Large model driven data analysis method and system based on mcp

Abstract

The invention discloses a data analysis method and system driven by a large model based on mcp, and relates to the technical field of big data analysis. Comprising the following steps: step 1, creating a data analysis system driven by a large model based on mcp; step 2, performing full life cycle management of service metadata and automatic generation of multi-language codes through a unified service abstraction module; 3, a protocol conversion and verification rule is built in through a protocol adaptation layer module and used for heterogeneous data source access and transmission; 4, flow control, fusing degradation and real-time monitoring functions are integrated through a service governance engine module, and multi-model routing and dynamic resource scheduling are carried out in combination with an intelligent analysis engine module; according to the method, the problems of the existing data analysis system in the aspects of multi-source heterogeneous data processing, real-time guarantee, model generalization ability, collaboration and the like are solved.

Description

TECHNICAL FIELD

[0001] The application discloses a big model driven data analysis method and system based on mcp, and relates to the technical field of big data analysis. BACKGROUND

[0002] The existing microservice architecture has many limitations when processing complex data analysis. Database services, distributed computing, cache systems and message queues are usually deployed as independent microservices, which leads to low data flow efficiency, large communication overhead between systems and difficulty in realizing real-time large-scale data collision calculation. In particular, in the trace scenario, it is necessary to simultaneously analyze the correlation of massive data in multiple dimensions, and the existing architecture often cannot meet the real-time requirements. In addition, when directly accessing and processing distributed data, the big model faces challenges such as non-uniform interfaces and complex data formats, which limits the application effect of the big model in large-scale data analysis. SUMMARY

[0003] The application provides a big model driven data analysis method and system based on mcp to solve the problems of the existing data analysis system in multi-source heterogeneous data processing, real-time guarantee, model generalization ability and cooperation.

[0004] The specific scheme provided by the application is as follows:

[0005] The application provides a big model driven data analysis method based on mcp, comprising:

[0006] Step 1: creating a big model driven data analysis system based on mcp, the system comprising a unified service abstraction module, a protocol adaptation layer module, a service governance engine module and an intelligent analysis engine module,

[0007] Step 2: performing full life cycle management of service metadata and automatic generation of multi-language code through the unified service abstraction module: the unified service abstraction module maintains the full life cycle of service metadata, performs version control and dependency relationship mapping, supports service grouping based on tags and dynamic binding of routing strategies, automatically generates multi-language client stubs and server framework code based on interface definition language (IDL) for cross-platform calling, and integrates a registration center to provide plug-in access rules;

[0008] Step 3: Protocol interface definition through the protocol adaptation layer module: the protocol adaptation layer module defines a unified service interface description specification using the Protocol Buffers language, ensuring that all data service components have consistent access patterns. The module uses a declarative syntax to define service methods, request or response message structures, and data types, including strong-typed message structures such as integers, floats, enumerations, and nested message types. This ensures clear interface semantics and provides interface version identification, allowing each service method to be associated with an independent version number to support multi-version coexistence and smooth upgrades.

[0009] Step 4: Integration of traffic control, circuit breaker degradation, and real-time monitoring functions through the service governance engine module, combined with multi-model routing and dynamic resource scheduling through the intelligent analysis engine module: the service governance engine module uses a hybrid algorithm based on token bucket and leak bucket to smooth burst traffic and differentiates traffic based on service priority. It sets error rate thresholds to trigger automatic circuit breaking. The module integrates a time series database, the Prometheus index monitoring system, and the Jaeger distributed tracking system to provide real-time service quality dashboards and call chain topology visualization for AI-based anomaly detection and root cause analysis.

[0010] Further, in Step 2 of the method, the protocol interface is defined through the protocol adaptation layer module: a unified service interface description specification is defined using the Protocol Buffers language, ensuring that all data service components have consistent access patterns. The declarative syntax is used to define service methods, request or response message structures, and data types,

[0011] where strong-typed message structures are defined, including integers, floats, enumerations, and nested message types, ensuring clear interface semantics and providing interface version identification, allowing each service method to be associated with an independent version number to support multi-version coexistence and smooth upgrades.

[0012] Further, in Step 4 of the method, the unified query interface is integrated through the intelligent analysis engine module, which includes multi-granularity semantic analysis components to implement entity recognition, relationship extraction, and condition grouping in complex query instructions. The attention mechanism is used to enhance context association understanding, and a dynamic syntax mapping mechanism is introduced to convert the unified query descriptor structure into specific syntax forms of different query languages, supporting the generation of multiple query target languages such as SQL, Cypher, and MongoDBAggregation Pipeline. Query semantic verification is performed through a combination of rule engines and machine learning models to verify the compliance and logical consistency of the generated query syntax.

[0013] Further, in step 4 of the method, the intelligent analysis engine module integrates a cost-based query optimizer, dynamically selects the optimal query path through statistical information collection and execution plan evaluation, monitors query performance in real time, automatically triggers index recommendation or condition reconstruction for slow queries, and embeds data desensitization and access audit components to dynamically apply privacy protection policies and record complete operation logs during execution, meeting data security compliance requirements.

[0014] Further, in step 4 of the method, when the intelligent analysis engine module performs multi-model routing and dynamic resource scheduling, priority queues and pre-allocation strategies are introduced, the computing task is divided into independent sub-blocks according to data dependency, and the sub-tasks are mapped to virtual buckets and then allocated to computing nodes through consistent hashing algorithm. Meanwhile, a heartbeat detection mechanism is combined to monitor node survival state every Δt seconds, and if a computing node is disconnected, the task is redistributed to a backup node.

[0015] Further, in step 4 of the method, the intelligent analysis engine module adopts a dynamic load balancing mechanism to collect CPU utilization, memory occupancy, and network bandwidth data of computing nodes in real time, and dynamically adjusts the weight coefficient w i of each computing node based on a feedback control algorithm when the load index of computing node i exceeds the threshold T. i The value of w

[0016] The intelligent analysis engine module adopts an asynchronous pipeline processing architecture, allowing partial computing results to enter the aggregation stage before they are fully ready. A time window buffering mechanism is used to temporarily store and weight the partial results. The weighting formula is R final = Σ(w i · R i ) / Σw i , where R i is the returned result of the computing node. A fault tolerance verification mechanism is introduced to remove abnormal values and request re-computation.

[0017] The coordination function C(f,x) supports multi-objective optimization, considering both computing efficiency and energy consumption indicators during allocation. A penalty term λ·E i is introduced to optimize the load allocation strategy, with the formula: C(f,x) = Σ[w i · f i (x i ) + λ·E i ], where E iThe node energy consumption coefficient is denoted as C, and the global scheduler constructs a performance prediction model based on historical task execution data, trains the correlation between task execution time and resource consumption by using a machine learning method, and is used for subsequent optimization of the initial node allocation strategy of the task.

[0018] Further, in step 4 of the method, a lightweight agent program is deployed by the intelligent analysis engine module to poll the CPU utilization, memory occupancy, network I / O throughput and task queue length at a fixed sampling period, and encapsulate them into time series data packets, while normalizing the original indicators to eliminate dimensional differences, generating a standardized load state vector with a dimension consistent with the number of monitoring indicators, and the data buffering and filtering unit uses a sliding window mechanism to smooth transient fluctuations and avoid false scheduling caused by transient peaks.

[0019] The application further provides a large model-driven data analysis system based on MCP, which comprises a unified service abstraction module, a protocol adaptation layer module, a service governance engine module and an intelligent analysis engine module,

[0020] The unified service abstraction module performs full life cycle management of service metadata and automatic generation of multi-language code: the unified service abstraction module maintains the full life cycle of service metadata, performs version control and dependency relationship mapping, supports service grouping based on tags and dynamic binding of routing strategies, automatically generates multi-language client stubs and server framework code based on interface definition language (IDL) for cross-platform calls, and integrates a registration center to provide plug-in access rules;

[0021] The protocol adaptation layer module has built-in protocol conversion and verification rules for heterogeneous data source access and transmission: the protocol adaptation layer module has a built-in JSON Schema verification engine in the RESTful adapter to perform structure verification and automatic conversion on request or response bodies, and performs bidirectional mapping of XML to JSON; the gRPC adapter integrates TLS two-way authentication and message compression; the protocol conversion gateway provides GraphQL to REST query language translation, supports field-level data pruning to reduce network transmission overhead;

[0022] The service governance engine module integrates traffic control, fuse degradation and real-time monitoring functions, and combines the intelligent analysis engine module to perform multi-model routing and dynamic resource scheduling: the service governance engine module performs burst traffic smoothing based on the token bucket and leaky bucket hybrid algorithm, and performs differentiated traffic limiting based on service priority; an error rate threshold is set to trigger automatic fuse; a time series database, an indicator monitoring system (Prometheus) and a distributed tracking system (Jaeger) are integrated to provide real-time service quality dashboards and call chain topology visualization for abnormal detection and root cause analysis using AI.

[0023] The present application has the advantages of:

[0024] The present application constructs a service-oriented and scalable data processing architecture, realizing the whole-link technology integration from data access, protocol conversion, governance control to intelligent analysis. Through the unified service abstraction module, the whole life cycle management of service metadata and the automatic generation of multi-language code are realized, which improves the consistency and development efficiency of cross-platform service calling; through the protocol adaptation layer module, multiple protocol conversion and verification mechanisms are built in, which enhances the support capability and transmission performance of the system to heterogeneous data sources; through the service governance engine module, the functions of traffic control, fuse degradation and real-time monitoring are integrated, and combined with the intelligent analysis engine module, the multi-model routing and dynamic resource scheduling are realized, which significantly improves the reliability, response speed and intelligent level of the system. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 is a method flowchart of the present application. DETAILED DESCRIPTION

[0026] The present application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the present application and implement it, but the embodiments are not limiting to the present application.

[0027] Embodiment 1

[0028] The present application provides a large model driven data analysis method based on mcp, comprising:

[0029] Step 1: create a large model driven data analysis system based on mcp, the system comprising a unified service abstraction module, a protocol adaptation layer module, a service governance engine module and an intelligent analysis engine module.

[0030] Step 2: through the unified service abstraction module, the whole life cycle management of service metadata and the automatic generation of multi-language code are realized: the unified service abstraction module maintains the whole life cycle of service metadata, performs version control and dependency relationship mapping, supports service grouping based on tags and dynamic binding of routing strategy; according to the interface definition language IDL, multi-language client stub and server framework code are automatically generated for cross-platform calling; integrate the registration center to provide plug-in access rules.

[0031] The version control and dependency management of component metadata are implemented by a unified service abstraction module, the interface change level is identified by a semantic version number, backward compatibility is ensured, the calling constraints between components are parsed through a dependency relationship graph to avoid circular references, a directed acyclic graph model is constructed to describe the component dependency topology, dynamic addition and deletion of nodes and edges are supported, a dependency conflict detection algorithm is implemented, illegal references are intercepted in the compilation stage and a solution is prompted, and a dependency version locking mechanism is provided to ensure consistency of production environment deployment.

[0032] Step 3: Protocol conversion and verification rules are built in the protocol adaptation layer module for heterogeneous data source access and transmission: the protocol adaptation layer module uses the built-in JSON Schema verification engine of the RESTful adapter to perform structural verification and automatic conversion on the request or response body, and performs bidirectional mapping of XML to JSON; the gRPC adapter integrates TLS two-way authentication and message compression; the protocol conversion gateway provides GraphQL to REST query language translation, supports field-level data pruning to reduce network transmission overhead.

[0033] The protocol interface can be defined through the protocol adaptation layer module: a unified service interface description specification is defined using the Protocol Buffers language to ensure that all data service components have a consistent access mode, service methods, request or response message structures and data types are defined through a declarative syntax,

[0034] The strong type message structure is defined, including integer, floating point, enumeration and nested message types, to ensure clear interface semantics, and an interface version identifier is provided to associate an independent version number with each service method, supporting multi-version coexistence and smooth upgrade.

[0035] Step 4: The service governance engine module integrates traffic control, fuse degradation and real-time monitoring functions, and combines with the intelligent analysis engine module for multi-model routing and dynamic resource scheduling: the service governance engine module uses a hybrid algorithm of token bucket and leaky bucket to smooth burst traffic, and differentiates traffic limiting based on service priority; sets an error rate threshold to trigger automatic fuse; integrates a time series database, the Prometheus index monitoring system and the Jaeger distributed tracking system to provide real-time service quality dashboards and call chain topology visualization for abnormal detection and root cause analysis using AI.

[0036] Wherein the unified query interface is queried through the intelligent analysis engine module, multi-granularity semantic analysis components are integrated, entity recognition, relationship extraction and condition grouping in the composite query instruction are realized, the context association understanding is enhanced through the attention mechanism, and the dynamic syntax mapping mechanism is introduced. The unified query descriptor structure is converted into the specific syntax form of different query languages, multiple query target languages including SQL, Cypher, MongoDB Aggregation Pipeline are supported, and query semantic verification is performed. Through the combination of rule engine and machine learning model, the generated query syntax is verified for compliance and logical consistency.

[0037] Through the intelligent analysis engine module, the cost-based query optimizer is integrated. The optimal query path is dynamically selected through statistical information collection and execution plan evaluation. The query performance is monitored in real time. The index recommendation or condition reconstruction operation is automatically triggered for slow query. The data desensitization and access audit components are embedded. The privacy protection policy is dynamically applied in the execution process, and the complete operation log is recorded to meet the data security compliance requirements.

[0038] At the same time, through the intelligent analysis engine module, the priority queue and pre-allocation strategy are introduced. The calculation task is divided into independent sub-blocks according to the data dependency relationship. The sub-task is mapped to a virtual bucket and then distributed to the calculation node through the consistent hashing algorithm. At the same time, combined with the heartbeat detection mechanism, the node survival state is monitored every Δt seconds. If the calculation node is disconnected, the task is redistributed to the backup node.

[0039] The dynamic load balancing mechanism can be preferably adopted through the intelligent analysis engine module. The CPU utilization, memory occupancy and network bandwidth data of the calculation node are collected in real time. Based on the feedback control algorithm, the weight coefficient w i is dynamically adjusted when the load index of the calculation node i exceeds the threshold T i , the task is migrated to the low-load calculation node,

[0040] Through the intelligent analysis engine module, the asynchronous pipeline processing architecture is adopted. The local calculation result is allowed to enter the aggregation stage when it is not completely ready. The partial result is temporarily stored and weighted fusion through the time window buffering mechanism. The weighted formula is R final =Σ(w i ·R i ) / Σw i , wherein R i is the calculation node return result. The fault tolerance verification mechanism is introduced. The abnormal value is removed and the calculation is requested again.

[0041] The coordination function C(f,x) is provided to support multi-objective optimization. The calculation efficiency and energy consumption index are considered in the allocation process. The penalty term λ·Ei Optimize the load distribution strategy, the formula is: C(f,x) = Σ[w i ·f i (x i )+λ·E i ], wherein E i is the node energy consumption coefficient, and the global scheduler constructs a performance prediction model based on historical task execution data, trains the correlation between task execution time and resource consumption using machine learning methods, and is used for subsequent initial node allocation strategy optimization of tasks.

[0042] A lightweight agent program can be deployed through the intelligent analysis engine module to poll the CPU utilization, memory occupancy, network I / O throughput and task queue length at a fixed sampling period, and encapsulated as a time series data package, while the original indicators are normalized to eliminate dimensional differences, generating a standardized load state vector with the same number of dimensions as the number of monitoring indicators, and the data buffer and filtering unit uses a sliding window mechanism to smooth the instantaneous fluctuations, avoiding false scheduling caused by instantaneous peaks.

[0043] Embodiment 2

[0044] The application also provides a large model driven data analysis system based on mcp, which comprises a unified service abstraction module, a protocol adaptation layer module, a service governance engine module and an intelligent analysis engine module,

[0045] The unified service abstraction module performs full life cycle management of service metadata and automatic generation of multi-language code: the unified service abstraction module maintains the full life cycle of service metadata, performs version control and dependency relationship mapping, supports service grouping based on tags and dynamic binding of routing strategies; automatically generates multi-language client stubs and server framework code based on interface definition language (IDL) for cross-platform calls; integrates a registration center to provide plug-in access rules;

[0046] The protocol adaptation layer module has built-in protocol conversion and verification rules for heterogeneous data source access and transmission: the protocol adaptation layer module uses the built-in JSON Schema verification engine of the RESTful adapter to perform structure verification and automatic conversion on the request or response body, and performs bidirectional mapping of XML to JSON; through the gRPC adapter, integrate TLS two-way authentication and message compression; use the protocol conversion gateway to provide GraphQL to REST query language translation, support field-level data pruning to reduce network transmission overhead;

[0047] The service governance engine module integrates traffic control, fuse degradation and real-time monitoring functions, and combines the intelligent analysis engine module to perform multi-model routing and dynamic resource scheduling: the service governance engine module performs burst traffic smoothing based on a token bucket and leaky bucket hybrid algorithm, and performs differentiated traffic limiting based on service priority; an error rate threshold is set to trigger automatic fuse; a time series database, an index monitoring system Prometheus and a distributed tracking system Jaeger are integrated to provide real-time service quality dashboards and call chain topology visualization for abnormal detection and root cause analysis using AI.

[0048] The information interaction and execution process between the modules in the system are based on the same concept as the method embodiments of the present application, and the specific content can be referred to the description in the method embodiments of the present application, which will not be repeated here.

[0049] Similarly, the system of the present application constructs a service-oriented and scalable data processing architecture, realizing full-link technology integration from data access, protocol conversion, governance control to intelligent analysis. Through the unified service abstraction module, the full life cycle management of service metadata and the automatic generation of multi-language code are realized, improving the consistency and development efficiency of cross-platform service calls; through the built-in protocol conversion and verification mechanism in the protocol adaptation layer module, the support capability and transmission performance of the system for heterogeneous data sources are enhanced; through the service governance engine module, traffic control, fuse degradation and real-time monitoring functions are integrated, and combined with the intelligent analysis engine module, multi-model routing and dynamic resource scheduling are realized, significantly improving the reliability, response speed and intelligent level of the system.

[0050] It should be noted that not all steps and modules in the above processes and system structures are necessary, and some steps or modules can be omitted according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above embodiments can be a physical structure or a logical structure, i.e. some modules can be implemented by the same physical entity, or some modules can be implemented by multiple physical entities, or they can be implemented by some components in multiple independent devices.

[0051] The above-described embodiments are only preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Equivalent substitutions or transformations made by those skilled in the art based on the present application are within the protection scope of the present application. The protection scope of the present application is subject to the claims.

Claims

1. A large model-driven data analysis method based on MCP, characterized by: include: Step 1: Create a large model-driven data analysis system based on MCP. This system includes a unified service abstraction module, a protocol adaptation layer module, a service governance engine module, and an intelligent analysis engine module. Step 2: Manage the entire lifecycle of service metadata and automatically generate multi-language code through the unified service abstraction module: The unified service abstraction module maintains the entire lifecycle of service metadata, performs version control and dependency mapping, and supports dynamic binding of service grouping and routing strategies based on tags; Based on the Interface Definition Language (IDL), it automatically generates multilingual client stubs and server-side framework code for cross-platform calls; it also integrates a registry center and provides plugin access rules. Step 3: The protocol adaptation layer module uses built-in protocol conversion and validation rules for heterogeneous data source access and transmission: The protocol adaptation layer module uses a built-in JSON Schema validation engine in the RESTful adapter to perform structure validation and automatic conversion of the request or response body, and perform bidirectional mapping from XML to JSON; Integrate TLS two-way authentication and message compression via a gRPC adapter; A protocol conversion gateway is used to provide query language translation from GraphQL to REST, supporting field-level data pruning to reduce network transmission overhead; Step 4: Integrate traffic control, circuit breaking and degradation, and real-time monitoring functions through the service governance engine module, and combine it with the intelligent analysis engine module for multi-model routing and dynamic resource scheduling: The service governance engine module performs burst traffic smoothing based on a token bucket and leaky bucket hybrid algorithm, and performs differentiated rate limiting based on service priority; Set an error rate threshold to trigger automatic circuit breakers; integrate time-series databases and metrics monitoring systems like Prometheus and Jaeger distributed tracing systems to provide real-time service quality dashboards and call chain topology visualization for AI-powered anomaly detection and root cause analysis.

2. The data analysis method based on large model driven by MCP according to claim 1, characterized in that: Step 3 involves defining the protocol interface through the protocol adaptation layer module: The Protocol Buffers language is used to define a unified service interface description specification, ensuring that all data service components have a consistent access pattern. Service methods, request or response message structures, and data types are defined using declarative syntax. It defines a strongly typed message structure, including integer, floating-point, enumeration, and nested message types, to ensure that the interface semantics are clear, and provides an interface version identifier so that each service method is associated with an independent version number, supporting the coexistence of multiple versions and smooth upgrades.

3. The data analysis method based on large model driven by MCP according to claim 1, characterized in that: In step 4, the unified query interface is achieved through the intelligent analysis engine module, which integrates multi-granularity semantic parsing components to realize entity recognition, relation extraction, and condition grouping in compound query commands. The attention mechanism enhances the understanding of contextual associations, and a dynamic syntax mapping mechanism is introduced to convert the unified query descriptor structure into specific syntax forms of different query languages. It supports the generation of multiple query target languages, including SQL, Cypher, and MongoDB Aggregation Pipeline, and performs query semantic verification. By combining the rule engine and machine learning model, the generated query syntax is verified for both compliance and logical consistency.

4. The data analysis method based on large model driven by MCP according to claim 1, characterized in that: In step 4, a cost-based query optimizer is integrated through the intelligent analysis engine module. The optimal query path is dynamically selected through statistical information collection and execution plan evaluation. Query performance is monitored in real time. For slow queries, index recommendation or condition reconstruction operations are automatically triggered. Data anonymization and access auditing components are embedded. Privacy protection strategies are dynamically applied during execution and complete operation logs are recorded to meet data security compliance requirements.

5. A large model-driven data analysis method based on MCP according to claim 4, characterized in that: In step 4, when performing multi-model routing and dynamic resource scheduling through the intelligent analysis engine module, a priority queue and pre-allocation strategy are introduced. The computing tasks are divided into independent sub-blocks according to data dependencies, and the sub-tasks are mapped to virtual buckets and then allocated to computing nodes through a consistent hashing algorithm. At the same time, a heartbeat detection mechanism is combined to monitor the node's liveness status every Δt seconds. If a computing node loses connection, the task is redistributed to the backup node.

6. A large model-driven data analysis method based on MCP according to claim 5, characterized in that: In step 4, the intelligent analysis engine module employs a dynamic load balancing mechanism to collect real-time data on CPU utilization, memory usage, and network bandwidth of computing nodes, and dynamically adjusts the weight coefficient w based on a feedback control algorithm. i When the load metric of computing node i exceeds the threshold T, its load is reduced. i The value will be adjusted, and the task will be migrated to a low-load compute node. The intelligent analysis engine module employs an asynchronous pipelined processing architecture, allowing partial computation results to enter the aggregation stage before they are fully ready. A time window buffering mechanism temporarily stores and weights some results for fusion, with the weighting formula being R. final =Σ(w i ·R i ) / Σw i , where R i The compute nodes return results, and a fault tolerance mechanism is introduced to remove outliers and re-request computation. A coordination function C(f,x) is provided to support multi-objective optimization, which considers both computational efficiency and energy consumption indices during the allocation process by introducing a penalty term λ·E. i The optimized load balancing strategy is defined by the formula: C(f,x)=Σ[w i ·f i (x i )+λ·E i ], where E i The node energy consumption coefficient is used, and the global scheduler builds a performance prediction model based on historical task execution data. Machine learning methods are used to train the relationship between task execution time and resource consumption, which is used to optimize the initial node allocation strategy for subsequent tasks.

7. A large model-driven data analysis method based on MCP as described in claim 6, characterized in that: In step 4, a lightweight agent program is deployed through the intelligent analysis engine module to poll CPU utilization, memory usage, network I / O throughput, and task queue length at a fixed sampling period and encapsulate them into time-series data packets. At the same time, the original indicators are normalized to eliminate differences in dimensions and generate a standardized load state vector with dimensions consistent with the number of monitoring indicators. Furthermore, the data buffering and filtering unit uses a sliding window mechanism to smooth out instantaneous fluctuations and avoid misscheduling caused by instantaneous peaks.

8. A large model-driven data analysis system based on MCP, characterized by: It includes a unified service abstraction module, a protocol adaptation layer module, a service governance engine module, and an intelligent analysis engine module. The unified service abstraction module manages the entire lifecycle of service metadata and automatically generates multi-language code: The unified service abstraction module maintains the entire lifecycle of service metadata, performs version control and dependency mapping, and supports dynamic binding of tag-based service grouping and routing strategies; Based on the Interface Definition Language (IDL), it automatically generates multilingual client stubs and server-side framework code for cross-platform calls; it also integrates a registry center and provides plugin access rules. The protocol adaptation layer module has built-in protocol conversion and verification rules for heterogeneous data source access and transmission: The protocol adaptation layer module uses the RESTful adapter to build a JSON Schema verification engine to perform structure verification and automatic conversion of the request or response body, and perform bidirectional mapping from XML to JSON; Integrate TLS two-way authentication and message compression via a gRPC adapter; A protocol conversion gateway is used to provide query language translation from GraphQL to REST, supporting field-level data pruning to reduce network transmission overhead; The service governance engine module integrates traffic control, circuit breaking and degradation, and real-time monitoring functions, and combines with the intelligent analysis engine module to perform multi-model routing and dynamic resource scheduling: the service governance engine module performs burst traffic smoothing based on a token bucket and leaky bucket hybrid algorithm, and performs differentiated rate limiting based on service priority; Set an error rate threshold to trigger automatic circuit breakers; integrate time-series databases and metrics monitoring systems like Prometheus and Jaeger distributed tracing systems to provide real-time service quality dashboards and call chain topology visualization for AI-powered anomaly detection and root cause analysis.

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