120 results about "Global modeling" patented technology

The invention belongs to the technical field of artificial intelligence and big data, and discloses a customer data processing and insight system based on a big language model. The system is composed of a multi-source data access module, a data preprocessing and label fusion module, a large language model semantic understanding module, a knowledge enhancement and semantic linkage module, an insight generation and visualization module, an intelligent strategy output module and a feedback learning and self-optimization module. According to the method, multi-source heterogeneous data such as texts, voices and structured behaviors are integrated, and the deep semantic analysis capability of a large language model is combined, so that global modeling of customer behaviors and intentions is realized; a multi-modal synchronous acquisition and standardization mechanism eliminates data format barriers, and a dynamic label mechanism adapts to context changes, so that the system can capture deep semantic association in customer expression, and compared with a traditional keyword matching method, the semantic understanding accuracy is improved by more than 40%, and a more complete data base is provided for insight generation.

The invention discloses a three-dimensional attitude estimation method combining global modeling and local refinement, which comprises the following steps of: firstly, extracting a two-dimensional attitude sequence by using a human body video data set; secondly, inputting the two-dimensional attitude sequence into a structural modeling main branch, modeling a spatial topological relation and a time sequence dynamic state between joints, and outputting a global three-dimensional attitude sequence; and inputting the two-dimensional attitude sequence into a local refining branch, modeling dynamic change and detail information of a local area, and outputting a local three-dimensional attitude sequence. And finally, fusing the global three-dimensional attitude sequence and the local three-dimensional attitude sequence, generating a three-dimensional attitude sequence output, and completing three-dimensional attitude estimation. According to the method, the problem of insufficient cross-frame information transmission in a traditional method is relieved, and the accuracy and robustness of attitude estimation in a dynamic complex scene are remarkably improved.

The invention discloses a unified framework for solving automatic driving track prediction and planning consistency based on a world model. According to the method, through cooperative work of the automatic driving domain controller and the vehicle-mounted sensing system, end-to-end joint optimization of track prediction and planning in a complex traffic scene is realized, time sequence dependence and interaction dynamics among intelligent agents are accurately captured, and the prediction capability and robustness of a model are remarkably improved. The method comprises the following specific steps: firstly, constructing a generative world model, and generating potential future state representation by utilizing a behavior conditional and backtracking expansion technology; secondly, in combination with global modeling and a local convolutional network, multi-scale features are extracted, adaptive fusion is carried out, and a multi-modal prediction trajectory is generated; then, a multi-target planning model is adopted to integrate various driving indexes, and a track with the minimum loss function is generated; finally, path planning parameters are dynamically optimized through real-time environment perception and decision feedback, and the problems of prediction uncertainty and planning consistency of the automatic driving track are effectively solved.

The invention discloses a hyperspectral image and LiDAR data collaborative classification method based on double-domain mask and multi-scale local reconstruction, and belongs to the field of remote sensing image classification. According to the method, the problems of scarcity of annotation data and insufficient multi-source feature fusion precision in cross-modal classification of a traditional method are solved. According to the invention, feature learning is carried out through mask random image blocks and channels; a hierarchical multi-scale reconstruction architecture is designed, a lower-layer encoder learns fine-grained features, an upper-layer encoder recovers macroscopic semantic information, and multi-level feature space alignment is realized in combination with deconvolution up-sampling and adaptive pooling. According to the method, a multi-modal feature interaction mechanism and a cross-modal attention module are constructed by fusing the local feature extraction advantages of a convolutional neural network (CNN) and the global modeling capability of Transform, and the complementarity of heterogeneous data is enhanced. According to the method, through multi-level feature dynamic fusion and adaptive weight distribution, the collaborative classification precision of the hyperspectral image and the LiDAR data is improved. The method can be applied to remote sensing image classification.

The invention relates to the technical field of computer vision and fire monitoring, and provides a fire image detection and segmentation method based on an end-to-end unified framework and physical knowledge embedding. Aiming at the problems of computation redundancy, feature segmentation and strong dependence on visible light caused by traditional staged processing, the invention provides the following technical scheme: constructing an end-to-end network comprising an Officient Hybrid Ender encoder and a mask-dino decoder, and realizing global modeling and cross-scale feature fusion through a single-layer Transform; thermal imaging physical knowledge embedding is innovatively introduced, and three fusion modes of pixel-level addition, feature-level Embedding and interactive learning are adopted; a lightweight single-layer Transform architecture and a multi-task loss function are designed, and GIOU, Dice and a joint detection segmentation hybrid matching strategy are combined. According to the method, a bounding box and mask prediction are synchronously generated through a unified query mechanism, the adaptability of a low-illumination scene is enhanced by using thermal imaging data, and the training efficiency is improved by decoupling bounding box loss. According to the invention, the real-time performance, robustness and precision of fire monitoring are significantly improved in a complex scene.

The invention discloses a multi-degraded image restoration method based on semantic guidance. According to the method, for degraded images captured by a vehicle-mounted camera under severe weather conditions, potential features of the images are extracted by adopting a trunk network based on Transform, multi-modal semantic information is extracted in combination with a CLIP visual language model, and semantic guidance is provided for different degradation types through a dynamic text prompt generation mechanism. A self-adaptive feature fusion module is designed, channel attention and space attention mechanisms are combined to realize effective integration of multi-modal features, and a degradation feature extraction and fusion module is introduced to enhance the generalization ability of the model. According to the semantic guidance multi-type image recovery network SGIRN provided by the invention, the global modeling capability of the Transform and the cross-modal representation capability of the CLIP visual language model are combined, so that high-quality recovery of various weather degradation types is realized.

The invention discloses a short-term power load prediction method and system based on a CNN-Transform hybrid model, and the method comprises the steps: carrying out the data collection of historical load data and meteorological data of a power system, carrying out the data preprocessing of the collected data, and carrying out the coding of a periodic time feature, and obtaining periodic time coding information; local space-time features of the load data and the meteorological data are extracted by using a convolutional neural network, and hierarchical expression of the features is realized through a multi-layer convolutional structure during extraction; the local spatiotemporal features and the periodic time coding information are fused to obtain fusion features containing a load sequence, the long-period dependency relationship of the load sequence is modeled through a Transform network, global modeling of the fusion features is achieved through a multi-head self-attention mechanism, and a CNN-Transform hybrid model is obtained; a CNN-Transform hybrid model is used for prediction, and a load prediction result is output; according to the invention, the precision of load prediction and the generalization ability of the model are significantly improved.

The invention relates to the technical field of computer vision, in particular to a target tracking method based on adaptive template updating and lightweight Transform. The invention provides an efficient and robust target tracking algorithm for solving the problems that a traditional twin network is insufficient in robustness in a complex scene and a Transform architecture is highly dependent on computing resources. Firstly, a supervision feedback module is designed, supervision information related to a task is introduced in a feature extraction stage, and a network is guided to be more focused on a target area, so that the feature discrimination capability is improved, and background interference is effectively suppressed; secondly, a lightweight Transform structure is constructed, the calculation complexity and the parameter scale are remarkably reduced while the global modeling capability is maintained, and the balance between the model performance and the calculation efficiency is achieved; and finally, designing a self-adaptive template updating mechanism, and dynamically updating the template content in combination with the state information of the current frame and the environment change, thereby enhancing the adaptability of the model to the target appearance change and reducing the tracking drift risk.

The invention discloses an employee performance evaluation method and system based on multi-dimensional data, and belongs to the technical field of enterprise talent intelligent management, and the method comprises the steps of multi-dimensional employee data preparation, employee behavior modeling, employee performance fluctuation prediction, employee performance attribution analysis and employee performance evaluation. According to the method, employee behavior modeling is carried out by adopting a graph construction method combining multi-dimensional cooperation characteristics and behavior characteristics, and overall modeling and dynamic sensing of real positions, interaction strength and multi-dimensional behavior states of individual employees in an organization cooperation network are realized; employee performance fluctuation prediction is carried out by using a graph convolution bidirectional long-short term network model optimized by a joint loss function, and employee individual performance and dynamic evolution of mutual influence in an organization cooperation network are comprehensively modeled; the employee performance attribution analysis method based on anti-fact simulation is adopted to perform attribution analysis, and the influence of the key cooperation relation on employee performance change is quantitatively evaluated by simulating the hypothesis situation.

The invention belongs to the technical field of mechanical health monitoring and predictive maintenance, and discloses a rolling bearing residual life prediction method and system, and the method comprises the steps: carrying out the adaptive feature extraction, transforming a TimesFM large model structure to adapt to the performance degradation modeling of a rolling bearing, and carrying out the field fine tuning based on the bearing data; and carrying out online deployment on the trained time sequence large model, inputting online monitoring data into an adaptive feature extraction module, and finally realizing visual early warning. According to the method, a transfer learning strategy of freezing a Transform trunk and fine tuning a small sample is introduced, and only task related parameters are adjusted, so that the dependence on new data is greatly reduced; a 1D convolution + MLP parallel structure is introduced, and complementary advantages of local convolution and global modeling are combined. According to the method, starting from an actual application scene, a complete end-to-end industrial grade prediction system is constructed, a matched visual component supports real-time prediction result display and early warning feedback, and the man-machine interaction capability is enhanced.

The invention discloses a deep learning method for realizing mechanical fault diagnosis, and belongs to the technical field of intelligent manufacturing fault prediction and diagnosis. Aiming at the problem that the diagnosis precision is sharply reduced along with the improvement of noise due to insufficient front-end feature extraction and mutual superposition of time domain limitation of a self-attention mechanism, a fault diagnosis classification model composed of three levels of feature processing layers is constructed; each stage comprises a wavelet-guided adaptive multi-scale convolution module and a frequency domain enhanced self-attention module; the wavelet-guided adaptive multi-scale convolution module can extract abundant multi-scale features under high noise; the frequency domain enhanced self-attention module carries out global modeling in the frequency domain, and the influence of noise on the overall recognition precision is reduced. The method has strong multi-scale feature extraction capability and anti-noise interference capability, effectively solves the problem of inaccurate diagnosis precision caused by a high-noise environment under an actual industrial condition, and is suitable for fault diagnosis of rotating mechanical equipment such as bearings and gears.

The present invention discloses an improved remote sensing image road extraction method based on a dual encoder, comprising the following steps: preparing a data set; building a remote sensing image road extraction network; training the remote sensing image road extraction network; and testing the remote sensing image road extraction network. The present invention uses a dual encoder combining Swin Transformer and pre-trained Resnet‑34 in the encoder to perform feature extraction to improve the connectivity of road extraction, and combines a cascaded parallel hole convolution block to realize multi-scale feature representation and reduce the fragmentation of the extracted roads. The present invention designs a spatial pixel module in the encoder to capture the spatial information of pixel-level features, thereby alleviating the inaccurate semantic information caused by occlusion. And enhances the global modeling capability of Swin Transformer due to the limitation of the window mechanism. The present invention not only maintains the complete structure of the road network, but also enhances the connectivity and integrity of the road.

The invention discloses a point cloud classification method based on a state space model, relates to the field of deep learning and three-dimensional point cloud target classification, and solves the problem of insufficient extraction of local geometric features and global features of an existing point cloud deep learning model. According to the method, point cloud targets are classified through geometric feature coupling and a cross path feature coordination enhancer. Local feature discrimination capability is improved by utilizing local geometric pooling with a geometric feature coupling mechanism through coordinated propagation and dynamic aggregation of geometric information between a local center point and a neighborhood of the local center point; according to the method, a designed collaborative feature intensifier is used for adopting double-path hybrid interaction, local mutation and sparse key signals are effectively processed, the limitation of traditional SSM global modeling is broken through, and high-precision classification of point cloud targets is achieved.

The invention discloses a power grid ice melting strategy optimization method based on range icing, and particularly relates to the field of power grid ice melting, which comprises the following steps: identifying strategy adjustment behavior differences formed between regions based on actual ice melting equipment states, extracting key boundary paths with significant adjustment direction offset, and constructing an initial identification structure for cross-region consistency control; constructing a fusion type feedback path chain to cope with a boundary strategy direction confrontation problem, and embedding the fusion type feedback path chain into a dynamic weight function to realize structure self-adaptive updating of a boundary weight adjustment mechanism; the adjustment effect after the boundary strategy callback is evaluated, unbalanced nodes are judged and identified through a combined threshold value, and a structure rollback mechanism is triggered to maintain the stability and convergence of boundary scheduling; weight adaptive evolution is realized by constructing a region adjustment state vector, a boundary offset recognition mechanism and a dynamic weight adjustment function and fusing historical behavior evolution, so that the problem of lack of global modeling and boundary cooperative control in existing scheduling is solved.

The invention discloses a method for realizing three-dimensional point cloud classification based on an NCFMama network, relates to the technical field of three-dimensional data processing, and solves the problems that an existing point cloud classification method is insufficient in local feature extraction, difficult in consideration of calculation complexity and global modeling capability and the like. According to the method, three dimensions of spatial distribution, feature similarity and direction consistency of a local neighborhood of the point cloud are comprehensively analyzed through a neighborhood consistency feature modulator, adaptive feature aggregation of different geometric regions is realized, and meanwhile, a bidirectional spatial perception fusion module is provided. The module uses point cloud three-dimensional space distance information to guide feature interaction of a bidirectional state space model, information exchange between adjacent points of a point cloud space is enhanced, and high-precision and high-efficiency point cloud classification is realized. According to the method, the advantages of calculation efficiency, classification precision and robustness are combined, and a new technical path is provided for application of the Mama architecture in point cloud processing.

The invention provides a target tracking method and system based on multi-kernel convolution and grouping residual self-attention, and the method comprises the steps: constructing a feature fusion network which comprises a multi-kernel convolution module and a grouping residual self-attention module under a twin network framework, and extracting the features of a template image and a search image, the template features are used as static tokens to be connected with search features in series, fusion is carried out through a self-attention mechanism, and a fusion feature sequence is formed; calculating a maximum similarity score by using a multi-head self-attention encoder to generate a center score graph, and planarizing a search region in the fusion feature sequence into a 2D feature graph; and inputting the data into the head network for processing to obtain a tracking result. According to the method, the advantages of deep convolution and a self-attention mechanism are absorbed, the global modeling capability is achieved, meanwhile, the capability of fully learning local details is achieved, feature fusion is conducted through an asynchronous structure, and the robustness of the model is enhanced.

The invention discloses a multi-scale time sequence prediction method based on an adaptive sparse expert selection strategy and a closed continuous time neural network. The method comprises the following steps: carrying out normalization and low-dimensional feature mapping based on RevIN; performing trend-seasonal structure enhancement processing on the feature sequence after linear mapping; constructing a multi-scale expert model based on the feature sequence after trend-season enhancement; self-adaptive sparse expert selection and load balancing loss calculation are carried out; carrying out weighted aggregation and residual fusion on multi-scale expert output; global modeling of a closed continuous time neural network based on channel weighting is carried out; and finally performing prediction generation and reverse normalization. The multi-scale time series prediction method has the multi-time-scale adaptive modeling capability, the sparse expert efficient selection mechanism and the global continuous time modeling capability, and can be applied to various multivariable time series prediction scenes such as power load prediction, weather prediction, industrial production monitoring, traffic flow prediction and financial price prediction.

The invention discloses a handwritten text recognition method based on multi-stage enhancement. The handwritten text recognition method comprises the following steps: acquiring a handwritten text image; constructing a hierarchical dynamic multi-scale CNN backbone network to obtain a visual feature sequence; inputting the visual feature sequence into a time sequence multi-scale module to obtain a local enhanced feature sequence; performing global modeling on the local enhanced feature sequence by using a Transform encoder to obtain a global visual feature sequence; enhancing the global visual features to obtain a time sequence context feature sequence; dynamic weighted fusion is carried out on the global visual features and the time sequence context features through a gating fusion module; and sending the fused features into a linear classifier and a CTC decoder to obtain an identification result. According to the method, intelligent arbitration of global and time sequence features is realized through new technology application of a hierarchical multi-scale CNN trunk, time sequence context enhancement and a gating fusion mechanism, and the recognition accuracy and robustness are remarkably improved.

The invention belongs to the technical field of life prediction, and discloses a residual life prediction method based on a digital twinning collaborative residual network, and the specific technical scheme is as follows: step 1, analyzing the working mechanism of a hollow cathode, and constructing a dynamic simulation model based on the digital twinning collaborative residual network; step 2, introducing a sub-domain adaptive mechanism, and realizing alignment of sub-domain condition distribution by minimizing the difference of fine-grained features between a simulation domain and a real domain; and step 3, constructing a ResTCN-BIGRU-Attention model for life prediction, firstly extracting local long-term dependence features in a time sequence by using a ResTCN module, introducing a multi-head attention mechanism and a feedforward network, realizing channel-level fusion of multi-sensor data, and finally realizing global modeling of key features after fusion by combining BIGRU with the attention mechanism, so as to realize life prediction. The method is good in accuracy, stability and practicability in a hollow cathode life prediction task.

This application belongs to the field of aircraft structure simulation calculation, and specifically relates to a method for constructing a high-dimensional output aircraft structure global proxy model, including: step 1, according to the probability distribution f of the structural model input variable X X (x), extract N input samples {x (1) ,…,x (N)} T , build a sample pool for global modeling. Step 2: Select N0 input samples from the sample pool and substitute each input sample into the finite element model for calculation to obtain the corresponding output response sample. Step 3: Perform PCA dimensionality reduction decomposition on the output response sample and convert the output response sample into the mean-centered output principal component sample. Step 4: Construct the Kriging model g with the input sample output principal component sample. K (X); Step 5, use the variance learning function to learn the Kriging model g K (X) is updated to obtain the Kriging model g for calculation K (X); Step 6: Calculate the Kriging model g K (X), perform matrix reconstruction to obtain a high-dimensional output prediction model.

The invention discloses a power distribution area net load prediction method based on global modeling and fusion optimization, and the method comprises the steps: firstly constructing a global net load prediction model, then carrying out the construction and fine tuning of a local model, and finally carrying out the model integration and fusion prediction. According to the power distribution area net load prediction method based on global modeling and fusion optimization, global modeling, feature extraction and multi-model integration are fused, a global period modeling structure is constructed, area-level local fine tuning is performed on the basis, and a multi-model fusion mechanism is combined, so that the power distribution area net load prediction efficiency is improved. The problems that according to an existing method, modeling is conducted in multiple areas, and the area migration effect is unstable are solved. According to the method, the modeling sharing performance and generalization capability of the model are improved, the depiction capability of periodicity and disturbance characteristics in the transformer area load is enhanced, and high-precision, high-efficiency and high-adaptability net load prediction is realized.

The invention relates to the technical field of power construction automation, and discloses a power robot control method and system based on a visual voice action model.According to the method, a VLA visual voice action model is improved for a power construction scene, a full-connection layer of a feedforward neural network in an attention layer is enhanced through nonlinear transformation of the VLA visual voice action model; the method comprises the following steps of: replacing a combination of a deep separable convolutional layer and a full-connection layer with a combination of the deep separable convolutional layer and the full-connection layer, capturing'local space continuity 'implied in a Token sequence by using the deep separable convolutional layer, so that the feed-forward neural network obtains a local space convolution mechanism similar to a CNN local receptive field, and modeling global association by using the full-connection layer, so as to obtain the local space continuity of the local receptive field. According to the method, the spatial perception capability of the CNN and the global modeling capability of the Transform are effectively combined, the spatial understanding capability of the VLA visual voice action model is improved, and the VLA visual voice action model can more accurately control a power robot to complete a complex task in a complex power construction scene.

The invention relates to a CNN-Mama double-branch fusion network for outdoor fire detection, and belongs to the technical field of image detection, the CNN-Mama double-branch fusion network comprises a VMama branch and a CNN branch which are processed in four stages, the VMama branch provides a global receptive field and a global modeling capability for outdoor fire, the CNN branch is responsible for capturing local details, and the VMama branch is responsible for providing a global receptive field and a global modeling capability for outdoor fire. And the VMama branch and the CNN branch carry out feature fusion interaction and iteration of down-sampling processing in a parallel mode. The invention provides a novel CNN-Mmba double-branch fusion network, and the network combines the skilled local detail capturing capability of the CNN and the strong long-range context modeling capability of the Mmba, so that the robustness of OF detection is remarkably enhanced in a complex real scene. Different from ViT, Mamba realizes linear calculation complexity while maintaining strong global modeling capability, so that the method is more efficient and suitable for actual scenes.

The application discloses a kind of dry coal shed in cargo abnormal operation monitoring method, the method gathers the image information of measured object, constructs operation vehicle dataset, adds attention mechanism module on the basis of depth neural network model, improves the global modeling ability of model, improves the accuracy of real-time detection;The edge recognition and contour extraction of coal pile in image are carried out using image processing method, and then different coal piles are divided and numbered.According to the release instruction of customs and the movement of coal conveying belt, the change of operation vehicle corresponding coal pile is judged, and then it is judged whether there is irregular operation behavior, and an alarm is given for abnormal behavior, to achieve the purpose of real-time monitoring of dry coal shed interior operation.

The invention provides a wind power blade image super-resolution reconstruction method and system based on a graph neural network. The method comprises the following steps: S1, dynamically establishing edges between nodes according to the similarity between pixel features; s3, dynamically distributing an aggregation degree for each node based on a detail perception index; s4, performing multi-scale feature aggregation on the graph structure to obtain an aggregated graph structure, the multi-scale feature aggregation including local feature aggregation and global feature aggregation; and S5, performing up-sampling reconstruction on the aggregated graph structure to generate a high-resolution wind power blade image, and optimizing the reconstruction process by using a weighted loss function. The method solves the problems of weak global modeling capability, poor geometric adaptability and uneven calculation resource distribution when a traditional method is used for processing the wind power blade image, improves the reconstruction definition and detail accuracy of the blade defect area, and is suitable for supporting subsequent high-precision automatic detection and state monitoring of the wind power blade.

The application belongs to the technical field of artificial intelligence, and relates to a voice processing method based on artificial intelligence, which comprises the following steps: receiving an input voice signal; pre-processing the voice signal to obtain voice data; calling a preset voice processing model; wherein the voice processing model comprises a modeling module, an attention module and a convolutional neural network module; performing feature extraction on the voice data based on the modeling module to obtain feature data; performing global modeling processing on the feature data based on the attention module to obtain global features; processing the global features based on a skip connection mechanism in the convolutional neural network module to generate a target complex spectrum; and performing inverse transformation processing on the target complex spectrum to obtain a target voice signal. The application also provides a voice processing device based on artificial intelligence, a computer device and a storage medium. The application can be applied to the voice enhancement processing scene in the fields of financial technology and digital medical treatment, and effectively improves the generation quality of the target voice signal.

This invention provides an automatic modeling method and system for low-voltage power grid data based on visual data acquisition, relating to the field of low-voltage power grid technology. The method includes: dividing the low-voltage power grid area into multiple sub-regions and constructing multiple local coordinate systems; analyzing the power grid equipment to be acquired and determining at least one matching local coordinate system; configuring a visual data acquisition task template, performing data acquisition to obtain at least one set of equipment modeling data; constructing a multi-region coordinate system transformation model, performing coordinate system transformation to obtain at least one set of equipment modeling transformation data corresponding to the equipment modeling data, and generating visual modeling simulation results for the data acquisition task. This invention solves the technical problem in existing technologies where, in the process of low-voltage power grid data modeling, a large, unified global modeling coordinate system is typically established for data acquisition or modeling of all power grid equipment. While this achieves a unified effect, it is not accurate enough, affecting the true reflection of power grid data and its application effectiveness.

The method for classifying sparse point cloud based on MSE-Mamba network relates to the technical field of three-dimensional data processing, and solves the technical problem that the existing sparse point cloud classification method is affected by the sparsity of the point cloud, resulting in insufficient capture of local features, low efficiency of global correlation modeling, and difficulty in coordinating local feature extraction and global modeling, thereby causing the classification accuracy to decrease. The method uses a MSE-Mamba multi-scale local feature coding module to complete the extraction and enhancement of the local geometric features of the sparse point cloud, then captures the long-range semantic correlation between the core points through a Transformer module based on a global attention mechanism, realizes the fusion of the local geometric features and the global semantic information, and finally completes the class probability calculation through a multi-feature aggregation strategy to realize the high-precision and high-efficiency classification of the sparse point cloud. The method realizes the coordinated improvement in classification accuracy, calculation efficiency and anti-sparsity robustness.

The invention discloses a visual representation global modeling method and system based on feature extraction, and the method comprises the steps: processing input visual data through employing a visual vocabulary bag feature coding algorithm, generating a feature vector, and obtaining space-time continuous feature representation through the dimension reduction reconstruction of a space-time continuous visual autoencoder; pixel and semantic category mapping is established by using a pixel-level semantic feature mapping algorithm, a pixel-level semantic feature map is generated, and the step comprises the sub-steps of dimension analysis, model construction and the like; inputting the feature map into a high-dimensional visual representation intelligent analysis platform, and screening through sub-steps of segmentation, standardization, correlation analysis and the like to obtain high-dimensional screening features; and on the basis of the screening features, constructing and optimizing a global feature incidence matrix through a visual representation global modeling technology, and outputting a global model. The system comprises corresponding function units, the visual data global association rule is accurately captured, and adaptability and analysis accuracy in a complex scene are enhanced.

The present application relates to the technical field of target detection, and particularly relates to a small target detection method for improving YOLOX network structure, by introducing and improving CSPDarkNet network, integrating multi-scale spatial pyramid pooling layer, global self-attention and multi-scale feature fusion modules into the network model, small target features of images can be extracted from complex data sets, and the positioning and effective detection of small targets can be accurately detected. Three technical problems are mainly solved, one is that limited use of maximum pooling convolution makes the top convolution too sparse, resulting in incomplete features extracted; two is that CNN lacks the ability of global modeling and long-distance modeling; three is that single-level extracted features will cause the final prediction result to be far from the true situation.