749 results about "Structure diagram" patented technology

The invention relates to the technical field of industrial component detection, in particular to a transformer iron core detection method based on computer vision, which comprises the following steps of: acquiring an iron core image, extracting key pixel characteristics, screening a directional scattering abnormal region to generate an interference map, extracting a consistent gradient region correction image to generate a reconstruction map, and positioning a symmetric disturbance generation structure map by integral gray difference. And analyzing an overlapping relation by a superposition structure graph to generate an abnormal component graph, and evaluating a risk level by matching a reference index to generate an early warning graph layer. Interference reflection and structural features can be distinguished through linkage analysis of the pixel direction vector and the brightness change frequency, correction of a distorted area in an image is realized based on a gray statistical stable value, and the distortion of the image is corrected by constructing a symmetric point map and analyzing the change trend of a gradient difference value sequence. And the structural overlapping relation is quantitatively judged by combining a component mapping profile diagram, so that the relevance between an abnormal region and a key component is clearly expressed, and the grading evaluation capability of various fault risks in the iron core is improved.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention provides an unmanned aerial vehicle ad hoc network transmission and calculation integrated resource scheduling method based on task driving, and the method comprises the steps: building a multi-dimensional resource pool model which comprises the communication bandwidth, calculation resources and storage resources of an unmanned aerial vehicle, and collecting the resource state vector of each unmanned aerial vehicle node in real time; a dynamic topology sensing network is constructed, link duration is predicted through relative motion speed between unmanned aerial vehicle nodes, and a network structure chart with weights is generated; constructing a decision model based on a fusion architecture of a preset message passing neural network and a deep reinforcement learning network, and inputting the network topology features of the network structure chart and the resource state vector into the decision model; and outputting an optimal scheduling strategy including target node selection and multi-hop path planning through the decision model, and maximizing system benefits while meeting constraints of tasks on communication and computing resource quality. The problems that existing unmanned aerial vehicle networking communication is high in time delay, low in reliability and difficult to calculate and maximize utilization of resources are solved.

The invention relates to the technical field of state monitoring, in particular to an energy state monitoring deduction method and system based on digital twinning, and the method comprises the following steps: obtaining a power value and a direction to construct a chain structure map, marking a mutation point to extract a linkage path, screening out an abnormal path, screening an available index, and combining state behaviors to form an evolution trajectory. And comparing the measured data to generate a mapping result. According to the method, through cooperative calculation of the multi-period node power change direction and the response time difference, the energy transfer path relation is defined by utilizing the construction of an inter-node chain transmission structure, and the node coincidence frequency is superposed in the multi-path sudden change behavior time sequence intersection statistics to realize the judgment and elimination of the abnormal path; path availability screening is realized based on response direction consistency of terminal nodes in residual paths, and accuracy improvement of energy flow anomaly identification, continuity guarantee of path deduction results and verification of equipment response states are realized.

The invention discloses a data processing method and system based on intelligent correction and electronic equipment, and relates to the technical field of educational informationization, and the method comprises the steps: analyzing a standard answer through natural language processing, extracting a standard knowledge point node and a connection edge, distributing a logic priority, calculating a node weight, and constructing a standard cognitive path map; analyzing student answers, mapping student knowledge point nodes through semantic matching, and constructing a student cognitive path map; comparing the two maps, and generating a score mark through a node matching state and sequence offset; detecting and complementing missing nodes and fracture paths, and constructing an atlas residual error scoring structure atlas; and calculating a structured score, and generating a report containing explanatory feedback. The system comprises five corresponding modules, and the equipment comprises a memory and a processor. The correction accuracy and interpretability are improved, the output report adapts to student feedback and teacher rechecking, and the method is suitable for education informatization automatic correction.

The invention discloses a special reasoning model-oriented data set construction method, and particularly relates to the technical field of data set construction. According to the method, domain task text information, rule constraint information and causal dependency information are analyzed, and a dependency structure chart of a target reasoning task is constructed; on the basis of the dependency structure diagram, topology path expansion and anti-fact topology path backtracking analysis are carried out according to the semantic relation direction and the constraint reverse relation, forward path topology feature data and reverse topology verification data are formed, and therefore a forward and reverse dependency consistency matrix is established for topology conflict recognition, and a consistency correction result is output; and finally, according to a consistency correction result, carrying out topology label labeling, topology equivalence judgment and multi-path admission screening to obtain a topology fidelity sample set, and carrying out dependent signature labeling and topology label coding to generate a special reasoning model training data set of the target reasoning task. And the reasoning path reliability and the data set structuring level of the special reasoning model are improved.

The invention discloses a block chain risk address identification method for a dual-structure time perception graph neural network, and the method comprises the steps: carrying out the analysis and cleaning of original data, generating a transaction pair and an account pair based on an effective transaction record, and finally carrying out the standardization processing of a timestamp. Carrying out model training on the marked training data and dividing a data set; and then a double-structure graph model of an account graph and a transaction graph is constructed, heterogeneous characteristics of accounts and transactions in the block chain are distinguished for the first time, an interaction relationship and behavior evolution are modeled respectively, and the comprehensiveness and accuracy of risk identification are improved. Relative and absolute time coding is introduced, short-term behavior modes and long-term trends are captured in a differentiated mode, and the perception ability of dynamic risks is enhanced. Compared with a traditional single graph model, the scheme has the advantages that multi-dimensional information is effectively fused, the model generalization performance is remarkably improved, overfitting is reduced, and the method is suitable for cross-scene and cross-cycle risk detection.

The invention discloses a transformer fault diagnosis and root cause positioning method based on a space-time diagram neural network, and relates to the field of transformer fault diagnosis, and the method comprises the steps: S1, carrying out the preprocessing of the structure information and DGA time series data of a transformer, and obtaining a topological network structure diagram and a DGA time series; s2, obtaining a spatial vector based on a message passing mechanism of a graph convolutional network; s3, a time sequence vector is obtained in combination with a Transform encoder and multi-head self-attention; s4, obtaining space-time fusion features; s5, constructing a multi-task prediction head based on the space-time fusion feature, the fault type historical data and the fault root cause; and S6, carrying out fault detection and outputting a corresponding fault type and root cause positioning result. According to the application, the accuracy of fault type identification can be remarkably improved, and accurate positioning of the fault root cause is realized.

The invention relates to the technical field of image precision optimization, in particular to a surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, which comprises the following steps: acquiring and preprocessing image data, synthesizing an image gradient modulus length and a maximum principal curvature, and introducing a surface normal vector partial derivative to obtain a surface normal vector partial derivative; calculating a curvature-driven depth discretization step length, and generating an initial point cloud data set; constructing a point cloud complex network structure diagram and calculating the topology durability of a point cloud topology structure to obtain an optimized point cloud data set; based on the optimized point cloud data set, in combination with the target image information, the adaptive curvature gradient weight and the error diffusion control value, constructing an adaptive matching cost function, and calculating a point cloud matching cost matrix; and calculating an optimal point cloud transformation matrix based on the point cloud matching cost matrix. According to the surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, low-texture region point cloud matching error suppression and structure optimization of high-curvature region point cloud on a complex curved surface are realized.

The invention discloses a server configuration method and system, a server, equipment and a medium, and relates to the technical field of computers.The method comprises the steps that a configuration file sent by a front-end tool is received and analyzed through a unified configuration service interface, and a to-be-configured item and to-be-configured information corresponding to a current configuration event are obtained; obtaining an execution topological structure chart matched with the current configuration event; according to the execution topological structure diagram, sending a modification instruction carrying to-be-configured information to a to-be-configured module in a server; and according to a preset event atomization configuration rule, if any to-be-configured module runs abnormally, rolling back each to-be-configured module corresponding to the current configuration event to the previous configuration version. Centralized management of configuration is realized through a unified configuration service interface; and through the event atomization configuration rule, the consistency of the overall configuration state is ensured. The problem that the current configuration automation degree is low can be solved, and the effect of improving the configuration automation degree and the configuration efficiency is achieved.

The invention discloses an industrial drawing analysis method and system combined with a multi-modal large model and OCR, and relates to the technical field of drawing analysis, the method comprises the following steps: carrying out layout area segmentation, text recognition and geometric element extraction on an industrial drawing to obtain a structured information set; constructing a multi-relation structure chart set of the industrial drawings; performing structure embedding and feature bias enhancement to obtain a structure feature set; performing semantic understanding on the industrial drawing to obtain semantic features, and performing multi-channel coding to obtain a multi-modal feature set; obtaining a preliminary analysis result set of the industrial drawing; and performing rule verification, and outputting an industrial drawing analysis result. The technical problems of low industrial drawing analysis efficiency, inaccurate automatic scheme analysis and limited processing capacity in the prior art are solved, and the technical effects of realizing full-process automatic analysis of the industrial drawing by combining the multi-modal large model and the OCR, improving the precision and efficiency of industrial drawing analysis and standardizing output information are achieved.

The invention provides a 3D scene generation method and device guided by structure control information, equipment and a medium, and belongs to the technical field of image processing. A 3D building model of a target building structure is constructed based on a scene generation demand; generating a multi-view wireframe structure chart and a depth map based on the 3D building model; using the multi-view wireframe structure diagram and the depth map as structure control information, and combining with a target 3D scene text cue word to guide a pre-trained image generation model to generate a plurality of target style images under different views; and mapping the plurality of generated target style images under different visual angles to the 3D building model as texture information to obtain a target 3D scene model. According to the method, the consistency of scene styles is ensured according to the structured and controllably generated 3D scene model. Meanwhile, generation is guided through the target 3D scene text cue word, and the problem of scene homogenization caused by asset reuse in an existing digital asset library technology is solved.

The invention discloses a building structure stability intelligent evaluation method based on AI modeling, and the method comprises the following steps: S1, collecting building structure data, and constructing a structure diagram topology model; s2, performing spectral analysis, generating a frequency domain priori vector, and constructing a neural network input vector; s3, inputting the input vector into the improved physical information neural network; s4, constructing a neural network training objective function containing multi-source error information; s5, training the neural network by adopting a structural residual adaptive gating optimization algorithm to obtain a final model; s6, inputting the input vector into the final model to obtain a new state variable predicted value and a modal disturbance scoring result; and S7, generating a stability evaluation diagram and a risk scoring result based on an output result, and outputting a structure stability evaluation conclusion and early warning information. According to the method, high-precision modeling and multi-dimensional risk intelligent assessment of the dynamic state of the building structure are realized, and the method is suitable for health monitoring and early warning scenes of large civil structures and complex infrastructures.

The invention discloses an AI diffusion model pattern generation platform and method, the generation platform comprises a visual interaction module, an image generation module and a pattern mapping module, and the image generation module comprises a training sub-module and a reasoning sub-module; the generation method comprises the following steps: preprocessing a text input by a user and an optional structure image, and outputting standardized data; a semantic embedding vector is generated through text coding; carrying out LoRA fine tuning to obtain a style weight module; a ControlNet structure condition is injected; performing diffusion sampling to generate candidate patterns; performing CLIP scoring to screen an optimal pattern; and outputting the optimal pattern and previewing the carrier map. According to the platform and the method provided by the invention, the LoRA low-rank fine tuning, the ControlNet structure condition control and the CLIP similarity evaluation algorithm are fused, so that the effects of customizing the special style pattern with a small number of samples at low cost and accurately controlling the structure and style of the pattern are realized.

The invention discloses a construction method and system of a power distribution network visualization platform based on SG-CIM, and relates to the technical field of construction of power distribution network visualization platforms. According to the construction method of the power distribution network visualization platform based on the SG-CIM, power text information of a power distribution network is obtained and input into a pre-trained semantic recognition model for analysis, an equipment attribute triple and an equipment relation triple of each piece of power equipment are obtained, topology reconstruction processing is carried out on the equipment relation triple, and the power distribution network visualization platform based on the SG-CIM is constructed. According to the method, a power equipment topological structure diagram is generated, graph modeling processing is carried out, a semantic knowledge graph is generated, then quality verification analysis is carried out, and a semantic structure coordination index is obtained. Therefore, the uniformity of semantic expression and topological connection logic of each power device in the power distribution network is guaranteed from the source, and the intelligent analysis capability, the operation visualization capability and the automatic verification capability of the power distribution network structure are improved.

The invention discloses a remote sensing image semantic segmentation data enhancement method based on a controllable diffusion model, and the method comprises the steps: generating a text description based on an original remote sensing image through a vision-language model, and constructing a text prompt in combination with a class label in a semantic segmentation mask; the method comprises the following steps of: generating a refined edge structure diagram through a Holistic Nested Edge Detection (HED) edge detection algorithm on the basis of an original remote sensing image, and coding the refined edge structure diagram into a visual priori feature in combination with a semantic segmentation mask; and embedding the visual priori into a diffusion generation model through a control adapter, and generating a remote sensing image in combination with text prompt. According to the method, the bottleneck of the prior art is broken through through a semantic-guided diffusion process, multi-scale controllable generation and visual priori embedding, the denoising characteristic of the diffusion model can generate a high-fidelity image, and a conditional constraint mechanism can ensure semantic consistency and physical rationality of an enhanced sample; and a better data support is provided for a remote sensing semantic segmentation task.

The invention discloses an eSIM equipment intelligent network selection method based on environment perception and an AI strategy, and the method comprises the steps: collecting and preprocessing multi-source environment data, and generating a multi-mode original feature vector; constructing a three-dimensional situation tensor, generating a causal structure diagram, clustering to obtain a situation identifier, and embedding a situation; constructing a situation feature table, improving TabNet to perform feature selection, and outputting a high-dimensional situation representation vector; based on the situation characterization and the capability characteristics, performing chain type prediction on the performance and generating an anti-factual income score; constructing short-time domain and long-time domain preferences, generating a comprehensive preference value after fusion, and determining a target connection object; and an eSIM strategy configuration instruction is generated, intelligent network selection switching is carried out, and model parameters are incrementally updated. According to the method, active high-stability intelligent network selection of the eSIM equipment in a complex scene is realized through fusion of multi-source environment perception, causal inference and an AI intelligent strategy.

The invention provides a small-scale target crack detection method and device based on depth intelligent perception and a medium, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring a concrete structure image, constructing a concrete structure data set, and dividing into a training set and a verification set; constructing a CCFDNet model based on a YOLOv11 model, wherein the CCFDNet model comprises a backbone network, a neck structure and a detection head; training a CCFDNet model through a knowledge distillation strategy by using training set data, guiding a student network by using an amplified CCFDNet as a teacher network, and adjusting softmax probability distribution through temperature parameters to enable the student network to learn a soft label of the teacher network; and obtaining a to-be-detected concrete structure image, inputting the trained CCFDNet model, and outputting a small-scale target crack detection image. The method still has good adaptability and generalization ability in a complex environment, so that the stability and reliability of concrete crack detection are ensured.

The invention discloses an FTU-based power distribution network fault positioning method and system, and belongs to the technical field of power distribution automation, and the method comprises the steps: constructing a space-time correlation feature matrix according to a transient current sequence and a voltage drop sequence during a fault period, and extracting the convolution features of a graph to obtain a fault feature graph containing the fault correlation degree between nodes; according to a static topological structure in the power distribution information model, virtual impedance is calculated based on the fault feature graph, and network equivalent topology is dynamically identified to obtain a dynamic virtual topological structure graph; and based on the dynamic virtual topological structure diagram structure and the fault feature diagram, performing fault section confidence competing decision through the intelligent agent unit corresponding to each FTU based on an incomplete information game, and outputting a fault section positioning result. The power distribution network fault positioning method solves the problems that a traditional power distribution network fault positioning method is insufficient in positioning accuracy and poor in fault tolerance and excessively depends on centralized processing and global information synchronization when information is incomplete, fault features are complex and network topology dynamically changes.

The invention discloses a graph anomaly detection method and system based on comparative learning and difficult sample mining, and belongs to the technical field of graph data anomaly detection. In order to solve the problems of low abnormal node recognition efficiency, poor stability and insufficient expansibility in a complex structure diagram environment, the method mainly adopts a Beta hybrid model which combines multi-view self-center sub-diagram construction, diagram neural network coding and multi-scale contrast learning with difficult sample mining, and realizes model joint training based on a joint loss function. Through fusing cross-scale and same-scale comparison abnormal scores, potential abnormal nodes are accurately identified. According to the method, efficient, stable and extensible detection of abnormal nodes in a complex graph structure can be realized, and the perception and response capability of a system to risk behaviors is improved.

The invention discloses a software variation testing system and method based on structure-semantic fusion, and the system comprises a document analysis module, a structure diagram construction module, an agent module, a defect injection module and an influence domain verification module.The method comprises the steps that a function document or natural language description serves as an entry, and after a code structure diagram (such as a function call diagram) is constructed through static analysis, the influence domain is verified; an agent autonomously explores semantic information of a code under the guidance of a structure diagram, precise positioning from function description to a target code position is completed, and defect injection and influence domain verification are carried out.

The invention relates to the technical field of unmanned aerial vehicle detection, and discloses a highway bridge hidden danger target identification and position determination method based on an unmanned aerial vehicle. The method comprises the following steps: acquiring a real-time video of a bridge by using an unmanned aerial vehicle carrying a multispectral sensor, and performing multi-scale segmentation on a current frame image to obtain a bridge surface and a suspected hidden danger target area; extracting a suspected hidden danger target area texture feature through a gray level co-occurrence matrix, morphological closed operation and spatial domain filtering, and inputting a hidden danger classification model to obtain type information; based on the bridge surface geometric structure model and the center coordinate of the suspected hidden danger target area, calculating the offset of the suspected hidden danger target area relative to the bridge datum line; and generating three-dimensional space coordinates in combination with the offset and the type information, and marking the three-dimensional space coordinates in a bridge structure chart. According to the method, multi-spectral sensing, multi-scale image processing and three-dimensional modeling technologies are fused, accurate recognition and three-dimensional positioning of bridge hidden dangers are achieved, hidden danger information can be dynamically updated, and the detection efficiency and precision are improved.

The invention provides a data retrieval and chart generation method based on a large language model, and relates to the field of data processing. According to the method, natural language query, interface images and interaction behaviors are fused through a large language model, context features are generated by combining historical query modeling, a query semantic graph is constructed and matched with a database semantic structure graph to obtain field mapping, and a retrieval plan with caliber and time rules is generated and executed to obtain a result. Results are cleaned, an index set is generated according to the index dictionary and the life cycle library, chart types are determined in combination with interaction and data changes, and coding specifications are generated to output chart results. By implementing the technical scheme provided by the invention, the processing efficiency of data retrieval and chart generation can be improved.

The invention discloses a static vulnerability detection method and system based on a unified cross-language program structure diagram, and the method comprises the following steps: constructing the unified cross-language program structure diagram according to a to-be-detected source code and compiling metadata thereof, and building an interface semantic mapping relation on a cross-language interface edge; a cross-language path from a program entry to a sensitive operation point is extracted on the unified cross-language program structure chart, uncertain elements in the cross-language path are labeled, and the confidence coefficient of the path is calculated; performing semantic alignment on heterogeneous language states on a cross-language path based on a cross-language abstract domain, and converting a path condition and a vulnerability establishment condition into logic constraints; and carrying out satisfiability judgment on the logic constraint, outputting a vulnerability detection result according to a judgment result, and generating a structured evidence chain. According to the method, the cross-language evidence chain can be output, vulnerability paths, witness information of trigger conditions and cross-language conversion details can be clearly displayed, and work such as manual recheck and vulnerability repair is powerfully supported.

The invention relates to the field of production scheduling, in particular to an intelligent scheduling method and system for towel production plans. The method comprises the following steps: obtaining towel production order data and a process execution sequence of a towel production information platform, constructing a dependency relationship between process task nodes and tasks according to the process execution sequence and the towel production order data, and generating a towel process task chain set; analyzing resource occupation overlapping conditions among the towel process task chains to construct conflicting resource nodes, analyzing the conflicting resource nodes, constructing conflicting paths, generating a conflicting chain graph, and dividing conflicting release priority areas; analyzing the towel process task chain based on the conflict release priority area and the conflict chain atlas, generating an optimized task chain, and obtaining a schedulable task chain and a path adjustment record; and updating the process task node through the path adjustment record, and generating a towel scheduling table and a towel scheduling structure chart. The method can improve the reasonability of towel production resource scheduling.

The invention relates to the technical field of electric power, and particularly discloses a power distribution branch box monitoring system and method based on a fault indicator, which can compensate for short-time limitation of real-time data by performing compensation and correction through historical load data and operation instruction information, so that a dynamic topological structure diagram at a first moment is more fit with an actual power grid operation state, and the power distribution branch box monitoring accuracy is improved. It is guaranteed that the topological structure of the dynamic topological structure diagram is dynamically updated along with changes of a power grid, the corrected dynamic topological structure diagram can accurately reflect the real connection relation and the electrical distance of the branch box, the quantization result of the branch correlation degree matrix is made to be more suitable for the correlation degree of the actual power grid, and therefore the reliability of the branch correlation degree matrix is improved through dynamic construction logic of the dynamic topological structure diagram. The method can adapt to the dynamic change process of the topological graph of the power distribution network, achieves the effect of dynamically monitoring the fault of the power distribution branch box, and achieves the purpose of reducing the false alarm rate.

The invention discloses a personalized knowledge tracking method based on a cognitive dynamic graph and multi-expert mixing, and belongs to the technical field of intelligent education, and the method comprises the following steps: constructing a cognitive dynamic structure graph based on learning interaction data of students, the cognitive dynamic structure graph comprising a cognitive dependence knowledge point graph and a question-knowledge point association graph; information transmission is carried out on the cognitive dynamic structure diagram based on a graph convolutional network, and cognitive enhanced interaction feature representation is obtained; constructing a heterogeneous expert pool and a self-adaptive routing mechanism to differentiate cognition of the students based on the interaction feature representation of cognition enhancement, and generating a comprehensive learning state evaluation result; and on the basis of the comprehensive learning state evaluation result, predicting the performance of the student on the next question, and realizing personalized knowledge tracking of the student. According to the invention, through dynamic knowledge dependence modeling and a multi-expert mixing mechanism, accurate tracking of student knowledge states and efficient planning of personalized learning paths are realized.

The invention discloses a low-altitude intelligent target tracking method based on multi-modal fusion, and the method comprises the following steps: S1, constructing a multi-modal input sequence, and generating a fusion input data set; s2, inputting the semantic structure feature graph into an improved TimeSform model, and outputting a prediction structure graph; s3, constructing a consistency scoring matrix; s4, extracting candidate target samples of which the track distances meet a set similarity threshold value, and generating anchor point feature vectors; s5, extracting a target feature vector and a historical identity feature vector, and generating an identity similarity score; s6, performing identity regression judgment according to the identity similarity score; and S7, recording a fusion output result and an identity label state of each frame, and generating a target tracking trajectory sequence and an identity record set. According to the invention, accurate tracking under complex shielding and multi-modal interference conditions in a low-altitude scene is realized, and the method has the advantages of high tracking robustness, high identity recovery accuracy and excellent abnormal response timeliness.

The invention discloses an unmanned aerial vehicle wind power blade defect dynamic detection method based on AI vision, and relates to the technical field of JSLYMC, and the method comprises the following steps: S1, collecting an original dynamic image sequence; s2, generating an aligned image sequence; s3, generating a standardized blade surface image sequence; s4, constructing a blade topological structure diagram; s5, inputting the graph structure data into the improved TransGAT model, and outputting a defect candidate set; s6, inputting the defect candidate set into the FairMOT model, and outputting a defect space-time trajectory; and S7, generating a structured detection report. The method overcomes the limitations of dependence on manual inspection, poor dynamic adaptability and insufficient identification precision in a traditional wind power blade defect detection method, and provides an efficient and accurate solution for unmanned aerial vehicle wind power blade automatic inspection and intelligent maintenance decision.

The invention is suitable for the technical field of data detection, and provides an anomaly detection method and device, terminal equipment and a computer readable storage medium, and the method comprises the steps: constructing an original structure attribute graph according to interaction data of a solid object in an import and export process, carrying out the structural enhancement of the original structure graph based on a data enhancement strategy, and obtaining a plurality of high-order sub-graphs, acquiring an implicit interaction relationship among a plurality of import and export solid objects, and modeling local feature information of the import and export solid objects by using the trained multi-order contrast learning model; dividing the original structure attribute graph into a plurality of communities according to a topological connection relationship between entities, acquiring global feature information of the import and export solid articles by using a plurality of community structures, and performing anomaly detection on a target entity according to the local feature information and the global feature information of the import and export solid articles. According to the method, the attribute and structure information of each dimension of the solid article are utilized, and the anomaly detection precision of the import and export solid articles is improved.