Research on Maintenance and Guarantee Measures of Meteorological Instrument Equipment Based on DZZ5
In order to perform more effective daily maintenance on meteorological instruments and improve their operational stabil-ity,a DZZ5 meteorological instrument status monitoring model based on an improved class of support vector machines combined with long short-term memory networks is proposed,taking the DZZ5 meteorological instrument as the research object.Among them,a state recognition classification method based on a type of support vector machine is introduced,and particle swarm optimization algo-rithm is combined with long short-term memory network to optimize the support vector machine,further improving the comprehensive effect of state recognition.The results show that compared with traditional LSTM recognition models,the DZZ5 meteorological instru-ment abnormal state monitoring model based on PSO-OCSVM-LSTM has stronger recognition performance and can more accurately i-dentify and classify the instrument's state,with fewer misidentification cases;When the constructed instrument anomaly monitoring model is applied to practical work scenarios,the model performs well and meets practical work requirements.In summary,the DZZ5 meteorological instrument abnormal state monitoring model constructed has excellent performance,which can monitor the daily opera-tion status of meteorological instruments in real time and accurately identify instrument faults.It can be applied to actual meteorologi-cal instrument state monitoring,helping management personnel to carry out more convenient instrument daily maintenance and ensu-ring the stable operation of the instrument.