Resource usage prediction method of plasma control system based on lstm

A resource usage prediction method of a plasma control system based on LSTM, the method comprising the steps of:

• data preprocessing and feature extraction:
• collecting and processing operation data of the plasma control system, selecting and standardizing feature metrics reflecting an operation state of the system, which specifically comprises:
• data collection:
• collecting time series data about resource usage including CPU occupancy rate and memory allocation during operation of the system, ensuring that the data includes resource consumption conditions under different operation states of the system;
• feature selection:
• selecting feature metrics reflecting the running state of the system, wherein the feature metrics include:
• algorithm type:
• a type of control algorithm currently running;
• trigger state:
• a state of the system when the system is in different trigger conditions;
• operation stage:
• an operation stage of the control system;
• dynamic resource utilization:
• dynamically changing resource utilization data, including historical CPU occupancy rate and memory allocation;
• standardization processing:
• standardizing input features to eliminate a scale difference between different features;
• introduction of an LSTM network for time series prediction:
• constructing an LSTM model, training the model using historical data to predict a resource usage standard value at a future time point, and enhancing an adaptability of the model through a sliding window method;
• feature embedding and feature separation strategies:
• generating embedded vectors for a system state, and separating a memory waveform according to busy and non-busy regions, which specifically comprises the following steps:
• feature embedding:
• performing a statistical analysis on the system state to generate the embedded vectors instead of traditional digital coding, wherein the system state includes the algorithm type and the trigger state;
• feature separation:
• in view of an obvious square wave characteristic of the memory waveform, separating the memory occupancy rate into busy regions and non-busy regions, and using the separated busy and non-busy regions as independent input features respectively for the LSTM model to learn, the obvious square wave characteristic of the memory waveform is that the busy regions and the non-busy regions change alternately;
• standard value prediction and memory leakage detection:
• collecting operation data of the system in real time, applying the LSTM model to predict a resource usage standard value in the future, calculating and monitoring a difference between the actual memory waveform and the predicted standard value and an increment of the difference to detect a memory leakage;
• health index calculation and feedback adjustment:
• calculating a health index of the system based on the actual difference, introducing a time decay factor, and adjusting the health index when a memory leakage is detected;
• feedback adjustment in the prediction period:
• according to a memory leakage detection result, performing feedback and adjustment on the input of the prediction, and performing cyclic predictions at a certain time step.