Electronic control system measuring time-dependent composite index values for steering risk-driven system interactions

A measuring and indexing method for a control system for steering risk-driven interaction between an automated system and operating units, a steering of a risk-driven interaction being provided by a signal generation of an electronically interacting control unit controller based on a measured composite index parameter value, wherein a measured value of a composite index is a measure of a change of risk-dependent measuring factors of region-dependent and event-exposed objects, wherein the measured value of the composite index is generated from measurement parameters provided by measuring devices, the measurement parameters measuring an exposure of the region-dependent and event-exposed objects by measuring risk drivers associated with occurrence of defined events providing event-exposure of the region-dependent and event-exposed objects, and wherein the measurement parameters are assigned to a parameterization of the risk drivers, which is transmitted to a processing circuitry of the control system for generating a measured time-dependent composite index parameter the method comprising:

• scanning the operating units for measurement parameters capturing dynamically static characteristics of at least one risk driver of a plurality of parameterized risk drivers;
• identifying risk drivers, which are either contracting or expanding a measured risk exposure, wherein, in case the identified risk drivers expand the measured risk exposure, additional risk drivers are selected until the measured risk exposure is contracting due to the additional risk drivers, and initiate mutual normalization of the risk drivers, wherein an initiation is triggered if the identified risk drivers contract the measured risk exposure,
• selecting a first set of risk drivers by parametrizing their contribution to the exposure, wherein the first set of risk drivers at least comprises a risk driver parametrizing measures related to Gross Domestic Product (GDP) growth, a risk driver parametrizing measures related to health-care expenditure growth, and a risk driver parametrizing measures related to real wage growth, wherein the selection is based on an impact of captured alterations to the measured composite index parameter value, the risk drivers being mutually normalized to each other, and select the additional risk drivers by parametrizing measures related to at least societal and/or legal and/or political based contributions to a general liability exposure loss,
• dynamically applying the additional risk drivers based on their impact to the measured composite index parameter, and dynamically normalize the risk drivers to each other, wherein the processing circuitry is configured to automatically perform a weighting process of the selected risk drivers by applying a transformation to normalize the selected risk drivers to a final time series of the parameters by providing individual set weights and/or individual driver weights to determine weighted risk drivers, wherein historic exposure and loss data assigned to a geographic region are selected from a dedicated data storage comprising region-specific data, wherein historic measurement parameters are generated corresponding to the measurement parameters, and wherein an exposure signal is weighted by the historic measurement parameters,
• automatically selecting, for optimization, based on the weighted risk drivers and sets of risk drivers, a minimum number of risk drivers in relation to maximized statistical significance using a selection grid, and provide the minimum number of risk drivers as a reduced set of risk drivers out of all available risk drivers using best fit characteristics, and the processing circuitry is configured to apply Akaike Information Criterion (AIC)-maximization compared to a number of selected risk drivers to select the minimum number of risk drivers in relation to maximized statistical significance based on the weighted risk drivers and sets of risk drivers, wherein the processing circuitry is configured to apply Akaike Information Criterion (AIC)-maximization compared to a number of selected liability risk drivers,
• scaling, for stabilization, impacts of different risk drivers, to a same scale by applying as normalization the transformation to a final time series
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• and select a minimum number of risk drivers in relation to maximized statistical significance based on the weighted risk drivers and sets of risk drivers, the processing circuitry being configured to apply R2-maximization compared to a number of selected risk drivers,
• dynamically adapting the set of risk drivers varying the minimum number of risk drivers in relation to a measured exposure signal by periodic time response, and transmit a request for a measurement parameter update periodically to the measuring devices for dynamic detection of variations of the measure parameters adapting measurable characteristics of the selected risk drivers by direct measurements, and
• generating the measured time-dependent composite index parameter based on the adapted set of risk drivers, wherein the operation of the automated systems is adapted based on the measured time-dependent composite index parameter providing a reduce-to-the-max operation of a steered automated system regarding a time-dependent flow of the selected minimum number of risk drivers.