridor based on a maximum similarity model determines ecosystem type according to the determined ecological source, and assigns a habitat suitability index; and according to a maximum similarity matrix between different ecosystems, assigns the habitat suitability index to peripheral pixels of the selected ecological source, to obtain a similar value of the habitat suitability index. The habitat suitability index is corrected according to the surface curvature data and population density data, and resistance surface data is calculated according to the corrected value. Based on the resistance surface data, an ecological corridor is delineated comprehensively to find one or more paths corresponding to a minimum cost distance as ecological corridors. The influence of ecological environment on ecological corridor establishment is considered more comprehensively, so that the ecological corridor is delineated more accurately, and applied into practice to maximum extent.

The invention belongs to the technical field of image information processing, and discloses a scene guidance prompt representation enhanced infrared and visible light image fusion method, which is divided into two stages: a first stage, constructing a scene prompt generation network, and learning global visual semantic information covering a source image through a semantic segmentation task; in order to further enhance the prompt representation capability, a visual perception context prompt module is designed, interaction is performed by using a correlation matrix between modal specific features and text features, and the text features are refined in a dynamic weighting mode, so that scene prompt representation with richer semantics is obtained. In the second stage, a cross-modal alignment fusion network guided by prompt is provided, and infrared and visible light features are mapped to a unified shared embedding space by utilizing learned scene prompt. In the process, a pixel-text similarity matrix is established through a prompt driving feature alignment module, and accurate alignment of cross-modal features is realized, so that a fusion result of semantic consistency and detail fidelity is obtained.

The invention provides a knowledge graph-based public security multi-source risk factor association identification analysis method, which relates to the technical field of risk identification, and comprises the steps of obtaining multi-source risk factor data, extracting information from unstructured data, constructing an initial association network, performing feature analysis and calculating a similarity matrix; and the close association subgroups are identified through community discovery, a multi-level association network is constructed, a conduction path is analyzed, a weight is calculated, and finally risk early warning information is generated. According to the invention, the complex association between public security risk factors can be effectively identified, and the risk prediction accuracy is improved.

The invention relates to the technical field of bridge safety monitoring, and comprises a bridge safety monitoring analysis system based on big data, and the system comprises a data collection processing module, a manifold feature dimension reduction module, an entropy change partition evaluation module, a health state dynamic analysis module, and a bridge safety early warning module. According to the method, a bridge measuring point topological relation matrix is established, measuring point spatial distribution characteristics are analyzed, spatial relevance of a bridge structure is reflected, measuring point local curvatures are calculated, a local curvature matrix is established, a neighborhood similarity matrix is combined, low-dimensional projection transformation is executed, local topological consistency of data is kept in the dimensionality reduction process, an entropy change partition matrix is established, and the spatial relevance of the bridge structure is reflected. The health states of different structural parts of the bridge are subjected to differential analysis based on mechanical characteristics, the entropy change trend and damage probability of a measuring point are calculated, and the overall safety state level of the bridge is judged through a damage risk threshold, so that the local damage severity of the bridge can be comprehensively considered in safety assessment, and the stability and adaptability of safety state judgment are improved.

The invention discloses a high and cold arid region slope soil stability safety risk evaluation system and method, and relates to the technical field of slope engineering. The state of each side slope under multi-dimensional indexes such as freeze-thaw cycle frequency, dry-wet alternation index, wind erosion strength, shear strength, water content, porosity and fracture density is quantified, the feature similarity between any two side slopes is calculated, and then a side slope feature similarity matrix is formed. Based on the matrix, an unsupervised clustering algorithm (such as spectral clustering, similarity propagation and the like) can be adopted to divide a plurality of side slopes into similar subsets with structural characteristics similar to environmental response, and category attribution of risks is achieved. On the basis, structural variation analysis, historical instability statistics and central risk difference extraction are performed on similar slope samples, so that the internal instability tendency of the slope can be identified, and a risk prediction model suitable for the type of slope can be constructed through feature training.

The invention discloses a multi-scale ReID network and double-domain joint measurement target tracking method for a complex scene. The method comprises the following steps: S1, constructing and training a target detection model YOLOv5; s2, designing an improved ReID network IncepSPA-DSC fusing a depth separable convolution and a spatial pyramid channel attention mechanism; s3, a DeepTrack-SPAE tracking framework is constructed, and a DeepTrack-SPAE tracking framework is According to the invention, by constructing a multi-scale feature fusion mechanism and an attention enhancement module, on the premise of maintaining the real-time processing speed, an anti-interference feature vector with strong discrimination is generated, and the cooperative capture capability of the network on local features and global context information is significantly improved. A space-feature double-domain joint measurement method is innovatively proposed, and by establishing a feature similarity matrix fused with Euclidean distance constraint, the spatial proximity and feature consistency of a target are considered in cost calculation, so that the problem of trajectory breakage caused by short-time shielding is effectively solved.

The invention relates to a photovoltaic bearing capacity assessment method and system for a distributed photovoltaic transformer area, belongs to the field of intelligent power distribution network and distributed energy access, and solves the problem that the photovoltaic bearing capacity of a low-voltage power distribution transformer area under a distributed photovoltaic high-proportion access scene is difficult to assess in a refined manner in the prior art. Comprising the steps of obtaining a physical adjacency matrix and a behavior similarity matrix of a transformer area based on collected voltage time sequence data of users in a non-photovoltaic output time period and a photovoltaic output time period in the transformer area; fusing the physical adjacency matrix and the behavior similarity matrix to obtain a user clustering result; based on the user clustering result and the observation time sequence data of the multiple operation conditions of the transformer area, obtaining an impedance matrix between transformer area clusters; and based on the clustering result and the impedance matrix, establishing a photovoltaic bearing capacity assessment implicit optimization network which aims at maximizing the total photovoltaic capacity of the transformer area and meets multiple constraint conditions, and solving by adopting an energy function and implicit layer optimization to obtain a photovoltaic bearing capacity assessment result of the transformer area.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

The invention discloses a remote sensing small sample semantic segmentation method and system based on multi-scale prototype fusion, and belongs to the technical field of remote sensing image semantic segmentation. Extracting features through a feature extraction network; carrying out weighted average pooling operation on the multi-scale support features through a multi-scale prototype fusion network, carrying out fusion and carrying out foreground and background prototype separation to obtain a foreground prototype and a background prototype; calculating a similarity matrix and an attention weight between the target support set data and the query set data through a co-attention module, and generating a co-attention result; and splicing the multi-scale prototype, the co-attention result and the query feature, and inputting a splicing result into a decoder to generate a semantic segmentation result. The method can better deal with the condition that the target scale difference between the support set and the query set is too large, improves the segmentation precision of the model for different scale targets, and is applied to semantic segmentation of ground feature categories with a small number of labels in a remote sensing image.

The invention belongs to the technical field of data encryption processing, and provides a multi-modal cross-domain few-sample learning lung medical image segmentation method and system, and the technical scheme is as follows: respectively extracting visual features and text features of a multi-modal X-ray data set in a query data set and a support data set; performing alignment and cross-modal fusion on the visual features and the text features of different modals to obtain fused lung features; mapping the fused lung features to a domain-independent representation space, and quantifying the similarity between different modal features to obtain feature similarity matrixes of different hierarchies; fusing the feature similarity matrixes of different levels, capturing to obtain semantic matching relationships of different abstract levels, and decoding to obtain segmentation mask features; and performing interpolation calculation on the mask features to obtain a segmentation mask, and segmenting the image to obtain a segmentation result. The X-ray images generated by different medical devices can be efficiently migrated, and the generalization ability of medical image segmentation is improved.

The invention provides a near infrared spectrum qualitative discrimination method and system, and the method comprises the steps: collecting a near infrared spectrum data set, and converting a one-dimensional spectrum sequence into a polar coordinate image; constructing a lightweight convolutional network architecture, and processing the polar coordinate image; k samples of each type are dynamically extracted from the training set, a support set is constructed based on the K samples and a few-sample strategy, and a query support similarity matrix is constructed through a cosine similarity algorithm; and judging the test set based on the query support similarity matrix, the support set and a preset threshold to obtain the highest similarity between the test set and the support set, and if the highest similarity exceeds the preset threshold, judging that the test set is a corresponding category. According to the method, the cost and time of sample collection and preparation can be reduced, the feature information of the spectral data is effectively extracted, and the method has relatively high flexibility during discrimination.

The invention discloses a remote sensing image feature matching and splicing method based on an improved LoFTR algorithm, and the method comprises the steps: S1, obtaining a to-be-spliced remote sensing image pair, and respectively extracting a coarse-grained feature map and a fine-grained feature map from the remote sensing image pair; s2, performing dimension feature flattening and position coding processing to obtain a sequence form feature vector, acquiring matching feature correlation and a rough matching point set by using an improved LoFTR algorithm module, and constructing a local similarity matrix; s3, obtaining a sub-pixel-level matching point set under the scale of the fine-grained feature map; and S4, carrying out weighted fusion of overlapped regions on the remote sensing image pair. According to the method, two stages of rough matching and fine matching are adopted, local fine-grained optimization is carried out on candidate areas in the high-resolution feature map on the basis of a rough matching result in the fine matching stage, a high-precision matching result is finally obtained in combination with a sub-pixel-level optimization strategy, and the consistency of splicing boundaries and the global fusion quality are effectively improved.

The invention discloses a multi-sensor fusion processing method, perception method and equipment based on LiDAR point cloud pseudo image conversion, a multi-view projection strategy is adopted to convert a preprocessed LiDAR point cloud into an aerial view, a front view and a side view, the aerial view, the front view and the side view are subjected to feature coding and then fused through a SENet attention mechanism to generate a multi-view fusion pseudo image, and a large amount of space information is reserved. Meanwhile, in order to effectively improve the multi-sensor feature fusion efficiency, after a visual image is preprocessed, an improved CNN network is adopted to extract the features of a LiDAR pseudo image and the visual image, feature fusion is achieved through a cross-modal attention mechanism, attention weights are generated by calculating a similarity matrix, and the feature fusion efficiency is improved. And after the features are enhanced, the features are fused in a channel splicing and element-level adding mode. The method can effectively improve the precision and robustness of automatic driving environment perception, enhances the performance in a complex scene, and is suitable for tasks such as target detection and semantic segmentation in automatic driving.