Orchestrator reporting of probability of downtime from machine learning process
First Claim
1. A non-transitory computer readable medium including instructions that, when executed by at least one processor, cause the at least one processor to perform operations for reporting Electronic Control Unit (ECU) errors or faults to a remote monitoring server, comprising:
- receiving, at a processor in a communications network of a vehicle, operational data from a plurality of ECUs in the vehicle, the operational data being indicative of a plurality of runtime attributes of the plurality of ECUs;
generating, through a machine learning process, a statistical model of the operational data, wherein the machine learning process is based on the plurality of runtime attributes;
receiving live, runtime updates from the plurality of ECUs in the communications network of the vehicle;
identifying, based on the live, runtime updates, an ECU error associated with an ECU in the communications network of the vehicle, the ECU error being determined by a comparison of the live, runtime updates with the statistical model of the operational data to identify at least one deviation from the operational data;
determining a probability of downtime for the plurality of ECUs based on the operational data and the live, runtime updates; and
wirelessly sending a report from the vehicle to the remote monitoring server based on the live, runtime updates, the report identifying the ECU and the identified ECU error,wherein ECU software on the ECU is mapped to a plurality of functional units, and the ECU is configured to utilize a virtual file system (VFS) to manage and track one or more versions of each of the plurality of functional units.
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Accused Products
Abstract
Disclosed embodiments relate to reporting Electronic Control Unit (ECU) errors or faults to a remote monitoring server. Operations may include receiving operational data from a plurality of ECUs in the vehicle, the operational data being indicative of a plurality of runtime attributes of the plurality of ECUs; generating, through a machine learning process, a statistical model of the operational data; receiving live, runtime updates from the plurality of ECUs in the communications network of the vehicle; identifying an ECU error associated with an ECU in the communications network of the vehicle, the ECU error being determined by a comparison of the live, runtime updates with the statistical model of the operational data to identify at least one deviation from the operational data; and wirelessly sending a report to the remote monitoring server based on the live, runtime updates, the report identifying the ECU and the identified ECU error.
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Citations
18 Claims
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1. A non-transitory computer readable medium including instructions that, when executed by at least one processor, cause the at least one processor to perform operations for reporting Electronic Control Unit (ECU) errors or faults to a remote monitoring server, comprising:
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receiving, at a processor in a communications network of a vehicle, operational data from a plurality of ECUs in the vehicle, the operational data being indicative of a plurality of runtime attributes of the plurality of ECUs; generating, through a machine learning process, a statistical model of the operational data, wherein the machine learning process is based on the plurality of runtime attributes; receiving live, runtime updates from the plurality of ECUs in the communications network of the vehicle; identifying, based on the live, runtime updates, an ECU error associated with an ECU in the communications network of the vehicle, the ECU error being determined by a comparison of the live, runtime updates with the statistical model of the operational data to identify at least one deviation from the operational data; determining a probability of downtime for the plurality of ECUs based on the operational data and the live, runtime updates; and wirelessly sending a report from the vehicle to the remote monitoring server based on the live, runtime updates, the report identifying the ECU and the identified ECU error, wherein ECU software on the ECU is mapped to a plurality of functional units, and the ECU is configured to utilize a virtual file system (VFS) to manage and track one or more versions of each of the plurality of functional units. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A system for reporting Electronic Control Unit (ECU) errors or faults to a remote monitoring server, the system comprising:
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one or more processors; and one or more memories having instructions that, when executed by the one or more processors, cause the one or more processors to perform the operations of; receiving, at a processor in a communications network of a vehicle, operational data from a plurality of ECUs in the vehicle, the operational data being indicative of a plurality of runtime attributes of the plurality of ECUs; generating, through a machine learning process, a statistical model of the operational data, wherein the machine learning process is based on the plurality of runtime attributes; receiving live, runtime updates from the plurality of ECUs in the communications network of the vehicle; identifying, based on the live, runtime updates, an ECU error associated with an ECU in the communications network of the vehicle, the ECU error being determined by a comparison of the live, runtime updates with the statistical model of the operational data to identify at least one deviation from the operational data; determining a probability of downtime for the plurality of ECUs based on the operational data and the live, runtime updates; and wirelessly sending a report from the vehicle to the remote monitoring server based on the live, runtime updates, the report identifying the ECU and the identified ECU error, wherein ECU software on the ECU is mapped to a plurality of functional units, and the ECU is configured to utilize a virtual file system (VFS) to manage and track one or more versions of each of the plurality of functional units.
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18. A computer-implemented method for reporting Electronic Control Unit (ECU) errors or faults to a remote monitoring server, the method comprising:
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receiving, at a processor in a communications network of a vehicle, operational data from a plurality of ECUs in the vehicle, the operational data being indicative of a plurality of runtime attributes of the plurality of ECUs; generating, through a machine learning process, a statistical model of the operational data, wherein the machine learning process is based on the plurality of runtime attributes; receiving live, runtime updates from the plurality of ECUs in the communications network of the vehicle; identifying, based on the live, runtime updates, an ECU error associated with an ECU in the communications network of the vehicle, the ECU error being determined by a comparison of the live, runtime updates with the statistical model of the operational data to identify at least one deviation from the operational data; determining a probability of downtime for the plurality of ECUs based on the operational data and the live, runtime updates; and wirelessly sending a report from the vehicle to the remote monitoring server based on the live, runtime updates, the report identifying the ECU and the identified ECU error, wherein ECU software on the ECU is mapped to a plurality of functional units, and the ECU is configured to utilize a virtual file system (VFS) to manage and track one or more versions of each of the plurality of functional units.
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Specification