Natural Language Control Policy Generation for IT Operations
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Solution Overview
Problem
In complex network environments, efficient IT operation and maintenance face challenges due to the need for deep understanding of IT architecture, service processes, and dynamic adjustments, requiring highly skilled personnel and manual policy optimization.
Innovation Solution
A method and apparatus that utilize a machine learning model to convert user-input natural language rules into control policies, employing a control policy generation module to generate and add policies in an information system, enabling non-experts to efficiently manage IT systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual policy optimization is performed by highly skilled personnel, then policy quality and system reliability are improved, but operation time and labor costs increase
Solution Approach 1:
The system enables self-service by allowing operators to input natural language requirements without needing deep expertise in IT architecture or policy formulation. The machine learning model automatically generates optimized control policies, making the system serve itself by converting simple inputs into complex policy configurations that previously required skilled personnel.
Solution Approach 2:
The patent replaces the mechanical system of manual policy creation and optimization with an automated machine learning-based system. Instead of relying on human experts to manually analyze requirements and formulate policies, the system uses natural language processing and machine learning models to automatically generate control policies, substituting human cognitive work with automated computational processes.
2Manufacturing precision
If comprehensive understanding of IT architecture and service processes is required, then policy accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The patent introduces natural language as an intermediary between the operator and the complex IT architecture. Instead of requiring operators to directly understand and manipulate complex technical parameters and architecture details, they interact through natural language which the machine learning model translates into accurate control policies. This intermediary layer shields users from technical complexity while maintaining policy accuracy.
Solution Approach 2:
The system substitutes the need for human understanding of complex IT architecture with automated machine learning models that inherently understand these relationships. The models have been trained on architectural knowledge and service processes, replacing the need for human operators to possess this expertise while maintaining high policy accuracy.
3Adaptability or versatility
If dynamic adjustment of policies is implemented, then system adaptability is improved, but complexity of management increases
Solution Approach 1:
The patent implements dynamic policy adjustment by enabling the system to continuously monitor operational data and automatically update control policies in response to changing conditions. The machine learning models can retrain and regenerate policies based on new information, allowing the system to adapt dynamically without manual intervention for each change.
Solution Approach 2:
The system provides self-service for dynamic adjustments by automatically detecting when policy changes are needed and generating updated policies without requiring manual management intervention. The machine learning models continuously optimize policies based on operational feedback, making the system self-managing and reducing the complexity of dynamic policy management.
Data Source
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AI summary
A method, an apparatus, a device and a storage medium for generating a control policy in an information system are provided. The method comprises: receiving first information input by a user in a natural language, the first information indicating a target rule for managing the information system; determining, using a first machine learning model, a first control policy generation module matching the first information from a plurality of control policy generation modules; presenting target configuration information generated for a target control policy, the target configuration information being generated using the first control policy generation module, and the target control policy indicating a control policy for implementing the target rule in the information system; and adding, based on the target configuration information, the target control policy corresponding to the target rule in the information system in response to receiving a positive indication for the target configuration information.