CN118936369A - A system and method for detecting sensitive components of an angle measurement sensor - Google Patents
A system and method for detecting sensitive components of an angle measurement sensor Download PDFInfo
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Abstract
The invention relates to the technical field of sensor testing, and discloses a detection system and a detection method for a sensitive component of an angle measurement sensor, wherein the detection system comprises the following steps: acquiring an original pulse signal set acquired by an angle measurement sensor; when judging that pulse abnormality occurs in the original pulse signal set according to a preset pulse time interval threshold, acquiring pulse characteristic data, and inputting the pulse characteristic data into a pre-trained pulse abnormality detection model to acquire the number of lost pulses or the number of repeated pulses occurring in a measurement time period; inputting the original measured pulse number M, the lost pulse number and the repeated pulse number into a pre-trained angle correction detection model together to obtain a real rotation angle measured value of the angle measuring sensor; the invention is favorable for continuously ensuring that the angular position sensor has high measurement precision and high reliability under the condition of dynamic environment change.
Description
Technical Field
The invention relates to the technical field of sensor testing, in particular to a system and a method for detecting a sensitive component of an angle measuring sensor.
Background
The angular position sensor is key equipment for measuring the rotation angle, is widely applied to various fields such as industrial automation, automotive electronics, aerospace, precision instruments and the like, and can be divided into photoelectric angular position sensors, capacitive angular position sensors, inductive angular position sensors and magnetic angular position sensors according to different sensitive components; the photoelectric angular position sensor has the advantages of quick response, low power consumption and the like; however, in practical applications, due to environmental changes, electromagnetic interference, and the sensor itself, deviations may occur in measurement accuracy and stability of the photoelectric angular position sensor; such deviations, if not effectively detected and adjusted, may lead to reduced performance of the application equipment and even to system failure; therefore, how high measurement accuracy and reliability of the angular position sensor must be ensured.
At present, the traditional angular position sensor testing method usually focuses on the detection of overall performance, is difficult to deeply detect the tiny change of a sensitive component in the sensor, and relies on a static calibration mode to adjust the tiny change; however, static calibration cannot cope with dynamic environmental changes, which causes cumulative errors in the angular position sensor; there are, of course, some related technical improvements, for example, patent CN111256735B discloses a method and apparatus for processing data of an optical-electrical encoder, which can solve the measurement error caused by water drops or dust, but studies and practical applications of the above method and the prior art find that the above method and the prior art have at least the following partial drawbacks:
(1) The lack of an abnormality detection mechanism cannot judge whether pulse loss or pulse repetition occurs during the measurement of the angular position sensor, and further, nonlinear influences caused by ambient temperature and electromagnetic noise are difficult to avoid as much as possible;
(2) The number of lost or repeated pulses cannot be accurately and automatically obtained on the basis of finding pulse anomalies, and further, the measurement result of the angular position sensor cannot be corrected according to the number of lost or repeated pulses, so that it is difficult to continuously ensure that the angular position sensor has high measurement accuracy and high reliability under the dynamic environment change condition.
Disclosure of Invention
In order to overcome the above-described drawbacks of the prior art, embodiments of the present invention provide an angle measurement sensor sensitive component detection system and method.
In order to achieve the above purpose, the present invention provides the following technical solutions:
An angle measurement sensor sensitive component detection system, the system comprising:
The data acquisition module is used for acquiring an original pulse signal set acquired by the angle measurement sensor during the measurement time; the original pulse signal set comprises M original measurement pulses acquired in a measurement time period, wherein M is an integer larger than zero;
The pulse detection module is used for acquiring pulse characteristic data when judging that pulse abnormality occurs in an original pulse signal set according to a preset pulse time interval threshold value, inputting the pulse characteristic data into a pre-trained pulse abnormality detection model to acquire the number of lost pulses or the number of repeated pulses occurring in a measurement time period, wherein the pulse abnormality comprises pulse loss or pulse repetition;
The measurement correction module is used for inputting the original measurement pulse number M, the lost pulse number and the repeated pulse number into a pre-trained angle correction detection model together so as to obtain a real rotation angle measurement value of the angle measurement sensor.
Further, before determining that a pulse abnormality occurs in the original pulse signal set, the method includes:
Taking every two adjacent original measurement pulses in the original pulse signal set as a group of measurement pulse pairs to obtain N groups of measurement pulse pairs, wherein N is an integer greater than zero;
taking one original measurement pulse in each measurement pulse pair as a first measurement pulse, and taking the other original measurement pulse in each measurement pulse pair as a second measurement pulse;
Respectively extracting time stamps of the first measurement pulse and the second measurement pulse, calculating a time stamp difference value between the first measurement pulse and the second measurement pulse, and taking the time stamp difference value as a pulse time interval Ti;
comparing the pulse time interval Ti with a preset pulse time interval threshold Td, wherein Td is more than 0;
if Ti is more than Td, judging that pulse abnormality which is pulse loss occurs in the original pulse signal set, and marking the corresponding measuring pulse pair as a pulse loss data pair;
if ti=td, determining that no pulse abnormality exists in the original pulse signal set;
If Ti is less than Td, judging that pulse abnormality which is pulse repetition occurs in the original pulse signal set, and marking the corresponding measuring pulse pair as a pulse repetition data pair.
Further, the pulse characteristic data includes a number G of pulse missing data pairs, a number H of pulse repetition data pairs, a pulse time interval ratio of each pulse missing data pair, and a pulse time interval ratio of each pulse time interval, G and H being integers greater than zero.
Further, the pulse feature data acquisition logic includes:
a1: counting all pulse missing data pairs to obtain the number G of the pulse missing data pairs;
a2: extracting a pulse time interval Ti of a g-th pulse missing data pair, wherein g is an integer greater than zero;
a3: calculating the ratio of the pulse time interval Ti of the g-th pulse missing data pair to the preset pulse time interval threshold Td to obtain the pulse time interval ratio of the g-th pulse missing data pair, enabling g to be equal to g+1, and returning to the step a2;
a4: repeating the steps a 2-a 3 until G=g, ending the cycle, and obtaining the pulse time interval ratio of each pulse loss data pair.
Further, the pulse feature data acquisition logic further comprises:
b1: counting all pulse repetition data pairs to obtain the number H of the pulse repetition data pairs;
b2: extracting a pulse time interval Ti of an h pulse repetition data pair, wherein h is an integer greater than zero;
b3: calculating the ratio of the pulse time interval Ti of the h pulse repetition data pair to the preset pulse time interval threshold Td to obtain the pulse time interval ratio of the h pulse repetition data pair, enabling h=h+1, and returning to the step b2;
b4: repeating the steps b 2-b 3 until H=h, ending the cycle, and obtaining the pulse time interval ratio of each pulse time interval.
Further, the training logic of the pulse anomaly detection model is as follows:
Acquiring historical pulse abnormal training data, and dividing the historical pulse abnormal training data into a pulse abnormal training set and a pulse abnormal testing set, wherein the historical pulse abnormal training data comprises pulse characteristic data and the corresponding lost pulse number or repeated pulse number;
Constructing a first regression network, taking pulse characteristic data in a pulse abnormal training set as input data of the first regression network, taking the lost pulse number or repeated pulse number in the pulse abnormal training set as output data of the first regression network, and training the first regression network to obtain an initial pulse abnormal detection network;
And performing model verification on the initial pulse abnormality detection network by using the pulse abnormality test set, and outputting the initial pulse abnormality detection network with the value smaller than or equal to a preset first test error threshold as a pre-trained pulse abnormality detection model.
Further, the training logic of the angle correction detection model is as follows:
Acquiring historical angle correction training data, dividing the historical angle correction training data into an angle correction training set and an angle correction testing set, wherein the historical angle correction training data comprises angle correction characteristic data and corresponding real rotation angle measurement values;
wherein the angle correction characteristic data comprises an original measurement pulse number M, a lost pulse number and a repeated pulse number;
the logic for generating the actual rotation angle measured value in the historical angle correction training data is as follows:
Extracting the original measured pulse number M, the lost pulse number and the repeated pulse number in the angle correction characteristic data;
Inputting the original measured pulse number M, the lost pulse number and the repeated pulse number into a pre-constructed mathematical calculation model to obtain a real rotation angle measurement value;
Wherein the mathematical calculation model is expressed as follows:
;
Wherein: As a true rotation angle measurement value, For the number M of pulses originally measured,In order to lose the number of pulses,For the number of the repeated pulses,In order to achieve a resolution of the image,The value of the circumferential angle is 360 degrees;
Constructing a second regression network, taking angle correction characteristic data in an angle correction training set as input data of the second regression network, taking a real rotation angle measured value in the angle correction training set as output data of the second regression network, and training the second regression network to obtain an initial angle correction detection network;
And performing model verification on the initial angle correction detection network by using angle correction detection, and outputting the initial angle correction detection network with the value smaller than or equal to the second test error threshold as a trained angle correction detection model.
A method of detecting an angular measurement sensor sensitive component, the method comprising:
acquiring an original pulse signal set acquired by an angle measurement sensor during measurement time; the original pulse signal set comprises M original measurement pulses acquired in a measurement time period, wherein M is an integer larger than zero;
When judging that pulse abnormality occurs in the original pulse signal set according to a preset pulse time interval threshold, acquiring pulse characteristic data, and inputting the pulse characteristic data into a pre-trained pulse abnormality detection model to acquire the number of lost pulses or the number of repeated pulses occurring in a measurement time period, wherein the pulse abnormality comprises pulse loss or pulse repetition;
the original measured pulse number M, the lost pulse number and the repeated pulse number are input into a pre-trained angle correction detection model together to obtain a real rotation angle measured value of the angle measuring sensor.
Compared with the prior art, the invention has the beneficial effects that:
The application discloses a detection system and a detection method for a sensitive component of an angle measurement sensor, wherein the detection system comprises the following steps: acquiring an original pulse signal set acquired by an angle measurement sensor; when judging that pulse abnormality occurs in the original pulse signal set according to a preset pulse time interval threshold, acquiring pulse characteristic data, and inputting the pulse characteristic data into a pre-trained pulse abnormality detection model to acquire the number of lost pulses or the number of repeated pulses occurring in a measurement time period; inputting the original measured pulse number M, the lost pulse number and the repeated pulse number into a pre-trained angle correction detection model together to obtain a real rotation angle measured value of the angle measuring sensor; based on the above, the application can judge whether the pulse is lost or repeated when the angular position sensor is used for measuring, thereby avoiding nonlinear influence caused by ambient temperature and electromagnetic noise as much as possible; in addition, the number of lost or repeated pulses is accurately and automatically obtained on the basis of finding pulse anomalies, and further, the measurement result of the angular position sensor is corrected according to the number of lost or repeated pulses, so that the angular position sensor is favorable for continuously ensuring high measurement accuracy and high reliability under the dynamic environment change condition.
Drawings
FIG. 1 is a block diagram of a sensing system for an angular measurement sensor sensing assembly in accordance with the present invention;
FIG. 2 is a flow chart of a method for detecting an angular measurement sensor sensitive component provided by the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to the present invention;
fig. 4 is a schematic structural diagram of a computer readable storage medium according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the disclosure provides a detection system for a sensing component of an angle measurement sensor, which includes:
A data acquisition module 110 for acquiring a set of raw pulse signals acquired by the angular measurement sensor during a measurement time; the original pulse signal set comprises M original measurement pulses acquired in a measurement time period, wherein M is an integer larger than zero;
It should be appreciated that: for the angle measuring sensor (i.e., the photoelectric angle sensor), in addition to the measurement error caused by water drops or dust, the measurement error caused by the ambient temperature and electromagnetic noise is more obvious, and the angle measuring sensor (i.e., the photoelectric angle sensor) is also more susceptible to the ambient temperature and electromagnetic noise; however, for such effects, most of the existing methods solve the problem of measurement errors caused by ambient temperature and electromagnetic noise by training a linear model with ambient temperature and electromagnetic noise as inputs and measurement errors as outputs; the problem is that such a model is a linear model, but the measurement error caused by the ambient temperature and the electromagnetic noise belongs to the problem of nonlinearity, and further explaining that the measurement error caused by the ambient temperature and the electromagnetic noise does not represent a linear development mode, which leads to inaccurate error detection results of the existing linear model, further, the nonlinear influence caused by the ambient temperature and the electromagnetic noise cannot be avoided as much as possible, and further, it cannot be ensured that the angle measurement sensor (i.e., the photoelectric angular position sensor) still can maintain higher measurement accuracy under the influence of the ambient temperature and the electromagnetic noise.
The pulse detection module 120 is configured to, when it is determined that a pulse abnormality occurs in the original pulse signal set according to a preset pulse time interval threshold, acquire pulse feature data, and input the pulse feature data into a pre-trained pulse abnormality detection model to acquire a missing pulse number or a repeated pulse number occurring in a measurement time period, where the pulse abnormality includes a pulse missing or a pulse repetition;
in an implementation, before determining that a pulse abnormality occurs in the original pulse signal set, the method includes:
Taking every two adjacent original measurement pulses in the original pulse signal set as a group of measurement pulse pairs to obtain N groups of measurement pulse pairs, wherein N is an integer greater than zero;
It should be noted that: each set of measurement pulse pairs allows for the presence of repeated raw measurement pulses, an exemplary explanation being that assuming that there are 4 raw measurement pulses in the raw pulse signal set, Z1, Z2, Z3 and Z4 respectively, thus, when every two adjacent raw measurement pulses in the raw pulse signal set are taken as a set of measurement pulse pairs, a measurement pulse pair Y1, a measurement pulse pair Y2 and a measurement pulse pair Y3 are obtained, wherein Y1 comprises Z1 and Z2, Y2 comprises Z2 and Z3, and Y3 comprises Z2 and Z3, then 3 sets of measurement pulse pairs are obtained, wherein each two adjacent sets of measurement pulse pairs have repeated raw measurement pulses;
taking one original measurement pulse in each measurement pulse pair as a first measurement pulse, and taking the other original measurement pulse in each measurement pulse pair as a second measurement pulse;
Respectively extracting time stamps of the first measurement pulse and the second measurement pulse, calculating a time stamp difference value between the first measurement pulse and the second measurement pulse, and taking the time stamp difference value as a pulse time interval Ti;
comparing the pulse time interval Ti with a preset pulse time interval threshold Td, wherein Td is more than 0;
if Ti is more than Td, judging that pulse abnormality which is pulse loss occurs in the original pulse signal set, and marking the corresponding measuring pulse pair as a pulse loss data pair;
if ti=td, determining that no pulse abnormality exists in the original pulse signal set;
if Ti is less than Td, judging that pulse abnormality which is pulse repetition occurs in the original pulse signal set, and marking the corresponding measuring pulse pair as a pulse repetition data pair;
The pulse characteristic data comprises the number G of pulse missing data pairs, the number H of pulse repeated data pairs, a pulse time interval ratio of each pulse missing data pair and a pulse time interval ratio of each pulse time interval, wherein G and H are integers larger than zero;
in one embodiment, the pulse feature data acquisition logic comprises:
a1: counting all pulse missing data pairs to obtain the number G of the pulse missing data pairs;
a2: extracting a pulse time interval Ti of a g-th pulse missing data pair, wherein g is an integer greater than zero;
a3: calculating the ratio of the pulse time interval Ti of the g-th pulse missing data pair to the preset pulse time interval threshold Td to obtain the pulse time interval ratio of the g-th pulse missing data pair, enabling g to be equal to g+1, and returning to the step a2;
by way of example, assuming that there are 1 pulse missing data pairs, U1, where U1 includes a first measurement pulse and a second measurement pulse, and the time stamps of the first measurement pulse and the second measurement pulse are 0 seconds and 0.00010 seconds, respectively, the pulse time interval Ti of U1 is 0.00010 seconds, and if the preset pulse time interval threshold Td is assumed to be 0.00005 seconds, the pulse time interval ratio of U1 is 2, which means that the actual time interval is twice the preset value, so it can be inferred that one pulse may be missing in the measurement period, and it can be understood that by using the pulse time interval ratio as the pulse characteristic data, important data support can be provided for accurately predicting the missing pulse number or the repetition pulse number;
a4: repeating the steps a2 to a3 until G=g, ending the cycle, and obtaining the pulse time interval ratio of each pulse loss data pair;
In another embodiment, the pulse feature data acquisition logic further comprises:
b1: counting all pulse repetition data pairs to obtain the number H of the pulse repetition data pairs;
b2: extracting a pulse time interval Ti of an h pulse repetition data pair, wherein h is an integer greater than zero;
b3: calculating the ratio of the pulse time interval Ti of the h pulse repetition data pair to the preset pulse time interval threshold Td to obtain the pulse time interval ratio of the h pulse repetition data pair, enabling h=h+1, and returning to the step b2;
b4: repeating the steps b 2-b 3 until H=h, ending the cycle, and obtaining the pulse time interval ratio of each pulse time interval;
in practice, the training logic of the pulse anomaly detection model is as follows:
Acquiring historical pulse abnormal training data, and dividing the historical pulse abnormal training data into a pulse abnormal training set and a pulse abnormal testing set, wherein the historical pulse abnormal training data comprises pulse characteristic data and the corresponding lost pulse number or repeated pulse number;
It should be noted that: pulse characteristic data, missing pulse number or repeated pulse number in the historical pulse abnormal training data are obtained by actually collecting and recording by technicians according to experimental data or historical data;
Constructing a first regression network, taking pulse characteristic data in a pulse abnormal training set as input data of the first regression network, taking the lost pulse number or repeated pulse number in the pulse abnormal training set as output data of the first regression network, and training the first regression network to obtain an initial pulse abnormal detection network;
Performing model verification on an initial pulse abnormality detection network by using a pulse abnormality test set, and outputting the initial pulse abnormality detection network which is smaller than or equal to a preset first test error threshold value as a pre-trained pulse abnormality detection model;
It should be noted that: the first regression network is a specific one of algorithm models such as decision tree regression, random forest regression, support vector machine regression, polynomial regression, LSTM neural network or RNN neural network.
The measurement correction module 130 is configured to input the number M of original measurement pulses, the number M of lost pulses, and the number of repeated pulses into a pre-trained angle correction detection model together, so as to obtain a real rotation angle measurement value of the angle measurement sensor;
Specifically, the training logic of the angle correction detection model is as follows:
Acquiring historical angle correction training data, dividing the historical angle correction training data into an angle correction training set and an angle correction testing set, wherein the historical angle correction training data comprises angle correction characteristic data and corresponding real rotation angle measurement values;
wherein the angle correction characteristic data comprises an original measurement pulse number M, a lost pulse number and a repeated pulse number;
It should be noted that: angle correction characteristic data in the historical angle correction training data are obtained by actual acquisition and recording of technicians according to experimental data or historical data;
the logic for generating the actual rotation angle measured value in the historical angle correction training data is as follows:
Extracting the original measured pulse number M, the lost pulse number and the repeated pulse number in the angle correction characteristic data;
Inputting the original measured pulse number M, the lost pulse number and the repeated pulse number into a pre-constructed mathematical calculation model to obtain a real rotation angle measurement value;
Wherein the mathematical calculation model is expressed as follows:
;
Wherein: As a true rotation angle measurement value, For the number M of pulses originally measured,In order to lose the number of pulses,For the number of the repeated pulses,In order to achieve a resolution of the image,The value of the circumferential angle is 360 degrees;
It should be noted that: in the application of angular position sensors and encoders, resolution (PPR, pulses Per Revolution) is a key parameter that represents the number of pulses output by the sensor per revolution; the minimum angle change which can be measured by the sensor is determined, and the measurement precision and detail are affected; thus, the resolution The specific value of (2) is determined according to the specific model and specification of the angular position sensor;
Constructing a second regression network, taking angle correction characteristic data in an angle correction training set as input data of the second regression network, taking a real rotation angle measured value in the angle correction training set as output data of the second regression network, and training the second regression network to obtain an initial angle correction detection network;
Performing model verification on the initial angle correction detection network by using angle correction detection, and outputting the initial angle correction detection network which is smaller than or equal to a second test error threshold value as a trained angle correction detection model;
It should be noted that: the second regression network is a specific one of algorithm models such as decision tree regression, random forest regression, support vector machine regression, polynomial regression, LSTM neural network or RNN neural network;
By analyzing and acquiring the lost pulse number or the repeated pulse number of the angle measurement sensor, the invention can deeply detect the tiny change of the sensitive component in the angle measurement sensor and predict the real rotation angle measured value according to the learned tiny change, compared with the prior art, the invention can excavate the nonlinear influence caused by the environmental temperature and electromagnetic noise, further, it can be ensured that the angular position sensor (namely, the photoelectric angular position sensor) still can maintain high measurement accuracy under the influence of ambient temperature and electromagnetic noise, and the angular position sensor can be continuously ensured to have high measurement accuracy and high reliability under the dynamic environment change condition.
Example 2
Referring to fig. 2, the disclosure provides a method for detecting a sensitive component of an angle measurement sensor, which includes:
s201: acquiring an original pulse signal set acquired by an angle measurement sensor during measurement time; the original pulse signal set comprises M original measurement pulses acquired in a measurement time period, wherein M is an integer larger than zero;
It should be appreciated that: for the angle measuring sensor (i.e., the photoelectric angle sensor), in addition to the measurement error caused by water drops or dust, the measurement error caused by the ambient temperature and electromagnetic noise is more obvious, and the angle measuring sensor (i.e., the photoelectric angle sensor) is also more susceptible to the ambient temperature and electromagnetic noise; however, for such effects, most of the existing methods solve the problem of measurement errors caused by ambient temperature and electromagnetic noise by training a linear model with ambient temperature and electromagnetic noise as inputs and measurement errors as outputs; the problem is that such a model is a linear model, but the measurement error caused by the ambient temperature and the electromagnetic noise belongs to the problem of nonlinearity, and further explaining that the measurement error caused by the ambient temperature and the electromagnetic noise does not represent a linear development mode, which leads to inaccurate error detection results of the existing linear model, further, the nonlinear influence caused by the ambient temperature and the electromagnetic noise cannot be avoided as much as possible, and further, it cannot be ensured that the angle measurement sensor (i.e., the photoelectric angular position sensor) still can maintain higher measurement accuracy under the influence of the ambient temperature and the electromagnetic noise.
S202: when judging that pulse abnormality occurs in the original pulse signal set according to a preset pulse time interval threshold, acquiring pulse characteristic data, and inputting the pulse characteristic data into a pre-trained pulse abnormality detection model to acquire the number of lost pulses or the number of repeated pulses occurring in a measurement time period, wherein the pulse abnormality comprises pulse loss or pulse repetition;
in an implementation, before determining that a pulse abnormality occurs in the original pulse signal set, the method includes:
Taking every two adjacent original measurement pulses in the original pulse signal set as a group of measurement pulse pairs to obtain N groups of measurement pulse pairs, wherein N is an integer greater than zero;
It should be noted that: each set of measurement pulse pairs allows for the presence of repeated raw measurement pulses, an exemplary explanation being that assuming that there are 4 raw measurement pulses in the raw pulse signal set, Z1, Z2, Z3 and Z4 respectively, thus, when every two adjacent raw measurement pulses in the raw pulse signal set are taken as a set of measurement pulse pairs, a measurement pulse pair Y1, a measurement pulse pair Y2 and a measurement pulse pair Y3 are obtained, wherein Y1 comprises Z1 and Z2, Y2 comprises Z2 and Z3, and Y3 comprises Z2 and Z3, then 3 sets of measurement pulse pairs are obtained, wherein each two adjacent sets of measurement pulse pairs have repeated raw measurement pulses;
taking one original measurement pulse in each measurement pulse pair as a first measurement pulse, and taking the other original measurement pulse in each measurement pulse pair as a second measurement pulse;
Respectively extracting time stamps of the first measurement pulse and the second measurement pulse, calculating a time stamp difference value between the first measurement pulse and the second measurement pulse, and taking the time stamp difference value as a pulse time interval Ti;
comparing the pulse time interval Ti with a preset pulse time interval threshold Td, wherein Td is more than 0;
if Ti is more than Td, judging that pulse abnormality which is pulse loss occurs in the original pulse signal set, and marking the corresponding measuring pulse pair as a pulse loss data pair;
if ti=td, determining that no pulse abnormality exists in the original pulse signal set;
if Ti is less than Td, judging that pulse abnormality which is pulse repetition occurs in the original pulse signal set, and marking the corresponding measuring pulse pair as a pulse repetition data pair;
The pulse characteristic data comprises the number G of pulse missing data pairs, the number H of pulse repeated data pairs, a pulse time interval ratio of each pulse missing data pair and a pulse time interval ratio of each pulse time interval, wherein G and H are integers larger than zero;
in one embodiment, the pulse feature data acquisition logic comprises:
a1: counting all pulse missing data pairs to obtain the number G of the pulse missing data pairs;
a2: extracting a pulse time interval Ti of a g-th pulse missing data pair, wherein g is an integer greater than zero;
a3: calculating the ratio of the pulse time interval Ti of the g-th pulse missing data pair to the preset pulse time interval threshold Td to obtain the pulse time interval ratio of the g-th pulse missing data pair, enabling g to be equal to g+1, and returning to the step a2;
by way of example, assuming that there are 1 pulse missing data pairs, U1, where U1 includes a first measurement pulse and a second measurement pulse, and the time stamps of the first measurement pulse and the second measurement pulse are 0 seconds and 0.00010 seconds, respectively, the pulse time interval Ti of U1 is 0.00010 seconds, and if the preset pulse time interval threshold Td is assumed to be 0.00005 seconds, the pulse time interval ratio of U1 is 2, which means that the actual time interval is twice the preset value, so it can be inferred that one pulse may be missing in the measurement period, and it can be understood that by using the pulse time interval ratio as the pulse characteristic data, important data support can be provided for accurately predicting the missing pulse number or the repetition pulse number;
a4: repeating the steps a2 to a3 until G=g, ending the cycle, and obtaining the pulse time interval ratio of each pulse loss data pair;
In another embodiment, the pulse feature data acquisition logic comprises:
b1: counting all pulse repetition data pairs to obtain the number H of the pulse repetition data pairs;
b2: extracting a pulse time interval Ti of an h pulse repetition data pair, wherein h is an integer greater than zero;
b3: calculating the ratio of the pulse time interval Ti of the h pulse repetition data pair to the preset pulse time interval threshold Td to obtain the pulse time interval ratio of the h pulse repetition data pair, enabling h=h+1, and returning to the step b2;
b4: repeating the steps b 2-b 3 until H=h, ending the cycle, and obtaining the pulse time interval ratio of each pulse time interval;
in practice, the training logic of the pulse anomaly detection model is as follows:
Acquiring historical pulse abnormal training data, and dividing the historical pulse abnormal training data into a pulse abnormal training set and a pulse abnormal testing set, wherein the historical pulse abnormal training data comprises pulse characteristic data and the corresponding lost pulse number or repeated pulse number;
It should be noted that: pulse characteristic data, missing pulse number or repeated pulse number in the historical pulse abnormal training data are obtained by actually collecting and recording by technicians according to experimental data or historical data;
Constructing a first regression network, taking pulse characteristic data in a pulse abnormal training set as input data of the first regression network, taking the lost pulse number or repeated pulse number in the pulse abnormal training set as output data of the first regression network, and training the first regression network to obtain an initial pulse abnormal detection network;
Performing model verification on an initial pulse abnormality detection network by using a pulse abnormality test set, and outputting the initial pulse abnormality detection network which is smaller than or equal to a preset first test error threshold value as a pre-trained pulse abnormality detection model;
It should be noted that: the first regression network is a specific one of algorithm models such as decision tree regression, random forest regression, support vector machine regression, polynomial regression, LSTM neural network or RNN neural network.
S203: inputting the original measured pulse number M, the lost pulse number and the repeated pulse number into a pre-trained angle correction detection model together to obtain a real rotation angle measured value of the angle measuring sensor;
Specifically, the training logic of the angle correction detection model is as follows:
Acquiring historical angle correction training data, dividing the historical angle correction training data into an angle correction training set and an angle correction testing set, wherein the historical angle correction training data comprises angle correction characteristic data and corresponding real rotation angle measurement values;
wherein the angle correction characteristic data comprises an original measurement pulse number M, a lost pulse number and a repeated pulse number;
It should be noted that: angle correction characteristic data in the historical angle correction training data are obtained by actual acquisition and recording of technicians according to experimental data or historical data;
the logic for generating the actual rotation angle measured value in the historical angle correction training data is as follows:
Extracting the original measured pulse number M, the lost pulse number and the repeated pulse number in the angle correction characteristic data;
Inputting the original measured pulse number M, the lost pulse number and the repeated pulse number into a pre-constructed mathematical calculation model to obtain a real rotation angle measurement value;
Wherein the mathematical calculation model is expressed as follows:
;
Wherein: As a true rotation angle measurement value, For the number M of pulses originally measured,In order to lose the number of pulses,For the number of the repeated pulses,In order to achieve a resolution of the image,The value of the circumferential angle is 360 degrees;
It should be noted that: in the application of angular position sensors and encoders, resolution (PPR, pulses Per Revolution) is a key parameter that represents the number of pulses output by the sensor per revolution; the minimum angle change which can be measured by the sensor is determined, and the measurement precision and detail are affected; thus, the resolution The specific value of (2) is determined according to the specific model and specification of the angular position sensor;
Constructing a second regression network, taking angle correction characteristic data in an angle correction training set as input data of the second regression network, taking a real rotation angle measured value in the angle correction training set as output data of the second regression network, and training the second regression network to obtain an initial angle correction detection network;
Performing model verification on the initial angle correction detection network by using angle correction detection, and outputting the initial angle correction detection network which is smaller than or equal to a second test error threshold value as a trained angle correction detection model;
It should be noted that: the second regression network is a specific one of algorithm models such as decision tree regression, random forest regression, support vector machine regression, polynomial regression, LSTM neural network or RNN neural network;
By analyzing and acquiring the lost pulse number or the repeated pulse number of the angle measurement sensor, the invention can deeply detect the tiny change of the sensitive component in the angle measurement sensor and predict the real rotation angle measured value according to the learned tiny change, compared with the prior art, the invention can excavate the nonlinear influence caused by the environmental temperature and electromagnetic noise, further, it can be ensured that the angular position sensor (namely, the photoelectric angular position sensor) still can maintain high measurement accuracy under the influence of ambient temperature and electromagnetic noise, and the angular position sensor can be continuously ensured to have high measurement accuracy and high reliability under the dynamic environment change condition.
Example 3
Referring to fig. 3, the disclosure provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the method for detecting the angle measurement sensor sensitive component provided by the above methods when executing the computer program.
Since the electronic device described in this embodiment is an electronic device for implementing the method for detecting an angle sensor sensitive component in the embodiment of the present application, based on the method for detecting an angle sensor sensitive component described in the embodiment of the present application, those skilled in the art can understand the specific implementation of the electronic device in this embodiment and various modifications thereof, so how to implement the method in the embodiment of the present application for this electronic device will not be described in detail herein. As long as the person skilled in the art implements the electronic device used in the detection method of the sensitive component of the angle measurement sensor in the embodiment of the application, the electronic device belongs to the scope of protection of the application.
Example 4
Referring to fig. 4, a computer readable storage medium has a computer program stored thereon, which when executed implements the method for detecting an angular measurement sensor sensitive component described above.
The above formulas are all formulas with dimensionality removed and numerical value calculated, the formulas are formulas with the latest real situation obtained by software simulation by collecting a large amount of data, and preset parameters, weights and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
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