# Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge Graphs
## 3 Conceptual Diagram of Multilevel Adaptive Synthesis
Given the specified and formalized knowledge of the monitoring object environment \(A\) (\(O\) \(Q\) \(D\) \(C\)), it is required to develop a model of the context event \(S\), represented by application context \(Context(t)=(S\big{|}O_{p},\)\(Q_{p},\)\(D_{p},\)\(C_{p},\)\(R_{p},\)\(t),O_{p}\subseteq O,\)\(Q_{p}\subseteq Q,\)\(D_{p}\subseteq D,\)\(C_{p}\subseteq C,\)\(R_{p}\subseteq R\). When determining the context event based on the analysis of the monitoring object state at time \(t_{k}\), a three-level gradation for the values of the node and edge attributes is proposed. The entire range of values for the attributes of nodes and edges is divided into three ranges:  
* Green range (corresponds to the normal state of the object. All the object elements are functioning properly);
* Yellow range (corresponds to the boundary state of the object. It is required to pay attention to the functioning of one or more elements);
* Red range (faulty state of the object, when it is necessary to urgently take certain actions to return its element or elements to a good state). This approach allows using a fairly simple algorithm to determine the current context event \(S_{i}\). The presence of the current context allows rebuilding the synthesis processes of the object models. A policy mechanism is used for rebuilding processes. For the context event \(S_{i}\) the policy \(\overline{P}_{S_{i}}\) is defined as follows: \(\overline{P}_{S_{i}}=p_{S_{i},1}\big{(}\overline{G}_{S_{i},t_{k}}\big{)},\)\(p_{S_{i},2}\big{(}\overline{G}_{S_{i},t_{k}}\big{)},\)\(\ldots,p_{S_{i}W}\big{(}\overline{G}_{S_{i},t_{k}}\big{)},\) where \(p_{S_{i},j}\big{(}\overline{G}_{S_{i},t_{k}}\big{)}\)--the rules for the policy \(\overline{P}_{S_{i}}\), \(W\)--the number of the policy \(\overline{P}_{S_{i}}\) rules, \(\overline{G}_{S_{i},t_{k}}\)--a subset of nodes and edges that a context event can change \(S_{i}\) (context metrics). Policies define a set of rules that are based on the statistics of the monitoring object's functioning or by expert advice, taking into account the context for which the policy is defined. At each step of the synthesis, to determine the sets of admissible values of nodes and edges of models based on a set of rules \(P\), the following functions are defined: \(\phi_{V}(FV_{t_{k}},V_{t_{k}}^{L},E_{t_{k}}^{H},\)\(E_{t_{k}}^{R},Context(t_{k}))\), \(\phi_{E}(FE_{t_{k}},V_{t_{k}}^{L},E_{t_{k}}^{H},\)\(E_{t_{k}}^{R},Context(t_{k}))\). To determine the values of nodes and edges from the sets of their admissible values, the following functions are constructed \(\psi_{V}\Big{(}FV_{t_{k+1}},\)\(V_{t_{k}}^{L}\Big{)}\), \(\psi_{E^{H}}\Big{(}FE_{t_{k+1}},\)\(E_{t_{k}}^{H}\Big{)}\) and \(\psi_{E^{R}}\Big{(}FE_{t_{k+1}},\)\(E_{t_{k}}^{R}\Big{)}\). Using the obtained functional dependencies, the transformation \(s_{k}\to s_{k+1}\) is performed. Thus, the proposed scheme provides the possibility of adaptive synthesis of the processes, of forecasting the state of the observed objects, which makes it possible to solve the forecast problems, taking into account the context determined by their current state.