Intelligent contract automatic optimization and risk early warning method and system based on AI and block chain

Abstract

The invention discloses a smart contract automatic optimization and risk early warning method and system based on an AI and a block chain, and the method comprises the steps: obtaining original data of a smart contract, and carrying out the preprocessing of the original data, and extracting a feature number; constructing a contract risk assessment model through a deep neural network and a graph convolutional network, and outputting an assessment result; constructing a risk vulnerability prediction model based on a long short-term memory network, generating a risk probability distribution result, performing dynamic matching with a historical risk database, generating a risk assessment result, and triggering a cross-chain early warning signal; transmitting the early warning signal to a contract administrator node through a block chain cross-chain relay service, and generating an intelligent optimization instruction set; and testing the optimized contract through the block chain node, and deploying the contract to a block chain network. The method has the advantages that through combination of the neural network model and the block chain technology, automatic optimization and risk early warning of the smart contract are realized, vulnerabilities are accurately identified, risks are predicted, optimization suggestions are provided, and the security and reliability of the contract are improved.

Description

Technical Field

[0001] The present invention relates to contract optimization and early warning, and in particular to a method and system for automatic optimization and risk early warning of smart contracts based on AI and blockchain. Background Art

[0002] Automated contract optimization and risk warning methods have emerged in recent years with the development of artificial intelligence, big data analytics, and blockchain technology. With technological advancements, smart contracts and automated tools can automatically optimize and adjust contract terms, ensuring that contracts are more aligned with market demands and evolving laws and regulations, thereby improving contract execution efficiency and compliance.

[0003] Current automated contract optimization and risk warning methods rely on traditional static analysis and rule-based checking, which are unable to effectively address the complex logic and dynamic transaction features within contract code, resulting in inaccurate identification of potential risks. Furthermore, the lack of intelligent evaluation mechanisms that combine deep learning and graph neural networks makes it difficult to comprehensively analyze the multi-dimensional characteristics of contracts, such as syntax tree structure, logical control flow, and transaction timing data. This limits the accuracy of vulnerability prediction and risk assessment. Furthermore, current methods lack a high degree of automation in vulnerability remediation and contract optimization, with most systems still requiring manual intervention to generate optimization suggestions and implement remediation measures. Furthermore, the lack of cross-chain early warning mechanisms makes it difficult to uniformly monitor contract security across different blockchain platforms, resulting in the failure to provide timely warnings and action for certain high-risk contracts. Finally, existing contract verification processes often lack distributed verification and multi-node participation, making it easy to overlook the security of contracts in different environments and increasing the risk of issues arising after deployment. Summary of the Invention

[0004] In order to improve the existing methods and systems, a method and system for automatic optimization and risk warning of smart contracts based on AI and blockchain is provided. This method combines AI and blockchain technology to achieve automatic optimization and risk warning of smart contracts. It can accurately identify vulnerabilities, predict risks and provide optimization suggestions, thereby improving the security and reliability of contracts.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] The original data of smart contracts is obtained in real time through blockchain network nodes, and the original data is preprocessed to extract features, including the syntax tree structure features of the contract code, the graph structure features of the logical control flow, and the time series correlation features of the transaction data;

[0007] A contract risk assessment model is constructed using deep neural networks and graph convolutional networks. The model is trained using historical vulnerability data and outputs assessment results including security scores, vulnerability types, and vulnerability location parameters.

[0008] Build a risk vulnerability prediction model based on the long-short-term memory network to generate risk probability distribution results within the future time window. Dynamically match the risk probability distribution results with the historical risk database to generate risk assessment results and trigger cross-chain early warning signals.

[0009] The early warning signal is transmitted to the contract administrator node through the blockchain cross-chain relay service, and an intelligent optimization instruction set including code logic reconstruction suggestions, a list of safe function replacements, and a resource allocation plan is generated;

[0010] The optimized contract is tested through the distributed verification mechanism of the blockchain nodes, and the optimized contract that passes the verification is deployed to the blockchain network.

[0011] Preferably, the raw data of the smart contract is obtained in real time through the blockchain network node, and the raw data is preprocessed to extract feature numbers, including the syntax tree structure features of the contract code, the graph structure features of the logical control flow, and the time series correlation features of the transaction data. Specifically, the following are included:

[0012] Generate an abstract syntax tree of the contract source code and extract the structural features of the contract, including function name, function parameter types, return value type, and extract its complexity and hierarchical structure;

[0013] Extract the control flow graph from the contract source code, obtain the execution probability and branching status of each path within the contract function, and identify code paths that may cause security issues;

[0014] Obtain all transaction data interacting with smart contracts through the node, including timestamp, transaction hash, initiator address, receiver address, and specific transaction parameters;

[0015] Perform feature extraction on contract code data and associated transaction time series data. By extracting the relationship between function calls in the contract, we can obtain complex logical branches and dependencies between functions, and obtain the transaction frequency, amount, participants, and time interval feature data of the transaction time series data.

[0016] The core feature data is obtained by selective dimensionality reduction processing of the feature data.

[0017] Preferably, the contract risk assessment model is constructed by using a deep neural network and a graph convolutional network, and the model is trained using historical vulnerability data. The output of the assessment results including security scores, vulnerability types, and vulnerability location parameters specifically includes:

[0018] A contract risk assessment model is constructed using a deep neural network and a graph convolutional network. The deep neural network is used to process time series correlation features, and the graph convolutional network is used to process syntax tree structure features and logical control flow features.

[0019] The model is trained based on historical vulnerability data, and classification loss function, regression loss function, and positioning loss function are designed to predict the security of the contract, vulnerability type, and positioning results;

[0020] Based on the trained contract risk assessment model, the obtained core feature data is input into the model, and the assessment results including security score, vulnerability type and vulnerability location parameters are output.

[0021] Preferably, the risk vulnerability prediction model is constructed based on the long short-term memory network, the risk probability distribution result is generated in the future time window, the risk probability distribution result is dynamically matched with the historical risk database, the risk assessment result is generated, and the cross-chain early warning signal is triggered, specifically including:

[0022] Build a risk vulnerability prediction model based on the long short-term memory network, and use the contract's historical transaction data and vulnerability data as training sets for model training;

[0023] Based on the trained risk vulnerability prediction model, the contract risk probability distribution in the future time period is output and the risks are divided into different levels;

[0024] Based on the output risk probability distribution results, it is dynamically matched with the records in the historical risk database to obtain and output the risks with the highest vulnerability pattern similarity. The historical database is updated based on the new risk prediction results and actual vulnerabilities.

[0025] Build a cross-chain monitoring system to monitor smart contracts on different blockchain platforms in real time. If the model predicts that the contract has high risk, the system will trigger an early warning mechanism.

[0026] Preferably, the method of transmitting the warning signal to the contract administrator node through the blockchain cross-chain relay service and generating an intelligent optimization instruction set including code logic reconstruction suggestions, a security function replacement list and a resource allocation plan specifically includes:

[0027] Transmit the risk assessment results of the smart contract to the target contract administrator node through the cross-chain relay service;

[0028] Conduct risk assessment by decoding and analyzing early warning signals to determine whether to take remedial measures;

[0029] Based on the repair measures taken, the logical structure of the contract code is optimized to reduce the computational complexity. Based on the vulnerability type, the specific functions that need to be replaced or repaired are listed, and safe function replacement is performed.

[0030] Obtain the required computing resources based on the complexity of vulnerability fixes and code refactoring, prioritize resource allocation based on risk level, and generate intelligently optimized instruction sets.

[0031] Preferably, the testing of the optimized contract through the distributed verification mechanism of the blockchain nodes and the deployment of the verified optimized contract to the blockchain network specifically include:

[0032] Optimize and fix vulnerabilities in smart contracts based on intelligent optimization instruction sets, and perform functional testing on the optimized contracts in a local test environment;

[0033] Based on the participation of multiple blockchain nodes in the contract verification process, contract verification is performed through a distributed verification framework, including functional verification, security verification, and resource consumption verification;

[0034] Based on the contract that has passed the test of all verification nodes, it is deployed to the blockchain network.

[0035] Furthermore, an AI and blockchain-based smart contract automatic optimization and risk warning system is proposed, including:

[0036] Data acquisition module: The data acquisition module is used to obtain the original data of smart contracts from the blockchain network nodes in real time, and perform feature extraction and preprocessing;

[0037] Contract risk assessment model construction module: The contract risk assessment model construction module processes the contract's feature data through deep neural networks and graph convolutional networks, trains the model based on historical vulnerability data, and generates the contract's security score, vulnerability type, and vulnerability location parameters;

[0038] Risk vulnerability prediction module: Based on the LSTM network model, the risk vulnerability prediction module predicts the probability distribution of contract risks in the future and matches it with historical risk data to generate risk assessment results;

[0039] Optimization instruction generation module: The optimization instruction generation module transmits early warning signals through the blockchain cross-chain relay service and generates an intelligent optimization instruction set including code refactoring suggestions, safe function replacement and resource allocation;

[0040] Contract Verification and Deployment Module: This module uses a distributed verification mechanism to verify functionality, security, and resource consumption on multiple blockchain nodes, and deploys the optimized contracts that have passed verification to the blockchain network.

[0041] Processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

[0042] Compared with the prior art, the advantages of the present invention are:

[0043] By acquiring raw smart contract data in real time and performing multi-dimensional feature extraction, this approach accurately captures the structure, logic, and transaction characteristics of the contract code, providing detailed data support for subsequent risk assessment and optimization. Secondly, by combining deep neural networks and graph convolutional networks, this method effectively assesses contract security, identifies potential vulnerabilities, and precisely locates them, enhancing contract security management. Risk vulnerability prediction using long-short-term memory networks provides accurate early warnings of risks within future time windows, and a cross-chain early warning mechanism provides real-time responses, ensuring timely preventive measures. Furthermore, a blockchain-based distributed verification mechanism ensures that optimized contracts are verified by multiple nodes, improving the security and stability of contract deployment. Finally, through intelligently optimizing the instruction set, code refactoring suggestions, function replacement, and resource allocation plans are automatically generated, enabling dynamic contract optimization and vulnerability remediation, significantly improving the intelligent management of smart contracts and the overall security of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of the method proposed in the present invention;

[0045] Figure 2 This is a schematic diagram of feature extraction proposed by the present invention;

[0046] Figure 3 This is a schematic diagram of the contract risk assessment model proposed by the present invention;

[0047] Figure 4 This is a schematic diagram of the risk vulnerability prediction model proposed by the present invention;

[0048] Figure 5 This is a schematic diagram of the optimized instruction set proposed by the present invention;

[0049] Figure 6 This is a schematic diagram of the contract test deployment proposed by the present invention;

[0050] Figure 7 This is a diagram of the architecture of the electronic equipment in this solution;

[0051] Figure 8 This is a schematic diagram of the computer-readable storage medium structure in this solution. DETAILED DESCRIPTION

[0052] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0053] An AI- and blockchain-based smart contract automatic optimization and risk warning system, including:

[0054] Data acquisition module: The data acquisition module is used to obtain the original data of smart contracts from the blockchain network nodes in real time, and perform feature extraction and preprocessing;

[0055] Contract risk assessment model construction module: The contract risk assessment model construction module processes the contract's feature data through deep neural networks and graph convolutional networks, trains the model based on historical vulnerability data, and generates the contract's security score, vulnerability type, and vulnerability location parameters;

[0056] Risk vulnerability prediction module: Based on the LSTM network model, the risk vulnerability prediction module predicts the probability distribution of contract risks in the future and matches it with historical risk data to generate risk assessment results;

[0057] Optimization instruction generation module: The optimization instruction generation module transmits early warning signals through the blockchain cross-chain relay service and generates an intelligent optimization instruction set including code refactoring suggestions, safe function replacement and resource allocation;

[0058] Contract Verification and Deployment Module: This module uses a distributed verification mechanism to verify functionality, security, and resource consumption on multiple blockchain nodes, and deploys the optimized contracts that have passed verification to the blockchain network.

[0059] Processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

[0060] See Figure 1 As shown in the figure, the automatic optimization and risk warning method of smart contracts based on AI and blockchain includes:

[0061] Step 1: Obtain the raw data of the smart contract in real time through the blockchain network nodes, and pre-process the raw data to extract features, including the syntax tree structure features of the contract code, the graph structure features of the logical control flow, and the time series correlation features of the transaction data;

[0062] Step 2: Build a contract risk assessment model using deep neural networks and graph convolutional networks, train the model using historical vulnerability data, and output an assessment result including security score, vulnerability type, and vulnerability location parameters;

[0063] Step 3: Build a risk vulnerability prediction model based on the long short-term memory network to generate the risk probability distribution results within the future time window. Dynamically match the risk probability distribution results with the historical risk database to generate risk assessment results and trigger a cross-chain warning signal.

[0064] Step 4: The warning signal is transmitted to the contract administrator node through the blockchain cross-chain relay service, and an intelligent optimization instruction set containing code logic reconstruction suggestions, a list of safe function replacements, and a resource allocation plan is generated;

[0065] Step 5: Test the optimized contract through the distributed verification mechanism of the blockchain node, and deploy the verified optimized contract to the blockchain network.

[0066] See Figure 2 As shown in the figure, the original data of the smart contract is obtained in real time through the blockchain network nodes, and the original data is preprocessed to extract feature numbers, including the syntax tree structure features of the contract code, the graph structure features of the logical control flow, and the time series correlation features of the transaction data. Specifically, the following features are included:

[0067] Generate an abstract syntax tree of the contract source code and extract the structural features of the contract, including function name, function parameter types, return value type, and extract its complexity and hierarchical structure;

[0068] Extract the control flow graph from the contract source code, obtain the execution probability and branching status of each path within the contract function, and identify code paths that may cause security issues;

[0069] Obtain all transaction data interacting with smart contracts through the node, including timestamp, transaction hash, initiator address, receiver address, and specific transaction parameters;

[0070] Perform feature extraction on contract code data and associated transaction time series data. By extracting the relationship between function calls in the contract, we can obtain complex logical branches and dependencies between functions, and obtain the transaction frequency, amount, participants, and time interval feature data of the transaction time series data.

[0071] The core feature data is obtained by selective dimensionality reduction processing of the feature data.

[0072] Specifically, the abstract syntax tree is a structured representation of the smart contract source code. It converts the code into a tree structure, where each node represents a grammatical element of the language. The structural features of the contract are extracted from the AST, including function names, parameter types, return types, and cyclomatic complexity. The cyclomatic complexity formula is:

[0073] C=E-N+2P

[0074] Where C is the cyclomatic complexity, E is the number of edges in the graph, N is the number of nodes in the graph, and P is the number of independent connected regions;

[0075] By parsing the contract functions, we build a control flow graph for each function to represent possible execution paths. Through the control flow graph, we can identify paths that may lead to security vulnerabilities.

[0076] Obtain all transaction data that interacts with the contract through a node, such as an Ethereum node.

[0077] See Figure 3 As shown in the figure, a contract risk assessment model is constructed through deep neural networks and graph convolutional networks. The model is trained using historical vulnerability data, and the output includes assessment results including security scores, vulnerability types, and vulnerability location parameters. Specifically, the following are included:

[0078] A contract risk assessment model is constructed using a deep neural network and a graph convolutional network. The deep neural network is used to process time series correlation features, and the graph convolutional network is used to process syntax tree structure features and logical control flow features.

[0079] The model is trained based on historical vulnerability data, and classification loss function, regression loss function, and positioning loss function are designed to predict the security of the contract, vulnerability type, and positioning results;

[0080] Based on the trained contract risk assessment model, the obtained core feature data is input into the model, and the assessment results including security score, vulnerability type and vulnerability location parameters are output.

[0081] Specifically, the time series features are processed through deep neural networks, and the syntax tree structure features are processed through graph convolutional networks. The outputs of the DNN and GCN models are fused through connections to obtain the final feature vector, which is then output through a fully connected layer to obtain the final risk assessment result of the contract.

[0082] The classification loss function is used to predict the security category of the contract, using cross entropy loss;

[0083] The regression loss function is used to predict the vulnerability type of the contract, using mean square error loss;

[0084] The localization loss function is used to predict the specific location of the vulnerability in the contract. It uses position regression loss and performs regression based on coordinates or line numbers.

[0085] The final total loss function is the weighted sum of classification, regression and localization losses;

[0086] Based on the trained model, core feature data is input and the contract's security score, vulnerability type, and location parameters are output.

[0087] See Figure 4 As shown in the figure, a risk vulnerability prediction model is constructed based on the long short-term memory network to generate the risk probability distribution results in the future time window. The risk probability distribution results are dynamically matched with the historical risk database to generate risk assessment results and trigger cross-chain early warning signals. Specifically,

[0088] Build a risk vulnerability prediction model based on the long short-term memory network, and use the contract's historical transaction data and vulnerability data as training sets for model training;

[0089] Based on the trained risk vulnerability prediction model, the contract risk probability distribution in the future time period is output and the risks are divided into different levels;

[0090] Based on the output risk probability distribution results, it is dynamically matched with the records in the historical risk database to obtain and output the risks with the highest vulnerability pattern similarity. The historical database is updated based on the new risk prediction results and actual vulnerabilities.

[0091] Build a cross-chain monitoring system to monitor smart contracts on different blockchain platforms in real time. If the model predicts that the contract has high risk, the system will trigger an early warning mechanism.

[0092] Specifically, vulnerability data records known vulnerabilities of the contract and their types, such as reentrancy attacks, overflows, and unauthorized access. The characteristics of each vulnerability include vulnerability type (e.g., overflow, reentrancy, and unauthorized access), and vulnerability location (the location in the contract code where the vulnerability occurs, such as line number or function name).

[0093] LSTM is a classic neural network for processing time series data. It can capture long-term dependencies in contract transaction data. The model input is the contract's historical transaction data, and the output is the contract's risk prediction.

[0094] Based on the trained LSTM model, risk prediction is performed. The output risk score can be converted into a risk probability distribution and normalized using the softmax function. The formula is:

[0095]

[0096] Among them, p i is the output probability of the i-th category, y i is the original input value corresponding to the i-th category, K is the total number of categories, is the sum of the index values ​​of all categories;

[0097] According to the probability distribution of risk scores, risks are divided into different levels: low risk: probability between 0-0.3, medium risk: probability between 0.3-0.7, high risk: probability greater than 0.7;

[0098] Use the features in historical vulnerability data to calculate vulnerability patterns similar to the current prediction results. If the new vulnerability pattern is highly similar to the pattern in the historical data, the new vulnerability data is added to the historical database, the vulnerability pattern library is updated, the currently predicted vulnerability type and location are added to the historical database, and the records of similar vulnerabilities are updated, including the predicted risk level and location;

[0099] Build a cross-chain monitoring system that can monitor smart contracts on multiple blockchain platforms in real time. If the model predicts that the contract has high risk, the early warning mechanism will be triggered and a warning notification will be sent to the relevant parties.

[0100] See Figure 5 As shown in the figure, the early warning signal is transmitted to the contract administrator node through the blockchain cross-chain relay service, and an intelligent optimization instruction set containing code logic reconstruction suggestions, a list of safe function replacements, and a resource allocation plan is generated. Specifically, it includes:

[0101] Transmit the risk assessment results of the smart contract to the target contract administrator node through the cross-chain relay service;

[0102] Conduct risk assessment by decoding and analyzing early warning signals to determine whether to take remedial measures;

[0103] Based on the repair measures taken, the logical structure of the contract code is optimized to reduce the computational complexity. Based on the vulnerability type, the specific functions that need to be replaced or repaired are listed, and safe function replacement is performed.

[0104] Obtain the required computing resources based on the complexity of vulnerability fixes and code refactoring, prioritize resource allocation based on risk level, and generate intelligently optimized instruction sets.

[0105] Specifically, the assessment results are transmitted to the target contract administrator node through the cross-chain relay service using the cross-chain protocol. The security score, vulnerability type, and vulnerability location are packaged into a message and encrypted. The message is encrypted using an encryption algorithm to ensure data security and privacy. The relay node receives data from multiple blockchains and transmits the encrypted risk assessment information to the target contract administrator.

[0106] After determining that a fix is ​​needed, optimize the logic of the contract code based on the vulnerability type, reduce the computational complexity, reduce the number of nested loops, or avoid repeated calculations. If the original contract contains nested loops, try to optimize it by caching the results or improving the algorithm.

[0107] Estimate the required computing resources based on the complexity of vulnerability repair and code refactoring. Reentrancy attack repair requires modifying function call methods and adding locking mechanisms. Overflow repair requires replacing arithmetic operations and adding the SafeMath library.

[0108] According to the risk level and vulnerability type of the contract, repair tasks are sorted by priority. The higher the risk, the higher the repair priority of the contract. According to resource requirements and priority, the corresponding intelligent optimization instruction set is generated to guide the system to perform contract optimization.

[0109] See Figure 6As shown in the figure, the optimized contract is tested through the distributed verification mechanism of the blockchain node, and the verified optimized contract is deployed to the blockchain network, which specifically includes:

[0110] Optimize and fix vulnerabilities in smart contracts based on intelligent optimization instruction sets, and perform functional testing on the optimized contracts in a local test environment;

[0111] Based on the participation of multiple blockchain nodes in the contract verification process, contract verification is performed through a distributed verification framework, including functional verification, security verification, and resource consumption verification;

[0112] Based on the contract that has passed the test of all verification nodes, it is deployed to the blockchain network.

[0113] Specifically, deploying contracts on multiple blockchain nodes and verifying them mainly includes:

[0114] Functional verification: Verify whether all functions of the contract meet expectations, especially whether the fixed vulnerabilities have been eliminated; Security verification: Ensure that no new vulnerabilities have appeared in the contract and there are no security risks; Resource consumption verification: Test the resource consumption during the contract execution, especially the gas fee, to ensure that the optimized contract is more efficient in resource consumption;

[0115] Resource consumption verification ensures that the optimized contract is more efficient by measuring the gas consumption during contract execution. In particular, it checks whether the optimized contract effectively reduces unnecessary calculation and storage operations.

[0116] Based on the selected blockchain platform, the contract is deployed to the blockchain network. This process includes: selecting the deployment platform: determining the target platform and selecting the appropriate node for deployment; deployment steps: interacting with the smart contract platform through the blockchain network and deploying the contract to the blockchain network.

[0117] Furthermore, the method according to the embodiment of the present application can also be used with the aid of Figure 7 The electronic device architecture shown in FIG. Figure 7 As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store the method and system for automatic optimization and risk warning of smart contracts based on AI and blockchain provided in this application. The electronic device 500 may also include a terminal interface 508. Of course, Figure 7 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 7One or more components of an electronic device are shown.

[0118] Figure 8 This is a schematic diagram of the computer-readable storage medium structure provided by an embodiment of the present application. Figure 8 1 shows a computer-readable storage medium 600 according to one embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are executed by a processor, the method and system for automatic optimization and risk warning of smart contracts based on AI and blockchain according to the embodiments of the present application described with reference to the above figures can be executed. The storage medium 600 includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0119] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0121] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The method of automatic optimization and risk warning of smart contracts based on AI and blockchain is characterized by: include: The original data of smart contracts is obtained in real time through blockchain network nodes, and the original data is preprocessed to extract features, including the syntax tree structure features of the contract code, the graph structure features of the logical control flow, and the time series correlation features of the transaction data; A contract risk assessment model is constructed using deep neural networks and graph convolutional networks. The model is trained using historical vulnerability data and outputs assessment results including security scores, vulnerability types, and vulnerability location parameters. Build a risk vulnerability prediction model based on the long-short-term memory network to generate risk probability distribution results within the future time window. Dynamically match the risk probability distribution results with the historical risk database to generate risk assessment results and trigger cross-chain early warning signals. The early warning signal is transmitted to the contract administrator node through the blockchain cross-chain relay service, and an intelligent optimization instruction set including code logic reconstruction suggestions, a list of safe function replacements, and a resource allocation plan is generated; The optimized contract is tested through the distributed verification mechanism of the blockchain nodes, and the optimized contract that passes the verification is deployed to the blockchain network.

2. The method for automatic optimization and risk warning of smart contracts based on AI and blockchain according to claim 1 is characterized in that: The raw data of the smart contract is obtained in real time through the blockchain network nodes, and the raw data is preprocessed to extract feature numbers, including the syntax tree structure features of the contract code, the graph structure features of the logical control flow, and the time series correlation features of the transaction data. Specifically, the following are included: Generate an abstract syntax tree of the contract source code and extract the structural features of the contract, including function name, function parameter types, return value type, and extract its complexity and hierarchical structure; Extract the control flow graph from the contract source code, obtain the execution probability and branching status of each path within the contract function, and identify code paths that may cause security issues; Obtain all transaction data interacting with smart contracts through the node, including timestamp, transaction hash, initiator address, receiver address, and specific transaction parameters; Perform feature extraction on contract code data and associated transaction time series data. By extracting the relationship between function calls in the contract, we can obtain complex logical branches and dependencies between functions, and obtain the transaction frequency, amount, participants, and time interval feature data of the transaction time series data. The core feature data is obtained by selective dimensionality reduction processing of the feature data.

3. The method for automatic optimization and risk warning of smart contracts based on AI and blockchain according to claim 1 is characterized in that: The contract risk assessment model is constructed by using a deep neural network and a graph convolutional network. The model is trained using historical vulnerability data, and the output includes assessment results including security scores, vulnerability types, and vulnerability location parameters. Specifically, the following are included: A contract risk assessment model is constructed using a deep neural network and a graph convolutional network. The deep neural network is used to process time series correlation features, and the graph convolutional network is used to process syntax tree structure features and logical control flow features. The model is trained based on historical vulnerability data, and classification loss function, regression loss function, and positioning loss function are designed to predict the security of the contract, vulnerability type, and positioning results; Based on the trained contract risk assessment model, the obtained core feature data is input into the model, and the assessment results including security score, vulnerability type and vulnerability location parameters are output.

4. An AI and blockchain-based smart contract automatic optimization and risk warning system, used to implement the AI ​​and blockchain-based smart contract automatic optimization and risk warning method according to any one of claims 1 to 3, characterized in that: include: Data acquisition module: The data acquisition module is used to obtain the original data of smart contracts from the blockchain network nodes in real time, and perform feature extraction and preprocessing; Contract risk assessment model construction module: The contract risk assessment model construction module processes the contract's feature data through deep neural networks and graph convolutional networks, trains the model based on historical vulnerability data, and generates the contract's security score, vulnerability type, and vulnerability location parameters; Risk vulnerability prediction module: Based on the LSTM network model, the risk vulnerability prediction module predicts the probability distribution of contract risks in the future and matches it with historical risk data to generate risk assessment results; Optimization instruction generation module: The optimization instruction generation module transmits early warning signals through the blockchain cross-chain relay service and generates an intelligent optimization instruction set including code refactoring suggestions, safe function replacement and resource allocation; Contract Verification and Deployment Module: This module uses a distributed verification mechanism to verify functionality, security, and resource consumption on multiple blockchain nodes, and deploys the optimized contracts that have passed verification to the blockchain network. Processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

5. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the AI ​​and blockchain-based smart contract automatic optimization and risk warning method as described in any one of claims 1-3.

6. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by the processor, the method for automatic optimization and risk warning of smart contracts based on AI and blockchain according to any one of claims 1 to 3 is implemented.

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