Intelligent contract vulnerability detection and repair system based on heterogeneous graph neural network

Abstract

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.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of blockchain security, in particular to an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network. BACKGROUND

[0002] With the rapid development of blockchain technology and the vigorous rise of decentralized finance ecology, the security of intelligent contracts is directly related to the security of digital assets and the stability of the blockchain ecology. The existing technology has the following main problems: low detection accuracy, traditional static analysis tools mainly rely on rule matching, and the recognition ability of complex semantic vulnerabilities is limited, and the overall accuracy is generally lower than 75%; high false positive rate, lack of deep understanding of execution semantics and business logic, and the false positive rate is usually more than 30%; difficulty in identifying complex interaction vulnerabilities, most of the existing methods can only analyze single contract internal problems, and lack effective analysis means for cross-contract calling, contract state dependence and ecosystem-level security risks; lack of automatic repair capability, most tools can only find problems and cannot provide specific repair solutions; poor adaptability to new attack patterns, rule-based detection methods are difficult to quickly adapt to the constantly evolving attack technology.

[0003] The existing related technology mainly includes detection methods based on static analysis, dynamic analysis, formal verification and machine learning, but each has obvious limitations and is not up to the task when facing increasingly complex intelligent contract systems. SUMMARY

[0004] The purpose of the present application is to provide an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, which solves the problems of low intelligent contract vulnerability detection accuracy, high false positive rate and inability to identify complex interaction vulnerabilities in the prior art.

[0005] To achieve the above purpose, the present application provides an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, comprising: A contract analysis module, which parses the input intelligent contract source code into an abstract syntax tree structure through a recursive descent parsing algorithm based on an abstract syntax tree and semantic analysis technology, adopts static taint analysis combined with a control flow graph to identify key code paths, and uses a contract standard library, an ABI interface specification and code standardization; A multi-layer graph construction module, which adopts a multi-layer network modeling method based on graph theory, constructs an internal heterogeneous graph of the contract through function call relationship extraction and data dependence analysis, constructs an interaction graph between contracts through external call tracking and state synchronization detection, and constructs an ecosystem relationship graph based on protocol relationship mining and fund flow analysis; The heterogeneous graph neural network module adopts a graph attention network based on the Transformer architecture. It improves the ability to understand code structure through node type embedding and edge type encoding, and uses multi-head attention mechanism and graph convolutional neural network to extract deep feature information including function vulnerabilities, interaction risks and systemic risks. The vulnerability signature database adopts a distributed knowledge graph to store the CVE vulnerability database and standardized vulnerability signature patterns such as OWASP security rules. Version management and conflict detection are used to achieve dynamic updates and consistency maintenance of the signature database. The vulnerability identification engine receives graph feature information based on asynchronous task scheduling, performs vulnerability classification through feature similarity matching and risk scoring algorithms, generates multi-level risk assessments using a hierarchical reasoning method, and uses a Bayesian network-based uncertainty reasoning system to handle fuzzy boundary situations. The automatic repair module combines formal verification technology and code generation algorithms to automatically generate vulnerability repair solutions. It uses a strategy optimization system based on genetic algorithms to evaluate the repair effect and employs a multi-objective optimization algorithm to balance security and performance indicators. The system features a visual interface that provides multi-format contract input interfaces and real-time detection capabilities. It displays the detection and processing progress through a distributed monitoring system, presents vulnerability analysis results based on graph visualization technology, and supports the export of encrypted detection reports.

[0006] Preferably, the contract parsing module includes: Abstract syntax tree construction unit, based on the LL(1) recursive descent parsing algorithm of the Solidity compiler front end, parses the source code of smart contracts into a complete abstract syntax tree structure containing 47 node types. Among them, there are 12 types of declaration nodes, including the SourceUnit root node, ContractDefinition contract definition node, FunctionDefinition function definition node, etc.; 15 types of statement nodes, including Block statement, IfStatement conditional statement, ForStatement loop statement, etc.; 14 types of expression nodes, including BinaryOperation binary operation, FunctionCall function call, Identifier identifier, etc.; and 6 types of type nodes, including ElementaryTypeName basic type name, Mapping mapping type, etc. The syntax tree structure with an average depth of 8-12 layers and 150-2000 nodes is constructed through a recursive traversal algorithm, and parent-child relationship, sibling relationship and semantic reference relationship between nodes are established. The semantic analysis processing unit constructs a complete standard interface semantic library containing 9 interface functions of ERC-20 standard, 11 interface functions of ERC-721 standard and 7 interface functions of ERC-1155 standard, integrates 29 core modules of OpenZeppelin standard library, covers 8 categories such as access control, security mechanism, token implementation, mathematics library, cryptography, utility tool, proxy contract and governance, maintains variable scope, function signature and modifier semantic constraint through symbol table management algorithm, verifies the legality of implicit conversion and the consistency of inheritance relationship by using type deduction algorithm; The static taint analysis unit establishes a detection dictionary containing 14 external input taint sources such as msg.sender, msg.value, block.timestamp and tx.origin, and 13 sensitive operation taint sinks such as transfer, call, delegatecall and selfdestruct, constructs def-use definition-use chain, reaching definition set and active variable set through data flow analysis algorithm, uses worklist algorithm for fixed point iteration calculation, traces the taint data propagation path, and identifies security risk patterns such as re-entrant attack, integer overflow, access control bypass and timestamp dependency; The control flow graph construction and path identification unit divides the function body into a basic block sequence with single entry and single exit based on basic block division algorithm, identifies control dependency relationship and loop structure through dominance tree analysis algorithm, constructs a directed graph structure containing five types of control flow edges including sequential execution, conditional branching, loop iteration, function call and exception handling, uses depth-first search combined with backtracking algorithm to enumerate the execution paths from the function entry to all reachable exits, filters infeasible paths by using path condition collection and constraint solving technology, maintains independent program state for each feasible path by using path-sensitive analysis algorithm, identifies key security property violation paths such as unauthorized fund transfer, state inconsistency, re-entrant attack and integer overflow, and quantitatively scores the risk paths from 1 to 10; The code standardization execution unit establishes a standardized rule library covering Solidity 0.4.0 to 0.8.19 versions, including 1247 syntax standardization rules and 863 semantic standardization rules, covering variable naming standardization, function visibility unification, event parameter indexed mark standardization, modifier usage order standardization, SafeMath library replacement with built-in overflow check, old version interface adaptation to the latest ERC standard, etc., automatically modifies the non-standard code through abstract syntax tree rewriting algorithm, ensures the function consistency of the standardized code by using semantic equivalence transformation technology, ensures the correct compilation and execution of the code by using compiler compatibility check, and generates detailed standardization log records of modification content, reason and influence range.

[0007] Preferably, the multi-layer graph construction module comprises: The graph theory multi-layer network modeling unit constructs a mathematical abstract model of the smart contract based on directed acyclic graph (DAG) and multigraph theory, adopts a node-edge-attribute triple representation method, the node set includes four categories of functions, variables, states, and events, a total of 23 subtypes, the edge set includes five categories of control flow, data flow, call relationship, dependency relationship, and state transition, a total of 31 subtypes, a multi-layer network model including five abstract levels of function call layer, data dependency layer, state transition layer, event trigger layer, and external interaction layer is established, the corresponding relationship between different levels is defined through an inter-layer mapping matrix, and a network layering algorithm is adopted to decompose a complex contract structure into a hierarchical representation of 3-7 layers; The function call and data dependency analysis unit identifies three types of direct calls, including direct call, delegate call, and static call, and indirect call modes such as callback, fallback, and modifier, through an abstract syntax tree traversal algorithm, establishes a function node descriptor including 12 attributes such as function signature hash, parameter type, return value type, and visibility modifier, constructs a function call directed graph with a call depth of 5-15 layers, and establishes a dependency graph covering four types of data entities, including state variables, local variables, parameter variables, and return variables, traces three dependency modes of def-use chain, use-def chain, and def-def chain through a data flow analysis algorithm, identifies four types of dependency relationships, including read-write dependency, control dependency, address dependency, and value dependency, and calculates indirect dependency relationships using a transitive closure algorithm. The contract internal heterogeneous graph construction unit fuses the function call graph, data dependency graph, and control flow graph to construct a unified contract internal heterogeneous graph representation, the node types include six categories of function nodes, variable nodes, statement nodes, expression nodes, modifier nodes, and event nodes, the edge types include five categories of call edges, dependency edges, control edges, data flow edges, and state transition edges, the function module boundaries are identified through a graph partitioning algorithm, the vulnerability patterns are detected using a subgraph matching technique, and a heterogeneous graph structure including 150-800 nodes and 300-2000 edges is established. The external call and state synchronization unit establishes a tracking dictionary containing five types of external call instructions, namely CALL, DELEGATECALL, STATICCALL, CREATE and CREATE2, identifies the external call position through contract bytecode analysis and opcode sequence pattern matching, records the execution trajectory such as call depth, parameter passing and return value processing by using a call stack tracking algorithm, simultaneously establishes a consistency detection mechanism containing four types of state space, namely storage state, memory state, calldata state and stack state, verifies the correctness of state synchronization through a Merkle tree hash comparison algorithm, identifies inconsistent state changes by using a state difference detection algorithm, and processes synchronization exceptions by using state rollback and forward recovery algorithms. The inter-contract interaction graph construction unit constructs contract instance nodes based on contract address mapping and ABI interface matching, identifies the calling relationship, fund transfer relationship and state dependency relationship among contracts through transaction tracking and event log analysis, establishes an interaction edge weight vector containing 12 dimensions such as calling frequency, transfer amount, gas consumption and time interval, identifies tightly coupled contract clusters by using a community discovery algorithm, calculates the importance ranking of contracts by using centrality analysis and PageRank algorithm, and constructs a directed weighted graph containing 50-500 contract nodes and 100-2000 interaction edges. The protocol relationship and fund flow direction analysis unit establishes a feature library containing 127 standard protocols in five categories, such as ERC standard protocol, DeFi protocol, NFT protocol, DAO governance protocol and cross-chain protocol, automatically identifies the protocol type implemented by the contract through function signature matching, event pattern recognition and state variable analysis, verifies the consistency of multi-protocol combinations by using a protocol compatibility detection algorithm, simultaneously constructs a complete fund flow direction graph through blockchain transaction analysis and event log analysis, identifies the source address, transfer address, target address and flow path, establishes a fund flow edge descriptor containing 16 attributes such as transfer amount, fee, timestamp and transaction hash, and traces multi-hop fund flow by using a graph traversal algorithm. The ecosystem relationship graph construction unit integrates the contract internal graph, inter-contract interaction graph, protocol relationship graph and fund flow direction graph to construct a unified blockchain ecosystem relationship graph, generates a low-dimensional dense representation by using a multi-layer network fusion algorithm and graph neural network embedding technology, identifies functionally similar contract groups by using graph clustering analysis, analyzes the scope of security incidents by using an influence propagation model, establishes multi-dimensional ecosystem health evaluation indexes containing node importance, edge tightness, subgraph modularity and network robustness, and constructs a large-scale complex network model containing 1000-10000 nodes and 5000-50000 edges on average.

[0008] Preferably, the heterogeneous graph neural network module comprises: Transformer graph attention architecture unit, based on the Multi-Head Graph Attention Transformer architecture, constructs a heterogeneous graph neural network backbone, adopts a 12-layer encoder structure, each layer contains a 768-dimensional hidden state and 12 attention heads, fuses the topological information of nodes in the graph through position encoding and graph structure encoding, uses residual connection and layer normalization technology to ensure the stability of deep network training, uses GELU activation function and Dropout regularization to prevent overfitting, supports parallel processing of heterogeneous graphs with a maximum of 2048 nodes, and optimizes the computing efficiency of large-scale graphs through gradient accumulation and mixed precision training; Node type embedding and encoding unit, establishes a node type vocabulary containing six categories including function nodes, variable nodes, statement nodes, expression nodes, modifier nodes and event nodes, a total of 23 subtypes, uses a learnable type embedding matrix to map discrete node types to 256-dimensional dense vector representations, processes structured attributes such as function signatures, variable types, access permissions, and state variability through a node attribute feature extractor, uses a pre-trained code language model to encode the semantics of the source code fragments associated with the nodes, and fuses type embedding, attribute features and semantic features to generate a 1024-dimensional node initial representation vector. Feature normalization and dropout technology improves model generalization ability; Edge type encoding and relationship modeling unit, establishes a relationship vocabulary covering five categories including call relationship, data dependency, control flow, state transition, and external interaction, a total of 31 edge types, uses an edge type embedding layer to map relationship types to 128-dimensional vector representations, processes numerical attributes such as call frequency, dependency strength, risk level, and timing constraints through an edge attribute encoder, uses graph relative position encoding to capture the structural information of edges in the graph, designs a learnable edge weight calculation function to dynamically adjust the attention weight according to the edge type and attribute, and uses edge dropout and edge noise injection technology to enhance model robustness. Multi-head graph attention mechanism unit, designs 12 attention heads to focus on different types of graph structure patterns, each attention head maintains an independent query matrix Q, key matrix K, and value matrix V, integrates node features and edge relationship information through the heterogeneous graph attention calculation formula attention(Q,K,V)=softmax(QK^T / √d_k+edge_bias)V, uses a type-aware attention mask mechanism to constrain information propagation between different types of nodes, uses a multi-scale attention window to capture local neighborhood and global context information, and uses attention weight visualization technology to provide explainability analysis of model decision-making. The graph convolution feature extraction unit adopts an integrated architecture of three graph convolution operators, i.e., Graph Convolutional Network (GCN), Graph Attention Network (GAT) and GraphSAGE, aggregates neighbor node information through a message passing mechanism, designs a heterogeneous graph convolution operator to process information propagation of different types of nodes and edges, extracts subgraph-level feature representation by using graph pooling operation, fuses graph features at different levels by using a skip connection, constrains the feature learning process by using graph regularization technology, and constructs hierarchical feature representation including node-level, edge-level, subgraph-level and full graph-level; The deep risk feature learning unit designs a feature extractor containing 12 types of vulnerability patterns, such as reentrant attack, integer overflow, access control bypass and unchecked call return value, for function vulnerability risk, identifies known vulnerability patterns by using graph pattern matching and subgraph isomorphism detection algorithms, and learns discriminative features of normal code and vulnerability code by using contrastive learning technology; for interaction risk, a cross-contract call risk evaluator is designed, potential cascading risks are identified by analyzing the call links, state dependencies and fund flow directions between contracts, and a graph propagation algorithm is used to calculate the risk propagation path and impact range; for systemic risk, an ecosystem stability analyzer is designed, and the systemic risk of the entire DeFi ecosystem is evaluated by network topology analysis, centrality calculation and community discovery algorithm; The feature fusion and output unit fuses function-level, contract-level and ecosystem-level risk features by using an attention mechanism, generates a compact risk representation vector by using feature selection and dimension reduction technology, simultaneously predicts vulnerability types, risk levels and repair suggestions by using a multi-task learning framework, designs a confidence estimation module to quantify the uncertainty of the prediction results, realizes end-to-end feature learning by using gradient backpropagation and parameter update mechanism, and outputs multi-dimensional analysis results including vulnerability detection probability, risk score, feature importance and attention weight.

[0009] Preferably, the vulnerability feature library includes: The distributed knowledge graph architecture unit constructs an intelligent contract vulnerability knowledge graph based on the RDF triple (Subject, Predicate, Object) model, distributes the knowledge graph in 3-7 storage nodes by using a sharding storage strategy, realizes data sharding and load balancing by using a consistent hashing algorithm, uses Apache Jena Fuseki as a SPARQL query endpoint, supports complex graph queries and reasoning operations, establishes an ontology model containing core concepts such as vulnerability entities, attack patterns, repair schemes and impact ranges, generates vector representations of entities and relationships by using knowledge graph embedding technology, and supports semantic similarity calculation and knowledge reasoning; CVE and OWASP standard integration unit, establish a standardized database covering CVE-2016 to CVE-2025, a total of 847 smart contract related vulnerability entries, using XML and JSON hybrid format to store vulnerability description, CVSS score, affected version, attack vector and other structured information, while integrating OWASP Smart Contract Top 10, OWASP Testing Guide and other security standards, establishing a specification library containing 156 security rules in five categories including access control, input validation, state management, cryptography application and error handling, extracting keywords, attack patterns, affected components and other semantic features from vulnerability descriptions through natural language processing technology, and establishing a vulnerability severity classification system including Critical(9.0-10.0), High(7.0-8.9), Medium(4.0-6.9), Low(0.1-3.9) four levels; Vulnerability feature pattern modeling unit, establish a vulnerability representation model based on code pattern, execution trajectory and state change three-dimensional features, code pattern features include abstract syntax tree substructure, function call sequence, variable dependency relationship and other static features, execution trajectory features include instruction sequence, branch coverage, exception handling and other dynamic features, state change features include storage modification, balance change, permission transfer and other state features, use graph embedding algorithm to map vulnerability patterns into 512-dimensional feature vectors, construct vulnerability pattern classification system through similarity calculation and clustering analysis, and use pattern matching algorithm to support similarity-based vulnerability detection; Version management and conflict detection unit, use Git-like distributed version control mechanism to manage the evolution history of vulnerability feature library, generate SHA-256 hash identifier for each feature library snapshot, support parallel feature library development and testing through branch management, record the specific content, impact range and responsible person information of each update through change log, identify backward compatibility through semantic version number(Major.Minor.Patch), and design conflict detection algorithm based on semantic similarity and structural similarity, identify duplicate or conflicting vulnerability patterns through feature vector cosine similarity calculation, use graph isomorphism algorithm to detect vulnerability features with the same structure but different descriptions, and use conflict classifier combining expert rules and machine learning models to classify conflict types into four categories: complete duplication, partial duplication, semantic conflict and structural conflict; The dynamic updating and synchronization unit establishes an event-driven real-time updating mechanism, automatically obtains the latest vulnerability information through channels such as CVE data source monitoring, security announcement subscription, and community vulnerability reporting, integrates new vulnerability features using an incremental learning algorithm without retraining the entire model, verifies the effectiveness and accuracy of new features using an A / B testing framework, distributes feature library updates to downstream systems through push notifications and API interfaces, establishes an updating frequency control mechanism to avoid excessively frequent updates affecting system stability, and supports rapid response to urgent vulnerabilities and hot patch deployment. The consistency maintenance and verification unit maintains the global consistency hash of the feature library using a Merkle tree structure, ensures data consistency between multiple nodes through a distributed consensus algorithm, detects errors in data transmission and storage processes using a checksum verification mechanism, establishes regular consistency check tasks to scan and repair data inconsistency problems, balances performance and consistency requirements through read-write separation and eventual consistency models, ensures that repeated updates do not cause data corruption using idempotent operation design, establishes backup and recovery mechanisms to ensure data security and business continuity, and handles different types of conflicts through conflict resolution strategies such as automatic merging, manual review, and priority selection, and establishes conflict resolution history to support decision review and experience accumulation.

[0010] Preferably, the vulnerability identification engine includes: The asynchronous task scheduling and feature receiving unit adopts an asynchronous task scheduling architecture based on message queues, receives graph feature information from heterogeneous graph neural network modules through Apache Kafka message middleware, establishes task queues with three levels of high, medium, and low priority, supports up to 10,000 concurrent task processing, implements streaming processing of feature data using a producer-consumer mode, distributes large-scale graph features to 8-16 worker nodes using task sharding and load balancing algorithms, uses backpressure control mechanisms to prevent task queue overflow, establishes task timeout and retry mechanisms to ensure processing reliability, supports real-time feature streams and batch feature file input modes, and performs data cleaning, format conversion, and dimension alignment operations through a feature preprocessing module. The feature similarity matching and classification unit establishes a vulnerability feature index library containing 512-dimensional graph feature vectors, uses the local sensitive hashing (LSH) algorithm to construct a high-dimensional feature fast retrieval index, calculates the similarity of input features and known vulnerability patterns through three measurement methods of cosine similarity, Euclidean distance, and Manhattan distance, uses the K-nearest neighbor (KNN) algorithm and support vector machine (SVM) classifier for coarse-grained vulnerability type recognition, establishes a classification system containing 32 types of vulnerabilities such as reentrant attack, integer overflow, access control, unchecked call, timestamp dependency, denial of service, front-end running, and lightning loan attack, uses an ensemble learning method to fuse the prediction results of multiple classifiers, controls the false positive rate and false negative rate through a threshold adjustment and confidence filtering mechanism, supports two modes of accurate matching with a similarity threshold of 0.85 and fuzzy matching with a similarity threshold of 0.6-0.85; The risk scoring and quantitative analysis unit designs a risk scoring algorithm based on a multi-factor model, comprehensively considers five-dimensional factors of vulnerability severity (weight 0.3), influence range (weight 0.25), exploitation difficulty (weight 0.2), detection confidence (weight 0.15), and historical exploitation frequency (weight 0.1), uses weighted average and nonlinear transformation to generate a risk score of 0-100, establishes a risk classification system containing five levels of extremely high risk (90-100), high risk (70-89), medium risk (50-69), low risk (30-49), and extremely low risk (0-29), evaluates the uncertainty range of the risk score through the Monte Carlo simulation method, calculates the base score, time score, and environment score using the CVSS3.1 standard, and analyzes the potential impact of a single vulnerability on the entire contract system using a risk propagation model; The hierarchical reasoning and multi-level evaluation unit establishes a risk reasoning framework containing three levels of function level, contract level, and system level, identifies the security risks of individual functions through static analysis and dynamic execution trajectory at the function level, evaluates the overall contract security through function interaction analysis and state consistency checking at the contract level, and evaluates the systemic risk through cross-contract dependency analysis and ecosystem modeling at the system level, uses a bottom-up risk aggregation algorithm to propagate low-level risks to high-level risks, dynamically adjusts the weight contribution of risks at different levels using an attention mechanism, simulates the impact of security mechanisms on risks through risk suppression and amplification factors, establishes a risk correlation graph to represent the interaction and cascading effect between different vulnerabilities, and supports risk scenario simulation and stress test analysis; The Bayesian network inference and uncertainty processing unit constructs a Bayesian network model containing random variables such as vulnerability types, attack paths, impact ranges, detection confidence, and environmental factors, learns the conditional probability distribution between variables through expert knowledge and historical data, performs probability inference calculation using variational inference and Markov Chain Monte Carlo (MCMC) algorithm, establishes a quantitative framework containing three types of uncertainty: data uncertainty, model uncertainty, and parameter uncertainty, describes the credibility of the inference results using confidence intervals and probability distributions, identifies the key factors that have the greatest impact on the inference results through sensitivity analysis, uses the evidence propagation algorithm to handle partial observation and missing data scenarios, supports hypothesis testing and counterfactual reasoning analysis; The fuzzy boundary and edge case processing unit establishes a fuzzy logic-based boundary situation processing mechanism, uses membership functions to describe the fuzzy attribution relationship of vulnerability features between different categories, processes edge cases with similar feature similarity near the classification threshold through fuzzy inference rules, improves the classification stability of boundary cases using ensemble learning and voting mechanisms, establishes an artificial review queue to handle difficult cases with automatic classification confidence below 0.7, uses active learning algorithms to select the most valuable boundary samples for manual annotation, continuously optimizes the boundary processing strategy through online learning mechanisms, establishes a case library to record the processing results and expert decisions of historical boundary situations, supports quick retrieval and experience reuse of similar cases, and verifies the effectiveness of the boundary processing strategy through A / B testing.

[0011] Preferably, the automatic repair module includes: The formal verification and code generation unit uses a formal verification framework based on Hoare logic to describe the safety property specifications of smart contracts through preconditions, postconditions, and invariants, uses the symbolic execution engine Z3-solver for constraint solving and counterexample generation, establishes formal specification templates containing 12 types of security properties such as arithmetic overflow, reentrant attack, access control, and resource leakage, verifies the equivalence and security improvement of the code before and after repair through model detection algorithms, uses code generation algorithms based on abstract syntax trees to establish a rule library containing 8 types of code transformation modes such as security check insertion, boundary condition verification, exception handling wrapping, and permission verification strengthening, generates repair code that meets the Solidity syntax specifications through template instantiation and syntax-directed translation techniques, and verifies the syntax correctness and type safety of the generated code using the compiler front-end; The vulnerability repair scheme automatic generation unit establishes a scheme library containing 15 types of repair strategies such as access control reinforcement, input verification enhancement, state check addition, exception handling improvement, re-entry protection mechanism, and overflow check insertion, automatically selects an applicable repair strategy combination through a vulnerability type mapping algorithm, generates a candidate repair scheme by using a rule-based expert system and a case-based reasoning (CBR) algorithm, retrieves a successful repair mode of a similar vulnerability from a historical repair case library by using code similarity matching, verifies the correctness of the repair scheme through abstract syntax tree comparison and semantic equivalence analysis, establishes a repair scheme scoring mechanism to comprehensively consider four dimensions of repair integrity (weight 0.4), code complexity increase (weight 0.3), performance impact degree (weight 0.2), and compatibility maintenance (weight 0.1), supports two repair modes of incremental repair and complete reconstruction, and generates a complete repair package containing repair code, test cases, and deployment scripts; The genetic algorithm strategy optimization unit optimizes repair strategy parameters by using a genetic algorithm framework based on chromosome coding, encodes the repair scheme into a binary chromosome with a length of 64 bits, with each 8 bits representing the enabled state and parameter configuration of a repair strategy, establishes an evolution population containing 100-200 individuals, evaluates the comprehensive performance of the repair scheme through an adaptive function, uses three selection strategies of roulette selection, elite reservation, and tournament selection, generates offspring by using three crossover operators of single-point crossover (probability 0.8), multi-point crossover (probability 0.6), and uniform crossover (probability 0.4), maintains population diversity through bit flip mutation (probability 0.05) and Gaussian mutation (probability 0.03), sets the maximum evolution generation to 500 generations and the convergence condition to an improvement of less than 0.001 in the adaptive degree for 50 consecutive generations, and dynamically adjusts the crossover probability and mutation probability through an adaptive parameter adjustment mechanism; The repair effect evaluation and verification unit establishes a repair effect evaluation system containing four dimensions of security improvement degree, functional integrity, performance impact, and code quality, calculates the security improvement degree by comparing the number and severity of vulnerabilities before and after repair through a vulnerability scanning tool, verifies the functional correctness of the repaired code through unit testing, integration testing, and regression testing, evaluates the impact of repair on contract performance through gas consumption analysis, execution time testing, and memory usage monitoring, measures code quality through static analysis indicators such as cyclomatic complexity, code line increase, and maintainability index, calculates the comprehensive repair effect score by using a weighted scoring algorithm, establishes a repair effect grading system containing five levels of excellent (90-100 points), good (80-89 points), general (70-79 points), poor (60-69 points), and failure (0-59 points), and verifies the effectiveness of the repair scheme through A / B testing and controlled experiments; The multi-objective optimization and performance balancing unit realizes multi-objective optimization of security and performance by using NSGA-II (non-dominated sorting genetic algorithm), establishes a security objective function , performance objective function , identify the optimal trade-off point between security and performance through Pareto frontier analysis, use fast non-dominated sorting algorithm to sort the repair scheme, use crowded distance calculation to maintain the diversity of solution set, establish five key balance indicators including gas consumption increase rate (≤15%), execution time extension rate (≤10%), storage space increase rate (≤20%), code complexity growth rate (≤25%), and security vulnerability elimination rate (≥95%), select the optimal repair scheme through multi-objective decision-making methods such as weight vector method, ideal point method, compromise programming method, support user to customize the priority weight of security and performance, establish repair strategy library including conservative type (priority security), balanced type (balanced consideration), and aggressive type (priority performance), continuously optimize the balance strategy parameters through real-time monitoring and feedback adjustment mechanism.

[0012] Preferably, the visualization interface provides multi-format contract input interface and real-time detection function, displays the detection processing progress through distributed monitoring system, demonstrates the vulnerability analysis results based on graph visualization technology, and supports encrypted transmission of detection report export. Including: Multi-format input and real-time detection unit, establish a unified interface supporting six input formats of Solidity source code (.sol), bytecode (.bin), ABI interface (.json), Vyper source code (.vy), compressed package (.zip / .tar.gz), and GitHub repository link, adopt two input methods of drag-and-drop upload and online editor, determine the input format through file type automatic identification algorithm and MIME type detection, establish a limit mechanism of maximum file size 50MB and maximum project file number 1000, use syntax highlighting and intelligent completion technology to improve code editing experience, support multi-version Solidity compiler (0.4.0-0.8.19) selection, at the same time, use WebSocket long connection technology to realize real-time communication between front and back end, establish a state machine including six detection stages of queue waiting, code analysis, graph construction, feature extraction, vulnerability identification, and repair generation, use progress bar and percentage to display current detection progress, use color state indicator to show detection state including five states of waiting (gray), in progress (blue), success (green), warning (yellow), and failure (red); Distributed monitoring and processing progress unit, establish a distributed monitoring system based on micro-service architecture, through Prometheus monitoring index collection and Grafana dashboard display cluster running state, monitoring includes CPU usage, memory occupation, GPU utilization, network bandwidth, storage I / O and other system resource indicators, track task queue length, processing throughput, average response time, error rate and other business indicators, adopt heat map and topology diagram to show the distribution of detection tasks among different nodes, through the load balancing state display to show the work load and health status of each node, establish the expected remaining time algorithm based on historical detection data and current queue length to calculate the completion time, support detection task pause, cancel and re-submit operation, establish alarm mechanism when system resource usage exceeds 85% or error rate exceeds 5%, support visual operation interface for cluster expansion and node fault switching; Graph visualization and vulnerability analysis display unit, adopt D3.js and Three.js hybrid rendering technology to build high-performance graph visualization engine, support 2D plane layout and 3D solid layout two display modes, through force-directed layout, hierarchical layout, circular layout, grid layout four automatic layout algorithm to optimize the display effect of graph, establish a visual coding scheme including node size, color, shape, transparency, use gradient color spectrum to represent the continuous change of risk level from green (safe) to red (high risk), through node clustering and edge filtering technology to process large-scale graph structure containing 1000+ nodes, support zoom, pan, rotate, highlight and other interactive operations, establish vulnerability details panel to display vulnerability type, risk score, impact range, repair suggestion and other information, use animation effect to show vulnerability propagation path and risk diffusion process, support graph export to PNG, SVG, PDF and other formats, through heat map to show code complexity and risk density distribution; Detection report generation and export unit, establish a detection report automatic generation system based on template engine, provide three report levels of executive summary, detailed analysis and repair suggestion, executive summary includes total number of vulnerabilities, risk level distribution, key findings, repair priority sorting and other core information, detailed analysis includes specific location, trigger condition, attack scenario, impact assessment and other complete description of each vulnerability, repair suggestion includes specific code modification scheme, test case, deployment guidance and other operable content, adopt Markdown and HTML mixed format to support rich text display, through chart library to generate risk distribution pie chart, trend line chart, comparison column chart and other data visualization charts, establish report template library to support enterprise customized report format, provide bilingual report generation capability, support report version management and historical comparison analysis; The encryption transmission and security protection unit protects the detection report content by using an AES-256-GCM symmetric encryption algorithm, generates and exchanges a key by using an RSA-2048 asymmetric encryption algorithm, establishes a TLS 1.3 secure transmission channel to ensure the confidentiality and integrity of the data transmission process, generates a report digest by using an HMAC-SHA256 algorithm to verify the integrity of the file, supports three export security levels of PDF encryption, ZIP password protection and PGP digital signature, establishes an access control mechanism to control the report access range through user identity authentication and permission management, uses a timestamp service to ensure the non-repudiation of the report generation time, embeds user information and the generation time in the report through watermark technology, establishes an audit log to record the operation track of report generation, download and sharing, supports the functions of automatic expiration and remote destruction of the report to protect the safety of sensitive information, provides an API interface to support third-party system integration and batch report processing, verifies the authenticity and authority of the report source through a digital certificate, and establishes a multiple backup mechanism to ensure the safe storage of the detection results and report data.

[0013] Therefore, the intelligent contract vulnerability detection and repair system based on the heterogeneous graph neural network has the following beneficial effects: (1) The accuracy of intelligent contract vulnerability detection is significantly improved. Through multi-layer heterogeneous graph modeling and graph neural network deep learning technology based on the Transformer architecture, the overall detection accuracy of the system reaches more than 92%, which is more than 20% higher than that of traditional static analysis tools, and the false positive rate is reduced to less than 8%; (2) Multi-level and all-round security risk assessment capability is realized. The system can analyze function-level vulnerabilities, contract-level interaction risks and ecosystem-level systematic risks at the same time, and establishes a comprehensive detection system including 32 types of vulnerabilities; (3) Strong automatic repair and optimization capability is possessed. The repair success rate is more than 87%, and the gas consumption is controlled within 15% by balancing safety and performance indicators through a multi-objective optimization algorithm; (4) Deep processing and fusion of complex multi-modal information are supported. Compared with the simple feature splicing method, the performance is improved by more than 35%; (5) Good engineering practicability and system expansibility are possessed. Horizontal expansion and load balancing are supported, and a single cluster can process 10,000+ contract detection tasks per second.

[0014] The technical solutions of the present application will be further described in detail below with the help of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is the overall architecture schematic diagram of the intelligent contract vulnerability detection and repair system based on the heterogeneous graph neural network of the present application; Figure 2 is a structural schematic diagram of a contract analysis module of the present application; Figure 3 is a structural schematic diagram of a multi-layer graph construction module of the present application; Figure 4 is a structural schematic diagram of a heterogeneous graph neural network module of the present application; Figure 5 is a structural schematic diagram of a vulnerability identification engine of the present application; Figure 6 is a structural schematic diagram of an automatic repair module of the present application. DETAILED DESCRIPTION

[0016] The following detailed description of embodiments of the application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0017] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0018] As shown in Figure 1 , the intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network provided by the present application includes a contract analysis module, a multi-layer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability identification engine, an automatic repair module, and a visualization interface. The entire system is deployed in a Kubernetes cluster environment using a distributed cloud native architecture, and is connected in real time with various blockchain platforms and development tools through standardized RESTful API interfaces, supporting smart contract security detection for mainstream blockchain networks such as Ethereum, BSC, Polygon, Arbitrum, and Optimism. The overall data flow of the system starts from contract input, goes through the complete process of analysis, graph construction, feature extraction, vulnerability identification, and repair scheme generation, and each functional module communicates asynchronously through an Apache Kafka message queue, ensuring high availability, high concurrency, and high scalability of the system. The specific implementation is as follows: As shown in Figure 2 , the contract analysis module specifically includes: Taking a smart contract of a DeFi lending protocol as an example, the contract contains core business logic such as lending function, repayment function, liquidation function, and the source code size is about 2000 lines. The abstract syntax tree construction unit based on the LL(1) recursive descent parsing algorithm of the Solidity compiler front end parses the smart contract source code of the lending protocol into a complete abstract syntax tree structure containing 1847 nodes, including 186 declaration class nodes including contract definition node LendingPool, function definition node borrow(), deposit(), withdraw(), liquidate() and 47 business functions, variable declaration node including user balance mapping userBalances, interest rate configuration interestRates, collateral price feed, etc. 156 state variables, a syntax tree structure with a depth of 11 layers is constructed through a recursive traversal algorithm, and a complete parent-child relationship network is established for subsequent semantic analysis and feature extraction.

[0019] The semantic analysis processing unit constructs a complete DeFi ecological standard interface semantic library including ERC-20 token standard interface, Oracle price oracle interface, decentralized exchange interface, etc. The lending protocol implements all 9 core interfaces of the ERC-20 standard for token operations, integrates 5 interfaces of the Chainlink price oracle for real-time price data, calls 12 interfaces of Uniswap V3 for liquidity management and token exchange, and integrates 15 core modules of the OpenZeppelin security library such as ReentrancyGuard reentry protection, SafeMath safe mathematical operation, AccessControl permission control, etc.

[0020] The static taint analysis unit establishes a detection dictionary containing 16 external input taint sources such as msg.sender user address, msg.value transfer amount, block.timestamp block timestamp, Oracle price data and 11 sensitive operation taint sinks such as transfer fund transfer, liquidate liquidation execution, updatePrice price update, etc. Through data flow analysis algorithm, a def-use definition-use chain covering all business logic of the lending protocol is constructed, and 3 potential security risk points are found, including integer overflow risk caused by insufficient user input verification in the lending function, price manipulation risk caused by excessive dependence on external price data in the liquidation function, and front-end running attack risk caused by improper use of timestamp in the interest rate update function.

[0021] The control flow graph construction and path identification unit decomposed each function of the lending agreement into a total of 127 basic blocks based on a basic block division algorithm, identified control dependency relationships and 3 main loop structures through a dominance tree analysis algorithm, constructed a directed graph structure containing 167 nodes and 234 edges, enumerated 312 feasible execution paths from contract deployment to various business scenarios using a depth-first search combined with a backtracking algorithm, identified 12 unauthorized fund transfer paths, 8 state inconsistency paths, 3 re-entrant attack paths, and 5 integer overflow paths, and quantitatively scored these risk paths at levels 1-10.

[0022] As shown in Figure 3 , the multi-layer graph construction module specifically includes: Taking a decentralized exchange (DEX) protocol as an example, the protocol includes complex function modules such as liquidity pool management, token exchange, fee distribution, and governance voting, involves 5 core contracts, 3 interface contracts, and 4 library contracts, and has a total code size of about 8000 lines. The graph theory multi-layer network modeling unit constructs a mathematical abstract model of the DEX protocol based on directed acyclic graphs and multigraph theory, uses a node-edge-attribute triple representation method to construct a multi-layer network containing 673 nodes, including 156 function class nodes such as addLiquidity (add liquidity), removeLiquidity (remove liquidity), and swapExactTokensForTokens (exact input exchange) core transaction functions, 234 variable class nodes such as reserves (reserve mapping), liquidityTokens (liquidity token mapping), and feeRates (fee rate configuration) key state variables, and establishes a five-level multi-layer network model including a transaction execution layer, a liquidity management layer, a fee distribution layer, a governance decision layer, and a risk control layer.

[0023] The function call and data dependency analysis unit identified 234 function call relationships in the DEX protocol through an abstract syntax tree traversal algorithm, including 167 direct calls, 23 delegate calls, and 44 static calls, established a function node descriptor containing 15 attributes such as function signature hash, parameter type list, return value type, gas consumption estimation, and re-entry risk level, traced 456 data dependency relationships through a data flow analysis algorithm, and calculated a total of 178 indirect dependency relationships using a transitive closure algorithm.

[0024] The contract internal heterogeneous graph construction unit fuses the function call graph, data dependency graph, and control flow graph to construct a unified DEX protocol internal heterogeneous graph representation. The graph contains 673 heterogeneous nodes and 1247 heterogeneous edges. Six functional module boundaries are identified through a graph partitioning algorithm, including a core transaction module, a liquidity management module, a price calculation module, a fee management module, a governance module, and a security control module. Twelve known DeFi vulnerability patterns are detected using subgraph matching technology, providing a structured input representation for subsequent neural network feature learning.

[0025] The external call and state synchronization unit establishes a complete tracking mechanism for the interaction between the DEX protocol and external systems, identifies 78 external call instances, accurately locates all external call code locations through bytecode analysis and opcode sequence pattern matching, establishes an external call risk assessment model, identifies 3 reentrant attack risk points, 2 privilege escalation risk points, and 5 state pollution risk points, and establishes a consistency detection mechanism for four types of state space, including storage state, memory state, calldata call data, and stack state.

[0026] As shown in Figure 4 the heterogeneous graph neural network module specifically includes: Taking a cross-chain bridge protocol as an example, the protocol implements asset cross-chain transfer function between Ethereum and BSC, including lock contract, verification contract, release contract, and other core components, with more than 10,000 daily cross-chain transactions. The Transformer graph attention architecture unit constructs a heterogeneous graph neural network backbone based on the Multi-Head Graph Attention Transformer architecture. It uses a 12-layer encoder structure to process the heterogeneous graph representation of the cross-chain bridge protocol, which contains 2034 nodes and 4567 edges. Each layer of the encoder contains a 768-dimensional hidden state vector and 12 attention heads. The relative position relationship of nodes in the graph is represented by learnable position encoding. Residual connection and layer normalization techniques are used to ensure the training stability of the 12-layer deep network.

[0027] The node type embedding and encoding unit establishes a complete vocabulary table containing 23 types of subdivided node types related to the cross-chain bridge protocol. The function nodes are subdivided into 4 key types: lock function lock(), verify function verify(), release function release(), and admin function admin(). A 256-dimensional learnable embedding matrix is used to map discrete node types to dense vector representations. Type embedding, attribute features, and semantic features are fused to generate a 1024-dimensional node initial representation vector.

[0028] The edge type coding and relationship modeling unit establishes a relationship vocabulary covering 31 edge types of the cross-chain bridge protocol, including 6 cross-chain business calls such as lock call, verification call, release call, management call, callback function, and exception handling, and 5 key dependency relationships such as asset dependency, verification dependency, timing dependency, state dependency, and configuration dependency. The relationship type is mapped to a vector representation using a 128-dimensional edge type embedding layer.

[0029] The deep risk feature learning unit designs special feature extractors for the special security risks of the cross-chain bridge protocol, including 12 types of cross-chain specific vulnerability patterns such as cross-chain re-entrant attack, multi-signature verification bypass, timestamp manipulation attack, Oracle price manipulation, and asset lock vulnerability. The known cross-chain attack patterns are detected by a graph pattern matching algorithm, and 3 potential cross-chain re-entrant attack paths, 2 multi-signature verification bypass risk points, and 1 timestamp dependency vulnerability are found. Discriminative features of cross-chain attacks and normal cross-chain transactions are trained using contrast learning techniques.

[0030] As shown in Figure 5 , the vulnerability identification engine specifically includes: Taking a certain DAO governance protocol as an example, the protocol implements governance functions such as proposal creation, voting execution, fund management, and permission allocation, and manages assets exceeding 80 million US dollars. The asynchronous task scheduling and feature receiving unit adopts an asynchronous task scheduling architecture based on ApacheKafka message middleware, establishes three levels of task queues including high-priority queue for handling emergency security events, medium-priority queue for handling regular detection tasks, and low-priority queue for handling batch analysis tasks, and supports up to 20,000 concurrent task processing to meet the high-frequency governance activity detection needs of the DAO protocol.

[0031] The feature similarity matching and classification unit establishes a vulnerability feature index library containing 512-dimensional graph feature vectors, which stores 89,734 known governance protocol vulnerability pattern feature vectors. A Faiss high-dimensional vector retrieval library is used to construct a fast retrieval index of LSH local sensitive hashing algorithm, and a classification system is established containing 32 types of governance protocol-specific vulnerabilities such as governance proposal manipulation, voting power concentration, lightning loan governance attack, multi-signature bypass, and time lock vulnerability.

[0032] The risk scoring and quantitative analysis unit designs a risk scoring algorithm based on a multi-factor model, considering six dimensions of factors including vulnerability severity, impact range, exploit difficulty, detection confidence, historical exploit frequency, and governance importance, and using weighted average and nonlinear sigmoid transformation to generate a risk score of 0-100. A five-level risk classification system is established, and the uncertainty range of the risk score is evaluated by the Monte Carlo simulation method.

[0033] The hierarchical reasoning and multi-level evaluation unit establishes a risk reasoning framework containing three levels of function level, contract level and system level. The function level reasoning identifies the security risks of individual governance functions through static analysis and dynamic execution trajectory. The contract level reasoning assesses the overall governance contract security through inter-function interaction analysis and state consistency check. The system level reasoning assesses the systemic risk through cross-contract dependency analysis and governance ecosystem modeling. A bottom-up risk aggregation algorithm is used to propagate low-level risks to high-level risks.

[0034] As shown in Figure 6 , the automatic repair module specifically includes: Taking a decentralized lending protocol as an example, the protocol realizes complex functions such as multi-asset mortgage lending, dynamic interest rate adjustment, and automatic clearing execution, with a total locked value (TVL) of over 300 million US dollars. The formal verification and code generation unit adopts a formal verification framework based on Hoare logic to establish formal specifications for the core functions of the lending protocol. The preconditions of the borrow() lending function are that the user's mortgage asset value is greater than 150% of the loan amount, and the postconditions are that the user's loan balance is correctly increased and the mortgage asset is correctly locked. Formal specification templates are established for 12 types of security properties including arithmetic overflow protection, reentrant attack protection, and access control verification.

[0035] The vulnerability repair scheme automatic generation unit establishes a scheme library containing 15 types of repair strategies including access control strengthening, input verification enhancement, state check addition, exception handling improvement, reentrant protection mechanism, and overflow check insertion. For the reentrant attack vulnerability identified in the lending protocol, the system automatically selects three repair strategies: ReentrancyGuard reentrant protection modifier, function state variable check, and external call postcondition. A repair scheme scoring mechanism is established to consider four dimensions: repair integrity, code complexity increase, performance impact, and compatibility preservation.

[0036] The genetic algorithm strategy optimization unit adopts a genetic algorithm framework based on chromosome coding to optimize the repair strategy parameters. The repair scheme is coded as a 64-bit binary chromosome. An evolution population containing 200 individuals is established. The comprehensive performance of the repair scheme is evaluated by an adaptive function. Three selection strategies are used: tournament selection, roulette wheel selection, and elite reservation. Single-point crossover, multi-point crossover, and uniform crossover are used to generate offspring.

[0037] The repair effect evaluation and verification unit establishes a repair effect evaluation system including four dimensions of security improvement degree, functional integrity, performance influence and code quality. 23 vulnerabilities are detected before repair, including 12 high-risk vulnerabilities, 7 medium-risk vulnerabilities and 4 low-risk vulnerabilities. 3 vulnerabilities are detected after repair, all of which are low-risk vulnerabilities. The security improvement degree is calculated as 87%. The comprehensive repair effect score is calculated as 0.866 by using a weighted scoring algorithm, and is rated as excellent level.

[0038] The multi-objective optimization and performance balancing unit realizes multi-objective optimization of security and performance by using NSGA-II non-dominated sorting genetic algorithm. The indicators of the current optimal solution are that the gas consumption increases by 12.8%, the execution time is prolonged by 8.7%, the storage space is increased by 16.3%, the code complexity is increased by 22.1%, and the security vulnerability elimination rate is 96.7%, all of which are within the acceptable range.

[0039] Therefore, the intelligent contract vulnerability detection and repair system based on the heterogeneous graph neural network is adopted, advanced deep learning technology and multi-modal information processing capability are integrated, high-precision identification of the intelligent contract vulnerability and accurate generation of the repair scheme are realized, and important technical support and solutions are provided for the healthy development of the blockchain industry.

[0040] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application but not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements also cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A system for smart contract vulnerability detection and repair based on heterogeneous graph neural network, characterized in that: The contract parsing module, the multi-layer graph construction module, the heterogeneous graph neural network module, the vulnerability feature library, the vulnerability identification engine, the automatic repair module, and the visualization interface are included. The source code of the smart contract is first input into the system through the multi-format contract input interface of the visualization interface. Subsequently, the contract parsing module uses a recursive descent parsing algorithm based on an abstract syntax tree and semantic analysis techniques to parse the source code into an abstract syntax tree structure, while using static taint analysis combined with a control flow graph to identify key code paths, and using a contract standard library and ABI interface specifications to perform code standardization. The code-related data processed by the contract parsing module is transmitted to the multi-layer graph construction module, which uses a multi-layer network modeling method based on graph theory to first construct a contract internal heterogeneous graph through function call relationship extraction and data dependency analysis. Then, it constructs an inter-contract interaction graph using external call tracking and state synchronization detection, and finally constructs an ecosystem relationship graph based on protocol relationship mining and capital flow analysis. On the one hand, the multi-layer heterogeneous graph data generated by the multi-layer graph construction module is passed to the heterogeneous graph neural network module, which uses a graph attention network based on the Transformer architecture to improve code structure understanding through node type embedding and edge type encoding, and uses a multi-head attention mechanism and a graph convolutional neural network to extract deep feature information including function vulnerabilities, interaction risks, and systemic risks. On the other hand, the vulnerability feature library stores standardized vulnerability feature patterns such as the CVE vulnerability database and the OWASP security rules in a distributed knowledge graph, providing a basis for subsequent vulnerability matching. The deep feature information extracted by the heterogeneous graph neural network module is sent to the vulnerability identification engine, which also provides standardized vulnerability feature patterns from the vulnerability feature library. The vulnerability identification engine receives graph feature information based on asynchronous task scheduling, performs vulnerability classification through feature similarity matching and risk scoring algorithms, generates multi-level risk assessments using a hierarchical reasoning method, and uses a Bayesian network-based uncertainty reasoning system to handle fuzzy boundary situations. The vulnerability-related assessment results obtained by the vulnerability identification engine are transmitted to the automatic repair module, which automatically generates vulnerability repair solutions using formal verification techniques and code generation algorithms, evaluates repair effectiveness using a strategy optimization system based on genetic algorithms, and balances safety and performance indicators using a multi-objective optimization algorithm.

2. The smart contract vulnerability detection and repair system based on a heterogeneous graph neural network according to claim 1, characterized in that: The repair scheme information generated by the automatic repair module and the detection-related data at each stage are passed to the visualization interface, which displays the detection processing progress through a distributed monitoring system, visualizes vulnerability analysis results based on graph visualization techniques, supports encrypted transmission of detection reports for export, and finally outputs detection reports and repair solutions. The contract parsing module includes an abstract syntax tree construction unit, a semantic analysis processing unit, a static taint analysis unit, a control flow graph construction and path identification unit, and a code standardization execution unit. The processing operations on the smart contract source code are as follows: Abstract syntax tree construction unit: based on the recursive descent parsing algorithm of the Solidity compiler front end, the smart contract source code is converted into an abstract syntax tree, which is the core input basis for all subsequent units; Semantic analysis processing unit: taking the abstract syntax tree as input, combined with the constructed standard interface semantic library, output semantic information, providing semantic constraints for static taint analysis and control flow graph construction; Static taint analysis unit: taking the abstract syntax tree and semantic information as input, establishing a detection dictionary containing external input taint sources and sensitive operation taint sinks, constructing def-use definition-use chains, reaching definition sets and active variable sets through data flow analysis algorithms, outputting taint propagation paths and security risk patterns, providing the basis for risk path identification of the control flow graph; Control flow graph construction and path identification unit: based on the abstract syntax tree and semantic information, the function body is divided into a sequence of basic blocks with single entry and single exit based on the basic block partitioning algorithm, the control dependence relationship and loop structure are identified through the dominance tree analysis algorithm, the directed graph structure of the control flow edge is constructed, the execution paths from the function entry to all reachable exits are enumerated using the depth-first search combined with the backtracking algorithm, the infeasible paths are filtered using path condition collection and constraint solving techniques, and the independent program state is maintained for each feasible path using the path-sensitive analysis algorithm, the violation paths are identified, and the risk paths are quantitatively scored from 1 to 10; Code standardization execution unit: taking the abstract syntax tree, semantic information, and risk path as input, according to the standardized rule library, automatically correcting non-standard code through abstract syntax tree rewriting algorithm, outputting standardized code structure and security analysis results, and feeding back standardized logs to the visualization interface.

3. The smart contract vulnerability detection and repair system based on a heterogeneous graph neural network according to claim 1, characterized in that, The multi-layer graph construction module includes: Graph theory multi-layer network modeling unit, based on directed acyclic graph and multigraph theory, constructs a mathematical abstract model of smart contract, uses node-edge-attribute triple representation method, establishes a multi-layer network model containing function call layer, data dependency layer, state transition layer, event trigger layer, and external interaction layer, defines the corresponding relationship between different layers through layer mapping matrix, and uses network layering algorithm to decompose complex contract structure into 3-7 layers of hierarchical representation; Function call and data dependency analysis unit, through abstract syntax tree traversal algorithm, identifies direct and indirect call modes, establishes function node descriptor, constructs function call directed graph with call depth of 5-15 layers, and establishes dependency graph covering state variables, local variables, parameter variables, and return variables, through data flow analysis algorithm to track def-use chain, use-def chain and def-def chain three dependency modes, identify four types of dependency relationship including read-write dependency, control dependency, address dependency and value dependency, and use transitive closure algorithm to calculate indirect dependency relationship; The contract internal heterogeneous graph construction unit fuses the function call graph, the data dependency graph and the control flow graph to construct a unified contract internal heterogeneous graph representation, identifies the functional module boundary through a graph partition algorithm, detects the vulnerability pattern using a subgraph matching technology, and establishes a heterogeneous graph structure containing 150-800 nodes and 300-2000 edges; The external call and state synchronization unit establishes a tracking dictionary containing external call instructions, identifies the external call position through contract bytecode analysis and opcode sequence pattern matching, records the execution trajectory using a call stack tracking algorithm, establishes a consistency detection mechanism containing a state space, verifies the state synchronization correctness through a Merkle tree hash comparison algorithm, identifies the inconsistent state change using a state difference detection algorithm, and processes the synchronization exception using a state rollback and forward recovery algorithm; The contract interaction graph construction unit constructs contract instance nodes based on contract address mapping and ABI interface matching, identifies the interaction edge weight vector between contracts through transaction tracking and event log analysis, identifies the tightly coupled contract cluster using a community discovery algorithm, calculates the contract importance ranking through centrality analysis and PageRank algorithm, and constructs a directed and weighted graph containing 50-500 contract nodes and 100-2000 interaction edges; The protocol relationship and fund flow analysis unit establishes a feature library containing standard protocols, automatically identifies the protocol type implemented by the contract, verifies the consistency of multi-protocol combination using a protocol compatibility detection algorithm, constructs a complete fund flow graph through blockchain transaction analysis and event log analysis, identifies the fund source address, transfer address, target address and flow path, establishes a fund flow edge descriptor, and traces the multi-hop fund flow using a graph traversal algorithm; The ecosystem relationship graph construction unit integrates the contract internal graph, the contract interaction graph, the protocol relationship graph and the fund flow graph to construct a unified blockchain ecosystem relationship graph, generates a low-dimensional dense representation using a multi-layer network fusion algorithm and a graph neural network embedding technology, identifies the functionally similar contract group through graph clustering analysis, analyzes the spread range of security events using an influence propagation model, establishes an ecosystem health evaluation index, and constructs a large-scale complex network model containing 1000-10000 nodes and 5000-50000 edges on average.

4. The smart contract vulnerability detection and repair system based on a heterogeneous graph neural network according to claim 1, characterized in that, The heterogeneous graph neural network module includes: a Transformer graph attention architecture unit that constructs a heterogeneous graph neural network backbone based on a Multi-HeadGraph Attention Transformer architecture, adopts a 12-layer encoder structure containing 768-dimensional hidden states and 12 attention heads, and fuses the topological information of nodes in the graph through position encoding and graph structure encoding; The node type embedding and coding unit establishes a node type vocabulary table, maps discrete node types to 256-dimensional dense vector representations using a learnable type embedding matrix, processes structured attributes through a node attribute feature extractor, encodes the semantic features of the source code fragments associated with the nodes using a pre-trained code language model, and fuses the type embedding, attribute features, and semantic features to generate a 1024-dimensional node initial representation vector; The edge type coding and relationship modeling unit establishes a relationship vocabulary table, maps relationship types to 128-dimensional vector representations using an edge type embedding layer, processes numerical attributes through an edge attribute encoder, captures structural information of edges in the graph using graph relative position coding, and designs a learnable edge weight calculation function to dynamically adjust the attention weight according to the edge type and attribute; The multi-head graph attention mechanism unit designs 12 attention heads to focus on different types of graph structure patterns, integrates node features and edge relationship information through heterogeneous graph attention, uses a type-aware attention mask mechanism to constrain information propagation between different types of nodes, captures local neighborhood and global context information using a multi-scale attention window, and provides interpretable analysis of model decision-making through attention weight visualization technology; The graph convolution feature extraction unit uses an integrated architecture of Graph Convolutional Network, Graph Attention Network, and GraphSAGE three graph convolution operators, aggregates neighbor node information through a message passing mechanism, designs a heterogeneous graph convolution operator to process information propagation of different types of nodes and edges, extracts subgraph-level feature representations using graph pooling operations, integrates graph features at different levels using skip connections, constrains the feature learning process using graph regularization techniques, and constructs hierarchical feature representations; The deep risk feature learning unit designs a feature extractor for function vulnerability risks, identifies known vulnerability patterns through graph pattern matching and subgraph isomorphism detection algorithms, and learns discriminative features of normal code and vulnerability code using contrastive learning techniques; designs a cross-contract call risk evaluator for interaction risks, identifies potential cascading risks by analyzing the call links, state dependencies, and fund flow directions between contracts, calculates risk propagation paths and impact ranges using graph propagation algorithms; designs an ecosystem stability analyzer for systemic risks, assesses the systemic risks of the entire DeFi ecosystem through network topology analysis, centrality calculation, and community discovery algorithms; The feature fusion and output unit uses an attention mechanism fusion function to integrate risk features at the function level, contract level, and ecosystem level, generates risk representation vectors through feature selection and dimensionality reduction techniques, simultaneously predicts vulnerability types, risk levels, and repair suggestions using a multi-task learning framework, designs a confidence estimation module to quantify the uncertainty of the prediction results, and realizes end-to-end feature learning through gradient backpropagation and parameter update mechanisms, and outputs multi-dimensional analysis results. 5.The smart contract vulnerability detection and repair system based on heterogeneous graph neural network of claim 1, wherein, The vulnerability feature library includes: Distributed knowledge graph architecture unit, based on RDF triple model to build smart contract vulnerability knowledge graph, using sharding storage strategy to distribute knowledge graph in 3-7 storage nodes, through consistent hashing algorithm to realize data sharding and load balancing, using Apache Jena Fuseki as SPARQL query endpoint, establish ontology model, through knowledge graph embedding technology to generate vector representation of entity and relationship; CVE and OWASP standard integration unit, establish standardized database covering smart contract related vulnerability entries, adopt XML and JSON mixed format storage structured information, integrate security standards at the same time, establish specification library containing security rules, extract semantic features from vulnerability description through natural language processing technology, establish vulnerability severity grading system; Vulnerability feature pattern modeling unit, establish vulnerability representation model based on code pattern, execution trajectory and state change three-dimensional features, use graph embedding algorithm to map vulnerability pattern to 512-dimensional feature vector, through similarity calculation and clustering analysis to build vulnerability pattern classification system, use pattern matching algorithm to support similarity-based vulnerability detection; Version management and conflict detection unit, use Git-like distributed version control mechanism to manage the evolution history of vulnerability feature library, generate SHA-256 hash identifier for each feature library snapshot, support parallel feature library development and testing through branch management, establish change log to record specific content, impact scope and responsible person information of each update, identify backward compatibility through semantic version number, design conflict detection algorithm based on semantic similarity and structural similarity, identify duplicate or conflicting vulnerability patterns through feature vector cosine similarity calculation, use graph isomorphism algorithm to detect vulnerability features with the same structure but different descriptions, use conflict classifier combining expert rules and machine learning models to classify conflicts into four categories: complete duplication, partial duplication, semantic conflict and structural conflict; Dynamic update and synchronization unit, establish real-time update mechanism based on event-driven, get the latest vulnerability information, use incremental learning algorithm to integrate new vulnerability features without retraining the entire model, use A / B testing framework to verify the effectiveness and accuracy of new features, distribute feature library updates to downstream systems through push notifications and API interfaces; Consistency maintenance and verification unit, use Merkle tree structure to maintain global consistent hash of feature library, establish regular consistency check tasks to scan and repair data inconsistency problems, balance performance and consistency requirements through read-write separation and eventual consistency model, handle different types of conflicts through conflict resolution strategy, establish conflict resolution history to support decision review and experience accumulation. 6.The smart contract vulnerability detection and repair system based on heterogeneous graph neural network of claim 1, wherein: Vulnerability identification engine includes: The asynchronous task scheduling and feature receiving unit adopts an asynchronous task scheduling architecture based on a message queue, receives graph feature information from a heterogeneous graph neural network module through an Apache Kafka message middleware, establishes a task queue containing three levels, distributes large-scale graph features to 8-16 working nodes through a task fragmentation and load balancing algorithm, establishes a task timeout and retry mechanism, and performs data cleaning, format conversion and dimension alignment operations through a feature preprocessing module; The feature similarity matching and classification unit establishes a vulnerability feature index library, uses a local sensitive hashing algorithm to construct a fast retrieval index of high-dimensional features, calculates the similarity between input features and known vulnerability patterns through three measurement methods of cosine similarity, Euclidean distance and Manhattan distance, uses a K-nearest neighbor algorithm and a support vector machine classifier to perform coarse-grained vulnerability type identification, establishes a classification system, uses an ensemble learning method to fuse the prediction results of multiple classifiers, controls the false positive rate and the false negative rate through a threshold adjustment and confidence filtering mechanism, supports two modes of accurate matching with a similarity threshold of 0.85 and fuzzy matching with a similarity threshold of 0.6-0.85; The risk scoring and quantitative analysis unit designs a risk scoring algorithm based on a multi-factor model, considers five-dimensional factors of vulnerability severity, impact range, exploitation difficulty, detection confidence and historical exploitation frequency, uses weighted average and nonlinear transformation to generate a risk score of 0-100, establishes a risk grading system containing five levels, evaluates the uncertainty range of the risk score through a Monte Carlo simulation method, calculates the base score, time score and environment score using the CVSS 3.1 standard, and uses a risk propagation model to analyze the potential impact of a single vulnerability on the entire contract system; The hierarchical reasoning and multi-level evaluation unit establishes a risk reasoning framework, uses a bottom-up risk aggregation algorithm to propagate low-level risks to high-level risks, uses an attention mechanism to dynamically adjust the weight contribution of different levels of risks, simulates the impact of security mechanisms on risks through risk suppression and amplification factors, establishes a risk correlation graph to represent the interaction and cascading effect between different vulnerabilities, and supports risk scenario simulation and stress test analysis; The Bayesian network reasoning and uncertainty processing unit constructs a Bayesian network model, learns the conditional probability distribution between variables through expert knowledge and historical data, uses variational inference and Markov chain Monte Carlo algorithm for probability reasoning calculation, establishes a quantitative framework containing three types of uncertainty of data uncertainty, model uncertainty and parameter uncertainty, uses confidence interval and probability distribution to describe the credibility of the reasoning result, identifies the key factors that have the greatest impact on the reasoning result through sensitivity analysis, and uses evidence propagation algorithm to handle partial observation and missing data scenarios. The fuzzy boundary and edge case processing unit establishes a fuzzy logic-based boundary case processing mechanism, adopts a membership function to describe the fuzzy attribution relationship of vulnerability characteristics between different categories, processes edge cases with similar characteristics near the classification threshold through fuzzy inference rules, improves the classification stability of boundary cases using ensemble learning and voting mechanisms, establishes an artificial review queue to process difficult cases with an automatic classification confidence lower than 0.7, selects the most valuable boundary samples for manual annotation using an active learning algorithm, continuously optimizes the boundary processing strategy through an online learning mechanism, records the processing results and expert decisions of historical boundary cases in a case library to support quick retrieval and experience reuse of similar cases, and verifies the effectiveness of the boundary processing strategy through A / B testing. 7.The smart contract vulnerability detection and repair system based on heterogeneous graph neural network of claim 1, wherein: The automatic repair module includes: The formal verification and code generation unit adopts a Hoare logic-based formal verification framework, describes the safety property specifications of the smart contract through preconditions, postconditions and invariants, performs constraint solving and counterexample generation using a symbolic execution engine Z3-solver, establishes a formal specification template, verifies the equivalence and security improvement of the code before and after repair through a model checking algorithm, adopts an abstract syntax tree-based code generation algorithm, establishes a generation rule library, generates repair code conforming to the Solidity syntax specification through template instantiation and syntax-directed translation techniques, and verifies the syntax correctness and type safety of the generated code using a compiler front end; The vulnerability repair scheme automatic generation unit establishes a scheme library containing repair strategies, automatically selects applicable repair strategy combinations through a vulnerability type mapping algorithm, generates candidate repair schemes using a rule-based expert system and a case-based reasoning algorithm, retrieves successful repair modes of similar vulnerabilities from the historical repair case library using code similarity matching, verifies the correctness of the repair scheme through abstract syntax tree comparison and semantic equivalence analysis, establishes a repair scheme scoring mechanism to comprehensively consider repair integrity, code complexity increase, performance impact degree and compatibility preservation, supports two repair modes of incremental repair and complete reconstruction, and generates a complete repair package containing repair code, test cases and deployment scripts; The genetic algorithm strategy optimization unit adopts a chromosome coding-based genetic algorithm framework to optimize repair strategy parameters, encodes the repair scheme into a 64-bit binary chromosome, with each 8-bit representing the enable state and parameter configuration of a repair strategy, establishes an evolution population containing 100-200 individuals, evaluates the comprehensive performance of the repair scheme through an adaptive function, adopts three selection strategies of roulette selection, elite reservation and tournament selection, uses three crossover operators of single-point crossover, multi-point crossover and uniform crossover to generate offspring, maintains population diversity through bit flip mutation and Gaussian mutation, and dynamically adjusts the crossover probability and mutation probability through an adaptive parameter adjustment mechanism. The repair effect evaluation and verification unit establishes a repair effect evaluation system including security improvement, functional integrity, performance impact and code quality, calculates the comprehensive repair effect score using a weighted scoring algorithm, establishes a repair effect grading system including five levels, and verifies the effectiveness of the repair scheme through A / B testing and controlled experiments; The multi-objective optimization and performance balancing unit realizes multi-objective optimization of safety and performance by using a non-dominated sorting genetic algorithm, establishes a safety objective function , a performance objective function , wherein , , , , are weight coefficients, an optimal trade-off point of safety and performance is identified through Pareto frontier analysis, a fast non-dominated sorting algorithm is used for hierarchical sorting of the repair scheme, a crowding distance calculation is used to maintain the diversity of the solution set, five key balancing indicators including a gas consumption increase rate, an execution time extension rate, a storage space increase rate, a code complexity growth rate and a security vulnerability elimination rate are established, an optimal repair scheme is selected through a multi-objective decision method, user-defined priority weights of safety and performance are supported, a repair style of the repair strategy library is established, and balancing strategy parameters are continuously optimized through a real-time monitoring and feedback adjustment mechanism. 8.The smart contract vulnerability detection and repair system based on heterogeneous graph neural network of claim 1, wherein, The visual interface includes: The multi-format input and real-time detection unit establishes a unified interface, uses drag-and-drop upload and online editor input methods, determines the input format through file type automatic identification algorithm and MIME type detection, supports multi-version Solidity compiler selection, simultaneously uses WebSocket long connection technology to realize real-time communication between front and back ends, establishes a state machine, uses a progress bar and percentage to display the current detection progress, and uses a color state indicator to display the detection state; The distributed monitoring and processing progress unit establishes a distributed monitoring system based on microservice architecture, collects Prometheus monitoring indicators and displays cluster running status through Grafana dashboard, monitors system resource indicators, tracks business indicators, uses heat map and topology map to display the distribution of detection tasks among different nodes, uses load balancing state display to display the work load and health status of each node, establishes a predicted remaining time algorithm based on historical detection data and current queue length to calculate completion time, supports detection task suspension, cancellation and re-submission operations, establishes an alarm mechanism to automatically notify administrators when system resource usage exceeds 85% or error rate exceeds 5%, and supports visual operation interface for cluster scaling and node fault switching; The graph visualization and vulnerability analysis display unit uses D3.js and Three.js hybrid rendering technology to build a high-performance graph visualization engine, supports 2D plane layout and 3D solid layout display modes, optimizes graph display effect through four automatic layout algorithms: force-directed layout, hierarchical layout, circular layout and grid layout, establishes a visual coding scheme including node size, color, shape and transparency, uses a gradient color spectrum to represent the continuous change of risk level, processes large-scale graph structures containing 1000+ nodes through node clustering and edge filtering technology, uses animation effects to display vulnerability propagation paths and risk diffusion process, and uses a heat map to display code complexity and risk density distribution; The detection report generation and export unit establishes a detection report automatic generation system based on template engine, provides three report levels: executive summary, detailed analysis and repair suggestion, supports rich text display using Markdown and HTML mixed format, and generates data visualization charts through a chart library. The encryption transmission and security protection unit protects the detection report content by using the AES-256-GCM symmetric encryption algorithm, generates and exchanges the key by using the RSA-2048 asymmetric encryption algorithm, establishes the TLS1.3 secure transmission channel to ensure the confidentiality and integrity of the data transmission process, supports three export security levels of PDF encryption, ZIP password protection and PGP digital signature, establishes an access control mechanism to control the report access range through user identity authentication and permission management, adopts the timestamp service, the watermark technology, the audit log record report generation, the download and the sharing operation track, supports the report automatic expiration and the remote destruction function to protect the safety of sensitive information, provides the API interface to support the third party system integration and the batch report processing, ensures the authenticity and authority of the report source through the digital certificate verification, and establishes a multiple backup mechanism to ensure the safe storage of the detection result and the report data.

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