Cross-bore risk assessment and risk management tool
AssigneeHydromax USA, LLC
InventorMatthew Scharpf
Life of this patent
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Abstract
A method for cross-bore risk management involves receiving at least one dataset comprising a plurality of assets and cross-bore data. A risk probability value is calculated, using a processor, based on the cross-bore data for each asset of the plurality of assets using machine learning techniques. The risk probability values are spatially distributed around each respective asset. A graphical output is produced that illustrates the risk probability for a specified geographical area based on the spatially distributed risk probability values.
Claims20
A method for cross-bore risk management, comprising:
- receiving at least one dataset comprising a plurality of assets and cross-bore data;
- calculating, using a processor, a risk probability value based on the cross-bore data for each asset of the plurality of assets using machine learning techniques;
- spatially distributing the risk probability values around each respective asset;
- and
- producing a graphical output illustrating the risk probability for a specified geographical area based on the spatially distributed risk probability values.
A system for cross-bore risk management, comprising:
- a processor;
- and
- a memory storing computer program instructions which when executed by the processor cause the processor to perform operations comprising:
- receiving at least one dataset comprising a plurality of assets and cross-bore data;
- calculating a risk probability value based on the cross-bore data for each asset of the plurality of assets using machine learning techniques;
- spatially distributing the risk probability values around each respective asset;
- and
- producing a graphical output illustrating the risk probability for a specified geographical area based on the spatially distributed risk probability values.
A method for risk determination, comprising:
- receiving at least one dataset comprising a plurality of assets and risk data;
- calculating, using a processor, a risk probability value based on the risk data for each asset of the plurality of assets using machine learning techniques;
- spatially distributing the risk probability values around each respective asset;
- and
- producing a graphical output illustrating the risk probability for a specified geographical area based on the spatially distributed risk probability values.
Dependencies are inferred from the claim text.