US-20260127335-A1 - METHOD FOR PREDICTING A TIME COURSE OF A PHYSICAL TARGET VARIABLE BY MEANS OF A MACHINE LEARNING MODEL

US20260127335A1US 20260127335 A1US20260127335 A1US 20260127335A1US-20260127335-A1

Abstract

A method for predicting a time course of a physical target. The method includes: providing multivariate sensor data including, for each of a plurality of physical variables, respective sensor data representing a time course of the physical variable, wherein each physical variable is assigned a respective text description describing it and its measurement environment; for each physical variable: dividing the respective sensor data into a respective plurality of sensor data segments; for each sensor data segment of the plurality of sensor data segments: determining a respective sensor data segment representation representing the sensor data segment and having a predefined dimension, determining a respective input element using the respective sensor data segment representation, time-related position information representing a position of the sensor data segment within the time period, and the respective text description of the physical variable; predicting the time course of the physical target variable.

Inventors

Sebastian Gerwinn
Martin Schiegg
Michal Moshkovitz

Assignees

ROBERT BOSCH GMBH

Dates

Publication Date: 20260507
Application Date: 20251031
Priority Date: 20241104

Claims (9)

1 . A method for predicting a time course of a physical target variable using a machine learning model, the method the following steps: providing multivariate sensor data assigned to a time period and including, for each physical variable of a plurality of physical variables, respective sensor data representing a time course of the physical variable within the time period, wherein each physical variable of the physical variables is assigned a respective text description describing the physical variable; for each physical variable of the plurality of physical variables: dividing the respective sensor data into a respective plurality of sensor data segments, and for each sensor data segment of the plurality of sensor data segments: determining a respective sensor data segment representation representing the sensor data segment and having, independently of a number of data points of the sensor data segment, a predefined dimension, and determining a respective input element using the respective sensor data segment representation, time-related position information representing a position of the sensor data segment within the time period, and the respective text description of the physical variable; predicting the time course of the physical target variable using the machine learning model in response to an input of all of the respective input elements and at least one target variable query representing a position of the time course to be predicted, within the time period, and a text description of the physical target variable, into the machine learning model.
2 . The method according to claim 1 , wherein the respective plurality of sensor data segments of at least one of the physical variables includes at least two sensor data segments with a different number of data points.
3 . The method according to claim 1 , wherein the time-related position information represents a start time and an end time within the time period.
4 . The method according to claim 1 , wherein the machine learning model includes a transformer model having an encoder and/or decoder which includes an attention layer to which all of the respective input elements are fed.
5 . The method according to claim 1 , wherein: (i) the respective sensor data segment representation for a sensor data segment is determined by means of an attention unit having a learned sensor-data-segment-specific parameter vector as the query and the sensor data segment as the key and as the value, and/or (ii) the respective input element is determined using the respective sensor data segment representation, a respective position representation, and the respective text description of the physical variable, wherein the position representation is determined using an attention unit having a learned position-specific parameter vector as the query and the time-related position information as the key and as the value.
6 . The method according to claim 1 , wherein the machine learning model includes a transformer model including an encoder and/or decoder having one or more attention layers which include an attention unit to which the target variable query is fed.
7 . A system, comprising: a device configured to carry out a technical process; one or more sensors configured to acquire multivariate sensor data; and a control device configured to predict a time course of a physical target variable using a machine learning model, by performing the following steps: providing the multivariate sensor data assigned to a time period and including, for each physical variable of a plurality of physical variables, respective sensor data representing a time course of the physical variable within the time period, wherein each physical variable of the physical variables is assigned a respective text description describing the physical variable; for each physical variable of the plurality of physical variables: dividing the respective sensor data into a respective plurality of sensor data segments, and for each sensor data segment of the plurality of sensor data segments: determining a respective sensor data segment representation representing the sensor data segment and having, independently of a number of data points of the sensor data segment, a predefined dimension, and determining a respective input element using the respective sensor data segment representation, time-related position information representing a position of the sensor data segment within the time period, and the respective text description of the physical variable; predicting the time course of the physical target variable using the machine learning model in response to an input of all of the respective input elements and at least one target variable query representing a position of the time course to be predicted, within the time period, and a text description of the physical target variable, into the machine learning model; wherein the control unit is configured to control the technical process, taking into account the prediction.
8 . A system, comprising: a data processing unit configured to predict a time course of a physical target variable using a machine learning model, the data processing unit configured to perform the following steps: providing multivariate sensor data assigned to a time period and including, for each physical variable of a plurality of physical variables, respective sensor data representing a time course of the physical variable within the time period, wherein each physical variable of the physical variables is assigned a respective text description describing the physical variable; for each physical variable of the plurality of physical variables: dividing the respective sensor data into a respective plurality of sensor data segments, and for each sensor data segment of the plurality of sensor data segments: determining a respective sensor data segment representation representing the sensor data segment and having, independently of a number of data points of the sensor data segment, a predefined dimension, and determining a respective input element using the respective sensor data segment representation, time-related position information representing a position of the sensor data segment within the time period, and the respective text description of the physical variable; predicting the time course of the physical target variable using the machine learning model in response to an input of all of the respective input elements and at least one target variable query representing a position of the time course to be predicted, within the time period, and a text description of the physical target variable, into the machine learning model.
9 . A non-transitory computer-readable medium on which are stored commands predicting a time course of a physical target variable using a machine learning model, the commands, when executed by a processor, causing the processor to perform the following steps comprising: providing multivariate sensor data assigned to a time period and including, for each physical variable of a plurality of physical variables, respective sensor data representing a time course of the physical variable within the time period, wherein each physical variable of the physical variables is assigned a respective text description describing the physical variable; for each physical variable of the plurality of physical variables: dividing the respective sensor data into a respective plurality of sensor data segments, and for each sensor data segment of the plurality of sensor data segments: determining a respective sensor data segment representation representing the sensor data segment and having, independently of a number of data points of the sensor data segment, a predefined dimension, and determining a respective input element using the respective sensor data segment representation, time-related position information representing a position of the sensor data segment within the time period, and the respective text description of the physical variable; predicting the time course of the physical target variable using the machine learning model in response to an input of all of the respective input elements and at least one target variable query representing a position of the time course to be predicted, within the time period, and a text description of the physical target variable, into the machine learning model.

Description

CROSS REFERENCE The present application claims the benefit under 35 U.S.C. § 119 of Europe Patent Application No. EP 24 21 0615.1 filed on Nov. 4, 2024, which is expressly incorporated herein by reference in its entirety. BACKGROUND INFORMATION For various technical (e.g., physical or chemical) processes, it may be desirable to predict a time course of a physical variable based on multivariate time series data of other physical variables and/or to predict an anomaly based on the multivariate time series data of multiple physical variables. For example, it may be desirable to predict a state of health or hydrogen loading of a fuel cell based on a time course of current and voltage, or in the case of a drilling machine, to predict which material is being drilled based on a time course of current and voltage, or to predict an anomaly based on the time course of current and voltage, etc. Typically, a machine learning model can be trained for exactly one use case (e.g., for predicting the state of health of the fuel cell). SUMMARY The present invention relates to a method for predicting a time course of a physical target variable using a machine learning model based on multivariate sensor data, wherein the multivariate sensor data may be irregularly sampled sensor data. If sensor data are acquired from different sensors, they may have different sampling rates. Data points may also be missing from some sensor data (e.g., due to a measurement error or because they are removed due to excessive uncertainty, etc.). Time periods in which sensor data are available may also have different durations. Illustratively, it is possible that not every data point in first sensor data can be bijectively assigned to a data point in second sensor data differing from the first sensor data. The method according to the present invention described herein allows for the prediction of the time course of the physical target variable even in such cases of irregular sensor data. According to an example embodiment of the present invention, this is achieved, for example, by dividing the sensor data into sensor data segments and then determining a respective sensor data segment representation for each sensor data segment, which representation has the same predefined dimension for all sensor data segments. Thus, the dimension of the sensor data segment representation is independent of the regularity (e.g., the sampling rate, the presence of data points, etc.) of the data points in the sensor data segment. The machine learning model of the present invention described herein can also have been trained to predict a respective physical target variable of a plurality of different tasks with at least partially different physical variables. This allows, for example, the physical laws that apply across the various tasks to be efficiently learned. Such training is only possible because the method described herein can process irregular multivariate sensor data. Various aspects pf the present invention relate to a method for predicting a time course of a physical target variable by means of a machine learning model. According to an example embodiment of the present invention, the method comprises: providing multivariate sensor data assigned to a time period and comprising, for each physical variable of a plurality of physical variables, respective sensor data representing a time course of the physical variable within the time period, wherein each physical variable is assigned a respective text description describing the physical variable (and optionally also a measurement environment in which the respective sensor data were acquired) (as text); for each physical variable of the plurality of physical variables: dividing the respective sensor data into a respective plurality of (e.g., disjoint) sensor data segments; for each sensor data segment of the plurality of sensor data segments: determining a respective sensor data segment representation representing the sensor data segment and having (independently of a number of data points of the sensor data segment) a predefined dimension, determining a respective input element using the respective sensor data segment representation, time-related position information representing a (e.g., temporal) position of the sensor data segment within the time period, and the respective text description of the physical variable; predicting the time course of the physical target variable by means of the machine learning model in response to an input of all input elements and at least one target variable query representing a (e.g., temporal) position of the time course to be predicted, within the time period and a text description of the physical target variable, into the machine learning model. Various exemplary embodiments of the present invention are specified below. Example 1 is the method for predicting the time course of the physical target variable by means of the machine learning model as described above. Example 2 i