1. A method for building an adaptive soft sensor, comprising the following steps: using a distributed control system (DCS) for sampling and inspecting a manufacturing process; using sensors configured in the distributed control system to obtain online data for recording process variables to form input vectors of an interferential model; using the distributed control system to preprocess the online data to remove approximately identical data records and/or remove the data records that out of a reasonable range in order to be meaningful for model generation and optimization; operating an online subtractive cluster algorithm to divide the online data into a plurality of local regions according to a schedule vector, which is a sub set of the input vectors, and the plurality of local regions compose of a plurality of clusters; building a linear model in each of the local regions, and constructing the inferential model based on each of the linear models; converting the online subtractive cluster algorithm into a recursive regression algorithm; calculating density of the schedule vector with P Φ = κ - 1 ( k - 1 ) ⁢ ( γ ⁡ ( k ) + 1 ) - 2 ⁢ η ⁡ ( k ) ⁢ ϕ ⁡ ( k ) + σ ⁡ ( k ) , where Pφ is the density of the scheduled vector k is a time interval, γ is the covariance of scheduling vector at the current sampling time k, which denotes the current change of soft sensors, η is the accumulation of changes of the scheduling vector for the past sampling time 1 , 2 ,. .. k−1, which denotes the past change of soft sensor, σ is the accumulation covariance of scheduling vector for the past sampling time 1 , 2 ,. .. k−1, which denotes the known knowledge of soft sensors φ is the schedule vector, γ(k)=φ(k) T φ(k), η ⁡ ( k ) = ∑ j = 1 k - 1 ⁢ ϕ ⁡ ( j ) T = η ⁡ ( k - 1 ) + ϕ ⁡ ( k - 1 ) T , ⁢ σ ⁡ ( k ) = ∑ j = 1 k - 1 ⁢ ϕ ⁡ ( j ) T ⁢ ϕ ⁡ ( j ) = σ ⁡ ( k - 1 ) + ϕ ⁡ ( k - 1 ) T ⁢ ϕ ⁡ ( k - 1 ) ; merging two nearest clusters, updating existing clusters or adding a new cluster to eliminate the recording of the redundant online data for saving memory space of the distributed control system, wherein merging two nearest clusters, updating existing clusters or adding the new cluster further comprise: merging two nearest clusters into a new cluster when the two cluster centers are closer than a threshold; updating existing clusters; while obtaining new online data; and adding the new cluster when the new online data is far away from the plurality of clusters already exist; operating the recursive regression algorithm to update a plurality of local models in real-time according to the linear model of the local regions; and obtaining a predicted output value by using each of the linear models.