A data contract-oriented strategy real-time compilation and consistency hot deployment method and system

Abstract

The application belongs to the technical field of computer data security, and more particularly to a data contract-oriented strategy real-time compiling and consistency hot deployment method and system, which comprises the following steps: constructing a strategy abstract syntax tree with complete semantic information according to the user-input strategy, the strategy definition based on formalized semantics and the normalized representation; inputting the abstract syntax tree into a multi-stage compiling optimization pipeline to generate high-performance intermediate representation (IR) code by using an optimization decision mechanism driven by a cost model; realizing the distributed consistency synchronization and real-time hot deployment of the strategy by using a strategy distribution architecture based on a publish-subscribe model, combining an improved Gossip protocol and a Paxos atomic commit algorithm; and realizing the efficient and safe execution of the strategy by using a register micro virtual machine and an adaptive execution mode. The application solves the problems of bottleneck in execution performance, strategy update delay, weak support for complex strategies and difficulty in guaranteeing the consistency of cluster strategies in the prior art.

Description

Technical Field

[0001] This invention belongs to the technical field of computer data security, and more specifically, relates to a method and system for real-time compilation and consistent hot deployment of strategies for data contracts. Background Technology

[0002] With data officially becoming a key production factor, building a secure and reliable data circulation environment has become a national strategic priority for promoting the development of the digital economy. Against this backdrop, the concept of a trusted data space has emerged, its core being to ensure the compliance, security, and controllability of data elements during their flow through technological means. Data contracts, as the core rule carrier in this system, bear the important responsibility of defining the boundaries of data use—they clearly stipulate key constraints such as the purpose, access scope, validity period, and anonymization standards of data in a machine-readable form. The execution efficiency of data contracts directly determines the credibility of the entire data space; only with efficient, accurate, and reliable contract execution capabilities can the ideal state of data being "usable but invisible" and "controllable and measurable" be truly achieved.

[0003] For example, Chinese patent document CN117494218A discloses a data management method and system based on contract attachment in a trusted data space. Based on a trusted data space, starting with the data provider's publication of data catalog information, the data user initiates an electronic contract for the selected data catalog information. Under the coordination of a trusted data space management platform, the encrypted transmission and verification of the electronic contract between the data provider and the data user are realized. The data user then obtains and uses the target files of the selected data catalog information. The design scheme, based on the agreement of the electronic contract, automatically realizes the use control of data files throughout their entire lifecycle of transmission, storage, and use. Simultaneously, it performs security checks and constraints on the usage environment to avoid the use and operation of data files under high-risk conditions, significantly enhancing the security of data file circulation and sharing, and constraining and controlling the behavior of data files during the data user's usage.

[0004] Currently, mainstream solutions in the industry mainly revolve around two technical paths. The first is the API gateway-based policy enforcement solution. This solution deploys a gateway proxy outside the data service interface and processes pre-configured policy files (such as JSON or YAML format) using interpreted execution. While this type of solution achieves basic data access control, its architecture has significant drawbacks: each request requires re-parsing and interpreting the policy file, generating substantial computational overhead and easily becoming a systemic bottleneck in high-concurrency scenarios; simultaneously, policy changes require service restarts or waiting for periodic polling from the configuration center to take effect, resulting in policy update delays typically reaching seconds or even minutes, failing to meet real-time requirements; furthermore, this type of solution has significant limitations in supporting complex policies, making it difficult to implement advanced features such as dynamic de-identification and multi-level watermark injection.

[0005] The second approach is a database proxy-based SQL rewriting scheme. This scheme embeds a proxy component into the database access chain, parsing and rewriting SQL statements to achieve basic conditional filtering. While this method achieves data access control to some extent, its limitations are equally prominent: its policy expression capabilities are weak, typically only supporting simple condition appending, making it difficult to handle complex de-identification rules and watermark injection requirements; in a distributed deployment environment, policy management is often fragmented, making it difficult to guarantee strong consistency of policies across nodes, and easily leading to execution deviations.

[0006] Existing technical solutions generally suffer from systemic problems such as execution performance bottlenecks, policy update delays, weak support for complex policies, and difficulty in guaranteeing policy consistency across clusters. These shortcomings severely restrict the efficient operation of the data element market and hinder the comprehensive construction of a trustworthy data space. Summary of the Invention

[0007] This invention aims to overcome at least one of the defects of the prior art and provide a method for real-time compilation and consistent hot deployment of strategies for data contracts. By compiling high-level strategy languages ​​into efficient intermediate representation bytecode, it realizes the transformation from "interpreted execution" to "compiled execution" mode, which greatly improves processing performance. It adopts a publish-subscribe model to achieve millisecond-level hot deployment of strategies, completely eliminating the dependency on service restarts. Through a centralized version management mechanism, it ensures strong consistency of strategies in a distributed environment.

[0008] This invention also provides a system for implementing a real-time compilation and consistent hot deployment method for data contract-oriented strategies.

[0009] The detailed technical solution of this invention is as follows:

[0010] A method for real-time compilation and consistent hot deployment of strategies for data contracts, the method comprising:

[0011] S1. Based on the policy input by the user, construct a policy abstract syntax tree with complete semantic information based on the formal semantic policy definition and normalized representation. Specifically, this includes policy syntax modeling and formal definition, policy conflict detection and consistency verification, policy dependency construction and structured modeling, and static semantic analysis and abstract interpretation verification.

[0012] S2. Input the abstract syntax tree into a multi-stage compilation optimization pipeline and use the cost model-driven optimization decision mechanism to generate high-performance intermediate representation (IR) code. This stage includes lexical analysis, syntax analysis, control flow analysis, data flow analysis, cost model evaluation, SSA-style intermediate code generation, and instruction-level optimization.

[0013] S3. It adopts a policy distribution architecture based on the publish-subscribe model, combined with the improved Gossip protocol and Paxos atomic commit algorithm, to achieve distributed consistency synchronization and real-time hot deployment of policies in a multi-node environment, fundamentally avoiding version drift and execution deviation caused by network latency or concurrent updates.

[0014] S4. Through register-based microvirtual machines and adaptive execution mode, efficient and secure policy execution is achieved.

[0015] According to a preferred embodiment of the present invention, the specific steps of step S1 are as follows:

[0016] Step S11: Based on the original policy text input by the user, design an attribute-based access control ABAC policy description language (DSL), clarifying the policy's subject, resources, actions, and environment. Use the extended Backus paradigm to define unified syntax rules, generating a set of policy production rules that conform to the syntax rules, providing structured input for subsequent semantic analysis and compilation optimization.

[0017] Step S12: For the set of policy production rules that conform to the grammatical rules, implement the policy conflict detection algorithm, perform consistency analysis on the logical implication relationship between different policy conditions based on the description logic, eliminate policies with logical conflicts, and obtain a set of policies without logical conflicts to ensure the determinism and security of policy execution.

[0018] Step S13: Based on the set of strategies without logical conflicts output in step S12, extract the input conditions, execution results and resource reference relationships of each strategy through semantic parsing, and establish a dependency graph G=(V,E) between strategies, where vertex V is the set of strategy nodes and edge E is the set of strategy dependencies. The hierarchy and priority between strategies can be clearly defined through the dependency graph.

[0019] Step S14: Map each policy node to an element in the abstract semantic domain, calculate its variable initialization state, condition reachability, and dependency consistency, mark and record the detected unreachable branches, undefined variables, or semantic conflict rules, ensure that the logical relationship of all policies reaches a stable state, and finally output the verified policy abstract syntax tree (AST).

[0020] According to a preferred embodiment of the present invention, step S2 is implemented as follows:

[0021] Step S21: Construct a lexical analyzer based on a deterministic finite automaton (DFA), and perform character-by-character scanning and state recognition on the policy text through a state transition function, splitting it into lexical units (Tokens) with clear semantics, and generating a Token sequence;

[0022] Step S22: A parser based on the Earley algorithm is used to parse the syntactic structure of the token sequence, while processing recursive and fuzzy grammatical structures, and constructing an abstract syntax tree with semantic annotations. This tree structure is used to express the logical hierarchy and operational dependencies of the strategy and serves as the input carrier for control flow and data flow analysis.

[0023] Step S23: Perform control flow analysis based on the semantically annotated abstract syntax tree and construct the control flow graph. Where N represents the set of basic blocks, and each basic block consists of intermediate representation instructions executed sequentially. The control flow graph represents the set of control transfer edges, used to describe the execution jump relationships between basic blocks; the control flow graph provides the topological foundation for subsequent data flow analysis and execution path optimization.

[0024] Step S24: Based on the control flow graph, perform data flow analysis, including LiveIn analysis of active variables and ReachOut analysis of reaching a set value. By traversing each basic block in the control flow graph, calculate the life cycle and the range of values ​​that can be assigned to variables, and output the variable dependency matrix.

[0025] Step S25: Based on historical execution statistics of the variable dependency matrix, an execution path optimization mechanism is constructed using a cost model to quantitatively evaluate the performance overhead of different paths. The optimization decision function of the cost model is defined as follows:

[0026] (1)

[0027] in, This represents the predicate filtering cost on the database side. Indicates the execution cost of the virtual machine. Indicates the amount of data transmitted; , , These are the weight parameters obtained through linear regression of historical execution data;

[0028] By comparing the cost of different paths, the execution scheme with the best performance is selected to achieve predicate pushdown or local execution;

[0029] Step S26: Convert the path-optimized control flow graph into intermediate representation code based on static single-assignment SSA form. By introducing the Euler φ function to unify the variable version at the control flow merging point, it is ensured that each variable is assigned only once, thereby improving the efficiency of subsequent optimization and code generation.

[0030] Step S27: Apply local and global optimizations to the generated intermediate representation in SSA form, including peephole optimization and loop optimization. Peephole optimization is used to eliminate redundant instructions and invalid operations, while loop optimization reduces repeated calculations by using loop-invariant code hoisting and inductive variable substitution, significantly improving code execution efficiency and instruction locality, and finally outputting high-quality intermediate representation code.

[0031] According to a preferred embodiment of the present invention, the policy description language DSL adopts a modular syntax design, which can be extended with new keywords and semantic rules according to business needs, thereby supporting complex policy logic such as multi-condition combination and user-defined functions, and has good scalability.

[0032] The intermediate representation IR is designed as an extensible instruction set structure, which supports the dynamic addition of arithmetic, logic and access control instructions based on the new policy type, thereby quickly adapting to new policy models without changing the core execution architecture.

[0033] According to a preferred embodiment of the present invention, in step S23, the process of constructing the control flow graph includes the following steps:

[0034] S231. Basic block division: Based on the control instructions in the intermediate representation IR instruction sequence, the continuous instruction sequence is divided into independent basic blocks. Each basic block contains a single entry and a single exit, which facilitates subsequent data flow analysis and optimization processing.

[0035] S232, Edge Generation: Based on the jump target of the control instruction, forward edges and backward edges are established between basic blocks to form a control flow transfer relationship. Forward edges represent sequential execution paths, and backward edges represent loop or back jump paths.

[0036] S233. Entry and exit identification: Identify the global entry and exit nodes of the control flow graph to form the structural boundary of the directed acyclic subgraph, providing boundary conditions for generating the dominance tree and subsequent optimization.

[0037] S234. Dominant Tree Generation: A dominant tree is constructed using a dominant relationship algorithm to clarify the control dependency hierarchy between basic blocks. The dominant tree provides a topological reference for compilation optimizations such as register allocation, loop optimization, and code movement.

[0038] According to a preferred embodiment of the present invention, step S3 is implemented as follows:

[0039] S31: In the policy distribution phase, a distribution architecture based on the publish-subscribe model is adopted. The central node, as a message publisher, publishes an incremental update package containing version number and difference content to each execution node through a message queue or event bus. Each execution node, as a subscriber, automatically receives, verifies and loads the new version policy according to the policy topic it has subscribed to, so as to realize the asynchronous push and fast arrival of policy updates.

[0040] S32: After distribution is completed, the system enters the consistency detection phase. Based on high-quality intermediate representation code and its version information, a version-based distributed consistency protocol is designed. Each node maintains a state vector V=(NodeID,Version,Timestamp) to record the node identifier, policy version number and update time. Concurrent conflicts are detected by vector clock to determine the version deviation and update order, thus establishing a consistency foundation for policy distribution.

[0041] S33: Implement an improved Gossip protocol to accelerate policy synchronization. Use an anti-entropy mechanism and a push-pull hybrid message propagation method to exchange version information, so that the network state converges in O(log n) time to achieve full network synchronization, where n is the number of nodes.

[0042] S34: The Paxos-based atomic commit protocol is adopted to ensure the atomicity and consistency of policy updates in a multi-node environment. Each update must go through three stages: proposal, voting and commit. The policy update is only allowed to be written after a majority of nodes reach a consensus, which fundamentally prevents the situation where some nodes' policies are not synchronized or rolled back.

[0043] S35: Design a multi-mode conflict resolution mechanism to address potential concurrent policy update conflicts;

[0044] S36: To achieve real-time hot deployment of the strategy, a lock-free hot deployment mechanism is adopted, which uses CAS atomic operations to replace the strategy pointer: During the execution process, the current pointer value is continuously checked, and when a new version is detected to be available, the old version is replaced with the new version through atomic comparison and swap, so as to achieve instant loading of the strategy;

[0045] S37: A policy-based garbage collection mechanism based on reference counting and generational analysis. When the reference count of a certain policy version is detected to be zero, a mark-and-sweep operation is performed to safely reclaim the memory resources occupied by the old version.

[0046] According to a preferred embodiment of the present invention, in step S35, the multi-mode conflict resolution mechanism specifically includes:

[0047] The default mechanism is a timestamp-based last-write priority mechanism, which adds a globally monotonically increasing timestamp to each update operation. When multiple version conflicts are detected, the version with the largest timestamp is automatically selected as the valid version and the old version is discarded to achieve efficient decision-making.

[0048] For critical business strategies, an arbitration mechanism based on business semantics is adopted, allowing access to custom conflict handling procedures; the arbitration procedure receives a list of conflicting versions and determines the final version according to business rules.

[0049] For strategies that are idempotent and commutative, a coordinated merging mechanism is adopted. Based on the state machine replication principle, the consistency of each node is achieved by merging the strategy content, and the merging operation satisfies the commutative, associative and idempotent laws.

[0050] The default mechanism is a timestamp-based last write priority mechanism, and administrators are allowed to switch flexibly between different modes based on policy importance.

[0051] According to a preferred embodiment of the present invention, step S4 is implemented as follows:

[0052] S41: Design a fast matching algorithm based on Bloom filters. The algorithm maps policy identifiers to a set of Boolean bit arrays through multiple hash functions and uses this to determine whether the target policy exists in the set. At the same time, in order to achieve a balance between memory usage and false positive rate, the number of hash functions and the length of the bit array are dynamically adjusted according to the number of policies, so that the false positive rate is kept within a controllable range.

[0053] S42: A precise matching mechanism based on cuckoo hashing is adopted to perform secondary positioning of candidate strategies: the strategy key value is mapped to different table positions through two independent hash functions. When a conflict occurs, the existing entries are automatically migrated to another storage location to free up insertion space.

[0054] S43: Design a register-based microvirtual machine architecture, adopting a three-stage pipeline structure to complete the instruction fetch, decode and execute processes sequentially. Each instruction uses registers as the core operation object and performs arithmetic or logical operations directly between registers.

[0055] S44: Introduce a dynamic optimization mechanism based on trace-based just-in-time compilation in the micro-virtual machine execution layer. Monitor the execution frequency of the strategy in real time, compile the strategy path with the number of executions exceeding the threshold to generate native machine code in just-in-time, and adaptively optimize the compilation strategy by combining runtime performance data.

[0056] S45: A security sandbox is built using a capability-based access control mechanism: Each policy enforcement unit is assigned a capability token at startup, and during policy enforcement, access authorization is strictly based on this token. Any operation outside the authorized scope will be denied.

[0057] S46: During the execution phase, an adaptive execution mode is designed to dynamically select the running mode based on load characteristics and policy complexity: when under light load, the interpreted execution mode is used to reduce memory consumption; when the load is high or the policy is executed repeatedly, it automatically switches to just-in-time compilation mode to improve execution speed.

[0058] In another aspect of the present invention, a real-time compilation and consistent hot deployment system for data contracts is provided, including a policy management module, a policy compilation module, a policy distribution center, an executor cluster, and a monitoring and auditing module.

[0059] The policy management module receives the raw policy text input by the user and completes the normalization processing and abstract syntax tree construction of the policy based on formal semantics. The policy compilation module generates high-performance intermediate representation (IR) code from the policy abstract syntax tree through a multi-stage compilation optimization pipeline. The policy distribution center achieves distributed consistency synchronization and real-time hot deployment of policies through a publish-subscribe model-based policy distribution architecture, combined with the improved Gossip protocol and Paxos atomic commit algorithm. The executor cluster achieves efficient and secure policy execution through register-based microvirtual machines and adaptive execution mode. The monitoring and auditing module monitors the system's operating status and policy execution process in real time.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] (1) Significant performance improvement: By pre-compiling the policy into efficient IR code, the interpretation overhead of each request is avoided, and the policy execution is changed from "interpreted" to "compiled", which improves the performance by more than an order of magnitude and greatly improves the system throughput.

[0062] (2) True real-time hot update: Based on the incremental distribution and dynamic loading mechanism of the publish-subscribe model, it completely gets rid of the dependence on service restart, realizes the global effect of policies at the millisecond level, and greatly improves the agility of data management.

[0063] (3) Excellent consistency and reliability: Through centralized version management and distribution, the absolute consistency of the policies of all nodes in a large-scale distributed deployment environment is ensured, effectively avoiding the risk of data leakage caused by inconsistent policies.

[0064] (4) Rich expressive power and extensibility: The introduction of the compiler makes it possible to support complex strategy logic (such as multi-condition combination and user-defined functions). By extending the DSL syntax and IR instruction set, new strategy types can be flexibly supported, and the extensibility is extremely strong. Attached Figure Description

[0065] Figure 1 This is a flowchart of the real-time compilation and consistent hot deployment method for data contract-oriented strategies described in this invention;

[0066] Figure 2 This is a flowchart illustrating the strategy compilation process described in an embodiment of the present invention;

[0067] Figure 3 This is a sequence diagram of the strategy consistency hot deployment and execution described in the embodiments of the present invention;

[0068] Figure 4 This is a system architecture diagram for real-time compilation and consistent hot deployment of strategies for data contracts, as described in an embodiment of the present invention. Detailed Implementation

[0069] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0070] Example 1

[0071] like Figure 1 As shown, this embodiment provides a method for real-time compilation and consistent hot deployment of strategies for data contracts. The method includes:

[0072] S1. This step takes the policy description file input by the user as input, and based on the formal semantic policy definition and normalized representation, it sequentially completes policy syntax modeling, logical consistency verification, dependency modeling and semantic verification, and finally constructs a policy abstract syntax tree (AST) with complete semantic information, providing structured input for the subsequent compilation and optimization stage.

[0073] S11: Policy Syntax Modeling and Formal Definition: The input object for this step is the original policy text input by the user. Based on the attribute-based access control ABAC model, the core components of the policy are abstractly modeled, clearly including four parts: Subject, Resource, Action, and Context. The Subject describes the entity performing the action, the Resource represents the data object being accessed, the Action defines the type of operation that can be authorized or prohibited, and the Context constrains the scope of application of the policy. Based on this model, a Policy Description Language (DSL) is designed, and a unified syntax rule is established using the Extended Backus Normative Form (EBNF) to ensure that the policy has the ability to be composable and nested in a structured manner. The policy semantics can be expressed as "allowing or denying a subject to perform a certain operation on a certain resource under specific conditions." Through this syntax definition, the system converts natural language or semi-structured policy text into a standardized form, realizing the mapping from unstructured descriptions to a computable semantic model. The policy description language DSL adopts a modular syntax design, which can be extended with new keywords and semantic rules according to business needs, thereby supporting complex policy logic such as multi-condition combinations and user-defined functions, and has good scalability. The output of this step is a set of policy production rules that conform to the syntax rules, providing structured input for subsequent semantic analysis and conflict detection.

[0074] S12: Policy Conflict Detection and Consistency Verification: For a set of policy production rules that conform to the syntax rules, based on description logic, consistency analysis is performed on the logical implications between different policies, i.e., possible logical overlap, mutual exclusion, and overriding, and policies with logical conflicts are eliminated. Specifically, through semantic matching and condition comparison, the system detects whether there are contradictory authorization results for the same subject, resource, and action under the same or overlapping conditions. For detected mutual exclusion conflicts, the system adopts the "rejection first" principle for adjudication; for overriding conflicts, the rules are rearranged according to the condition granularity and priority metadata; for completely equivalent redundant rules, they are merged or marked as duplicates. The system generates a conflict report during the analysis process and outputs a set of policies without logical conflicts after correction, ensuring the logical independence and global consistency of the policy set.

[0075] S13: Policy Dependency Construction and Structured Modeling: Based on the set of policies without logical conflicts output in step S12, the input conditions, execution results, and resource reference relationships of each policy are extracted through semantic parsing to establish a dependency graph between policies. Specifically, when the execution result of policy A is referenced or conditionally dependent on policy B, the system generates a directed edge from A to B in the dependency graph. The dependency graph adopts a directed acyclic graph (DAG) structure, with nodes representing policy units and edges representing dependencies. Through this structure, the execution order and parallelizability of policies can be analyzed. After topological sorting, the priority execution sequence of policies can be determined, and possible circular dependencies can be detected. The output of this step is the policy dependency graph G=(V,E), where vertex V is the set of policy nodes and edge E is the set of policy dependencies. The hierarchy and priority between policies can be clearly defined through the dependency graph.

[0076] S14: Static Semantic Analysis and Abstract Interpretation Verification. The input to this step is the policy dependency graph output from step S13. The system performs static semantic analysis of the policies based on the abstract interpretation method to verify the executability and completeness of the policy logic. During the analysis, the system maps each policy node to an element in the abstract semantic domain, calculates its variable initialization state, condition reachability, and dependency consistency. Unreachable branches, undefined variables, or semantically conflicting rules are marked and recorded. After completing the semantic analysis, the system integrates the output results of steps S11 to S13, and models the syntax nodes, dependencies, and semantic constraints in a unified manner according to the semantic domain mapping relationship, constructing a complete policy abstract syntax tree (AST). This syntax tree uses nodes to represent policy units and directed edges to represent dependencies, and adds static semantic annotation information to the nodes, realizing a unified expression of the policy from syntax structure, logical dependencies to semantic verification. Finally, the system outputs a policy abstract syntax tree (AST) that has passed semantic verification and consistency checks, ensuring the semantic completeness and executable feasibility of the policy model, and providing a unified input for the subsequent compilation and optimization stage.

[0077] S2. Input the abstract syntax tree into a multi-stage compilation and optimization pipeline. Utilize a cost model-driven optimization decision mechanism to generate high-performance intermediate representation (IR) code. This stage includes lexical analysis, syntax analysis, control flow analysis, data flow analysis, cost model evaluation, SSA-style intermediate code generation, and instruction-level optimization. This efficiently converts the policy description language into an executable intermediate code structure. The specific policy compilation process is as follows: Figure 2 As shown.

[0078] The intermediate representation IR is designed as an extensible instruction set structure, which supports the dynamic addition of arithmetic, logic and access control instructions according to the new policy type, so as to quickly adapt to the new policy model without changing the core execution architecture.

[0079] S21: The input to this step is the policy abstract syntax tree and its corresponding syntax rules from S1. The system constructs a lexical analyzer based on a deterministic finite automaton (DFA) to scan the policy text character by character and identify its state, breaking it down into lexical units (Tokens) with explicit semantics. The analysis process uses the state transition function δ(Q,Σ) to identify the lexical units (Tokens), where Q is the set of states, Σ is the set of input symbols, and δ is the state transition mapping function. During the scanning process, state transitions are completed based on the matching relationship between the current state and the input characters, and the corresponding Token is generated when the terminating state is reached. The final output Token sequence is fed into the parser to provide input for the construction of the semantic structure.

[0080] S22: The input to this step is the token sequence output from step S21. The system uses a parser based on the Earley algorithm to parse the syntactic structure of the token sequence. The Earley algorithm can handle both recursive and fuzzy grammatical structures, making it suitable for analyzing multi-condition nesting and hierarchical logic in policy languages. Through this process, the system generates an Abstract Syntax Tree (AST) with semantic annotations, clearly defining the hierarchical relationships and semantic dependencies of the policy logic.

[0081] S23: Perform control flow analysis based on an abstract syntax tree with semantic annotations, and construct a control flow graph. , where N represents the set of basic blocks, and each basic block consists of intermediate representation instructions executed sequentially; The control flow graph represents the set of control flow edges, which describes the execution jump relationships between basic blocks. The control flow graph provides the topological foundation for subsequent data flow analysis and execution path optimization.

[0082] The construction of a control flow graph involves four main processes:

[0083] S231. Basic Block Division: Based on the control instructions in the intermediate representation IR instruction sequence, such as conditional branches, loop jumps, function calls, etc., the continuous instruction sequence is divided into independent basic blocks. Each basic block contains a single entry point and a single exit point, which facilitates subsequent data flow analysis and optimization processing;

[0084] S232. Edge Generation: Based on the jump target of the control instruction, forward and backward edges are established between basic blocks to form control flow transfer relationships. Forward edges represent sequential execution paths, and backward edges represent loop or back jump paths;

[0085] S233. Entry and exit identification: Identify the global entry and exit nodes of the control flow graph to form the structural boundary of the directed acyclic subgraph, providing boundary conditions for generating the dominance tree and subsequent optimization.

[0086] S234. Dominance Tree Generation: A dominance tree is constructed using a dominance relation algorithm, such as the Lengauer-Tarjan dominance tree algorithm, to clarify the control dependency hierarchy between basic blocks. The dominance tree provides a topological reference for compiler optimizations such as register allocation, loop optimization, and code movement.

[0087] S24: Based on the control flow graph, perform data flow analysis, including live variable analysis and reaching definition analysis. During the analysis, the system traverses each basic block in the control flow graph to calculate the lifecycle and reachable range of variables. The data flow relationship can be formalized as IN[B] = ∪ OUT[P], where P is the predecessor set of basic block B. Through this calculation, the system obtains the variable dependency matrix, providing basic data for path optimization and register allocation. This step outputs the set of live variable information required for optimization.

[0088] S25: Based on historical execution statistics of the variable dependency matrix, an execution path optimization mechanism is constructed using a cost model to quantitatively evaluate the performance overhead of different paths. The optimization decision function is defined as follows:

[0089]

[0090] in, This represents the predicate filtering cost on the database side. Indicates the execution cost of the virtual machine. This represents the data transfer volume; α, β, and γ are weight parameters obtained through linear regression of historical execution data. The system compares the cost of different paths and selects the execution plan with the optimal performance, achieving dynamic optimization such as predicate pushdown or local execution. This step outputs the optimal execution plan.

[0091] S26: The path-optimized control flow graph is converted into intermediate representation code based on Static Single Assignment (SSA). By introducing the Euler's φ function to unify variable versions at control flow merging points, the system ensures that each variable is consistent, thereby simplifying dependencies and improving optimization efficiency. This step outputs intermediate representation code in SSA form, providing clear data flow semantics for subsequent instruction optimization stages.

[0092] S27: Perform local and global code optimizations on the intermediate representation code in SSA form, including peephole optimization and loop optimization. Peephole optimization eliminates redundant instructions and invalid operations, while loop optimization reduces redundant calculations through loop-invariant code hoisting and inductive variable substitution, significantly improving code execution efficiency and instruction locality. This step outputs high-quality intermediate representation code (Optimized IR), which serves as direct input for subsequent hot deployment and execution phases.

[0093] S3. The input to this step is the high-quality intermediate representation code and its version information output from step S2. First, a publish-subscribe model-based distribution architecture is used to trigger and distribute policy updates. The central node pushes policy update events through a message queue or event bus, and each execution node, as a subscriber, automatically receives policy change notifications, achieving asynchronous triggering and rapid delivery of policy distribution. Based on this, the system combines an improved Gossip protocol and the Paxos atomic commit algorithm to achieve distributed consistency synchronization and real-time hot deployment of policies in a multi-node environment. The core objective of this step is to ensure the global synchronization, atomicity, and traceability of policy version updates in a multi-node environment, thereby achieving real-time effectiveness and high reliability. The policy consistency hot deployment and execution sequence are as follows: Figure 3 As shown.

[0094] Step S31: In the policy distribution phase, the system adopts a distribution architecture based on a publish-subscribe (Pub / Sub) model. The central node, acting as a message publisher, publishes incremental update packages containing version numbers and differences to each execution node through a message queue or event bus. Each execution node, acting as a subscriber, automatically receives, verifies, and loads the new version policy according to the policy topic it has subscribed to, thus achieving asynchronous push and rapid delivery of policy updates.

[0095] Step S32: After distribution is complete, the system enters the consistency detection phase. Based on high-quality intermediate representation code and its version information, a version-based distributed consistency protocol is designed to monitor and resolve concurrent conflicts during policy distribution. Each node maintains a state vector V=(NodeID,Version,Timestamp), recording the node identifier, policy version number, and update time. The system compares the version vectors of each node to determine policy version differences; when all components of a node are not less than those of another node and at least one component is larger, it is determined to be an updated version. This mechanism uses vector clocks to implement causal consistency detection among multiple nodes, providing the basic input for subsequent Gossip synchronization and conflict resolution.

[0096] S33: The input to this step is the node version status information output from step S32. An improved Gossip protocol is implemented to accelerate policy synchronization, employing an anti-entropy mechanism and a push-pull hybrid message propagation method for version information exchange. In the specific implementation, each node periodically and randomly selects a communication partner to update its version status through bidirectional synchronization. When version differences exist between nodes, the system replaces the state based on the latest timestamp or version number, achieving rapid convergence. This algorithm can... The system achieves full network synchronization within a time complexity, where n is the number of nodes. Through this mechanism, the system can achieve efficient decentralized synchronization without relying on centralized scheduling.

[0097] S34: The input to this step is the network-wide node status output in step S33. To ensure the atomicity and consistency of the policy update operation, an atomic commit protocol based on the Paxos algorithm is adopted. This protocol means that each update must complete a consensus process through three phases: proposal Prepare, voting Accept, and commit. The proposal number is generated using the combination of ProposalNum = (RoundNumber, NodeID), where RoundNumber is a globally monotonically increasing round number, ensuring the uniqueness and temporal order of proposal numbers among different nodes. The policy update is only allowed to be officially written after a majority of nodes have completed voting confirmation and reached a consensus, thereby avoiding update failures on some nodes or version rollbacks. The output of this step is the consistent version confirmed by the entire network, which serves as the input to the conflict resolution phase.

[0098] S35: The input to this step is the set of candidate policy versions output from step S34. To address the potential for concurrent policy updates in a distributed environment, a multi-mode conflict resolution mechanism is designed to ensure eventual consistency and flexibility, specifically including:

[0099] The default mechanism is Last-Writer-Wins (LWW), which uses a timestamp-based last-write-wins mechanism. It adds a globally monotonically increasing timestamp to each update operation, such as a hybrid logical clock (HLC). When multiple version conflicts are detected, it automatically selects the version with the largest timestamp as the valid version and discards the old version to achieve efficient decision-making.

[0100] For critical business strategies, an arbitration mechanism based on business semantics is adopted, allowing the integration of custom conflict resolution procedures. The arbitration procedure receives a list of conflicting versions and determines the final version based on business rules (such as security level priority, risk threshold control, or manual review).

[0101] For strategies that are idempotent and commutative, a reconcilable mergeable policy mechanism is adopted. This mechanism is based on the principle of state machine replication and achieves consistency among nodes by merging policy content, such as the union of conditional sets. The merge operation satisfies the commutative, associative, and idempotent laws.

[0102] The system defaults to LWW mode and allows administrators to flexibly switch between different modes based on policy importance. This step outputs a unified and arbitrated policy version.

[0103] S36: The input to this step is the unified policy version output from step S35. Based on this, a lock-free hot deployment mechanism is adopted, using CAS (Compare-And-Swap) atomic operations to replace the policy pointer, achieving instant version switching. During execution, the current pointer value is continuously monitored. When a new version is detected, the old version is replaced with the new version through atomic comparison and swapping, achieving immediate policy loading. The typical process is to read the old value, calculate the updated value, and attempt atomic replacement. If the replacement fails, it is retried until successful. This design ensures the atomicity and thread safety of version switching, and updates can be completed in milliseconds without service restart, thus significantly improving the system's real-time performance and availability.

[0104] S37: The input to this step is the latest policy version and its reference count information output from step S36. A policy garbage collection mechanism based on reference counting and generational analysis is designed to clean up invalid older policy versions. Specifically, when the reference count of a policy version is detected to be zero, a mark-and-sweep operation is performed to safely reclaim the memory resources occupied by the old version. This method prevents memory leaks and excessive resource consumption, ensuring stability and maintainability during long-term operation. This step outputs a streamlined policy storage set, providing an efficient and reliable operating environment for subsequent policy execution phases.

[0105] S4. The input to this step is the unified policy version output from step S3 and its optimized intermediate representation (IR) code. The system achieves efficient and secure policy execution through a register-based microvirtual machine and an adaptive execution mode. This execution architecture balances performance and security, providing low-latency and scalable policy computation capabilities in a multi-tenant environment, while ensuring the trustworthiness of the policy runtime environment through a secure isolation mechanism.

[0106] S41: To address the high-frequency policy matching requirements during the execution phase, a fast matching algorithm based on a Bloom filter is designed for preliminary screening of a large-scale policy set. This algorithm maps policy identifiers to a set of Boolean bit arrays using multiple hash functions, and then determines whether a target policy exists in the set. To balance memory usage and false positive rate, the number of hash functions and the length of the bit array are dynamically adjusted based on the number of policies, keeping the false positive rate within a controllable range. This algorithm has extremely high space utilization, can complete preliminary matching in constant time, and significantly improves the response speed of policy retrieval. The formula for controlling the false positive rate of the fast matching algorithm based on a Bloom filter is as follows:

[0107]

[0108] By adjusting the number of hash functions k and the size of the bit array m, memory usage and false positive rate can be balanced. The optimal parameter is k = (m / n)ln2, where m represents the length of the bit array of the Bloom filter, which determines the system's memory overhead; n represents the expected number of elements to be inserted, i.e., the number of policy identifiers or rule entries that the system needs to store; and k represents the number of hash functions, which controls the balance between false positive rate and computational overhead. Under this parameter configuration, the false positive rate of the Bloom filter can be approximately minimized, thereby achieving optimal query performance under limited memory conditions.

[0109] S42: After the initial screening by the Bloom filter, a precise matching mechanism based on Cuckoo Hash is further employed to perform secondary positioning of candidate strategies. This algorithm maps strategy keys to different table locations using two independent hash functions. When a collision occurs, existing entries are automatically migrated to another storage location to free up insertion space. This "replacement" design effectively avoids the performance degradation of traditional hash tables under high load, ensuring that strategy lookup maintains a stable constant-level response time under any load condition. Finally, the system outputs precise matching results, providing definite instruction input for the strategy execution phase.

[0110] The combination of S41 and S42 achieves a two-level matching structure of "coarse first, then fine" in the strategy execution phase.

[0111] S43: After policy indexing is completed, a register-based microvirtual machine architecture is designed to efficiently execute policy logic. This virtual machine employs a three-stage pipeline structure, sequentially completing instruction fetching, decoding, and execution. Each instruction uses registers as its core operation object, allowing direct arithmetic or logical operations between registers, thus significantly reducing memory accesses. This design fully utilizes instruction-level parallelism, enabling the virtual machine to achieve high throughput and low latency during multi-tasking. This step outputs an intermediate execution flow, scheduled by the virtual machine, for just-in-time compilation.

[0112] S44: To further improve performance, a dynamic optimization mechanism based on tracing-based just-in-time (JIT) compilation is introduced into the micro-virtual machine execution layer. During runtime, the execution frequency of each strategy is monitored in real time, automatically identifying frequently executed paths. When the number of executions of a path exceeds a preset threshold, JIT compilation is triggered, converting that part of the logic into native machine code for execution. Simultaneously, the compilation strategy is adaptively optimized based on runtime performance data, ensuring that hot paths automatically converge to their optimal performance state after multiple executions. This mechanism significantly reduces interpretation overhead while maintaining responsiveness.

[0113] S45: To ensure the security and isolation of the policy execution process, a capability-based access control mechanism is used to build a security sandbox: each policy execution unit is assigned a capability token upon startup, containing the access target, permission scope, and delegateable rules. During policy execution, access authorization is strictly based on this token, and any operation outside the authorized scope will be denied. This capability-based access control mechanism effectively prevents unauthorized access and data leakage risks during policy execution.

[0114] S46: During the execution phase, an adaptive execution mode balances performance and resource utilization. The execution engine dynamically selects the most suitable running mode based on real-time load characteristics and policy complexity: under light load, interpreted execution mode is used to reduce memory consumption; under high load or when policies are frequently executed repeatedly, it automatically switches to Just-In-Time (JIT) mode to improve execution speed. The system's scheduling module continuously monitors the running status and dynamically adjusts the switching threshold, thereby maintaining optimal execution efficiency and resource utilization under different business loads. This step outputs the final policy execution result, providing execution feedback and performance metrics to the system policy management module.

[0115] Example 2

[0116] like Figure 4 As shown, this embodiment provides a real-time compilation and consistent hot deployment system for data contracts. Based on the method described in Embodiment 1, this system further incorporates a systematic implementation design, adopting a modular and distributed architecture. It mainly includes a policy management module, a policy compilation module, a policy distribution center, an executor cluster, and a monitoring and auditing module. The modules are interconnected through standardized interfaces and rely on a service mesh architecture to achieve fine-grained traffic control, service discovery, and observability. The system as a whole supports horizontal scaling and high-availability deployment, can operate in multi-datacenter environments, and ensures the real-time performance, consistency, and traceability of the policy execution process.

[0117] The policy management module receives the raw policy text input by the user and performs policy normalization and abstract syntax tree construction based on formal semantics, specifically including:

[0118] Based on the attribute-based access control ABAC model, a policy description language DSL is designed. An extended Backus paradigm is used to define unified syntax rules and generate a standardized set of policy production rules.

[0119] Implement a policy conflict detection and consistency verification algorithm, eliminate logical conflicts based on description logic, and output a set of conflict-free policies;

[0120] Extract the input conditions, execution results, and resource reference relationships between strategies, and construct a strategy dependency graph;

[0121] Through static semantic analysis and abstract interpretation verification, issues such as unreachable branches and undefined variables are marked, and a verified strategy abstract syntax tree (AST) is output.

[0122] The policy compilation module is used to generate high-performance intermediate representation (IR) code from the policy abstract syntax tree through a multi-stage compilation optimization pipeline, specifically including:

[0123] A lexical analyzer is constructed based on a deterministic finite automaton (DFA) to generate a sequence of lexical units (Tokens).

[0124] The Earley algorithm is used to construct a parser and generate an abstract syntax tree with semantic annotations;

[0125] Perform control flow analysis and data flow analysis, construct a control flow graph and output a variable dependency matrix;

[0126] Based on a cost model-driven optimization decision-making mechanism, the execution path overhead is quantitatively evaluated, and the optimal execution plan is selected.

[0127] The control flow graph is converted into intermediate code in the form of static single assignment SSA. Through instruction-level optimizations such as peephole optimization and loop optimization, high-quality IR code is output.

[0128] The policy distribution center is used to achieve distributed consistency synchronization and real-time hot deployment of policies through a publish-subscribe model-based policy distribution architecture, combined with an improved Gossip protocol and Paxos atomic commit algorithm. Specifically, it includes:

[0129] The publish-subscribe model architecture is adopted, and incremental update packages containing version numbers and differences are published to the executor cluster through message queues or event buses;

[0130] Maintain node state vectors, detect concurrent conflicts based on vector clocks, and establish a consistency foundation;

[0131] An integrated and improved Gossip protocol is used to achieve full network version synchronization through an anti-entropy mechanism and a push-pull hybrid propagation method.

[0132] The Paxos atomic commit protocol is adopted to ensure the atomicity and consistency of policy updates through a three-phase process of proposal, voting, and commit.

[0133] It provides a multi-mode conflict resolution mechanism, including three modes: timestamp priority, business semantic arbitration, and reconcilable merging, which can be flexibly switched by administrators;

[0134] A lock-free hot deployment mechanism is adopted, and policy pointer replacement is completed through CAS atomic operations to achieve millisecond-level policy loading;

[0135] Policy-based garbage collection is implemented using reference counting and generational analysis to safely reclaim memory resources occupied by older policy versions.

[0136] The executor cluster is used to achieve efficient and secure policy execution through register-based microvirtual machines and adaptive execution modes, specifically including:

[0137] A two-level strategy matching mechanism of Bloom filter fast matching + Cuckoo hash exact matching is adopted to improve strategy retrieval efficiency;

[0138] Deploy register-based microvirtual machines to achieve efficient instruction execution through a three-stage pipeline;

[0139] An integrated tracing-based Just-In-Time (JIT) compilation dynamic optimization mechanism generates native machine code for high-frequency execution paths, adaptively optimizing execution performance.

[0140] A capability-based access control mechanism is used to build a security sandbox, which strictly restricts policy execution permissions through capability tokens and rejects unauthorized operations.

[0141] It supports adaptive execution mode, dynamically switching between interpreted execution and just-in-time compilation mode based on load characteristics and strategy complexity, balancing memory consumption and execution speed.

[0142] The monitoring and auditing module is used to monitor the system's operating status and policy execution process in real time, specifically including:

[0143] The module utilizes a streaming processing framework to continuously analyze execution data and employs the Isolation Forest algorithm to identify abnormal execution patterns. The system establishes a multi-dimensional feature space based on factors such as policy execution latency, call frequency, and resource consumption, calculating deviation metrics in real time. When potential abnormal behavior is detected, such as excessive resource consumption or unauthorized calls, the module automatically triggers security alerts and can perform pause or isolation operations according to policy configurations. Furthermore, this module also undertakes policy execution auditing responsibilities, automatically recording execution logs, version changes, and access paths to ensure traceability throughout the policy lifecycle.

[0144] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solutions of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A method for real-time compilation and consistent hot deployment of strategies for data contracts, characterized in that, The method includes: Step S1: Based on the policy input by the user, construct a policy abstract syntax tree with complete semantic information based on the formal semantic policy definition and normalized representation. Specifically, this includes policy syntax modeling and formal definition, policy conflict detection and consistency verification, policy dependency construction and structured modeling, and static semantic analysis and abstract interpretation verification. Step S2: Input the abstract syntax tree into a multi-stage compilation optimization pipeline and use a cost model-driven optimization decision mechanism to generate high-performance intermediate representation (IR) code. This stage includes lexical analysis, syntax analysis, control flow analysis, data flow analysis, cost model evaluation, SSA-style intermediate code generation, and instruction-level optimization. Step S3: Adopt a policy distribution architecture based on the publish-subscribe model, combined with the improved Gossip protocol and Paxos atomic commit algorithm, to achieve distributed consistency synchronization and real-time hot deployment of policies in a multi-node environment. Step S4: Achieve efficient and secure execution of the policy through a register-based microvirtual machine and adaptive execution mode; The specific implementation steps of step S4 are as follows: S41: Design a fast matching algorithm based on Bloom filters. The algorithm maps policy identifiers to a set of boolean bit arrays through multiple hash functions and uses this to determine whether the target policy exists in the set. At the same time, the number of hash functions and the length of the bit array are dynamically adjusted according to the number of policies. S42: Employs a precise matching mechanism based on cuckoo hashing to perform secondary positioning of candidate strategies: Two independent hash functions are used to map strategy key values ​​to different table locations, and when a conflict occurs, the existing entries are automatically migrated to another storage location. S43: Design a register-based microvirtual machine architecture, adopting a three-stage pipeline structure to complete the instruction fetch, decode and execute processes sequentially. Each instruction uses registers as the core operation object and performs arithmetic or logical operations directly between registers. S44: Introduce a dynamic optimization mechanism based on trace-based just-in-time compilation in the micro-virtual machine execution layer. Monitor the execution frequency of the strategy in real time, compile the strategy path with the number of executions exceeding the threshold to generate native machine code in just-in-time, and adaptively optimize the compilation strategy by combining runtime performance data. S45: A security sandbox is built using a capability-based access control mechanism: Each policy enforcement unit is assigned a capability token at startup, and during policy enforcement, access authorization is strictly based on this token. Any operation outside the authorized scope will be denied. S46: During the execution phase, an adaptive execution mode is designed to dynamically select the running mode based on load characteristics and policy complexity: when under light load, the interpreted execution mode is used to reduce memory consumption; when the load is high or the policy is executed repeatedly, it automatically switches to just-in-time compilation mode to improve execution speed.

2. The method for real-time compilation and consistent hot deployment of strategies for data contracts according to claim 1, characterized in that, The specific steps of step S1 are as follows: Step S11: Based on the original policy text input by the user, design a policy description language (DSL) for attribute-based access control (ABAC), clarifying the policy subject, resources, actions, and environment. Then, define unified syntax rules using extended Backus paradigm to generate a set of policy production rules that conform to the syntax rules. Step S12: For the set of policy production rules that conform to the grammatical rules, implement the policy conflict detection algorithm, perform consistency analysis on the logical implication relationship between different policy conditions based on the description logic, eliminate policies with logical conflicts, and obtain a set of policies without logical conflicts. Step S13: Based on the set of strategies without logical conflicts output in step S12, extract the input conditions, execution results and resource reference relationships of each strategy through semantic parsing, and establish a dependency graph G=(V,E) between strategies, where vertex V is the set of strategy nodes and edge E is the set of strategy dependencies. Step S14: Map each policy node to an element in the abstract semantic domain, calculate its variable initialization state, condition reachability, and dependency consistency, mark and record the detected unreachable branches, undefined variables, or semantic conflict rules, ensure that the logical relationship of all policies reaches a stable state, and finally output the verified policy abstract syntax tree (AST).

3. The method for real-time compilation and consistent hot deployment of strategies for data contracts according to claim 2, characterized in that, The specific implementation steps of step S2 are as follows: Step S21: Construct a lexical analyzer based on a deterministic finite automaton (DFA), and perform character-by-character scanning and state recognition on the policy text through a state transition function, splitting it into lexical units (Tokens) with clear semantics, and generating a Token sequence; Step S22: Use an Earley algorithm-based parser to parse the syntactic structure of the token sequence, while handling recursive and fuzzy grammatical structures, and construct an abstract syntax tree with semantic annotations; Step S23: Perform control flow analysis based on the semantically annotated abstract syntax tree and construct the control flow graph. Where N represents the set of basic blocks, and each basic block consists of intermediate representation instructions executed sequentially. The control flow graph represents the set of control transfer edges, used to describe the execution jump relationships between basic blocks; the control flow graph provides the topological foundation for subsequent data flow analysis and execution path optimization. Step S24: Based on the control flow graph, perform data flow analysis, including LiveIn analysis of active variables and ReachOut analysis of reaching a set value. By traversing each basic block in the control flow graph, calculate the life cycle and the range of values ​​that can be assigned to variables, and output the variable dependency matrix. Step S25: Based on historical execution statistics of the variable dependency matrix, an execution path optimization mechanism is constructed using a cost model to quantitatively evaluate the performance overhead of different paths. The optimization decision function of the cost model is defined as follows: （1） in, This represents the predicate filtering cost on the database side. Indicates the execution cost of the virtual machine. Indicates the amount of data transmitted; , , These are the weight parameters obtained through linear regression of historical execution data; By comparing the cost of different paths, the execution scheme with the best performance is selected to achieve predicate pushdown or local execution; Step S26: Convert the path-optimized control flow graph into intermediate representation code based on static single-assignment SSA form. By introducing the Euler φ function to unify the variable version at the control flow merging point, it is ensured that each variable is assigned only once, thereby improving the efficiency of subsequent optimization and code generation. Step S27: Apply local and global optimizations to the generated intermediate representation in SSA form, including peephole optimization and loop optimization. Peephole optimization is used to eliminate redundant instructions and invalid operations, while loop optimization reduces repeated calculations by using loop-invariant code hoisting and inductive variable substitution, significantly improving code execution efficiency and instruction locality, and finally outputting high-quality intermediate representation code.

4. The method for real-time compilation and consistent hot deployment of strategies for data contracts according to claim 3, characterized in that, The policy description language DSL adopts a modular syntax design, which can be extended with new keywords and semantic rules according to business needs, thereby supporting complex policy logic such as multi-condition combination and user-defined functions, and has good scalability. The intermediate representation IR is designed as an extensible instruction set structure, which supports the dynamic addition of arithmetic, logic and access control instructions based on the new policy type, thereby quickly adapting to new policy models without changing the core execution architecture.

5. The method for real-time compilation and consistent hot deployment of strategies for data contracts according to claim 3, characterized in that, In step S23, the process of constructing the control flow graph includes the following steps: S231. Basic block division: Based on the control instructions in the intermediate representation IR instruction sequence, the continuous instruction sequence is divided into independent basic blocks. Each basic block contains a single entry and a single exit, which facilitates subsequent data flow analysis and optimization processing. S232, Edge Generation: Based on the jump target of the control instruction, forward edges and backward edges are established between basic blocks to form a control flow transfer relationship. Forward edges represent sequential execution paths, and backward edges represent loop or back jump paths. S233. Entry and exit identification: Identify the global entry and exit nodes of the control flow graph to form the structural boundary of the directed acyclic subgraph, providing boundary conditions for generating the dominance tree and subsequent optimization. S234. Dominant Tree Generation: A dominant tree is constructed using a dominant relationship algorithm to clarify the control dependency hierarchy between basic blocks. The dominant tree provides a topological reference for compilation optimizations such as register allocation, loop optimization, and code movement.

6. The method for real-time compilation and consistent hot deployment of strategies for data contracts according to claim 1, characterized in that, The specific implementation steps of step S3 are as follows: S31: In the policy distribution phase, a distribution architecture based on the publish-subscribe model is adopted. The central node, as a message publisher, publishes an incremental update package containing version number and difference content to each execution node through a message queue or event bus. Each execution node, as a subscriber, automatically receives, verifies and loads the new version policy according to the policy topic it has subscribed to, so as to realize the asynchronous push and fast arrival of policy updates. S32: After distribution is completed, the system enters the consistency detection phase. Based on high-quality intermediate representation code and its version information, a version-based distributed consistency protocol is designed. Each node maintains a state vector V=(NodeID,Version,Timestamp) to record the node identifier, policy version number and update time. Concurrent conflicts are detected by vector clock to determine the version deviation and update order, thus establishing a consistency foundation for policy distribution. S33: Implement an improved Gossip protocol to accelerate policy synchronization. Use an anti-entropy mechanism and a push-pull hybrid message propagation method to exchange version information, so that the network state converges in O(log n) time to achieve full network synchronization, where n is the number of nodes. S34: The Paxos-based atomic commit protocol is adopted to ensure the atomicity and consistency of policy updates in a multi-node environment. Each update must go through three stages: proposal, voting and commit. The policy update is only allowed to be written after a majority of nodes reach a consensus. S35: Design a multi-mode conflict resolution mechanism to address potential concurrent policy update conflicts; S36: To achieve real-time hot deployment of the strategy, a lock-free hot deployment mechanism is adopted, which uses CAS atomic operations to replace the strategy pointer: During the execution process, the current pointer value is continuously checked, and when a new version is detected to be available, the old version is replaced with the new version through atomic comparison and swap, so as to achieve instant loading of the strategy; S37: A policy-based garbage collection mechanism based on reference counting and generational analysis. When the reference count of a certain policy version is detected to be zero, a mark-and-sweep operation is performed to safely reclaim the memory resources occupied by the old version.

7. The method for real-time compilation and consistent hot deployment of strategies for data contracts according to claim 6, characterized in that, In step S35, the multi-mode conflict resolution mechanism specifically includes: The default mechanism is a timestamp-based last-write priority mechanism, which adds a globally monotonically increasing timestamp to each update operation. When multiple version conflicts are detected, the version with the largest timestamp is automatically selected as the valid version and the old version is discarded to achieve efficient decision-making. For critical business strategies, an arbitration mechanism based on business semantics is adopted, allowing access to custom conflict handling procedures; the arbitration procedure receives a list of conflicting versions and determines the final version according to business rules. For strategies that are idempotent and commutative, a coordinated merging mechanism is adopted. Based on the state machine replication principle, the consistency of each node is achieved by merging the strategy content, and the merging operation satisfies the commutative, associative and idempotent laws. The default mechanism is a timestamp-based last write priority mechanism, and administrators are allowed to switch flexibly between different modes based on policy importance.

8. A system for implementing the real-time compilation and consistent hot deployment method for data contract-oriented strategies as described in any one of claims 1-7, characterized in that, The system includes: a policy management module, a policy compilation module, a policy distribution center, an executor cluster, and a monitoring and auditing module; The policy management module receives the raw policy text input by the user and completes the normalization processing and abstract syntax tree construction of the policy based on formal semantics. The policy compilation module generates high-performance intermediate representation (IR) code from the policy abstract syntax tree through a multi-stage compilation optimization pipeline. The policy distribution center achieves distributed consistency synchronization and real-time hot deployment of policies through a publish-subscribe model-based policy distribution architecture, combined with the improved Gossip protocol and Paxos atomic commit algorithm. The executor cluster achieves efficient and secure policy execution through register-based microvirtual machines and adaptive execution mode. The monitoring and auditing module monitors the system's operating status and policy execution process in real time.

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