6056 results about "Graph based" patented technology

The invention discloses a machine vision-based resistor disc defect online detection system and a grading method, relates to the technical field of industrial machine vision detection, and solves the defect problems in the aspects of multi-scale defect dynamic perception, cross-level feature interaction and process adaptive optimization in the prior art. According to the scheme, metal reflection interference is inhibited through Retinex illumination correction and a combined denoising model; adopting a deformable convolution kernel and cavity space pyramid pooling to realize gradient entropy driving dynamic sensing of the multi-scale defect; constructing a bidirectional cross-layer attention network to realize early fusion of high-resolution details and high-level semantics; modeling local-global feature physical association based on a graph attention network and a self-supervised message passing mechanism; integrating reinforcement learning and a memristor random calculation unit to form a closed-loop parameter optimization system; according to the method, the multi-scale defect detection precision, the cross-modal feature fusion efficiency and the system adaptive capacity under complex working conditions are remarkably improved.

The invention provides a multi-source heterogeneous data integration and governance-oriented data weaving method, which comprises the following steps of: performing data acquisition from an accessed multi-source heterogeneous data source to generate an original multi-source heterogeneous data stream; performing standardization processing on the original multi-source heterogeneous data stream to generate a standardized multi-source heterogeneous data set; performing active content scanning processing on the standardized multi-source heterogeneous data set to determine business metadata, and performing consanguinity tracking processing on the business metadata to generate enhanced business metadata; calling a domain ontology framework to carry out standardized constraint on the enhanced service metadata to obtain standardized service metadata without cross-data source semantic ambiguity, and carrying out implicit association mining processing on the standardized service metadata based on a graph neural network to generate a semantic knowledge graph containing core entities and relationships; and performing logic abstraction processing on the distributed data resources according to the semantic knowledge graph to generate a unified data access interface.

The invention discloses a traffic flow prediction method based on graph diffusion and dynamic graph fusion. The method comprises the following steps: S1, acquiring historical traffic flow time sequence data of each traffic node in a target road network; s2, preprocessing historical traffic flow time series data to obtain a road network node adjacency matrix; taking the historical traffic flow time sequence data and the road network node adjacency matrix as sample data, and dividing a training set, a verification set and a test set according to a preset proportion; s3, constructing a traffic flow prediction model based on graph diffusion and dynamic graph fusion; and S4, performing model training and verification on the traffic flow prediction model through the training set and the verification set to obtain an optimal traffic flow prediction model, and realizing traffic flow prediction of the test set through the optimal traffic flow prediction model. The problems that an existing method does not have the dynamic topology modeling capacity, the high heterogeneous feature fusion capacity and the self-adaptive space-time modeling capacity, and consequently the bottleneck problem of a current model in the aspects of prediction precision, stability and practicability cannot be effectively solved.

The invention discloses an intelligent monitoring system for a municipal drainage pipe network, and particularly relates to the technical field of drainage pipe network monitoring. The node operation mode identification module carries out real-time classification and confidence evaluation on the operation state of the pipe network, constructs a multi-attribute pipe network weighted graph based on pipe diameter difference, gradient and confluence density, and extracts multi-scale features through graph Fourier transform. A hybrid anomaly detection link is constructed in combination with an LSTM self-encoder, an isolated forest model and chemical oxygen demand and turbidity water quality verification, and the problems that traditional single-index monitoring is prone to false alarm and missing alarm and inaccurate in positioning are solved; sensor data compensation is realized through cooperation with digital twinning, a rapid detection mode is started during rainstorm early warning, key nodes are processed preferentially, and drainage scheduling is controlled in a closed-loop mode; and target nodes which are easy to accumulate grease are screened based on pipe network topology connectivity, accumulation risks are predicted by fusing multi-sensor data, and a preventive clearing instruction is triggered.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention discloses a community intelligent monitoring and emergency linkage method and system fusing BIM spatial semantics, and the method comprises the steps: constructing a BIM scene map, and obtaining the attributes and mutual relationships of components and spatial regions in a BIM model; mapping a dynamic target detected in video monitoring into the BIM model, and obtaining spatial semantic information of the dynamic target; based on BIM spatial semantic information of a dynamic target, a target-environment interaction graph is constructed, a graph neural network model is used for training and reasoning, and specific complex events related to spatial contexts are recognized; taking the BIM model as a space-time reference, fusing multi-source heterogeneous data, and reconstructing by adopting a graph-based event association algorithm to form a complete event chain containing an atomic event sequence and an association relationship; and when an emergency event or an event chain is detected to indicate an emergency state, combining BIM preset information and real-time sensor data, dynamically generating an optimal emergency plan, and performing visual commanding and dispatching through a BIM three-dimensional scene and augmented reality.

The invention discloses an electric power system abnormal remote signaling detection method based on a graph auto-encoder model, and the method comprises the steps: collecting measurement data of an electric power system, carrying out the preprocessing, obtaining a graph data set with an abnormal label, and dividing the graph data set into a training set and a test set; inputting the graph data in the training set into the graph auto-encoder model for training; after training is completed, abnormal score distribution is counted based on normal edge samples in a training set, a threshold value is set to serve as a follow-up judgment basis, and threshold value selection takes the accuracy rate and the recall rate on a test set as an adjustment and optimization target; in a test stage, image data in a test set are input to carry out edge feature reconstruction and anomaly scoring, anomaly judgment is carried out on edges in combination with a set threshold value, and a preliminary abnormal edge detection result is output; and the output abnormal edge detection result is input into the graph restoration module, the restored edge structure and edge features are output, the damaged remote signaling state in the power grid is restored, and the integrity of the graph structure and the operation credibility of the power system are improved.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention relates to a graph-based network security event modeling method and system, and the method comprises the steps: obtaining topological data and security policy information of a network where network security equipment is located, and carrying out the hierarchical construction of a knowledge graph, and obtaining a multi-layer security graph; acquiring real-time monitoring data of the network security equipment, and performing graph adversarial learning association with the multilayer security graph to obtain a dynamic evolution graph sequence; obtaining alarm data of the network security device, and performing anomaly detection on the multilayer security map to obtain an anomaly propagation situation; performing security assessment construction on the dynamic evolution diagram sequence and the abnormal propagation situation to obtain an initial security assessment scheme; performing alarm identification and attack link prediction on the alarm data to obtain a link prediction result; and performing evaluation and prediction on the initial security evaluation scheme and the link prediction result to obtain a security situation evaluation strategy. According to the invention, the security condition of the current network can be evaluated more accurately.

The invention discloses an urban inland inundation risk multi-level prediction method and device based on time-space diagram learning, a storage medium and a computer program product, and relates to the technical field of natural disaster risk prediction, and the method comprises the steps: collecting multi-modal urban hydrological data; performing hierarchical time modeling on the multi-modal urban hydrological data, and extracting a time embedding vector; constructing a heterogeneous graph based on the time embedding vector, and performing spatial feature aggregation calculation on the heterogeneous graph to obtain spatial embedding representation; and performing multi-level prediction according to the spatial embedding representation to obtain a multi-granularity waterlogging risk index. Through multi-modal data acquisition and preprocessing, layered time modeling, heterogeneous graph construction, spatial feature aggregation calculation and multi-level prediction, multi-modal urban hydrological data are effectively fused, and comprehensive and accurate urban inland inundation risk prediction is realized.

The invention relates to the technical field of remote sensing image recognition, in particular to a remote sensing sewage area recognition method and system based on a graph structure and multi-stage enhancement. The method comprises the steps of performing data preprocessing and representation enhancement on an acquired remote sensing image; performing sewage salient region preliminary screening on the enhanced remote sensing image, including abnormal enhancement mapping construction based on local statistical distribution; pollution candidate graph extraction based on spatial structure prior driving; enhancing the response of the stable region based on a structure consistency enhancing mechanism of the polluted region; high-precision segmentation and identification of the sewage area comprises the following steps: constructing a multi-resolution residual pyramid structure; carrying out fine-grained boundary structure modeling and uncertainty suppression; generating a sewage distribution probability graph and optimizing structural consistency; according to the method, the multi-resolution residual pyramid structure is constructed, image context information under different perception scales is fully mined, and the sensitivity and edge integrity of the model to a sewage area under a complex texture background are remarkably enhanced.

The invention is suitable for the technical field of data analysis, and provides a transformer substation fault handling method combining causal reasoning knowledge graph modeling, comprising: acquiring multi-source heterogeneous data and performing data cleaning processing to obtain a space-time alignment data set, the space-time alignment data set comprising one or more quaternary data sets, the quaternary data set comprises a device identifier, a timestamp, a feature vector and an event tag; causal modeling processing is carried out on the time-space alignment data set to obtain a causal graph, and the causal graph comprises node information of nodes and relation information between the nodes; constructing a space-time diagram neural network model according to the causal diagram and the equipment connection relation diagram, wherein the space-time diagram neural network model realizes dynamic evolution of the graph based on an incremental updating strategy; and outputting fault root cause positioning information according to the time-space diagram neural network model.

The invention relates to the technical field of granary ventilation and energy consumption optimization, in particular to an intelligent granary ventilation and energy consumption optimization decision-making method based on reinforcement learning, and the method comprises the steps: obtaining the internal and external multi-dimensional state data of a granary, respectively constructing a basic state vector, a space correlation feature vector and a grain condition change trend vector based on the multi-dimensional state data inside and outside the granary; combining the basic state vector, the space correlation feature vector and the grain condition change trend vector into a space-time feature tensor, inputting the space-time feature tensor into a space-time topology perception decision model based on a graph attention network, and outputting an optimal composite action vector for controlling ventilation equipment; and analyzing and converting the optimal composite action vector to drive ventilation equipment of the granary to execute control. According to the method, through fusion of space-time topology perception and deep reinforcement learning, accurate prediction and active regulation and control of the granary state are realized, and energy consumption of a ventilation system can be reduced to the greatest extent while grain storage safety is ensured.

The invention relates to the technical field of intelligent logistics scheduling, in particular to a logistics scheduling planning method and system based on a graph neural network and reinforcement learning, and the method comprises the following steps: S1, constructing a dynamic graph structure of a logistics network; s2, carrying out embedded learning on the dynamic graph structure through a graph attention network, and extracting a multi-dimensional feature vector of each node; s3, inputting the multi-dimensional feature vector into a multi-agent reinforcement learning framework to generate an initial vehicle path planning scheme; s4, dynamically correcting the road section traffic state in the initial vehicle path planning scheme; s5, iteratively updating the vehicle path planning scheme through local reinforcement learning; and S6, outputting a final collaborative optimization cargo transportation track and a vehicle driving path. According to the method, dynamic modeling and multi-agent path collaborative optimization of a logistics network structure can be realized, and the method has adaptive adjustment capability on real-time traffic and environment change, so that the overall scheduling efficiency is improved.

The invention relates to the technical field of respiratory system risk prediction, and provides a respiratory system risk prediction method and system based on a graph neural network, and the method comprises the steps: collecting the multi-modal medical data of a patient, and constructing a multilayer heterogeneous graph based on the multi-modal medical data; constructing a weighted adjacency matrix and a node feature vector through the multi-layer heterogeneous graph; matrix product operation and convolution operation are carried out based on the weighted adjacent matrix and the node feature vector, splicing combination with historical moment state information is carried out, graph state representation is obtained, weighted aggregation of time dimensions is carried out, and time sequence attention features are obtained; performing coding processing based on the clinical examination data to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a risk classifier for classification calculation to obtain a respiratory system risk level prediction result, generating a risk assessment report, and outputting respiratory risk early warning information. The accuracy and clinical practicability of respiratory system risk prediction are improved.

The invention discloses a building elevator detection, diagnosis and decision-making method based on a graph retrieval enhanced agent. The method comprises the steps that 1, elevator detection data are prepared and processed; step 2), knowledge extraction; step 3), knowledge fusion; step 4), visualization and optimization of the knowledge graph; 5) performing graph retrieval enhancement generation; step 6), diagnosing a decision-making agent; according to the method, triple information can be extracted from structural data, text data, visual data and other multi-modal data in the elevator detection field by guiding a multi-modal large model through an elevator detection technical specification, and an elevator detection visual target entity and a text named entity are automatically aligned based on a pre-trained vision-language model; the multi-modal knowledge graph in the field of elevator detection is accurately and efficiently generated, and building elevator detection intelligent diagnosis is carried out on the basis of the multi-modal knowledge graph and the fusion graph retrieval enhancement technology.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

The invention discloses an intelligent operation and maintenance management system and method based on a charging pile, and belongs to the technical field of fault early warning, and the method comprises the steps: building a unified time sequence operation data matrix through collecting multi-source state data generated in the operation process of the charging pile; key features are extracted to construct feature vectors, and a multi-classification neural network model is utilized to evaluate a health state; a micro-degradation evolution path model is constructed in combination with the health trend in the continuous observation period, and a fault prediction curve is generated; performing similarity matching on a prediction result and a fault prior curve library, calculating a risk weight coefficient, identifying potential fault nodes and outputting an early warning list; constructing a regional task scheduling graph based on the high-risk pile position, fusing geographic position, power level and residual life information, and optimizing to generate an operation and maintenance path and a resource configuration scheme; according to the method, the fault prediction accuracy and operation and maintenance efficiency of the charging pile can be remarkably improved, and intelligent operation and maintenance and response optimization are realized.

The invention discloses a grape disease recognition and early warning method based on the Internet of Things, and relates to the technical field of plant disease recognition, image acquisition equipment and environment sensing nodes are arranged in a vineyard, and leaf images and corresponding temperature and humidity, illumination and soil moisture parameters are obtained; inputting the image into a neural network fusing dilated convolution and a residual attention mechanism, realizing extraction of a disease spot region and a disease spot variation feature, and generating a preliminary recognition result; constructing a multi-factor evolution sample set in combination with the recognition result and the environment state of the time node; constructing a space-time correlation graph model based on a graph neural network, estimating a disease propagation risk path and a diffusion probability, and performing early warning judgment at a gateway end through a multi-factor gating discrimination algorithm; the method disclosed by the invention is high in recognition precision and strong in response timeliness, has adaptive prediction and targeted treatment capabilities, and remarkably improves the intelligence and precision level of grape disease management.

The invention discloses a V2X-based global path planning method for a mine unmanned driving cloud control platform, and relates to unmanned driving. The V2X-based global path planning method comprises the following steps: constructing a traffic semantic map data structure # imgabs0 #; according to task issuing or operation plan adjustment, generating path request data R, and performing time constraint, resource constraint and path optimality constraint verification on the path request data R; according to the R and # imgabs1 #, adopting a heuristic search algorithm based on a graph theory to carry out optimal path search on the road topological structure, and generating a global path P containing a node sequence and driving parameters; and acquiring obstacle data detected by the vehicle end through a local sensor, performing obstacle avoidance correction through a local path optimization algorithm according to the obstacle data and the global path P, and generating a local path meeting the safety distance constraint and the path deviation constraint. According to the method, on the premise of meeting multi-dimensional coupling constraints such as time-space, priority-resource, safety-efficiency and the like, the optimal driving path dynamically adapting to the complex environment of the well industry and mining is generated.

The invention provides a patient information collection and medical record construction system and method based on multiple rounds of dialogues, and the system comprises an intelligent guide interaction module which receives the natural language input of a patient; the context-aware question and answer engine adopts a dialogue state representation method based on a graph structure to construct entities, relationships and attributes of each round of dialogue into knowledge sub-graphs; the medical record information dynamic builder monitors an updating event of the dialogue state diagram in real time; the abnormal information detection module adopts a mixed conflict detection method combining rules and learning; and the medical knowledge graph support system maps the oral expression of the patient to a standard medical term system in real time through a multi-level semantic matching strategy. According to the invention, the doctor does not need to distract the record in the inquiry process, and can pay more attention to patient observation and clinical thinking. The quality of the medical record first draft automatically generated by the system is high, a doctor only needs to perform a small amount of auditing and supplementing, and the medical record writing time is greatly shortened.

The present invention discloses a method and system for artificial intelligence-based cryptocurrency regulatory analysis capable of performing automated, adaptive, and verifiable compliance evaluation across multiple blockchain ecosystems. The invention integrates blockchain data acquisition, data normalization, graph-based behavioral modeling, artificial intelligence inference, and cryptographically anchored reporting within a unified architecture. The system comprises a blockchain data acquisition unit for retrieving multi-chain transaction data, a data normalization unit for harmonizing heterogeneous blockchain formats, a graph construction unit for generating dynamic transaction graphs, a regulatory knowledge base unit storing jurisdiction-specific regulatory rule graphs, an artificial intelligence processor configured for hybrid neural and symbolic reasoning, and a regulatory reporting unit for generating explainable compliance reports cryptographically anchored to a blockchain ledger.

The invention relates to the technical field of heat load prediction, and discloses a heat supply system load prediction method and system, and the method comprises the steps: collecting multi-source sensing data in real time, and collecting outdoor meteorological parameters and building structure information; based on building distribution, a pipe network structure and user load characteristics of a heat supply area, a multi-stage heat supply load prediction model is constructed. And constructing a thermal topological graph model of the heat supply area based on the graph neural network. And periodically collecting parameters of the building-level edge prediction model, the heat exchange station-level aggregation prediction model and the thermal topological graph model, performing global aggregation optimization, and updating and optimizing each edge node model. And obtaining an edge prediction result according to the optimized model, and jointly controlling the heat source output power, the main pump rotating speed and the area valve opening according to the edge prediction result and the heat source level scheduling prediction model. According to the method, the depiction capability of the system on the dynamic load change and the space heat conduction path is improved, and the generalization capability of model updating and the real-time responsiveness of edge deployment are ensured.

The invention relates to the technical field of metallurgical raw material batching control, and discloses a bottom blowing smelting furnace automatic batching intelligent control method and system based on deep learning, and the method comprises the steps: achieving intelligent batching through multi-source data fusion, physical constraint modeling and dynamic optimization control; edge calculation is adopted to realize data space-time alignment and purification, and physical and economic mixed features are constructed; modeling a reaction path based on a graph neural network, and embedding conservation law constraint to synchronously predict key process parameters; and finally, in combination with gradient sensitivity analysis and reinforcement learning, constructing a differentiable optimization framework to realize multi-target dynamic ratio decision and real-time compensation control, and forming a perception-decision-execution closed loop. The system comprises a global sensing and data purification module, an intelligent decision-making and optimization batching module and a high-precision execution and closed-loop control module. According to the invention, the batching strategy is adaptively adjusted, and optimal resource allocation and maximum economic benefit are realized.

The invention discloses an intelligent operation abnormity monitoring and positioning method for a polypropylene cable, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting multi-source physical signals in the operation process of the cable, and constructing a multi-dimensional feature matrix fusing multiple physical quantities through multi-scale time window division and space mapping processing; extracting space-time coupling characteristics among nodes by using a graph attention embedding network, and training a running state recognition model in combination with a label perception contrast learning mechanism; constructing a cable topological graph based on an identification result, introducing an improved Bayesian space reasoning network, and calculating abnormal probability distribution of each node; a weighted abnormal heat map is further generated, an abnormal propagation path is extracted through an abnormal state flow model and a directional propagation scoring algorithm, and abnormal node positioning and trend evolution prediction are achieved; the method has the advantages of high spatial resolution, high identification precision and good online adaptability, and is suitable for intelligent state perception and abnormity early warning of the polypropylene cable in a complex operation environment.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention discloses a financial risk assessment method based on big data, and relates to the technical field of finance, and the method comprises the following steps: S1, obtaining structured data, unstructured data and real-time streaming data of a target entity through a multi-source heterogeneous data collection module; s2, constructing an association relationship graph, and modeling risk propagation paths of a target entity and associated nodes thereof based on a graph neural network; s3, performing feature alignment and joint representation learning on the structured data, the unstructured text data and the time series data through a multi-modal data fusion module; and S4, based on the causal inference model, separating causal features and hybrid variables of the target entity risk event, generating causal risk factors, quantifying risk infection paths between nodes by setting an enterprise guarantee network and a supply chain relation graph dynamically constructed in a graph neural network, effectively identifying hidden risk nodes, and improving the risk assessment efficiency. And the chain reaction risk caused by the default of the associated enterprise is reduced.

The invention relates to the technical field of electric power communication, in particular to an electric power communication resource topological optimization method and system based on a graph database. The specific implementation process comprises the following steps: constructing an electric power communication resource topological graph comprising communication exchange nodes, data transmission links and attribute information through a graph database; analyzing and quantifying the received business type into a routing constraint condition according to a business service level protocol; performing dynamic weighting calculation on each candidate communication path meeting the routing constraint condition, evaluating comprehensive performance indexes and outputting an optimal communication path; and converting the optimal communication path into a topology configuration instruction capable of guiding network equipment to carry out data deployment, and carrying out dynamic optimization on the power communication resource topology. According to the method, the service quality requirements of different services are quantified into dynamically adjusted routing constraints and weights, so that reasonable allocation of power communication resources and effective optimization of network topology are realized, and the working efficiency of a power communication resource network is improved.

The invention relates to the field of electric power material quality detection, and discloses a test scheduling system for detection task collaboration and data acquisition, which comprises a task construction module, a state collaboration module, a graph reasoning module and a data acquisition module. And the task construction module generates a standardized test task packet including a task identifier, a project code, a target equipment identifier, an environment requirement parameter and a two-dimensional code according to the test rule base and the resource configuration state, and pushes the standardized test task packet to corresponding test equipment through a Web Service interface. And the state collaboration module receives an equipment state feedback event, constructs an event time sequence flow graph based on the task identifier and generates a task state sequence with a timestamp. The atlas reasoning module takes the state sequence and the environmental parameters as input, constructs a test atlas structure and generates an optimization execution path. And the data acquisition module controls the test equipment to complete a detection task according to the path, acquires test data and environmental parameters, and encapsulates the test data and the environmental parameters to form a structured task data packet, thereby realizing data collection and task tracing.

The invention discloses an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, and belongs to the technical field of block chain security, and the system comprises a contract analysis module, a multilayer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability recognition engine, an automatic repair module and a visual interface. After the source code of the intelligent contract is input, code analysis and standardization are completed by a contract analysis module; the multi-layer graph construction module constructs a contract internal heterogeneous graph, an inter-contract interaction graph and an ecosystem relation graph based on a graph theory; the heterogeneous graph neural network module learns a vulnerability feature mode; the vulnerability recognition engine combines the vulnerability feature library to realize vulnerability classification and risk assessment; the automatic repairing module generates a repairing scheme; and the visual interface realizes detection progress monitoring, result display and encrypted report export. The intelligent contract vulnerability detection and restoration system based on the heterogeneous graph neural network provided by the invention provides technical support for block chain digital asset security and ecological stability.