  Estimating the composition and concentration of ambient gases is crucial for
industrial gas safety. Even though other researchers have proposed some gas
identification and con-centration estimation algorithms, these algorithms still
suffer from severe flaws, particularly in fulfilling industry demands. One
example is that the lengths of data collected in an industrial setting tend to
vary. The conventional algorithm, yet, cannot be used to analyze the
variant-length data effectively. Trimming the data will preserve only
steady-state values, inevitably leading to the loss of vital information. The
gas identification and concentration estimation model called GCN-ViT(GViT) is
proposed in this paper; we view the sensor data to be a one-way chain that has
only been downscaled to retain the majority of the original in-formation. The
GViT model can directly utilize sensor ar-rays' variable-length real-time
signal data as input. We validated the above model on a dataset of 12-hour
uninterrupted monitoring of two randomly varying gas mixtures, CO-ethylene and
methane-ethylene. The accuracy of gas identification can reach 97.61%, R2 of
the pure gas concentration estimation is above 99.5% on average, and R2 of the
mixed gas concentration estimation is above 95% on average.
