A device for determining an operational status of a sensor, comprising: at least one processor , and at least one memory coupled to the at least one processor , wherein the at least one memory is configured to provide the at least one processor with instructions which, when executed, causes the at least one processor to: determine initial values of parameters of a Bayesian model and a degree of a polynomial regression model based on historical data of a target sensor and historical data of a reference sensor; infer posterior distributions of regression coefficients and an error term of a regression curve representing a correlation between the historical data of the target sensor and the historical data of the reference sensor using the polynomial regression model and the Bayesian model; set a credible interval based on the posterior distributions of the regression coefficients and the error term of the regression curve, and set control lines of data of the target sensor using the credible interval, wherein the control lines are upper limits or lower limits to determine whether the target sensor is normal or abnormal; determine an accuracy of the target sensor based on current data of the target sensor and the control lines; and control an operational status of the target sensor based on the accuracy. at least one processor , and at least one memory coupled to the at least one processor , wherein the at least one memory is configured to provide the at least one processor with instructions which, when executed, causes the at least one processor to: determine initial values of parameters of a Bayesian model and a degree of a polynomial regression model based on historical data of a target sensor and historical data of a reference sensor; infer posterior distributions of regression coefficients and an error term of a regression curve representing a correlation between the historical data of the target sensor and the historical data of the reference sensor using the polynomial regression model and the Bayesian model; set a credible interval based on the posterior distributions of the regression coefficients and the error term of the regression curve, and set control lines of data of the target sensor using the credible interval, wherein the control lines are upper limits or lower limits to determine whether the target sensor is normal or abnormal; determine an accuracy of the target sensor based on current data of the target sensor and the control lines; and control an operational status of the target sensor based on the accuracy. determine initial values of parameters of a Bayesian model and a degree of a polynomial regression model based on historical data of a target sensor and historical data of a reference sensor; infer posterior distributions of regression coefficients and an error term of a regression curve representing a correlation between the historical data of the target sensor and the historical data of the reference sensor using the polynomial regression model and the Bayesian model; set a credible interval based on the posterior distributions of the regression coefficients and the error term of the regression curve, and set control lines of data of the target sensor using the credible interval, wherein the control lines are upper limits or lower limits to determine whether the target sensor is normal or abnormal; determine an accuracy of the target sensor based on current data of the target sensor and the control lines; and control an operational status of the target sensor based on the accuracy. <EOS>