A web3 cross-chain interoperable smart contract dynamic generation and verification system

Abstract

The application provides a Web3 cross-chain interoperable smart contract dynamic generation and verification system, realizes knowledge-driven logical disassembly + neural network feature adaptation + rigorous symbol rule verification trinity dynamic contract generation and verification, solves the contradiction between contract customization and high security in heterogeneous cross-chain scenarios, improves the adaptability, security and generation efficiency of cross-chain contracts, and adapts to multiple cross-chain ecosystems. Through the above system, the application solves the problems of insufficient dynamic adaptation capability, low contract customization degree, poor verification efficiency and security, and low technology integration degree of existing Web3 cross-chain interoperable smart contract technology.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and cybersecurity, and in particular to a dynamic generation and verification system for Web3 cross-chain interoperable smart contracts. Background Technology

[0002] With the rapid development and widespread penetration of Web3 technology, the blockchain ecosystem has presented a distinct pattern of multi-chain parallelism and heterogeneous coexistence. Against this backdrop, asset transfer, data interaction, and business collaboration between different public chains have become the core foundation supporting the deepening development of decentralized applications. Cross-chain interoperability has thus become crucial for realizing the full circulation of value in the internet of things. Smart contracts, as the core carrier for automating and decentralizing the execution of cross-chain business logic, directly determine the ultimate efficiency, reliability, and application scope of cross-chain services through their adaptability, security, and dynamic generation and adjustment capabilities. They are a key technological support for the large-scale application of cross-chain technology.

[0003] Currently, in the practical application of Web3 cross-chain interoperability, the smart contracts relied upon have significant technical limitations in design and generation, restricting the expansion and in-depth exploration of complex cross-chain scenarios. Specifically, most existing technical solutions are developed or automatically generated based on preset, fixed cross-chain protocol templates. Smart contracts generated in this way can usually only adapt to a single or very limited simple cross-chain scenario, such as standardized fungible token transfers. When faced with increasingly complex business scenarios, such as the transfer of non-fungible assets (NFTs) with complex conditions between heterogeneous public chains, the linkage of DeFi protocol liquidation across multiple chains, or inter-chain business collaborations relying on dynamic event triggers, existing smart contracts, due to their rigid logic, cannot dynamically adjust and adapt to real-time changes in business needs, chain states, and external inputs, exhibiting serious insufficient adaptability.

[0004] Secondly, regarding customized contract generation, traditional AI-based contract generation technologies often focus on learning and imitating general code templates and patterns from historical codebases. This approach fails to deeply integrate the core constraints unique to cross-chain business scenarios for targeted generation, such as specific path dependencies in asset transfers, state consistency requirements under complex business logic, and network trigger conditions for liquidation or rebalancing. This results in generated contract code that, while syntactically correct, has a low degree of matching between its internal logic and real, complex cross-chain business needs, often requiring significant manpower for secondary modification and debugging, thus negating the benefits of automated generation.

[0005] Furthermore, in the contract verification phase, existing methods lack efficient and rigorous formal verification mechanisms. Most solutions still rely on manual code auditing by developers or are limited to static checks at the syntax and simple semantic levels. Such methods struggle to systematically and automatically identify potential logical vulnerabilities within smart contracts, cross-chain business rule conflicts, and state adaptation anomalies that may occur in different chain environments. Due to insufficient depth and breadth of verification, deployed contracts are highly susceptible to problems in complex cross-chain interactions, potentially leading to significant security risks such as accidental asset locking, improper transfers, or even permanent losses. This also results in a persistently high cross-chain transaction failure rate, severely impacting user experience and system reliability.

[0006] A deeper problem lies in the fact that existing technological approaches have failed to effectively integrate cutting-edge intelligent computing technologies with blockchain cross-chain mechanisms. In particular, they have failed to organically combine neural network technology, with its flexible learning and generation capabilities, with symbolic reasoning technology, which emphasizes rigorous logic and verifiability, and apply this combination to the generation and verification process of cross-chain contracts. This lack of integration makes it difficult for existing solutions to find a balance between the inherent contradiction of "meeting the flexibility required for highly customized business scenarios" and "ensuring the absolute reliability and security of contract logic," often resulting in a trade-off between the two.

[0007] The shortcomings of existing technologies can be summarized as follows: Insufficient dynamic adaptability: Existing cross-chain smart contracts are usually strongly bound to fixed cross-chain protocol templates, and their logical structure is rigid. They cannot respond to the dynamic business needs of complex scenarios between heterogeneous public chains (such as cross-chain NFT circulation and cross-chain DeFi liquidation linkage), and lack the ability to adaptively adjust the logic according to chain status and event input at runtime, resulting in poor cross-chain business scalability.

[0008] Low level of contract customization and precision: Traditional AI-assisted contract generation methods mainly rely on the matching and reuse of general code templates. They fail to deeply integrate the core constraints of cross-chain business (such as asset transfer paths, state dependencies, and complex event triggering thresholds) in a structured manner for targeted guidance and generation, resulting in deviations between the generated contracts and the refined needs of specific businesses, and low practicality.

[0009] The verification mechanism is inefficient and lacks security: after contract generation, there is a lack of integrated and rigorous formal verification processes. Current methods that rely on manual auditing or shallow syntax checks are inefficient and fail to systematically detect deep logical inconsistencies, cross-chain context adaptation conflicts, and security vulnerabilities, resulting in high asset security risks and business interruption risks after contract deployment.

[0010] The integration of cutting-edge technologies is insufficient: advanced artificial intelligence paradigms such as neural symbolic reasoning have not been effectively and systematically integrated with cross-chain blockchain technology. Existing methods cannot simultaneously leverage the data-driven learning capabilities of neural networks and the logical reasoning verifiability of symbolic systems. This makes it difficult to balance flexible customization of logic with rigorous and reliable generation during the generation of cross-chain smart contracts, failing to fundamentally resolve the inherent contradiction between customization and high security. Summary of the Invention

[0011] This application provides a dynamic generation and verification system for Web3 cross-chain interoperable smart contracts to solve the problems of insufficient dynamic adaptation capability, low contract customization, poor verification efficiency and security, and low technology integration in existing Web3 cross-chain interoperable smart contract technologies.

[0012] The system includes: A cross-chain knowledge graph construction module is configured to acquire core data information of various public chains, construct a cross-chain scenario knowledge graph based on the core data information, convert the cross-chain scenario knowledge graph into symbolic knowledge units, and store it in a knowledge graph database. The neural network training module is configured to acquire cross-chain business data and smart contract samples for model training, preprocess the cross-chain business data and smart contract samples, and use the preprocessed data to train a large neural network model based on the Transformer architecture to obtain a preliminary contract logic fragment model. The requirement standardization module is configured to acquire users' cross-chain business requirements in real time and perform natural language-based standardization processing on the cross-chain business requirements to obtain standardized requirement text. The neural symbolic reasoning engine module is configured to use a symbolic reasoning algorithm to match the standardized requirement text with the symbolic knowledge units in the cross-chain scenario knowledge graph library to obtain the core constraints of the cross-chain scenario, and generate a contract logic skeleton based on the core constraints and the preliminary contract logic fragment model. The symbol rule verification module is configured to perform formal verification on the contract logic skeleton and fix logical vulnerabilities and cross-chain adaptation conflicts in the contract logic skeleton to obtain the target contract logic skeleton. The contract output and deployment module is configured to generate corresponding smart contract code based on the target contract logic skeleton, output the smart contract code to the user, and provide a deployment interface.

[0013] In some implementations, the cross-chain knowledge graph construction module is further configured as follows: Obtain the core data information of various public chains in the cross-chain ecosystem; The core data information is constructed into a cross-chain scenario knowledge graph using knowledge graph construction technology; The cross-chain scenario knowledge graph is symbolically encoded to obtain symbolic knowledge units, and the symbolic knowledge units are stored in the knowledge graph database.

[0014] In some implementations, the core data information includes Polkadot's PoS consensus mechanism, Cosmos' Tendermint consensus mechanism, EVM and Wasm's smart contract virtual machines, ERC-721 and ERC-1155 asset standards, and the XCM cross-chain protocol and its corresponding data interaction format.

[0015] In some implementations, the preprocessing includes: The requirement description of the cross-chain business data is segmented and stop words are removed, and core features are extracted. The contract code of the smart contract sample is parsed to extract the contract logic structure and function definition features; Vulnerability cases in the cross-chain business data are classified and labeled, and a vulnerability feature library is established.

[0016] In some implementations, a cross-entropy loss function and the symbolic knowledge units are introduced as constraints during the training process of the large neural network model.

[0017] In some implementations, the natural language-based standardization process includes: The cross-chain business requirements are segmented, stop words are removed, and redundant information is filtered to extract core elements. These core elements include cross-chain participants, asset types, cross-chain protocols, core constraints, and business processes. Based on the premise of determining the relationship between each element in the core elements, the core elements are transformed into the standardized requirement text.

[0018] In some implementations, the neural symbolic reasoning engine module is further configured as follows: A set of reasoning rules for the standardized requirement text is constructed using a predicate logic-based symbolic reasoning algorithm; The matching degree between the set of inference rules and the symbolic knowledge unit is calculated to obtain the core constraints of the cross-chain scenario; the core constraints include cross-chain asset transfer constraints, liquidation constraints and adaptation constraints.

[0019] In some implementations, the formula for generating the contract logic skeleton is: ; In the formula, CL represents the generated contract logic skeleton. S is the output of the large neural network model; S is the standardized requirement text; F is the cross-chain business feature vector. Let C be the symbolic inference constraint function, C be the set of core constraints, and λ be the constraint weight coefficient.

[0020] In some implementations, the symbol rule verification module is further configured as follows: The contract logic skeleton is formally verified using Hall logic, and combined with the symbol rule verification algorithm, the contract logic skeleton is transformed into a formal model. Based on the core requirements of cross-chain scenarios, several verification test cases are constructed, and the preconditions and postconditions of each verification test case are defined. Each contract logic skeleton is verified to ensure that it meets the preconditions and postconditions in each verification test case. If all conditions are met, the target contract logic skeleton is output. If not, Wasm virtual machine adaptation code is added by combining the symbolic knowledge units of Polkadot adaptation Wasm virtual machine in the cross-chain knowledge graph until the contract logic skeleton is repaired, and the verification test cases are verified again.

[0021] In some implementations, the verification test cases include virtual machine adaptation verification, cross-chain protocol adaptation verification, liquidation threshold verification, logic flow verification, and exception handling verification.

[0022] As described above, this application provides a dynamic generation and verification system for Web3 cross-chain interoperable smart contracts. The system includes a cross-chain knowledge graph construction module, configured to acquire core data information from various public chains, construct a cross-chain scenario knowledge graph based on this core data information, convert the cross-chain scenario knowledge graph into symbolic knowledge units, and store it in a knowledge graph database; a neural network training module, configured to acquire cross-chain business data and smart contract samples for model training, preprocess the cross-chain business data and smart contract samples, and use the preprocessed data to train a large neural network model based on the Transformer architecture to obtain a preliminary contract logic fragment model; and a demand standardization module, configured to acquire users' cross-chain business requirements in real time and standardize the requirements. The system standardizes cross-chain business requirements using natural language to obtain standardized requirement text. A neural symbolic reasoning engine module is configured to use symbolic reasoning algorithms to match the standardized requirement text with symbolic knowledge units in the cross-chain scenario knowledge graph to obtain core constraints of the cross-chain scenario. Based on these core constraints and a preliminary contract logic fragment model, a contract logic skeleton is generated. A symbolic rule verification module is configured to formally verify the contract logic skeleton and fix logical vulnerabilities and cross-chain adaptation conflicts to obtain a target contract logic skeleton. A contract output and deployment module is configured to generate corresponding smart contract code based on the target contract logic skeleton and output the smart contract code to the user, providing a deployment interface. This application solves the problems of insufficient dynamic adaptation capabilities, low contract customization, poor verification efficiency and security, and low technology integration in existing Web3 cross-chain interoperable smart contract technologies through the above system. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of a dynamic generation and verification system for Web3 cross-chain interoperable smart contracts according to this application. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0027] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0028] Figure 1 This is a schematic diagram of a dynamic generation and verification system for Web3 cross-chain interoperable smart contracts according to this application.

[0029] See Figure 1 As can be seen, this embodiment provides a dynamic generation and verification system for Web3 cross-chain interoperable smart contracts, the system comprising: A cross-chain knowledge graph construction module is configured to acquire core data information of various public chains, construct a cross-chain scenario knowledge graph based on the core data information, convert the cross-chain scenario knowledge graph into symbolic knowledge units, and store it in a knowledge graph database.

[0030] Specifically, in this embodiment, firstly, core data information of various public chains in mainstream cross-chain ecosystems such as Polkadot and Cosmos is collected, including consensus mechanisms such as Polkadot's PoS consensus and Cosmos' Tendermint consensus, smart contract virtual machines such as EVM and Wasm, asset standards such as ERC-721 and ERC-1155, as well as cross-chain protocols such as XCM and their corresponding data interaction formats.

[0031] Secondly, knowledge graph construction technology is adopted, using the "entity-relationship-entity" triple as the basic unit to construct a cross-chain scenario knowledge graph. Entities include public chains, consensus mechanisms, virtual machines, asset standards, etc., and relationships include "adaptation", "support", "compatibility", etc., clarifying the relationships between entities.

[0032] Finally, the knowledge graph is symbolically encoded, assigning a unique symbolic identifier to each entity and relation, forming a set of symbolic knowledge units: public chain symbols are represented by C, consensus mechanisms by Cn, virtual machines by Vm, asset standards by As, and relations by R. For example, Ethereum (C1) adapted to the EVM virtual machine (Vm1) is encoded as (C1, R1, Vm1), and ERC-721 (As1) supports NFT assets (C2) as (As1, R2, C2). All symbolic knowledge units are stored in the knowledge graph database, providing a foundation for subsequent symbolic reasoning.

[0033] The system also includes: The neural network training module is configured to acquire cross-chain business data and smart contract samples for model training, preprocess the cross-chain business data and smart contract samples, and use the preprocessed data to train a large neural network model based on the Transformer architecture to obtain a preliminary contract logic fragment model.

[0034] Specifically, in this embodiment, firstly, a massive amount of cross-chain business data and smart contract samples are collected. The data covers business requirement descriptions, corresponding contract code, cross-chain constraints and logical vulnerabilities, adaptation conflicts and other vulnerability cases in complex scenarios such as cross-NFT asset transfer between heterogeneous public chains and cross-chain DeFi clearing linkage. The total number of contract samples is no less than 100,000, covering different cross-chain protocols and different asset types.

[0035] Secondly, the collected data is preprocessed: the business requirement description is segmented and stop words are removed to extract core features; the contract code is parsed to extract features such as contract logic structure and function definitions; and vulnerability cases are classified and labeled to establish a vulnerability feature library, providing high-quality data for model training.

[0036] Then, a large neural network model based on the Transformer architecture is constructed. The input is the preprocessed cross-chain business requirement features and contract features, and the output is the contract logic fragment. During training, a cross-entropy loss function is introduced, combined with symbolic knowledge units in the cross-chain knowledge graph as constraints, to optimize the model parameters. The specific training parameters are set as follows: batch size of 32, learning rate of 0.001, number of iterations of 100 rounds, dropout probability of 0.2, and an early stopping strategy is adopted, stopping training when the accuracy of the validation set reaches 95% or more. This results in a model that can learn the unstructured features of cross-chain business and generate preliminary contract logic fragments.

[0037] The system also includes: The requirement standardization module is configured to acquire users' cross-chain business requirements in real time and perform natural language-based standardization processing on the cross-chain business requirements to obtain standardized requirement text.

[0038] Specifically, in this embodiment, the user inputs the cross-chain business requirements through the system interface in natural language, clearly specifying key information such as cross-chain participants, asset types, business processes, and core constraints. The example is "to implement cross-chain transfer of Ethereum (ERC-721 standard NFT) to Polkadot and link with DeFi protocols. After the NFT cross-chain is completed, if the staking ratio of the DeFi account is lower than 15%, automatic liquidation will be triggered. The cross-chain data interaction adopts Polkadot's XCM protocol format."

[0039] Demand preprocessing: Natural language processing (NLP) technology is used to segment and remove stop words from the input natural language requirements, filter redundant information, and accurately extract core elements, including cross-chain participants (Ethereum, Polkadot), asset type (ERC-721 standard NFT), cross-chain protocol (XCM protocol), core constraints (liquidation trigger threshold of 15%), and business process (NFT cross-chain → staking rate detection → automatic liquidation).

[0040] Standardization transformation: The extracted core elements are integrated, the relationships between the elements are clarified, and the transformation is into a standardized requirement text that is clear, unambiguous, and logically coherent. This text is then output as input data for the neural symbolic reasoning engine.

[0041] The system also includes: The neural symbolic reasoning engine module is configured to use a symbolic reasoning algorithm to match the standardized requirement text with the symbolic knowledge units in the cross-chain scenario knowledge graph library to obtain the core constraints of the cross-chain scenario, and generate a contract logic skeleton based on the core constraints and the preliminary contract logic fragment model.

[0042] Specifically, in this embodiment, based on the constructed symbolic knowledge graph, a symbolic reasoning algorithm is used to accurately match the core elements in the standardized requirement text with the symbolic knowledge units in the knowledge graph, thereby decomposing the core constraints of the cross-chain scenario and clarifying the boundaries and requirements for contract generation, as follows: Data retrieval: The neural symbolic reasoning engine receives the standardized requirement text output and synchronously calls the set of symbolic knowledge units stored in the knowledge graph database to provide knowledge support for symbolic reasoning.

[0043] Algorithm and Formula Definition: A symbolic reasoning algorithm based on predicate logic is adopted to construct a set of reasoning rules. Through the symbol matching degree calculation formula, the precise matching of requirement elements and symbolic knowledge units is achieved. The core formula and parameter explanations are as follows: Let S = {s1, s2, ..., sn} be the set of core elements in the standardized requirement text (si is the i-th requirement element); let K = {k1, k2, ..., km} be the set of symbolic knowledge units in the cross-chain knowledge graph (kj is the j-th symbolic knowledge unit); and let R = {r1, r2, ..., rk} be the set of inference rules (rl is the l-th inference rule, such as "if there are NFT assets in the requirement, then match the ERC-721 or ERC-1155 asset standard symbols").

[0044] Formula for calculating symbol matching degree: ; Where Count(si∩kj) represents the number of intersection elements between demand element si and symbolic knowledge unit kj (the number of features they share). Count(si∪kj) represents the number of elements in the union of the two (the total number of all features of the two). Match(si, kj) is the matching degree, with a value range of [0, 1]. A successful match is determined when the matching degree is ≥0.8.

[0045] Specific reasoning and matching: Extract the core elements from the standardized requirements (s1=Ethereum, s2=Polkadot, s3=ERC-721, s4=NFT, s5=XCM protocol, s6=liquidation threshold 15%), and call the symbolic knowledge units in the knowledge graph (such as k1=(C1(Ethereum), R1(adapter), Vm1(EVM)), k2=(C2(Polkadot), R1(adapter), Vm2(Wasm)) etc.).

[0046] The matching degree is calculated one by one using the above formula. For matching results that do not reach the threshold, the inference rules are supplemented and optimized (such as adding rules such as "Ethereum supports ERC-721 standard NFTs" and "Polkadot supports XCM cross-chain protocol and is compatible with Wasm virtual machine") until the matching degree reaches the standard, thus completing the correspondence between the requirement elements and symbolic knowledge.

[0047] Constraint decomposition and transformation: Based on the successfully matched symbolic knowledge units and reasoning rules, three core constraints for cross-chain scenarios are identified: First, cross-chain asset transfer constraints (ERC-721 standard NFTs transferred from Ethereum to Polkadot, compatible with EVM and Wasm virtual machines, using the XCM protocol and corresponding data interaction format); second, liquidation constraints (automatic liquidation is triggered when the DeFi account staking ratio is ≤15%, and this is linked to the NFT cross-chain process); and third, adaptation constraints (contracts must be compatible with both virtual machines to ensure conflict-free cross-chain execution). These core constraints are then transformed into symbolic constraint expressions (such as the liquidation constraint expression). Where L is the real-time staking rate of the DeFi account and T is the 15% threshold, it is stored in the constraint database to provide clear constraints for the subsequent generation of the contract logic skeleton.

[0048] Furthermore, in some embodiments, the neural symbolic reasoning engine module dynamically trains a large neural network model, using the decomposed core constraints as criteria to generate a contract logic skeleton adapted to the needs of cross-chain scenarios, achieving the synergy of "feature learning + constraint adaptation". The specific process is as follows: Core Algorithm and Formula Definitions: The algorithm employs a neural-symbolic collaborative architecture, integrating the feature learning capabilities of neural networks with the constraint capabilities of symbolic reasoning. The core formulas and parameters are explained below: Contract logic skeleton generation formula: ; Parameter explanation: CL is the generated contract logic skeleton; f neural (S, F) represents the output of the large neural network model, where S is the standardized requirement text and F is the cross-chain business feature vector. The output is a preliminary contract logic fragment without considering constraint adaptation; g symbolic (C) is the symbolic reasoning constraint function, where C is the set of core constraints to be decomposed and used to correct the constraints on the initial contract logic fragment; λ is the constraint weight coefficient, with a value range of [0.5, 0.8]. In this embodiment, λ=0.6, which is used to balance the weight of the neural network output and the symbolic constraints; "+" indicates that the initial logic fragment and the constraint correction content are merged to form a complete contract logic skeleton.

[0049] Formula for calculating the cross-chain business feature vector F: ; Where n represents the number of cross-chain business features (in this embodiment, n=6, representing cross-chain participants, asset types, cross-chain protocols, liquidation thresholds, business processes, and virtual machine types, respectively); w iWeights are assigned to each feature based on its importance, with the sum of the weights being 1 (in this example: asset type 0.25, cross-chain protocol 0.2, liquidation threshold 0.2, cross-chain participant 0.15, business process 0.1, virtual machine type 0.1); x i Numericalized results of One-Hot encoding for each feature (e.g., ERC-721 encoding 100 corresponds to decimal 4, XCM protocol encoding 10 corresponds to decimal 2).

[0050] Feature vector calculation: Substitute the feature weights and encoding values, convert the encoding values ​​to decimal, and calculate the F value: F = 0.25×4 + 0.2×2 + 0.2×0.15 + 0.15×3 + 0.1×4 + 0.1×3 = 1 + 0.4 + 0.03 + 0.45 + 0.4 + 0.3 = 2.58.

[0051] Preliminary contract logic generation: The standardized demand text S and feature vector F are input into the trained neural network model to generate a preliminary contract logic fragment, which includes the preliminary definition of core functions such as NFT cross-chain transfer, staking rate detection, and liquidation. Constraints such as cross-chain protocol adaptation and virtual machine compatibility are not considered.

[0052] Constraint Modification and Skeleton Fusion: Calling the Symbolic Reasoning Constraint Function g symbolic (C) Based on the core constraints, generate constraint correction instructions (such as "add XCM protocol data interaction interface to NFT cross-chain transfer function", "set 15% threshold and 10-second / time detection frequency to pledge ratio detection function", "add EVM and Wasm virtual machine adaptation code"); substitute them into the contract logic skeleton generation formula, integrate the constraint correction instructions into the initial logic fragment, supplement the adaptation code and logic, and finally form a complete contract logic skeleton including contract header definition, cross-chain transfer module, pledge ratio detection module, automatic liquidation module, and exception handling module, ensuring that each module meets the core constraints.

[0053] The system also includes: The symbol rule verification module is configured to perform formal verification on the contract logic skeleton and fix logical vulnerabilities and cross-chain adaptation conflicts in the contract logic skeleton to obtain the target contract logic skeleton.

[0054] Specifically, in this embodiment, the generated contract logic skeleton undergoes rigorous formal verification to identify logical vulnerabilities and cross-chain adaptation conflicts, and is automatically corrected, forming a closed loop of "verification-identification-correction-re-examination" to ensure that the contract logic is compliant and executable. The specific process is as follows: Verification Method and Algorithm Definition: Formal verification is performed using Hall logic, combined with a symbolic rule-based verification algorithm. The contract logic skeleton is transformed into a formal model, defining preconditions, postconditions, and invariants. The core verification formulas and parameters are explained below: Formal verification correctness judgment formula: ; Where Valid(CL) is the validation result, taking a value of 0 (failure) or 1 (pass); m is the number of validation test cases (m=5 in this embodiment); P i Each verification test case is assigned a weight of 0.2 (the sum of the weights is 1); C i Q represents the preconditions (constraints that the contract must satisfy) for the i-th verification test case; i This is the postcondition for the i-th verification test case (the state that should be reached after the contract is executed). Indicate C i When Q was established i The condition must be true; if true, the test case passes (value 1); otherwise, it fails (value 0). “∏” represents the product of the results of all test cases. The contract logic skeleton is validated only when all test cases pass (product is 1).

[0055] Verification test case construction: Based on the core requirements of cross-chain scenarios, five verification test cases were constructed, and the preconditions C for each test case were defined. i With postcondition Q i Comprehensive coverage of core dimensions: Verification Test Case 1 (Virtual Machine Adaptation Verification): C1 = The contract logic skeleton contains EVM and Wasm virtual machine adaptation code; Q1 = The contract can be compiled and executed normally in both virtual machines without syntax errors; Verification Test Case 2 (Cross-chain Protocol Adaptation Verification): C2 = The NFT cross-chain transfer module includes the XCM protocol data interaction interface; Q2 = Cross-chain data can be transmitted normally in the XCM protocol format without loss or format errors; Verification Test Case 3 (Liquidation Threshold Verification): C3 = The collateral ratio detection module sets the liquidation threshold to 15%; Q3 = Liquidation is triggered when the collateral ratio L≤15%, and not triggered when L>15%; Verification Test Case 4 (Logical Flow Verification): C4 = The contract logic flow is "NFT cross-chain → Staking ratio detection → Automatic liquidation"; Q4 = The process execution order is correct, and the detection is performed immediately after the cross-chain is completed; Verification Test Case 5 (Exception Handling Verification): C5 = The contract contains exception handling code for cross-chain failure and exception detection; Q5 = When an exception occurs, the contract rolls back normally without asset loss or logic deadlock.

[0056] Verification and vulnerability identification: Following the order of verification test cases, check whether the contract logic skeleton satisfies C.i With Q i The requirements are met, and the validation formula is used to calculate Valid(CL). In this embodiment, because the core code for Wasm virtual machine adaptation is not added to the contract logic skeleton, validation test case 1 fails. The calculated Valid(CL) = 0.2×0 + 0.2×1 + 0.2×1 + 0.2×1 + 0.2×1 = 0.8 ≠ 1, and the validation fails, clearly identifying a "cross-chain adaptation conflict" vulnerability.

[0057] Vulnerability Correction and Re-examination: Combining the symbolic knowledge unit k2=(C2(Polkadot), R1(adaptation), Vm2(Wasm)) of the Polkadot-to-Wasm virtual machine in the cross-chain knowledge graph, automatically add Wasm virtual machine adaptation code (including syntax compatibility processing, function call adaptation, etc.); after the correction is completed, re-execute the above verification process until all verification test cases pass (Valid(CL)=1), and obtain the target contract logic skeleton.

[0058] The system also includes: The contract output and deployment module is configured to generate corresponding smart contract code based on the target contract logic skeleton, output the smart contract code to the user, and provide a deployment interface.

[0059] Specifically, in this embodiment, the system receives a verified contract logic skeleton, performs format standardization processing, and generates corresponding deployable smart contract code based on the compatible virtual machine type (EVM corresponds to Solidity code, Wasm corresponds to ink! code). After final verification confirms that there are no vulnerabilities and that the code conforms to the specifications, the system outputs the contract code and documentation to the user. Simultaneously, it supports integration with cross-chain ecosystem deployment interfaces such as Polkadot and Cosmos, completing contract deployment and automatically monitoring the contract execution status to ensure normal collaboration of cross-chain services.

[0060] This embodiment has the following advantages: This embodiment integrates neural symbolic reasoning algorithms and cross-chain blockchain technology, effectively addressing the technical pain points of existing Web3 cross-chain interoperable smart contracts, such as insufficient dynamic adaptation capabilities, low contract customization, and poor verification efficiency and security, demonstrating significant beneficial effects. Compared to traditional technologies, this invention constructs a cross-chain scenario knowledge graph and transforms it into symbolic knowledge units, combining this with a large neural network model to learn the unstructured characteristics of cross-chain businesses, achieving dual support of "knowledge-driven + data-driven" approaches. By deconstructing cross-chain constraints and generating customized contract logic skeletons through a neural symbolic reasoning engine, it solves the problem of contracts' inability to dynamically adapt in heterogeneous cross-chain scenarios, enabling adaptation to multiple cross-chain ecosystems and covering complex cross-chain business scenarios. Simultaneously, it employs formal verification methods and specific verification algorithms to achieve automatic identification and correction of contract vulnerabilities, significantly improving contract security and reliability and avoiding risks such as cross-chain transaction failures and asset loss. The entire process of contract generation, verification, and deployment is automated, improving generation efficiency, reducing development and deployment costs, and promoting the industrial application of Web3 cross-chain interoperability technology, demonstrating high practical value and promising prospects for wider adoption.

[0061] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the discussion in some embodiments is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the contents of this disclosure, thereby enabling those skilled in the art to better utilize the embodiments.

Claims

1. A system for dynamic generation and verification of Web3 cross-chain interoperable smart contracts, characterized in that, The system includes: A cross-chain knowledge graph construction module is configured to acquire core data information of various public chains, construct a cross-chain scenario knowledge graph based on the core data information, convert the cross-chain scenario knowledge graph into symbolic knowledge units, and store it in a knowledge graph database. The neural network training module is configured to acquire cross-chain business data and smart contract samples for model training, preprocess the cross-chain business data and smart contract samples, and use the preprocessed data to train a large neural network model based on the Transformer architecture to obtain a preliminary contract logic fragment model. The requirement standardization module is configured to acquire users' cross-chain business requirements in real time and perform natural language-based standardization processing on the cross-chain business requirements to obtain standardized requirement text. The neural symbolic reasoning engine module is configured to use a symbolic reasoning algorithm to match the standardized requirement text with the symbolic knowledge units in the cross-chain scenario knowledge graph library to obtain the core constraints of the cross-chain scenario, and generate a contract logic skeleton based on the core constraints and the preliminary contract logic fragment model. The symbol rule verification module is configured to perform formal verification on the contract logic skeleton and fix logical vulnerabilities and cross-chain adaptation conflicts in the contract logic skeleton to obtain the target contract logic skeleton. The contract output and deployment module is configured to generate corresponding smart contract code based on the target contract logic skeleton, output the smart contract code to the user, and provide a deployment interface.

2. The system for dynamic generation and verification of Web3 cross-chain interoperable smart contracts of claim 1, wherein, The cross-chain knowledge graph construction module is also configured as follows: Obtain the core data information of various public chains in the cross-chain ecosystem; The core data information is constructed into a cross-chain scenario knowledge graph using knowledge graph construction technology; The cross-chain scenario knowledge graph is symbolically encoded to obtain symbolic knowledge units, and the symbolic knowledge units are stored in the knowledge graph database.

3. The system for dynamic generation and verification of Web3 cross-chain interoperable smart contracts of claim 2, wherein, The core data information includes Polkadot's PoS consensus mechanism, Cosmos' Tendermint consensus mechanism, EVM and Wasm smart contract virtual machines, ERC-721 and ERC-1155 asset standards, and the XCM cross-chain protocol and its corresponding data interaction format.

4. The system for dynamic generation and verification of Web3 cross-chain interoperable smart contracts of claim 1, wherein, The preprocessing includes: The requirement description of the cross-chain business data is segmented and stop words are removed, and core features are extracted. The contract code of the smart contract sample is parsed to extract the contract logic structure and function definition features; Vulnerability cases in the cross-chain business data are classified and labeled, and a vulnerability feature library is established.

5. The dynamic generation and verification system for Web3 cross-chain interoperable smart contracts according to claim 4, characterized in that, The training process of the large neural network model incorporates a cross-entropy loss function and the symbolic knowledge units as constraints.

6. The system for dynamic generation and verification of Web3 cross-chain interoperable smart contracts of claim 1, wherein, The natural language-based standardization process includes: Perform word segmentation, stop word removal, and redundant information filtering on the cross-chain business requirements, and extract the core elements; the core elements include cross-chain participants, asset types, cross-chain protocols, core constraint conditions, and business processes; On the premise of determining the association relationships between each of the core elements, convert the core elements into the standardized requirement text.

7. The system for dynamic generation and verification of Web3 cross-chain interoperable smart contracts of claim 1, wherein, The neuro-symbolic reasoning engine module is further configured to: Construct an inference rule set for the standardized requirement text using a symbolic reasoning algorithm based on predicate logic; Calculate the matching degree between the inference rule set and the symbolic knowledge unit to obtain the core constraint conditions of the cross-chain scenario; the core constraint conditions include cross-chain asset transfer constraints, settlement constraints, and adaptation constraints.

8. The system for dynamic generation and verification of Web3 cross-chain interoperable smart contracts of claim 1, wherein, The generation formula of the contract logic skeleton is: ； In the formula, CL represents the generated contract logic skeleton. S is the output of the large neural network model; S is the standardized requirement text; F is the cross-chain business feature vector. Let C be the symbolic inference constraint function, C be the set of core constraints, and λ be the constraint weight coefficient.

9. The system for dynamic generation and verification of Web3 cross-chain interoperable smart contracts of claim 1, wherein, The symbolic rule verification module is further configured to: Perform formal verification on the contract logic skeleton using Hoare logic, and combine with the symbolic rule verification algorithm to convert the contract logic skeleton into a formal model; Construct a number of verification cases according to the core requirements of the cross-chain scenario, and define the preconditions and postconditions for each verification case; Verify one by one whether the contract logic skeleton meets the preconditions and postconditions in each verification case; If all are met, output the target contract logic skeleton; if not, add Wasm virtual machine adaptation code in combination with the symbolic knowledge unit of Polkadot adapting to the Wasm virtual machine in the cross-chain knowledge graph until the contract logic skeleton is repaired, and re-verify the verification cases.

10. The system for dynamic generation and verification of Web3 cross-chain interoperable smart contracts of claim 9, wherein, The verification cases include virtual machine adaptation verification, cross-chain protocol adaptation verification, settlement threshold verification, logic process verification, and exception handling verification.

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