Process management method and apparatus
Provided is a method of managing a target process. The method performed by a process management apparatus includes: generating a reference pattern indicating a normal state based on reference observed data on a process factor measured while the target process is maintained in the normal state; obtaining observed data on the process factor measured for a specified observation period; calculating a dissimilarity between the reference pattern and the observed data; and constructing a regression tree for the target process by using the observed data and the dissimilarity, wherein the process factor is set as an independent variable of the regression tree, and the dissimilarity is set as a dependent variable of the regression tree.
1. A method of managing a plurality of unit processes using a process management apparatus comprising at least one processor, the method comprising:
receiving, in real time, observed data, collected by at least one sensor, on at least one process factor measured while a first unit process is performed;
constructing a regression tree for the first unit process by using the observed data;
generating a ruleset for controlling a second unit process, which is performed after the first unit process, using the regression tree, the generated ruleset comprising a first ruleset indicating a normal state and a second ruleset indicating an abnormal state; and
identifying a process factor, among the at least one process factor, that causes an abnormality in the second unit process using the second ruleset;
providing, via a user interface, identification information of the identified process factor that causes the abnormality in the second unit process;
controlling a value of the identified process factor of the second unit process using the first ruleset.
2. The method of claim 1 , wherein the constructing of the regression tree comprises:
calculating a dissimilarity between a reference pattern indicating the normal state and the observed data; and
constructing the regression tree using the observed data and the dissimilarity,
wherein the at least one process factor is set as an independent variable of the regression tree, and the dissimilarity is set as a dependent variable of the regression tree.
3. The method of claim 1 , wherein the regression tree is constructed by setting the at least one process factor as an independent variable and a dissimilarity between a reference pattern indicating the normal state and the observed data as a dependent variable, and the generating of the ruleset comprises generating the first ruleset indicating the normal state based on an average and degree of deviation of dissimilarity values corresponding to each tree node included in the regression tree.
4. The method of claim 3 , wherein the first ruleset indicating the normal state comprises a range of process factor values of the identified process factor indicating the normal state, and
wherein the controlling the value of the identified process factor comprises controlling the value of the identified process factor of the second unit process according to the range of process factor values.