With the rapid development of the wind power industry, the production of wind power forgings has become a crucial part. In order to improve production efficiency, reduce cost and improve product quality, a decision support system for wind power forging production based on artificial intelligence was designed. This paper will introduce the requirements analysis, system architecture design, algorithm implementation and system testing of the system.
- Demand analysis
In the demand analysis stage, we need to clarify the requirements of wind power forging production decision support system. Specifically, the system needs to meet the following requirements:
It can collect and monitor the data in the production process of wind power forging in real time to ensure the safety and stability of the production process.
Can process and analyze the collected data to find the problems and bottlenecks in the production process, so as to propose improvement measures;
Ability to monitor and analyze market dynamics to develop more rational production plans and sales strategies;
It can provide corresponding prediction and decision support to help enterprises make more scientific and reasonable decisions.
- System architecture design
In the system architecture design stage, we need to design the overall architecture of the system according to the results of requirement analysis. Specifically, the system should include the following levels:
Data acquisition layer: This layer is mainly responsible for collecting various data in the production process of wind power forging, including process parameters, quality detection data, etc.
Data processing layer: This layer is mainly responsible for processing, analyzing and mining the collected data to find the problems and bottlenecks in the production process;
Data analysis layer: This layer is mainly responsible for in-depth analysis of the processed data to develop a more reasonable production plan and sales strategy;
Forecast and decision support layer: This layer is mainly responsible for monitoring and analyzing market dynamics, and providing corresponding forecast and decision support.
In addition, system stability is also an important aspect to consider during the architecture design phase. In order to ensure the stability of the system, we need to introduce fault tolerance mechanism and backup scheme in the system to avoid system crash due to unexpected circumstances.
- Algorithm implementation
In the algorithm implementation stage, we need to implement the algorithm of the system according to the results of requirement analysis and system architecture design. Specifically, the system should include algorithms for:
Data preprocessing algorithm: The algorithm is mainly responsible for cleaning, filtering and normalization of the collected data to ensure the quality and reliability of the data;
Data mining algorithm: The algorithm is mainly responsible for in-depth mining of the processed data to find the problems and bottlenecks in the production process;
Machine learning algorithm: The algorithm is mainly responsible for monitoring and analyzing market dynamics to develop more reasonable production plans and sales strategies;
Prediction and decision support algorithm: The algorithm is mainly responsible for forecasting and decision support for market dynamics to help enterprises make more scientific and reasonable decisions.
- System test
In the system test phase, we need to conduct a comprehensive test on the function, performance and reliability of the system. Specifically, the system should be tested in the following areas:
Functional test: The test is mainly responsible for testing the functions of the system to ensure that the system can correctly achieve the required functions;
Performance test: This test is mainly responsible for testing the performance of the system to ensure that the system can run efficiently under the premise of ensuring stability;
Reliability test: This test is mainly responsible for testing the reliability of the system to avoid the interruption of the production process or the loss of data due to system failure.
After the above tests, the decision support system for wind power forging production based on artificial intelligence can be successfully put into practical application.
In short, this paper introduces the demand analysis, system architecture design, algorithm implementation and system testing of wind power forging production decision support system, aiming to provide a design and implementation method of wind power forging production decision support system based on artificial intelligence for related enterprises.
