Industrial Process Decision Model With Domain Rules and Multi-Source Data
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Solution Overview
Problem
Complex industrial manufacturing processes present challenges in optimization decision-making due to their multi-scale, dynamic nature, involving diverse production factors and vast amounts of multi-source data with high frequency, density, and heterogeneity, which existing technologies struggle to effectively integrate with domain knowledge for automation.
Innovation Solution
The method involves acquiring domain knowledge using probability soft logic to build a domain rule knowledge base, fusing multi-source data semantics to construct a semantic knowledge base, and using posterior regularization to develop an optimization decision-making model embedded with domain rule knowledge, followed by knowledge distillation to migrate this knowledge into a posteriori distribution model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain knowledge and multi-source data are integrated to improve decision-making accuracy, then the complexity of the system increases
Solution Approach 1:
The patent segments the complex decision-making system into distinct modules: a domain knowledge base storing expert rules and mechanisms, a multi-source data processing module, and an integration layer using posterior regularization. This segmentation allows each component to be developed and optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces posterior regularization as an intermediary mechanism that bridges domain knowledge and multi-source data. This intermediary enables the harmonious integration of structured knowledge and unstructured data without direct complex interaction, reducing system complexity while preserving decision-making accuracy.
2Speed
If multi-source heterogeneous data is processed in real-time, then the processing speed improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary processing of multi-source heterogeneous data by constructing semantic knowledge bases and extracting features before real-time decision-making. This preliminary action reduces the computational burden during real-time processing, enabling fast processing without excessive computational complexity.
Solution Approach 2:
The patent transforms multi-source heterogeneous data into unified semantic representations and extracted features, changing the parameters from raw diverse data to standardized semantic entities. This parameter transformation simplifies real-time processing while maintaining the ability to handle heterogeneous data sources.
3Reliability
If domain knowledge rules are strictly enforced, then the reliability of decision-making improves, but the adaptability to new situations decreases
Solution Approach 1:
The patent implements dynamic weighting of domain knowledge rules through posterior regularization, allowing the system to adjust the strength of rule enforcement based on data evidence. This dynamic approach maintains reliability when rules are valid while enabling adaptability when new situations arise that contradict existing rules.
Solution Approach 2:
The patent incorporates feedback mechanisms where multi-source data provides continuous validation and adjustment of domain knowledge rules. The posterior regularization framework uses data feedback to modulate rule application, ensuring reliability from domain knowledge while adapting to new patterns discovered in the data.
Data Source
AI summary
Disclosed is an optimization decision-making method of an industrial process fusing domain knowledge and multi-source data. The method comprises the steps of: acquiring the domain knowledge of the industrial process by using probability soft logic, and building an domain rule knowledge base of the industrial process; fusing multi-source data semantics and multi-source data features to form a new semantic knowledge representation of the industrial process, and constructing a semantic knowledge base of the industrial process; under a posteriori regularization framework, utilizing the domain rule knowledge base of the industrial process and the semantic knowledge base of the industrial process to obtain an optimization decision-making model embedded with the domain rule knowledge and obtain a posteriori distribution model; and migrating knowledge in the optimization decision-making model embedded with the domain rule knowledge into the posteriori distribution model through the knowledge distillation technology.