Method and system of identifying environment features for use in analyzing asset operation
First Claim
1. A method carried out by a computing system that is communicatively coupled to one or more asset-related data sources, the method comprising:
- receiving asset attribute data associated with a plurality of assets, wherein the asset attribute data includes sensor data captured by sensors affixed to assets in the plurality of assets;
based on an analysis of the received asset attribute data, detecting a grouping of related assets in a locality and thereby determining that the locality is a possible instance of a given type of real-world environment in which assets operate;
in response to the determining, obtaining image data associated with the detected locality;
inputting the obtained image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of real-world environment, wherein the model was previously defined by applying a machine learning technique to training data that includes respective image data for each of a plurality of known instances of the given type of real-world environment;
based on the likelihood data, generating output data indicating a location of any portion of the detected locality that is likely to comprise the given feature; and
using the output data to simulate asset operation in the detected locality.
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Abstract
Based on an analysis of asset attribute data associated with a plurality of assets, a platform may detect a locality that is a possible instance of a given type of environment, such as a mine or construction site. In response, the platform may obtain image data associated with the detected locality and input that image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of environment (e.g., a boundary, navigation route, hazard, etc.), where this model is defined based on training data. Based on the likelihood data, the platform may then generate output data indicating a location of any portion of the detected locality that is likely to comprise the given feature. In turn, the platform may use the output data to simulate asset operation in the detected locality.
135 Citations
20 Claims
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1. A method carried out by a computing system that is communicatively coupled to one or more asset-related data sources, the method comprising:
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receiving asset attribute data associated with a plurality of assets, wherein the asset attribute data includes sensor data captured by sensors affixed to assets in the plurality of assets; based on an analysis of the received asset attribute data, detecting a grouping of related assets in a locality and thereby determining that the locality is a possible instance of a given type of real-world environment in which assets operate; in response to the determining, obtaining image data associated with the detected locality; inputting the obtained image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of real-world environment, wherein the model was previously defined by applying a machine learning technique to training data that includes respective image data for each of a plurality of known instances of the given type of real-world environment; based on the likelihood data, generating output data indicating a location of any portion of the detected locality that is likely to comprise the given feature; and using the output data to simulate asset operation in the detected locality. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computing system comprising:
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a network interface configured to facilitate communications over a communication network with one or more data sources; at least one processor; a non-transitory computer-readable medium; and program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to; receive, via the network interface, asset attribute data associated with a plurality of assets, wherein the asset attribute data includes sensor data captured by sensors affixed to assets in the plurality of assets; based on an analysis of the received asset attribute data, detect a grouping of related assets in a locality and thereby determine that the locality is a possible instance of a given type of real-world environment; in response to the determining, obtain, via the network interface, image data associated with the detected locality; input the obtained image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of real-world environment, wherein the model was previously defined by applying a machine learning technique to training data that includes respective image data for each of a plurality of known instances of the given type of real-world environment; based on the likelihood data, generate output data indicating a location of any portion of the detected locality that is likely to comprise the given feature; and use the output data to simulate asset operation in the detected locality. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19)
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20. A non-transitory computer-readable medium having program instructions stored thereon that are executable to cause a computing device to:
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receive asset attribute data associated with a plurality of assets, wherein the asset attribute data includes sensor data captured by sensors affixed to assets in the plurality of assets; based on an analysis of the received asset attribute data, detect a grouping of related assets in a locality and thereby determine that the locality is a possible instance of a given type of real-world environment; in response to the determining, obtain image data associated with the detected locality; input the obtained image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of real-world environment, wherein the model was previously defined by applying a machine learning technique to training data that includes respective image data for each of a plurality of known instances of the given type of real-world environment; based on the likelihood data, generate output data indicating a location of any portion of the detected locality that is likely to comprise the given feature; and use the output data to simulate asset operation in the detected locality.
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Specification