262 results about "Structure extraction" patented technology

The invention discloses a semantic segmentation method for a low-resolution road scene, and aims to solve the problems of difficulty in small target recognition, fuzzy details, texture information loss and the like existing in a low-resolution image in the conventional semantic segmentation technology. The method comprises the following steps: (1) collecting a low-resolution road scene image and a corresponding semantic tag; (2) constructing a semantic segmentation model consisting of an edge guidance module (BGM), a double-domain feature decomposer (DDFD), a domain alignment attention fusion module (DAAFM) and a double-layer attention context aggregation module (HACAM); (3) designing a joint loss function to carry out multi-scale supervision on semantic regions, edges and middle features; (4) carrying out model training by utilizing the road scene image; and (5) outputting a semantic segmentation result map and an edge prediction map. The boundary perception capability is enhanced by introducing learnable pixel difference convolution, the extraction precision of a small target and a global structure is improved by combining frequency domain and spatial domain feature alignment, and context semantic relationship expression is optimized by fusing a channel and a spatial attention mechanism. The method effectively improves the semantic segmentation precision and boundary restoration capability of the model in a low-resolution complex road environment, and is suitable for intelligent analysis tasks of road images in scenes of automatic driving, intelligent traffic, severe weather and the like.

The invention discloses a multi-mode fusion product document and source code association retrieval method based on a knowledge graph, and relates to the technical field of software engineering and artificial intelligence. The method comprises the steps that source codes are preprocessed, and code structure information and business semantics are mapped in combination with a predefined business term dictionary; performing controlled induction on a code file and a product document by utilizing a large model, extracting business terms, logic intentions and a subject relationship, fusing with original codes, and establishing a vector retrieval index system; further analyzing a code structure by using an abstract syntax tree, and extracting an entity and a calling relationship; semantic enhancement and relation normalization are performed in combination with the large model, entities and relations are stored in a graph database, and a knowledge graph is formed; and performing parallel processing on user query based on a full-text retrieval index, a vector retrieval index system and a knowledge graph, and finally generating a product concept. According to the method, the retrieval speed, the semantic depth and the logical reasoning ability can be considered at the same time, and the retrieval accuracy is improved.

The invention discloses a highly dense broken ice image segmentation method based on an iteration MGAC and an SAM model, and the method comprises the steps: carrying out the sea ice instance segmentation of a preprocessing image based on the SAM model, and obtaining a sea ice mask image corresponding to the preprocessing image; according to the binary image and the sea ice mask image, obtaining an initial sea ice residual region image which is not identified by the SAM model; obtaining initial sea ice residual region images under different gray threshold values to obtain an initial seed mask graph; taking the initial sea ice residual region image and the initial seed mask image as inputs of a preset MGAC contour model, and obtaining an MGAC sea ice recognition result based on a multi-round iteration partitioning mechanism; and performing union operation on the MGAC sea ice identification result and the sea ice mask image to obtain a crushed ice segmentation mask result. The method solves the problem that the existing method is insufficient in structure extraction precision and boundary integrity of the dense broken ice area.

The invention provides a method and system based on NLP file analysis, and relates to the technical field of natural language processing. According to the method, time and identifier unification and format and character set standardization are carried out on the multi-source file, layout segmentation, table structure extraction, reference analysis, term standardization and anaphora resolution are combined, semantic representation is constructed, a hierarchical index and a unique traceability identifier are generated, intention recognition, retrieval sorting, incremental updating and consistency verification are supported, and the method is suitable for large-scale popularization and application. Unification, semantization and traceability of the file analysis process are achieved, and the processing efficiency and accuracy are improved.

The invention discloses a PCB defect real-time detection method based on multi-scale feature fusion, and relates to the technical field of PCB defect real-time detection methods, and the method comprises the steps: obtaining a to-be-detected PCB image, carrying out the size normalization and pixel value standardization processing of the image, and obtaining a standardized image meeting the input requirements of a model; inputting the standardized image into a backbone network of a teacher detection model, and extracting a multi-scale primary feature map containing texture information in different directions through a grouping convolution structure; transmitting the multi-scale primary feature map to a neck network of a teacher detection model, and performing weighted fusion on feature maps of different scales by using a learnable weight to generate a multi-scale fusion feature map; and in an up-sampling path of the neck network, generating channel description information after global pooling is performed on the deep fusion feature map, generating a channel attention weight through nonlinear transformation, acting the weight on a primary feature map of a corresponding level, and outputting an enhanced feature map.

The invention relates to the technical field of hydrological prediction, in particular to a drainage basin distributed runoff prediction method and system based on a graph neural network, and the method comprises the following steps: obtaining a drainage basin multi-source runoff data set to construct a multi-relation dynamic graph structure, and extracting node feature vectors; based on the node feature vector, obtaining a watershed evolution trend forward feature by establishing a watershed diffusion fitting architecture; constructing a distributed runoff probability prediction model, and taking the watershed trend forward features as model input to obtain runoff initial condition probability distribution of each sub-watershed in multiple periods in the future; establishing a mixed loss function, and performing physical constraint optimization on the runoff initial condition probability distribution to obtain distributed runoff optimization probability distribution; and performing uncertainty quantification on the distributed runoff optimization probability distribution to obtain a drainage basin distributed runoff prediction result. According to the method, the hydrological process simulation capability of the complex watershed is improved, and accurate prediction of the distributed runoff volume is realized.

The invention relates to the technical field of topographic change analysis, in particular to an unmanned aerial vehicle-based topographic change detection system, which comprises a slope direction sensing track control module, a texture structure extraction module, a crack evolution track construction module, a direction trend comparison module and a patrol recheck positioning module. According to the method, a continuous elevation point column of an unmanned aerial vehicle scanning area is extracted, laser reflection point coordinates are fused, a space relation of transition point distribution is constructed, dynamic adjustment of a ground-imitated flight path is achieved, and a texture structure area with continuous directivity is recognized in combination with a high-angle image boundary communication relation; texture boundary evolution is compared at different time nodes to form a crack path, the stability of the path and the slope direction is judged through an included angle sequence, recognition and sorting of areas with the consistent direction are completed, a space comparison result is registered in a three-dimensional coordinate system, terrain change areas are accurately marked, and rapid positioning and continuous tracking of high-risk areas are achieved.

The invention discloses a multi-level detail automatic simplification method for an oblique photography live-action three-dimensional model, and relates to the technical field of three-dimensional model simplification and computer graphics, and the method comprises the steps: obtaining oblique photography original data and three-dimensional model basic information; preprocessing the model, performing adaptive Gaussian filtering denoising, improving RANSAC to remove outer points, compressing textures in a blocking manner, correcting mapping coordinates, and repairing a topological structure; extracting multi-scale features; constructing a simplified decision model, and determining a simplification rate and a priority by combining an observation distance, scene precision and hardware performance; performing hierarchical simplification, vertex hierarchical improved edge folding, patch hierarchical adaptive deletion and regional hierarchical grid reconstruction; performing multi-dimensional quality evaluation, and if the requirements are not met, performing backtracking adjustment; and outputting a simplified model stored according to the LOD hierarchy, wherein the simplified model comprises transition information and a simplified log. According to the method, the data quality is improved through refined preprocessing, the simplification pertinence is enhanced through multi-dimensional feature extraction and intelligent decision, and the application value of the model is improved.

The invention discloses an original script-oriented AI autonomous plot structure adaptive generation system, and relates to the technical field of creation assistance, and the system specifically comprises the following modules: a structure extraction analysis module, an emotional role analysis module, a plot inference module, a scene generation optimization module, a conservation target generation module, an adaptive control module, and a constraint punishment module. According to the method, a multi-level narrative structure and a causal relationship graph are constructed to form an emotion vector and a trajectory curve, an optimal causal path is generated based on a graph neural network, a graph convolution / attention mechanism and a graph generation algorithm, and emotion toning and conservation target driven text generation are performed on scene and dialogue levels. Structural consistency and emotional arcs are optimized in real time in combination with self-adaptive control, plot path weighting and rewriting triggering are performed by utilizing a multi-dimensional emotional space and a neural oscillator network, intelligent structured management of a script is realized, and script creation efficiency and quality are improved.

The invention discloses an automatic driving environment sensing method and system based on multispectral fusion, and relates to the field of auxiliary driving, and the method comprises the steps: collecting environment data in a complex environment through a sensor; performing time-space synchronization and standardized preprocessing on the environment data to obtain preprocessed data; constructing a sensor confidence estimation network, and inputting the preprocessed data into the confidence estimation network; features are extracted and fused through a structure combining a shared encoder and a branch encoder, and a real-time confidence score vector of each sensor is output; a preset mapping function of confidence and weight is obtained, the confidence score vector is converted into a dynamic weight vector, and the sum of all components of the weight vector is 1; and integrating the sensing results of the sensors by adopting a weighted fusion strategy, verifying the sensing results of the high-weight sensors through a cross validation mechanism, and outputting final environment sensing data.

The invention discloses a plant protection field literature information batch structured extraction method based on an AI large model, and belongs to the technical field of computer data processing and artificial intelligence application. According to the method, a PDF format literature is converted into a Markdown format text through a PDF literature self-adaptive preprocessing module and a Markdown conversion module; then, a double-strategy self-adaptive AI extraction module is adopted, different modes are adopted for processing according to a text length threshold value, and JSON data are extracted in combination with a JSON format data restoration strategy; then, the CPU intensive conversion task and the I / O intensive AI analysis task are processed in parallel through a two-stage parallel scheduling module, and a structured database file is generated through aggregation according to a predefined mapping rule through a multi-dimensional aggregation export module. According to the method, end-to-end automation from literature acquisition to structured data output is realized, and the problem of low extraction efficiency of manual literature reading information is solved.

The invention discloses a document structure extraction and model training method and device, equipment and a medium, and relates to the technical field of artificial intelligence and computer vision. The method comprises the following steps: constructing a special training data set containing data of at least two document understanding tasks (including optical character recognition, layout analysis, text positioning, regional text extraction, image description and chart title generation), and a fine tuning data set for converting a document image into a machine-readable structured text format; constructing a multi-modal large model comprising a shape adaptive cutting module, a visual encoder, a visual token compression module, a modal connector and a language decoder; pre-training the model by using the special training data set to jointly learn various document understanding tasks; and performing fine tuning on the pre-trained model by using the fine tuning data set, and adapting to a document structure extraction task to obtain a document structure extraction model. By means of the technical scheme, efficient and accurate document structure extraction can be achieved.

The invention relates to the technical field of intelligent power grid operation monitoring and state recognition, and discloses a method, a system and equipment for sensing the state of a high-proportion photovoltaic regional power distribution network based on a heterogeneous graph neural network and a medium. The limitation of a traditional method in processing complex network topology, multi-source heterogeneous data and disturbance driving behaviors is broken through. In the state sensing process, from acquisition of multi-source data, a series of preprocessing, construction of a reasonable graph structure, extraction of node embedding vectors and output of state labels and abnormal scores are performed by using a preset state recognition model, and all links are closely connected and have innovativeness. And key nodes or regions with potential risks or abnormal states in the power distribution network are identified through the abnormal score function, and state online sensing and risk early warning of the power distribution network containing the photovoltaic region are realized.

The invention relates to the field of underwater sound signal processing, in particular to an underwater DOA (direction of arrival) estimation method based on graph nerves and a convolutional neural network, which comprises the following steps: 1, establishing a linear array, and enabling narrow-band signals to simultaneously reach an underwater sound array; 2, performing signal preprocessing to obtain a signal covariance matrix, and performing normalization processing; 3, extracting correlation between array elements and spatial features of array signals, and performing data supplementation on sparse linear array information; 4, forming a double-branch structure, enhancing the information aggregation capability, and extracting features from a space path and a time domain path; and 5, constructing an adjacent matrix, filling node features of damaged array elements, adopting a double-branch structure, extracting spatial features and time domain features, carrying out feature integration, and outputting a DOA estimation result. The spatial correlation between array elements is extracted and the array sparsity problem is processed by using the graph neural network, and the time domain features of the signals are extracted in combination with the convolutional neural network, so that more accurate and more robust DOA estimation can be realized under the conditions of low signal-to-noise ratio and array sparsity.

The invention relates to the technical field of pattern design intelligent optimization, in particular to an embroidery pattern optimization design method and system based on a machine learning model.The method comprises the following steps that a grey-scale map structure is obtained, edges are extracted to construct a direction node map, node intersection points are positioned to generate path vectors, boundaries are drawn according to the symmetrical relation to generate annotation map blocks, and the annotation map blocks are obtained. Establishing a pattern code generation structure label group, and extracting a repeated paragraph classification style to generate a style training sample label set; according to the method, a composition node map is constructed through the direction continuity of edge pixel points, path candidate intersection points are positioned through the intensity of node space distribution and are connected into skeleton path line segments, an edge structure closed area is extracted by using a path symmetric projection relation, and ordered association of a pattern structure and a pattern is realized. The pattern design process has the capabilities of being controllable in structure, adjustable in style and traceable in feature, and the intelligent level of composition processing and style induction is remarkably improved.

The invention discloses a hydropower station corridor autonomous mobile robot path navigation method, system and device and a storage medium, and the method comprises the steps: collecting hydropower station multi-source environment data, constructing a laser two-dimensional map, and fusing image semantic segmentation labels to generate a two-dimensional semantic map; the current sensor data is matched with the two-dimensional semantic map, and the initial pose of the robot is determined; a navigation path is generated on the basis of the initial pose and the target point, and the pose of the robot is dynamically adjusted and updated in the navigation process; path planning control is carried out based on the updated pose, obstacles are recognized in real time, and a driving path is optimized; recording path driving navigation data, updating the two-dimensional semantic map, and returning to the starting point based on the updated map after the task is completed. According to the method, a two-dimensional semantic map with space consistency and semantic definition is constructed, key geometric structure extraction and navigation structure index generation are combined, accurate recognition and path topology understanding of passable areas in the hydropower station gallery are achieved, and the accuracy and robustness of autonomous path navigation are improved.

Disclosed herein is a cyberthreat detection system for detecting, in real-time, cyberthreats residing within a memory of a targeted computing device. The cyberthreat detection system features an undocumented structure extractor logic and an undocumented offset extractor logic. The undocumented structure extractor logic is configured to identify known, undocumented, memory structures associated with software operating on the targeted computing device. The undocumented offset extractor logic is configured to identify undocumented and unknown memory structures associated with software installed on the targeted computing device.

A machine abnormal sound detection method based on feature enhanced dynamic graph convolution belongs to the field of machine abnormal sound detection, and comprises the following steps: firstly, generating a time-frequency spectrum feature representation for an original audio signal of a machine through a feature extractor; the method comprises the following steps: extracting multi-scale features by using an FE module through 1D-FFT, multi-scale trend period decomposition and a Token-Transform structure to obtain an enhanced feature spectrogram; sDA-GCN and DCA-GCN network structures are used, shared features and dynamic differences between devices are mined based on an enhanced feature spectrum, and feature differences under domain offset are reduced; through a coarse-grained label classifier, a fine-grained label classifier and a domain classifier, in combination with GRL and CORAL losses, fine-grained alignment of the feature space is realized; a three-stage training strategy is used, and the model is optimized through perception consistency pre-training, unsupervised contrast classification learning and reconstruction error-based anomaly detection. According to the invention, high-performance anomaly detection is realized.

The invention discloses an optical flow estimation method, device, equipment and medium, and the method comprises the steps: extracting the multi-scale features of an image through a pyramid network structure, and obtaining the initial optical flow of each level through feature warping processing and cost body calculation; calculating an uncertainty map of each level of optical flow based on the initial optical flow; calculating the fusion weight of the current layer by adopting the uncertainty map of the current layer and the fused uncertainty map of the previous layer, and carrying out weighted fusion to obtain the fusion optical flow of the current layer; calculating the motion gradient consistency loss based on the optical flow gradient difference of the adjacent pyramid layers; constructing a total loss function based on the endpoint error loss, the uncertainty loss and the motion gradient consistency loss of the initial optical flow; and training the optical flow estimation network by using the total loss function to obtain an optimized optical flow estimation model, and performing optical flow prediction on the to-be-estimated image. According to the invention, the overall precision, robustness and interpretability of optical flow estimation in a complex scene are improved.

The invention relates to the technical field of space planning analysis, in particular to a land space planning analysis system based on big data, which comprises a boundary discrimination module, a purpose offset module, a coordination analysis module, a change identification module and an ownership selection module. According to the method, an efficient spatial information circulation and feedback chain is constructed by combining boundary space structure extraction, use weight distribution mapping, population use collaborative feature analysis, resource configuration change trend tracking and an ownership affiliation unit optimization mechanism; global linkage judgment of core elements such as spatial forms, purposes, population and resources in a complex territorial structure is achieved, the whole-course quantitative perception ability of planning and management is enhanced, land purpose optimization, resource allocation dynamic balance and accurate orientation of ownership relations are promoted, data transparency of a space governance link and continuity of a decision closed loop are improved, and the space governance efficiency is improved. Flexible adjustment and fine management of a spatial pattern in a multi-dimensional scene are supported, and the datamation and intelligence level of a territorial space treatment system is enhanced.

The invention relates to the technical field of anesthesia management, in particular to an anesthesia scheme evaluation system based on big data analysis, and the system comprises a response section recognition module, a lag offset linkage module, a risk association mapping module, a risk association mapping module and a scheme structure evaluation module. According to the method, by constructing a multi-section anesthesia depth dynamic structure based on time step division and slope analysis and extracting time synchronization changes of medicine injection information and physiological indexes, boundary recognition and rule induction of multi-section response characteristics in an operation are achieved, and the dynamic anesthesia depth is obtained by combining response delay characteristics and starting point difference calculation. A dose response relation label corresponding to index change is obtained, a preoperative visual induction task and a postoperative trajectory offset record are fused, intervention association between a path error area and a medication path is mined, structured mapping and label evaluation between an anesthesia strategy and postoperative behavior performance of a patient are achieved, and the patient postoperative behavior performance is evaluated. And the structure evaluation pertinence and response precision of the anesthesia scheme are effectively improved.

The invention provides a lightweight multi-modal fusion document information structured extraction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining a document image; preprocessing the document image to obtain an optimized image; through the MobileNetV3, text features of the optimized image are extracted; performing multi-scale feature fusion on the multiple text features to obtain a fused feature map; through a double-branch Tokenized MLP module, extracting a context relation feature between a position feature of the fusion feature map and a spatial position; detecting a text region of the optimized image according to the position features of the fused feature map and the context relationship features between the spatial positions; performing text recognition on the text region of the optimized image through LPRNet; through SLANetplus, a table area of the optimized image is detected; and extracting structured document information by combining the text region and the table region through a multi-modal encoder.

The invention relates to the field of cross application of electronic data processing and block chain technologies, in particular to a tax declaration data verification method based on a block chain. Comprising the following steps: outputting a global data abstract through an SHA-512 hash function; performing Hash processing to obtain field-level structural features, and generating structural perturbation terms in combination with the global data abstract; carrying out nonlinear transformation and weight weighted fusion, and packaging into a ciphertext; analyzing the tax declaration data ciphertext and the global abstract structure, and extracting a structural feature vector; matching the rule template library on the chain to generate a verification function set, outputting a Boolean type result vector, and generating a cross-chain key identifier; a comprehensive fusion credible score value is generated through nonlinear fusion, and whether the tax declaration data meet verification requirements in the aspects of structure verification and cross-chain consistency or not is judged. The technical problems that in the prior art, structural consistency multi-path verification is lacked, the self-adaptive rule generation capacity is weak, and a credible scoring mechanism does not have entropy perception discrimination capacity are solved.

The invention provides an expressway traffic flow prediction method, a storage medium and a system, and the method comprises the steps: generating a node state vector based on multi-dimensional feature data, calculating the real-time relation strength between nodes, generating a dynamic graph weight relation, fusing the dynamic graph weight relation with a prior static graph structure, generating a global graph structure, extracting a path, and generating multi-scale spatial-temporal features. And calculating a weight by attention fusion gating, generating fusion features, constructing an improved space-time synchronization graph convolutional network, and obtaining a traffic flow prediction result. According to the method, a dynamic graph weight relation is formed by generating a node state vector and combining an attention mechanism, and road section relevance mutation can be quickly responded; the dynamic and static fusion gating fuses dynamic and static graphs, and solves the problem of spatial correlation change. The parallel multi-scale feature extraction path captures features from three dimensions, and combines attention fusion gating adaptive weighting to break through the local space-time convolution limitation and improve the prediction adaptability and accuracy.

The invention relates to the technical field of three-dimensional modeling, in particular to a construction fence dynamic simulation method and system based on digital twinning, and the method comprises the following steps: obtaining task information and component coordinates to establish mapping, recognizing a path shielding relation and marking a coincident section, splicing component boundaries to construct an expansion structure, extracting direction offset to generate split segments, and constructing a construction fence. And marking overlapped components and distributing block sequences to generate a dynamic simulation scheme. According to the method, component space mapping is constructed based on a task time sequence, component shielding relation identification is achieved by combining shielding angle extraction and overlapping section positioning, an expansion structure is constructed through a boundary connection relation, direction offset analysis and path recombination are completed, and a block sequence is dynamically generated based on a component overlapping relation and a number distribution mechanism. Cooperative processing of shielding extraction, structure reconstruction and layer configuration is realized, the precision and integrity of shielding relation identification are enhanced, and the flexibility of block organization and the dynamic response capability of scene simulation are improved.

The invention discloses an unmanned aerial vehicle landslide image clustering method and device for emergency monitoring, and belongs to the technical field of landslide monitoring. The method comprises the following steps: acquiring an original image shot by a camera mounted on an unmanned aerial vehicle; extracting multi-modal features of the landslide instance from the original image, wherein the multi-modal features comprise a spatial position feature, a global semantic embedding feature and a local geometric matching feature; fusing the multi-modal features by using a neural network model based on an attention mechanism to generate an instance feature vector; on the basis of the spatial position features, the local geometric matching features and the instance feature vectors, through an iteration process including a feedback mechanism, a graph structure representing the incidence relation between the landslide instances is constructed; and connected components in the graph structure are extracted, and each connected component is determined as a landslide instance cluster. According to the invention, different image instances belonging to the same landslide can be accurately associated, and the error rate of cross-frame target matching under a complex terrain is reduced.

The invention relates to the technical field of resource scheduling, in particular to an AI-based road construction resource optimal configuration method and system, and the method comprises the following steps: analyzing an operation region and a time period, judging spatial coincidence and time concurrency between nodes, recognizing resource coding conflicts, calculating node priority ranking scores, and reconstructing a connection structure. Extracting path indexes to calculate delay contribution and risk distribution, screening abnormal equipment to calculate an adaptive score, and adjusting a scheduling sequence to generate a scheduling arrangement result. According to the method, the task conflict strength index and the priority ranking scoring mechanism are constructed, the delay contribution proportion and the path risk structure are combined, the equipment performance is quantified by adopting the scheduling adaptation grade score, and the instruction arrangement structure is adjusted and optimized by using the resource calling ranking; the dynamic recombination of the construction task scheduling chain and the delay risk suppression of the task path are realized, the equipment matching efficiency and the task execution stability are improved, and the response capability of resource allocation and the adaptive regulation and control capability of path scheduling are enhanced.

The invention relates to a three-dimensional model fingerprint generation method and system, a terminal and a storage medium. The method comprises the following steps: identifying a file type, analyzing a file structure for the file type containing structured entity information, and extracting triangular mesh entity data; executing a block sampling strategy based on the file size for the file type which does not contain the structured entity information; counting the total number of triangular facets for the triangular mesh entity data, and selecting an adaptive hierarchical sampling strategy according to the total number of the triangular facets; triangular surface area sampling is carried out on a triangular mesh entity, a sampling block-jumping block alternating traversal mode is adopted, and compared with an existing mode, the performance is improved by 69-83 times; besides, a two-level Hash structure is adopted, the first level is to calculate the MD5 value of the area accumulated sum for each sampling block, the second level is to calculate the overall MD5 value for the connection sequence of all block level MD5 values, and the two-level structure enhances the uniqueness and collision resistance of fingerprints.

The invention discloses an evaluation method and device for a modular large language model and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of receiving original input data containing at least one mode, and performing structure analysis and field extraction on the original input data according to a predefined task type to generate a standard structured input sample; according to the task type, dynamically matching and scheduling a corresponding multi-modal large language model from a registered model service to obtain original output content; performing structure extraction and semantic regularization processing on the original output content to obtain regularized answer content; calling a corresponding automatic evaluation algorithm to calculate the normalized answer content, scoring through a preset auxiliary scoring big language model, and generating an automatic index score and a subjective index score; and based on the automatic index score and the subjective index score, generating a structured evaluation report containing visual content.

This disclosure relates to the technical field of fault diagnosis, in particular, to a method and system for industrial equipment fault diagnosis based on graph structure joint optimization. The method includes: acquiring an original equipment dataset; constructing an original graph structure based on the original equipment dataset; extracting two basic views based on the original graph structure, calculating graph node embeddings of the basic views using a GCN, and recalculating a probability of an edge in the graph structure based on the graph node embeddings; performing view fusion based on the probability of the edge in the graph structure to obtain a preliminarily optimized view; and processing a fused view through a GAT network to obtain an enhanced view. According to this disclosure, the problems of low prediction accuracy, poor robustness, the like in traditional fault diagnosis are optimized, and thus the stability of industrial Internet equipment is greatly improved.