A model-driven data analysis method and system based on mcp

Abstract

The application discloses a kind of big model driven data analysis method and system based on mcp, it is related to big data analysis technical field;Including: step 1: create big model driven data analysis system based on mcp, step 2: through unified service abstraction module, service metadata is managed in whole life cycle and multilingual code is automatically generated;Step 3: through the protocol adaptation layer module built-in protocol conversion and check rule, for heterogeneous data source access and transmission;Step 4: through service governance engine module, integrate traffic control, fuse degradation and real-time monitoring function, and combine intelligent analysis engine module and carry out multi-model routing and dynamic resource scheduling;The present application solves the problem of the deficiency of existing data analysis system in the processing of multi-source heterogeneous data, real-time guarantee, model generalization ability and cooperation.

Description

Technical Field

[0001] This invention discloses a large model-driven data analysis method and system based on MCP, which relates to the field of big data analysis technology. Background Technology

[0002] Existing microservice architectures have numerous limitations when handling complex data analysis. Database services, distributed computing, caching systems, and message queues are typically deployed as independent microservices. This architecture leads to inefficient data flow, high inter-system communication overhead, and difficulty in achieving real-time, large-scale data collision calculations. Especially in traceability scenarios, where simultaneous correlation analysis of massive amounts of data across multiple dimensions is required, existing architectures often cannot meet real-time requirements. Furthermore, large models face challenges such as inconsistent interfaces and complex data formats when directly accessing and processing distributed data, limiting their effectiveness in large-scale data analysis. Summary of the Invention

[0003] This invention addresses the problems of existing technologies by providing a large model-driven data analysis method and system based on MCP, which solves the shortcomings of existing data analysis systems in areas such as multi-source heterogeneous data processing, real-time performance assurance, model generalization ability, and collaboration.

[0004] The specific solution proposed in this invention is as follows:

[0005] This invention provides a large model-driven data analysis method based on MCP, comprising:

[0006] 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.

[0007] 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; it automatically generates multi-language client stubs and server-side framework code based on the interface definition language (IDL) for cross-platform calls; and it integrates with the registry center to provide plugin access rules.

[0008] Step 3: 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 a RESTful adapter with a built-in JSON Schema verification engine to perform structure verification and automatic conversion of request or response bodies, and performs bidirectional mapping from XML to JSON; it integrates TLS bidirectional authentication and message compression through the gRPC adapter; and it uses a protocol conversion gateway to provide GraphQL to REST query language translation, supporting field-level data pruning to reduce network transmission overhead.

[0009] 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; sets error rate thresholds to trigger automatic circuit breaking; integrates the time-series database and indicator monitoring system Prometheus and the distributed tracing system Jaeger to provide real-time service quality dashboards and call chain topology visualization for anomaly detection and root cause analysis using AI.

[0010] Furthermore, in step 2 of the large model-driven data analysis method based on MCP, the protocol interface is defined through a 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, and service methods, request or response message structures, and data types are defined using declarative syntax.

[0011] 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.

[0012] Furthermore, in step 4 of the large model-driven data analysis method based on MCP, a unified query interface is established through the intelligent analysis engine module, integrating multi-granularity semantic parsing components to achieve entity recognition, relation extraction, and condition grouping in composite 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 grammatical forms of different query languages. This supports the generation of multiple query target languages, including SQL, Cypher, and MongoDBAggregation Pipeline, and performs query semantic verification. By combining a rule engine with a machine learning model, the generated query syntax is subjected to dual verification for compliance and logical consistency.

[0013] Furthermore, in step 4 of the large model-driven data analysis method based on MCP, 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, index recommendation or condition reconstruction operations are automatically triggered for slow queries, and data anonymization and access auditing components are embedded. During the execution process, privacy protection strategies are dynamically applied and complete operation logs are recorded to meet data security compliance requirements.

[0014] Furthermore, in step 4 of the large model-driven data analysis method based on MCP, 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, combined with a heartbeat detection mechanism, the node liveness status is monitored every Δt seconds. If a computing node loses connection, the task is redistributed to the backup node.

[0015] Furthermore, in step 4 of the large model-driven data analysis method based on MCP, a dynamic load balancing mechanism is adopted through the intelligent analysis engine module to collect CPU utilization, memory usage, and network bandwidth data of computing nodes in real time, and dynamically adjust the weight coefficient w based on the 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.

[0016] 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.

[0017] 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 iThe 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.

[0018] Furthermore, in step 4 of the large model-driven data analysis method based on MCP, 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, generating a standardized load state vector with dimensions consistent with the number of monitoring indicators. The data buffering and filtering unit uses a sliding window mechanism to smooth out instantaneous fluctuations and avoid misscheduling caused by instantaneous peaks.

[0019] This invention also provides a large model-driven data analysis system based on MCP, including 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 manages the entire lifecycle of service metadata and automatically generates multi-language code: It 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; it automatically generates multi-language client stubs and server-side framework code based on the interface definition language (IDL) for cross-platform calls; and it integrates a registry center to provide plugin 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 uses a RESTful adapter with a built-in JSON Schema verification engine to perform structure verification and automatic conversion of request or response bodies, and performs bidirectional mapping from XML to JSON; it integrates TLS bidirectional authentication and message compression through a gRPC adapter; and it uses a protocol conversion gateway to provide GraphQL to REST query language translation, supporting field-level data pruning to reduce network transmission overhead.

[0022] 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 for multi-model routing and dynamic resource scheduling: The service governance engine module uses a hybrid token bucket and leaky bucket algorithm to smooth burst traffic and performs differentiated rate limiting based on service priority; it sets error rate thresholds to trigger automatic circuit breaking; and it integrates a time-series database and the metrics monitoring system Prometheus and the distributed tracing system Jaeger to provide real-time service quality dashboards and call chain topology visualization for anomaly detection and root cause analysis using AI.

[0023] The advantages of this invention are:

[0024] This invention constructs a service-oriented, scalable data processing architecture, achieving end-to-end technology integration from data access, protocol conversion, governance control to intelligent analysis. A unified service abstraction module enables full lifecycle management of service metadata and automatic generation of multi-language code, improving consistency and development efficiency in cross-platform service calls. A protocol adaptation layer module incorporates multiple protocol conversion and verification mechanisms, enhancing the system's support for heterogeneous data sources and transmission performance. A service governance engine module integrates traffic control, circuit breaking and degradation, and real-time monitoring functions, and combined with an intelligent analysis engine module, enables multi-model routing and dynamic resource scheduling, significantly improving system reliability, response speed, and intelligence. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0026] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0027] Example 1

[0028] This invention 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 includes a unified service abstraction module, a protocol adaptation layer module, a service governance engine module, and an intelligent analysis engine module.

[0030] 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; it automatically generates multi-language client stubs and server-side framework code based on the interface definition language (IDL) for cross-platform calls; and it integrates the registry center to provide plugin access rules.

[0031] The unified service abstraction module implements version control and dependency management of component metadata, uses semantic version numbers to identify interface change levels to ensure backward compatibility, and resolves inter-component call constraints through dependency graphs to avoid circular references. This includes building a directed acyclic graph model to describe component dependency topology, supporting dynamic addition and deletion of nodes and edges, implementing a dependency conflict detection algorithm, intercepting illegal references and prompting solutions during the compilation phase, and providing a dependency version locking mechanism to ensure deployment consistency in the production environment.

[0032] Step 3: 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 a RESTful adapter with a built-in JSON Schema verification engine to perform structure verification and automatic conversion of request or response bodies, and performs bidirectional mapping from XML to JSON; it integrates TLS bidirectional authentication and message compression through the gRPC adapter; and it uses a protocol conversion gateway to provide GraphQL to REST query language translation, supporting 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, and service methods, request or response message structures and data types are defined through declarative syntax.

[0034] 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.

[0035] 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; sets error rate thresholds to trigger automatic circuit breaking; integrates the time-series database and indicator monitoring system Prometheus and the distributed tracing system Jaeger to provide real-time service quality dashboards and call chain topology visualization for anomaly detection and root cause analysis using AI.

[0036] The system utilizes a unified query interface through an intelligent analysis engine module, integrating multi-granularity semantic parsing components to achieve entity recognition, relation extraction, and condition grouping in complex query commands. It enhances contextual understanding through an attention mechanism and introduces a dynamic syntax mapping mechanism to convert the unified query descriptor structure into specific grammatical forms for 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 a rule engine with a machine learning model, it performs dual verification of compliance and logical consistency for the generated query syntax.

[0037] By integrating a cost-based query optimizer through the intelligent analysis engine module, the system 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 operations for slow queries, and embeds data anonymization and access auditing components. During execution, it dynamically applies privacy protection strategies and records complete operation logs to meet data security compliance requirements.

[0038] Meanwhile, when performing multi-model routing and dynamic resource scheduling through the intelligent analysis engine module, priority queues and pre-allocation strategies are introduced to divide computing tasks into independent sub-blocks according to data dependencies. The sub-tasks are then mapped to virtual buckets and redistributed to computing nodes through a consistent hashing algorithm. In addition, a heartbeat detection mechanism is used to monitor the node's liveness status every Δt seconds. If a computing node becomes unreachable, the task is redistributed to a backup node.

[0039] A dynamic load balancing mechanism can be preferably adopted through the intelligent analysis engine module to collect real-time data on CPU utilization, memory usage, and network bandwidth of computing nodes, and dynamically adjust 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.

[0040] 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.

[0041] 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.

[0042] Lightweight agent programs can be 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 monitored 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.

[0043] Example 2

[0044] This invention also provides a large model-driven data analysis system based on MCP, including 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 manages the entire lifecycle of service metadata and automatically generates multi-language code: It 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; it automatically generates multi-language client stubs and server-side framework code based on the interface definition language (IDL) for cross-platform calls; and it integrates a registry center to provide plugin 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 a RESTful adapter with a built-in JSON Schema verification engine to perform structure verification and automatic conversion of request or response bodies, and performs bidirectional mapping from XML to JSON; it integrates TLS bidirectional authentication and message compression through a gRPC adapter; and it uses a protocol conversion gateway to provide GraphQL to REST query language translation, supporting field-level data pruning to reduce network transmission overhead.

[0047] 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 for multi-model routing and dynamic resource scheduling: The service governance engine module uses a hybrid token bucket and leaky bucket algorithm to smooth burst traffic and performs differentiated rate limiting based on service priority; it sets error rate thresholds to trigger automatic circuit breaking; and it integrates a time-series database and the metrics monitoring system Prometheus and the distributed tracing system Jaeger to provide real-time service quality dashboards and call chain topology visualization for anomaly detection and root cause analysis using AI.

[0048] The information interaction and execution process between the modules in the above system are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description in the method embodiment of the present invention, and will not be repeated here.

[0049] Similarly, this invention constructs a service-oriented and scalable data processing architecture, achieving end-to-end technology integration from data access, protocol conversion, governance control to intelligent analysis. Through a unified service abstraction module, it achieves full lifecycle management of service metadata and automatic generation of multi-language code, improving the consistency and development efficiency of cross-platform service calls. Through a protocol adaptation layer module with built-in multiple protocol conversion and verification mechanisms, it enhances the system's support for heterogeneous data sources and transmission performance. Through a service governance engine module integrating traffic control, circuit breaking and degradation, and real-time monitoring functions, and combined with an intelligent analysis engine module to achieve multi-model routing and dynamic resource scheduling, it significantly improves the system's reliability, response speed, and intelligence level.

[0050] It should be noted that not all steps and modules in the above processes and system structures are mandatory; some steps or modules can be omitted as needed. The execution order of each step is not fixed and can be adjusted as required. The system structures described in the above embodiments can be physical or logical structures. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be jointly implemented by certain components in multiple independent devices.

[0051] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by 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 a RESTful adapter with a built-in 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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