CN118032027B - Hybrid absolute value encoder and position detection method thereof - Google Patents

Abstract

The invention belongs to the technical field of encoder detection, and provides a hybrid absolute value encoder and a position detection method thereof, wherein the method comprises the following steps: the method comprises the steps of collecting output data of a magnetic encoder and an optical encoder, preprocessing the data, calculating a weight coefficient of each sensor, correspondingly distributing the weight coefficient of each sensor to the output data collected by the sensors after determining the weight coefficient of each sensor, carrying out fusion processing on the output data by a weighted average method, outputting a fusion result of the weighted average method, calculating to obtain a data set, judging the relation between the collection temperature and the data contained in the data set, directly predicting the data set required to be obtained at different temperatures, and directly predicting each data in the data set required to be obtained at different temperatures.

Description

Hybrid absolute value encoder and position detection method thereof

Technical Field

The invention belongs to the technical field of encoder detection, and particularly relates to a hybrid absolute value encoder and a position detection method thereof.

Background

The conventional absolute value encoder mainly adopts a single detection mode, such as an optical or magnetic mode, and although the modes can realize position detection to a certain extent, due to respective limitations, such as performance degradation of the optical encoder in a severe environment, insufficient precision of the magnetic encoder in high-speed motion and the like, the requirements of certain high-precision and high-reliability applications cannot be met. Therefore, a new absolute value encoder position detection method is needed to solve this problem.

One chinese patent application with publication number CN107655399a discloses a multi-turn absolute value encoder and a position detection method based on the multi-turn absolute value encoder, comprising: the multi-circle absolute value encoder comprises a single chip microcomputer, a permanent magnet and at least one magnetic angle sensor, wherein the permanent magnet is installed on an object to be detected and rotates along with the object to be detected, the at least one magnetic angle sensor is arranged around the permanent magnet to detect a single circle absolute angle value of the object to be detected, zero positions of the single circle absolute angle value are calibrated and stored in the single chip microcomputer, and output ends of the at least one magnetic angle sensor are respectively connected with the single chip microcomputer. Compared with the existing multi-turn absolute value encoder, the multi-turn absolute value encoder has the advantages that the structure is simpler, the cost and the design difficulty can be effectively reduced, the reliability and the stability are higher, and multi-turn rotating position detection is realized at lower cost.

In the prior art, the position and detection are carried out only through a single encoder, the detection precision and stability of the single encoder are low, the anti-interference capability is poor, so that accurate detection cannot be realized in a severe environment.

To this end, the present invention provides a hybrid absolute value encoder and a position detection method thereof.

Disclosure of Invention

In order to overcome the deficiencies of the prior art, at least one technical problem presented in the background art is solved.

The invention aims to provide a hybrid absolute value encoder and a position detection method thereof, which are characterized in that a plurality of sensors are used for collecting output data of a magnetic encoder and an optical encoder, the data of each sensor is preprocessed, the weight coefficient of each sensor is calculated according to the characteristics and the data quality of the sensors, the weight coefficient of each sensor is correspondingly distributed to the output data collected by the sensors after being determined, and a weighted average method is applied to fuse the output data collected by each sensor and output the fusion result of the weighted average method.

The technical scheme adopted for solving the technical problems is as follows: a hybrid absolute value encoder position detection method, comprising:

Step one: collecting output data of the magnetic encoder and the optical encoder by using a plurality of sensors;

Step two: preprocessing the data of each sensor;

step three: calculating a weight coefficient of each sensor according to the characteristics and the data quality of the sensor;

The method comprises the steps of acquiring output data acquired by each sensor for a plurality of times, calculating error parameters of the data acquired by each sensor based on the output data, calculating error factors of each sensor based on the error parameters, and determining weight coefficients of each sensor according to the size of the error factors of each sensor;

step four: after the weight coefficient of each sensor is determined, correspondingly distributing the weight coefficient of each sensor to the output data collected by the sensor, and carrying out fusion processing on the output data collected by each sensor by using a weighted average method;

Step five: outputting a fusion result of a weighted average method;

step six: obtaining output data collected by a plurality of sensors at different collection temperatures, and calculating to obtain a final data set;

Step seven: constructing a change line graph based on the obtained unit average temperature and the data set, and judging the relation between the acquisition temperature and the data contained in the data set based on the change line graph;

Step eight: based on the generated growth signals, randomly selecting two acquisition temperatures and corresponding data sets, calculating to obtain a data change value at unit temperature, and directly predicting the data sets required to be acquired at different temperatures based on the obtained data change value at the unit temperature;

Step nine: based on the normal signal, obtaining a function equation of the sub-polyline according to the slope of the obtained sub-polyline, directly predicting each item of data in the data set required to be obtained at different temperatures based on the sub-polyline equation, and re-integrating each item of predicted data to generate the data set.

The invention further adopts the technical scheme that: the error parameters comprise deviation and random errors, and the error parameters of the data acquired by each sensor are calculated based on the output data, and the process is as follows:

Dividing output data collected by a sensor into a plurality of groups according to the types of physical quantities;

and calculating the average value and the standard deviation of each group of data, respectively summing the average value and the standard deviation of all groups of data to obtain the average value and the standard deviation of the data acquired by the sensor, and taking the standard deviation as the random error of the sensor data.

The invention further adopts the technical scheme that: the process of obtaining the deviation is: and obtaining a nominal value of the sensor, carrying out difference processing on the nominal value of the sensor and an average value of data acquired by the sensor, and carrying out absolute value processing on the difference value to obtain a deviation of the sensor.

The invention further adopts the technical scheme that: calculating an error factor of each sensor based on the error parameters, specifically: marking the random error of the sensor as Ai and the deviation of the sensor as Bi;

And carrying out data processing on the obtained random error Ai of the sensor number and the deviation B i of the sensor, and substituting the random error Ai into a formula: Obtaining an error factor Ci of the sensor, wherein gamma is a preset natural constant, the gamma value is 0.25896, As a logarithmic function of the base e.

The invention further adopts the technical scheme that: and based on the error factors, carrying out summation processing on the error factors of all the sensors to obtain a sum of the error factors, carrying out ratio processing on the sum of the error factors of each sensor and the error factors, and carrying out reciprocal processing on the result of the ratio processing to obtain a weight coefficient of each sensor.

The invention further adopts the technical scheme that: and carrying out fusion processing on the output data acquired by each sensor, carrying out product processing on the data contained by each sensor and the weight coefficient to obtain processed sensor data, carrying out corresponding summation on the processed sensor data, taking an average value, and integrating the obtained average value into a data set.

The invention further adopts the technical scheme that: the acquisition temperature is the unit average temperature of the acquisition environment of the sensor in the acquisition period, wherein the acquisition process of the unit average temperature is as follows: obtaining the highest temperature and the lowest temperature in the acquisition period, summing the highest temperature and the lowest temperature, and carrying out ratio processing on the summation result and the acquisition period duration to obtain the unit average temperature;

Based on the obtained unit average temperature and the data set, a change line graph is constructed, and based on the change line graph, the relation between the acquisition temperature and the data contained in the data set is judged, specifically: calculating the slopes of all the sub-folding lines included in the change folding line, if the slopes of all the sub-folding lines are equal, indicating that each data included in the data set is regularly increased along with the regular increase of the acquisition temperature, and generating an increase signal in a positive relation;

If no obvious equal relation exists between slopes of the sub-broken lines, the positive relation exists between the acquisition temperature and the data set, and a normal signal is generated.

The invention further adopts the technical scheme that: calculating to obtain a data change value of a unit temperature system, wherein the process comprises the following steps: performing difference processing on the two selected acquisition temperatures to obtain an acquisition temperature difference;

and carrying out difference processing on each item of data contained in the two data sets to obtain each item of data difference, and carrying out ratio processing on each item of data difference and the acquired temperature difference to obtain a data change value at unit temperature.

A hybrid absolute value encoder manufactured according to a hybrid absolute value encoder position detection method according to any one of the above.

The beneficial effects of the invention are as follows:

1. The invention relates to a hybrid absolute value encoder position detection method, which is characterized in that a plurality of sensors are used for collecting output data of a magnetic encoder and an optical encoder, the data of each sensor are preprocessed, the weight coefficient of each sensor is calculated according to the characteristics and the data quality of the sensors, wherein the output data collected by each sensor are acquired for a plurality of times, the error parameter of the data collected by each sensor is calculated based on the output data, the error factor of each sensor is calculated based on the error parameter, the weight coefficient of each sensor is determined according to the size of the error factor of each sensor, the weight coefficient of each sensor is correspondingly distributed to the output data of each sensor after the weight coefficient of each sensor is determined, and the fusion result of the weight average method is output.

2. According to the hybrid absolute value encoder position detection method, output data acquired by a plurality of sensors are obtained at different acquisition temperatures, a final data set is calculated and obtained, a change line graph is constructed based on the obtained unit average temperature and the data set, the relation between the acquisition temperature and the data contained in the data set is judged based on the change line graph, signals are generated, two acquisition temperatures and corresponding data sets are randomly selected based on the generated growth signals, the data change value at the unit temperature is calculated and obtained based on the acquisition temperatures and the corresponding data sets, the data sets required to be acquired at different later temperatures are directly predicted based on the obtained data change value at the unit temperature, a function equation of the sub-broken line is obtained based on the normal signals, each item of data in the data sets required to be acquired at different later temperatures is directly predicted based on the sub-broken line segment equation, and each item of data after prediction is recombined to generate the data set.

Drawings

The invention is further described below with reference to the accompanying drawings.

FIG. 1 is a flow chart of a method according to a first embodiment of the invention;

FIG. 2 is a flow chart of a method according to a second embodiment of the invention;

FIG. 3 is a variation line diagram in a second embodiment of the invention;

fig. 4 is a system block diagram of the present invention.

Detailed Description

The invention is further described in connection with the following detailed description in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.

Example 1

As shown in fig. 1, an embodiment of the present invention provides a hybrid absolute value encoder position detection method, including:

Step one: collecting output data of the magnetic encoder and the optical encoder by using a plurality of sensors;

Step two: preprocessing the data of each sensor, including removing noise, calibration errors and the like;

step three: calculating a weight coefficient of each sensor according to the characteristics and the data quality of each sensor;

The method comprises the steps of acquiring output data acquired by each sensor for a plurality of times, calculating error parameters of the data acquired by each sensor based on the output data, calculating error factors of each sensor based on the error parameters, and determining weight coefficients of each sensor according to the size of the error factors of each sensor;

the output data acquired by each sensor is obtained by measuring the same physical quantity of the same sensor under the same time and environment conditions;

step four: after the weight coefficient of each sensor is determined, correspondingly distributing the weight coefficient of each sensor to the output data collected by the sensor, and carrying out fusion processing on the output data collected by each sensor by using a weighted average method;

Step five: outputting a fusion result of a weighted average method;

it should be noted that, the result may be a single value, or may be a fused data set, where the fused result may represent information such as the position of the object;

The output data comprises absolute position information acquired by the magnetic encoder, position resolution information acquired by the optical encoder and an incremental pulse signal output by the encoder;

The absolute position information includes: the angle value, the length value, the displacement value and the like, the increment pulse signals are used for calculating the direction and the speed of the movement of the object, the position resolution information is expressed by the pulse number or the line number generated by each rotation of the encoder, and the position of the rotating shaft can be accurately determined by counting the pulse signals;

The error parameters comprise deviation and random errors, and the error parameters of the data acquired by each sensor are calculated based on the output data, and the process is as follows:

Processing the output data acquired by each sensor for multiple times, and particularly dividing the output data acquired by the sensors into multiple groups according to the types of physical quantities;

Calculating the average value and standard deviation of each group of data, respectively summing the average value and the standard deviation of all groups of data to obtain the average value and the standard deviation of the data collected by the sensor, taking the standard deviation as the random error of the sensor, and marking the random error as Ai;

Obtaining a nominal value of the sensor, carrying out difference processing on the nominal value of the sensor and an average value of data acquired by the sensor, carrying out absolute value processing on the difference value, obtaining a deviation of the sensor, and marking the deviation as B i;

it should be noted that, the nominal value of the sensor can be queried through the specification of the sensor;

Calculating an error factor of each sensor based on the error parameters, wherein the error factor comprises the following steps:

And carrying out data processing on the obtained random error Ai of the sensor number and the deviation B i of the sensor, and substituting the random error Ai into a formula: Obtaining an error factor Ci of the sensor, wherein gamma is a preset natural constant, the gamma value is 0.25896, A logarithmic function of the base e;

determining a weight coefficient of each sensor according to the size of each sensor error factor, wherein the weight coefficient comprises the following steps:

Obtaining an error factor of each sensor, and marking the error factor of each sensor as x1, x2 and x3 … … xi, wherein x represents the error factor, and i represents the sensor number;

the error factors of all the sensors are summed, and the method specifically comprises the following steps: s=x1+x2+x3+ … … +xi, where S represents the sum of all sensor error factors;

The ratio of the error factor and the sum of error factors S of each sensor is processed, the result is processed by reciprocal to obtain the weight coefficient of each sensor, for example, the description is given for a specific sensor, the ratio of x1 and the sum of error factors S is processed to obtain the result as And then carrying out reciprocal processing on the result of the ratio processing, specifically: obtaining a weight coefficient of the sensor;

It should be noted that, after the ratio processing is performed on the sum of the error factors of the sensors and the error factors of the sensors, reciprocal processing is performed, because the error of one sensor is larger, the weight coefficient of the sensor is smaller;

The output data collected by each sensor and the corresponding weight coefficient are weighted and averaged to obtain a fusion result, and specifically, the data such as angle values, length values, displacement values and the like contained in the output data collected by all the sensors are marked, for example:

the first sensor contains data labeled: a1, b1, c1 … … z1;

the second sensor contains data labeled: a2, b2, c2 … … z2;

The third sensor contains data labeled: a3, b3, c3 … … z3;

……

The ith sensor contains data labeled: ai. b i, c i … … z i;

multiplying the data contained in each sensor by the weight coefficient to obtain processed sensor data; the method comprises the following steps:

The data contained in the first sensor are subjected to product processing:

the data contained in the second sensor are subjected to product processing:

the data contained in the third sensor are subjected to product processing:

……

the data contained in the ith sensor is subjected to product processing:

Correspondingly summing the processed sensor data, and taking an average value:

……

integrating the obtained mean values Zk1, zk2 and Zk3 … … Zk i into a dataset, and taking the dataset as an output result, wherein the mean values Zk1, zk2 and Zk3 … … Zki are expressed as the data mean value of the ith sensor;

The working principle of the embodiment of the invention is as follows: the method comprises the steps of acquiring output data of a magnetic encoder and an optical encoder by utilizing a plurality of sensors, preprocessing the data of each sensor, calculating a weight coefficient of each sensor according to the characteristics and the data quality of the sensors, acquiring the output data acquired by each sensor for a plurality of times, calculating error parameters of the data acquired by each sensor based on the output data, calculating error factors of each sensor based on the error parameters, determining the weight coefficient of each sensor according to the size of the error factors of each sensor, correspondingly distributing the weight coefficient of each sensor to the output data of each sensor after determining the weight coefficient of each sensor, carrying out fusion processing on the output data acquired by each sensor by using a weighted average method, and outputting the fusion result of the weighted average method.

Example two

Based on the first embodiment, as shown in fig. 2, an embodiment of the present invention provides a hybrid absolute encoder position detection method, including:

step one: obtaining output data collected by a plurality of sensors at different collection temperatures, and calculating to obtain a final data set;

it should be noted that, the temperature difference between different collection temperatures is the same, for example, the first collection temperature is 10 ℃, the second collection temperature is 15 ℃, and the third collection temperature is 20 ℃; the acquisition temperature is the unit average temperature of the acquisition environment of the sensor in the acquisition period, wherein the acquisition process of the unit average temperature is as follows:

Obtaining the highest temperature and the lowest temperature in the acquisition period, summing the highest temperature and the lowest temperature, and carrying out ratio processing on the summation result and the acquisition period duration to obtain the unit average temperature;

step two: constructing a change line graph based on the obtained unit average temperature and the data set, determining the relation between the acquisition temperature and the data contained in the data set based on the change line graph, and generating a signal, wherein in the graph, an X axis represents the acquisition temperature, and a Y axis represents the data value in the corresponding data set, as shown in fig. 3;

The data points in the marked data set are connected in a straight line, a change broken line is obtained, the slopes of all the sub broken lines contained in the change broken line are calculated, and the slopes of all the sub broken lines are marked as follows: k1, k2, k3 … … kz, wherein kz represents the slope of the z-th sub-polyline, and z represents the sub-polyline number;

It should be noted that, the data type represented by each change broken line is different, and the sub broken line is a connection line between every two data points in the data set;

comparing the slopes of all the sub-folding lines, wherein the specific comparison process is as follows:

If k1=k2=k3= … … =kz, the data set contains data which increases regularly with the regular increase of the acquisition temperature, and the data set is in a forward relation to generate an increase signal;

If no obvious equality relation exists among k1, k2 and k3 … … kz, the positive relation exists between the acquisition temperature and the data set, and a normal signal is generated;

Step three, based on the generated growth signals, specifically, randomly selecting two acquisition temperatures and corresponding data sets, calculating to obtain data change values at unit temperatures based on the acquisition temperatures and the corresponding data sets, and directly predicting the data sets required to be obtained at different temperatures based on the obtained data change values at the unit temperatures;

specifically, performing difference processing on the two selected acquisition temperatures to obtain an acquisition temperature difference;

Carrying out difference processing on each item of data contained in the two data sets to obtain each item of data difference, and carrying out ratio processing on each item of data difference and the acquired temperature difference to obtain a data change value at unit temperature, wherein the data change value at unit temperature=the data difference/the acquired temperature difference;

Step four: based on the normal signal, obtaining a function equation of the sub-polyline according to the obtained slope of the sub-polyline: y=kz x, where kz represents the slope of the z-th sub-polyline, z represents the number of the sub-polyline, each item of data in the data set required to be acquired at different temperatures later is directly predicted based on a sub-polyline equation, each item of data after prediction is re-integrated to generate a data set, specifically, each item of data at the acquisition temperature can be obtained by substituting the later acquisition temperature into a function equation, and each item of data is integrated to form the data set;

It should be noted that, a plurality of sub-folding lines are corresponding between one temperature interval, and each sub-folding line corresponds to a specific type of data in the data set, and the data can be one of Zk1, zk2 and Zk3 … … Zki in the data set;

The working principle of the embodiment of the invention is as follows: the method comprises the steps of obtaining output data collected by a plurality of sensors at different collected temperatures, calculating to obtain a final data set, constructing a change line graph based on the obtained unit average temperature and the data set, judging the relation between the collected temperatures and data contained in the data set based on the change line graph, generating signals, randomly selecting two collected temperatures and corresponding data sets based on generated growth signals, calculating to obtain data change values at the unit temperature based on the collected temperatures and the corresponding data sets, directly predicting the data sets required to be obtained at different temperatures later based on the obtained data change values at the unit temperature, directly predicting each item of data in the data sets required to be obtained at different temperatures based on normal signals according to the obtained slope of the sub-broken line, and re-integrating each item of predicted data to generate the data set based on the sub-broken line segment equation.

Example III

The embodiment of the invention provides a hybrid absolute value encoder, which is prepared by the position detection method of the hybrid absolute value encoder according to the first embodiment or the second embodiment, and can save production cost, detect object positions at different temperatures and improve detection efficiency and accuracy;

Example IV

As shown in fig. 4, an embodiment of the present invention provides a hybrid absolute value encoder position detection system, including:

the data acquisition module is used for acquiring output data of the magnetic encoder and the optical encoder;

The data preprocessing module is used for preprocessing the data of each sensor

The weight coefficient calculation module is used for calculating the weight coefficient of each sensor according to the characteristics and the data quality of each sensor;

The fusion processing module is used for correspondingly distributing the weight coefficient of each sensor to the output data acquired by the sensor after determining the weight coefficient of each sensor, and carrying out fusion processing on the output data acquired by each sensor by applying a weighted average method;

The output module is used for: outputting a fusion result of a weighted average method;

The acquisition module is used for acquiring output data acquired by the plurality of sensors at different acquisition temperatures and calculating to acquire a final data set;

The construction module is used for constructing a change line graph according to the obtained unit average temperature and the data set;

the judging module is used for judging the relation between the acquisition temperature and the data contained in the data set based on the change line graph and generating signals, wherein the signals comprise normal signals and growth signals;

The change value calculation module is used for calculating and obtaining a data change value of the unit temperature system according to the acquired temperature and the corresponding data set;

The prediction module is used for directly predicting the data sets required to be acquired at different temperatures based on the obtained data change values at the unit temperatures and also used for directly predicting various data in the data sets required to be acquired at different temperatures;

and the integration module is used for integrating various data in the data set directly predicted at different temperatures to form a data set.

The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (8)

1. A hybrid absolute value encoder position detection method is characterized in that: comprising the following steps:

Step one: collecting output data of the magnetic encoder and the optical encoder by using a plurality of sensors;

Step two: preprocessing the data of each sensor;

step three: calculating a weight coefficient of each sensor according to the characteristics and the data quality of the sensor;

The method comprises the steps of acquiring output data acquired by each sensor for a plurality of times, calculating error parameters of the data acquired by each sensor based on the output data, calculating error factors of each sensor based on the error parameters, and determining weight coefficients of each sensor according to the size of the error factors of each sensor;

step four: after the weight coefficient of each sensor is determined, correspondingly distributing the weight coefficient of each sensor to the output data collected by the sensor, and carrying out fusion processing on the output data collected by each sensor by using a weighted average method;

Step five: outputting a fusion result of a weighted average method;

step six: obtaining output data collected by a plurality of sensors at different collection temperatures, and calculating to obtain a final data set;

Step seven: constructing a change line graph based on the obtained unit average temperature and the data set, and judging the relation between the acquisition temperature and the data contained in the data set based on the change line graph;

Step eight: based on the generated growth signals, randomly selecting two acquisition temperatures and corresponding data sets, calculating to obtain a data change value at unit temperature, and directly predicting the data sets required to be acquired at different temperatures based on the obtained data change value at the unit temperature;

Step nine: based on the normal signal, obtaining a function equation of the sub-polyline according to the slope of the obtained sub-polyline, directly predicting each item of data in the data set required to be obtained at different temperatures based on the sub-polyline equation, and re-integrating each item of predicted data to generate the data set.

2. The hybrid absolute value encoder position detection method of claim 1, wherein: the error parameters comprise deviation and random errors, and the error parameters of the data acquired by each sensor are calculated based on the output data, and the process is as follows:

Dividing output data collected by a sensor into a plurality of groups according to the types of physical quantities;

and calculating the average value and the standard deviation of each group of data, respectively summing the average value and the standard deviation of all groups of data to obtain the average value and the standard deviation of the data acquired by the sensor, and taking the standard deviation as the random error of the sensor data.

3. The hybrid absolute value encoder position detection method of claim 2, wherein: the process of obtaining the deviation is: and obtaining a nominal value of the sensor, carrying out difference processing on the nominal value of the sensor and an average value of data acquired by the sensor, and carrying out absolute value processing on the difference value to obtain a deviation of the sensor.

4. A hybrid absolute value encoder position detection method according to claim 3, wherein: and based on the error factors, carrying out summation processing on the error factors of all the sensors to obtain a sum of the error factors, carrying out ratio processing on the sum of the error factors of each sensor and the error factors, and carrying out reciprocal processing on the result of the ratio processing to obtain a weight coefficient of each sensor.

5. The hybrid absolute value encoder position detection method of claim 1, wherein: and carrying out fusion processing on the output data acquired by each sensor, carrying out product processing on the data contained by each sensor and the weight coefficient to obtain processed sensor data, carrying out corresponding summation on the processed sensor data, taking an average value, and integrating the obtained average value into a data set.

6. The hybrid absolute value encoder position detection method of claim 1, wherein: the acquisition temperature is the unit average temperature of the acquisition environment of the sensor in the acquisition period, wherein the acquisition process of the unit average temperature is as follows: obtaining the highest temperature and the lowest temperature in the acquisition period, summing the highest temperature and the lowest temperature, and carrying out ratio processing on the summation result and the acquisition period duration to obtain the unit average temperature;

Based on the obtained unit average temperature and the data set, a change line graph is constructed, and based on the change line graph, the relation between the acquisition temperature and the data contained in the data set is judged, specifically: calculating the slopes of all the sub-folding lines included in the change folding line, if the slopes of all the sub-folding lines are equal, indicating that each data included in the data set is regularly increased along with the regular increase of the acquisition temperature, and generating an increase signal in a positive relation;

If no obvious equal relation exists between slopes of the sub-broken lines, the positive relation exists between the acquisition temperature and the data set, and a normal signal is generated.

7. The hybrid absolute value encoder position detection method of claim 1, wherein: calculating to obtain a data change value of a unit temperature system, wherein the process comprises the following steps: performing difference processing on the two selected acquisition temperatures to obtain an acquisition temperature difference;

and carrying out difference processing on each item of data contained in the two data sets to obtain each item of data difference, and carrying out ratio processing on each item of data difference and the acquired temperature difference to obtain a data change value at unit temperature.

8. A hybrid absolute value encoder, characterized by: the hybrid absolute value encoder is manufactured according to a hybrid absolute value encoder position detection method according to any one of claims 1 to 7.