Risk management system and risk management method

A risk management system for improving prediction accuracy of labor-related risk assessment through adaptive machine learning, the risk management system being an upper layer system connected to a plurality of lower layer systems, the risk management system comprising:

• a first processor;
• and
• a first memory device configured to store a program executed by the first processor to cause the first processor to:
• acquire, from a first lower layer system comprising a first sensor type connected to a second processor and a second memory device, first sensing data comprising heat index measurements;
• acquire, from a second lower layer system comprising a second sensor type, different from the first sensor type, and connected to a third processor and a third memory device, second sensing data comprising workload measurements;
• train a machine learning model to predict risk data using historical first sensing data, historical second sensing data, and historical risk data serving as teacher data, the historical data being acquired in advance;
• acquire user-specific information related to a human worker, the user-specific information including two or more of a length of service, environment information, age, and sex;
• generate user-adapted first normalized data by:
• determining a first normalized reference value based on the acquired user-specific information;
• and
• applying a first normalization arithmetic formula that transforms the first sensing data relative to the first normalized reference value, wherein the transformation adjusts the heat index measurements in the acquired first sensing data to account for individual worker characteristics;
• generate user-adapted second normalized data by:
• determining a second normalized reference value based on the acquired user-specific information;
• and
• applying a second normalization arithmetic formula that transforms the second sensing data relative to the second normalized reference value, wherein the transformation adjusts the workload measurements in the acquired second sensing data to account for the individual worker characteristics;
• synthesize the user-adapted first normalized data and the user-adapted second normalized data into integrated normalized data through an arithmetic process that generates one coherent data for input into the machine learning model;
• generate improved predicted risk data by inputting the integrated normalized data into the trained machine learning model, wherein the predicted risk data exhibits increased accuracy through the user-specific normalization that adapts the machine learning model inputs to individual worker characteristics;
• and
• periodically retrain the trained machine learning model after an elapsed period of time using updated user-specific information, newly acquired first sensing data, newly acquired second sensing data, and actual risk outcomes from the elapsed period of time as new teacher data,
• wherein the machine learning model generates the predicted risk data by assigning a weight value to at least one of the first normalized data, the second normalized data and the integrated normalized data based on a predetermined condition, and
• wherein the predicted risk data is adjusted based on the weight value.