Radiometric
measuring instrument (1), comprising: - a number n of sensors (2_1 to 2_n), wherein each sensor (2_i) of the number n of sensors (2_1 to 2_n) is configured to provide associated sensor data (x i ) to generate a total of n sensor data (x1, ...,x n ) is generated using the number n of sensors (2_1 to 2_n), - a measurement unit (4) designed to calculate a number m of measurement values (ŷ1, ...,y) m ) depending on the number n of sensor data (x1, ...,x n ) based on values of a number d of parameters (θ1, ..., θ d to calculate, and - a
learning unit (5), wherein the
learning unit (5) is trained to perform based on training data ( ( xt 1 ( i ) ,..., xtn ( i ) ) ; ( ys 1 ( i ) ,..., ysm ( i ) ) ) the values of the number d of parameters (θ1, ..., θ d to calculate - where the number n of sensors (2_1 to 2_n) is selected from the set of sensors consisting of: - at least one radiometric sensor, in particular a radiometric sensor designed to generate sensor data in the form of a count rate or
radiation intensity data, and / or a radiometric sensor designed to generate sensor data in the form of information about radiometric spectra, - at least one sensor designed to generate sensor data in the form of temperature data, - at least one sensor designed to generate sensor data in the form of acceleration data, - at least one sensor designed to generate sensor data in the form of speed data, - at least one sensor designed to generate sensor data in the form of position data, - at least one sensor, in particular in the form of an
ultrasonic sensor or
laser sensor, which is designed to generate sensor data in the form of information about a load height profile, and - at least one sensor designed to generate sensor data in the form of
humidity data, - where at least one measurement variable is selected from the set of measurement variables consisting of: - Fill level, - Positions and / or thicknesses of individual material
layers, - Density, - Delivery rate, in particular total delivery
mass, -
Throughput, especially
mass flow rate, and - Material composition, in particular content levels, - wherein the
learning unit (5) is trained to extract data from the training data ( ( xt 1 ( i ) ,..., xtn ( i ) ) ; ( ys 1 ( i ) ,..., ysm ( i ) ) ) a number n of training sensor data ( xt 1 ( i ) ,..., xtn ( i ) ) to extract and a number m of associated setpoints ( ys 1 ( i ) ,..., ysm ( i ) ) the number m of
measured quantity values (y1, ..., y m ) to extract, and - wherein the measurement unit (4) is configured to provide a number m of training values ( yt 1 ( i ) ,..., ytm ( i ) ) the number m of measured variable values (y1, ...,y m ) depending on the number n of training sensor data ( xt 1 ( i ) ,..., xtn ( i ) ) to calculate, and - wherein the learning unit (5) is trained to, based on the number m of setpoints ( ys 1 ( i ) ,..., ysm ( i ) ) the number m of
measured quantity values (y1, ..., y m ) and the number m of training values ( yt 1 ( i ) ,..., ytm ( i ) ) the number m of
measured quantity values (ŷ1, ..., y m) the values of the number d of parameters (θ1, ..., θ d to calculate.