CN113514076A

CN113514076A - Data processing method, device, equipment and storage medium

Info

Publication number: CN113514076A
Application number: CN202010273948.XA
Authority: CN
Inventors: 续立军; 林晨; 赵远东; 李名杨
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2020-04-09
Filing date: 2020-04-09
Publication date: 2021-10-19
Anticipated expiration: 2040-04-09
Also published as: CN113514076B

Abstract

The embodiment of the application provides a data processing method, a data processing device, data processing equipment and a storage medium, wherein the temperature compensation is performed on IMU data output by an IMU by using the external temperature of the IMU and the internal temperature of the IMU, so that the measurement precision of the IMU is improved.

Description

Data processing method, device, equipment and storage medium

Technical Field

The present application relates to the field of electronic technologies, and in particular, to a data processing method, apparatus, device, and storage medium.

Background

An Inertial Measurement Unit (IMU) is a sensor capable of providing navigation information, and is widely applied to the fields of aerospace, vehicles, ships, robots, warehousing, logistics, navigation, and the like.

In practical applications, the IMU is susceptible to temperature, thereby reducing the measurement accuracy of the IMU.

Disclosure of Invention

In view of this, embodiments of the present application provide a data processing method, an apparatus, a device, and a storage medium, which can improve the measurement accuracy of an IMU.

The embodiment of the application mainly provides the following technical scheme:

in a first aspect, an embodiment of the present application provides a data processing method, including: acquiring a first temperature outside the inertial measurement unit IMU and a second temperature inside the IMU; and according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU so as to correct a measurement error caused by temperature change.

In one illustrative example, the temperature compensating the IMU data output by the IMU according to the first and second temperatures includes: calculating a first error parameter value corresponding to the first temperature and the second temperature according to a fitting curve which is generated in advance and used for representing the relation between the temperature outside the IMU and the temperature inside the IMU and the error parameter; and according to the calculated first error parameter value, carrying out temperature compensation on IMU data output by the IMU.

In one illustrative example, prior to said acquiring a first temperature external to the inertial measurement unit, IMU, and a second temperature internal to the IMU, the method further comprises: calibrating the IMU to obtain multiple groups of sample data, wherein each group of sample data comprises: a first sample temperature external to the IMU, a second sample temperature internal to the IMU, and a sample error parameter value of the IMU corresponding to the first sample temperature and the second sample temperature; and performing curve fitting according to the multiple groups of sample data to generate the fitting curve. .

In an exemplary instance, said performing a curve fit according to the plurality of sets of sample data, generating the fitted curve, includes: and respectively processing each group of sample data as follows: calculating the derivative of the second sample temperature in the group of sample data to the time to obtain a first temperature change rate value corresponding to the group of sample data; performing curve fitting on the multiple groups of sample data and the calculated multiple first temperature change rate values to generate a first fitting curve for representing the relationship among the temperature outside the IMU, the temperature inside the IMU and the temperature change rate and the intermediate variable, and a second fitting curve for representing the relationship between the intermediate variable and the error parameter; wherein the fitting curve comprises: the first fitted curve and the second fitted curve.

In one illustrative example, the calculating a first error parameter value corresponding to the first temperature and the second temperature includes: calculating the derivative of the second temperature to the time to obtain a second temperature change rate value; calculating intermediate variable values corresponding to the first temperature, the second temperature and the second temperature change rate value according to the first fitted curve; calculating a second error parameter value corresponding to the intermediate variable value according to the second fitting curve; wherein the calculated second error parameter value is the first error parameter value.

In an exemplary instance, said performing a curve fit according to the plurality of sets of sample data, generating the fitted curve, includes: and respectively processing each group of sample data as follows: calculating a third sample temperature corresponding to the set of sample data according to the first sample temperature and the second sample temperature in the set of sample data; and performing curve fitting on the plurality of sample error parameter values in the plurality of groups of sample data and the plurality of calculated third sample temperatures to generate the fitting curve.

In one illustrative example, the calculating a first error parameter value corresponding to the first temperature and the second temperature includes: calculating a third temperature according to the first temperature and the second temperature; calculating a third error parameter value corresponding to a third temperature according to the fitted curve; wherein the calculated third error parameter value is the first error parameter value.

In one illustrative example, said calculating a third temperature from said first temperature and said second temperature comprises: weighting the first temperature and the second temperature to obtain a third temperature; or calculating a derivative of the second temperature to obtain a third temperature change rate value; and weighting the first temperature, the second temperature and the third temperature change rate value to obtain the third temperature.

In one illustrative example, after the temperature compensating the IMU data output by the IMU according to the first and second temperatures, the method further comprises: and displaying the temperature compensated IMU data.

In a second aspect, an embodiment of the present application provides a computer-readable storage medium, including: a stored program, wherein the electronic device where the storage medium is located is controlled to execute the steps of any of the data processing methods described above when the program runs.

In a third aspect, an embodiment of the present application provides an electronic device, including: at least one processor; and at least one memory, bus connected with the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to call program instructions in the memory to perform the steps of any of the data processing methods described above.

In a fourth aspect, an embodiment of the present application provides a data processing apparatus, including: the temperature measurement device comprises a shell, and a processor, a first temperature sensor and an Inertial Measurement Unit (IMU) which are arranged in the shell; wherein,

the first temperature sensor is used for acquiring a first temperature outside the IMU; outputting the first temperature to a processor;

the IMU includes: the temperature sensor comprises a shell and a second temperature sensor arranged in the shell; the second temperature sensor is used for acquiring a second temperature inside the IMU; outputting the second temperature to the processor;

and the processor is used for carrying out temperature compensation on the IMU data output by the IMU according to the first temperature and the second temperature so as to correct the measurement error caused by temperature change.

In an exemplary example, the first temperature sensor is disposed at a position away from a position at which the heat generation source is disposed within the housing.

In an exemplary embodiment, a thermal insulation layer is disposed between the first temperature sensor and an inner wall of the housing.

In one illustrative example, the housing is a closed structure.

In one illustrative example, at least one of the first temperature sensor and the second temperature sensor is a target temperature sensor, and the target temperature sensor is provided in plural number.

In an exemplary embodiment, the target sensors are disposed at uniformly distributed or symmetrically distributed positions.

In a fifth aspect, an embodiment of the present application provides a data processing method, including: acquiring a first temperature outside an Inertial Measurement Unit (IMU) and a second temperature inside the IMU, wherein the IMU is used for acquiring IMU data of a walker; according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation; determining the number of the moving steps of the walker according to the IMU data after temperature compensation; and displaying the number of the motion steps of the walker.

In one illustrative example, the determining the number of steps the walker walks based on the temperature compensated IMU data includes: calculating the newly added steps of the walker according to the IMU data after the temperature compensation; acquiring the historical step number of the walker in a preset time period; and adding the historical step number and the newly added step number to obtain the exercise step number of the walker.

In a sixth aspect, an embodiment of the present application provides a data processing method, including: acquiring a first temperature outside an Inertial Measurement Unit (IMU) and a second temperature inside the IMU, wherein the IMU is used for acquiring IMU data of a first object, and the first object is one of an unmanned aerial vehicle, a robot, a vehicle and a pedestrian; according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation; determining a motion trail of the first object according to the IMU data after temperature compensation and pre-stored initial position information; and displaying the motion trail in the electronic map layer.

In a seventh aspect, an embodiment of the present application provides a data processing method, including: acquiring a first temperature outside an Inertial Measurement Unit (IMU) and a second temperature inside the IMU, wherein the IMU is used for acquiring IMU data of a second object, and the second object is one of an unmanned aerial vehicle, a robot, a vehicle and a head-mounted device; according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation; acquiring other measurement data, wherein the other measurement data is one or more of GPS data, radar data, magnetometer data and image data of a second object measured by other sensors; and performing data fusion on the IMU data subjected to temperature compensation and the other measurement data, and determining the pose information of the second object, wherein the pose information of the second object comprises one or more of speed information, position information, attitude angle and distance information.

In an eighth aspect, an embodiment of the present application provides a data processing method, including: acquiring a first temperature outside an Inertial Measurement Unit (IMU) and a second temperature inside the IMU, wherein the IMU is used for acquiring IMU data of a building; according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation; determining whether the building has potential safety hazards or not according to the IMU data after temperature compensation; and if the potential safety hazard of the building is determined, outputting preset warning information for warning the potential safety hazard of the building.

In one illustrative example, the determining whether the building has a potential safety hazard based on the temperature compensated IMU data includes: performing integral operation on the temperature compensated IMU data to obtain attitude information of the building, wherein the attitude information of the building comprises one or more of speed information and attitude angle; and determining whether the building has potential safety hazards or not based on the attitude information of the building.

The IMU data output by the IMU are subjected to temperature compensation by utilizing the temperature outside the IMU and the temperature inside the IMU, and the measurement precision of the IMU is improved.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. Other advantages of the present application may be realized and attained by the instrumentalities and combinations particularly pointed out in the specification and the drawings.

Drawings

The accompanying drawings are included to provide an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples serve to explain the principles of the disclosure and not to limit the disclosure.

FIG. 1 is a schematic flow chart diagram illustrating a first embodiment of a data processing method according to the present application;

FIG. 2A is a schematic diagram of a first embodiment of a data processing apparatus according to the present application;

FIG. 2B is a schematic diagram of a data processing apparatus according to a second embodiment of the present application;

FIG. 3 is a schematic structural diagram of an electronic device according to the present application;

FIG. 4 is a schematic flow chart diagram illustrating a data processing method according to a second embodiment of the present application;

FIG. 5 is a schematic flow chart diagram illustrating a data processing method according to a third embodiment of the present application;

FIG. 6 is a schematic flow chart diagram illustrating a fourth embodiment of the data processing method of the present application;

fig. 7 is a flowchart illustrating a fifth embodiment of the data processing method of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

Fig. 1 is a schematic flowchart of a first embodiment of the data processing method of the present application, as shown in fig. 1, the method may include:

step 101: a first temperature external to the IMU and a second temperature internal to the IMU are obtained.

Step 102: and performing temperature compensation on IMU data output by the IMU according to the first temperature and the second temperature.

Here, the first temperature external to the IMU may refer to a temperature of an external environment in which the IMU is located; the second temperature internal to the IMU may refer to a temperature of an environment internal to the IMU in which the sensor internal to the IMU is located.

It should be understood that temperature compensating the IMU data output by the IMU based on the first and second temperatures refers to: in order to weaken the influence of the ambient temperature on the performance of the IMU, the temperature compensation technical measure of the sensor is realized according to the first temperature and the second temperature, and the sensor can correct the measurement error of the IMU caused by temperature change.

The inventor of the application finds out in the process of realizing the application that: in practical application, the accuracy of the measurement data output by the IMU is greatly affected by the environmental temperature change, and the positions of the sensors such as the gyroscope and the accelerometer inside the IMU and the temperature sensor inside the IMU cannot be strictly superposed, so that the temperature measured by the temperature sensor inside the IMU, namely the second temperature inside the IMU, and the actual real environmental temperature of the IMU are greatly different to a certain extent. Particularly, when the external environment temperature of the IMU has a large temperature rise or decrease rate, the temperature distribution inside the IMU also changes continuously and drastically, and at this time, the influence of the environmental temperature change on the IMU is very large. Accordingly, the inventors of the present application have innovatively proposed temperature compensation of the IMU by a combination of a first temperature external to the IMU and a second temperature internal to the IMU to comprehensively correct for measurement errors due to temperature variations. Therefore, the influence of the environmental temperature change on the IMU can be effectively overcome, and the measurement precision of the IMU is improved. Moreover, the temperature hysteresis phenomenon caused by temperature compensation of IMU data output by the IMU only by directly using temperature data measured by a temperature sensor inside the IMU can be overcome.

In an exemplary embodiment, the step 101 may include: acquiring a first temperature through a first temperature sensor outside the IMU; a second temperature is acquired by a second temperature sensor within the IMU.

Step 102 in the present application is described in detail below with reference to specific examples.

In an exemplary embodiment, the step 102 can include the following steps 1021 ~ 1022:

step 1021: calculating a first error parameter value corresponding to the first temperature and the second temperature according to a fitting curve which is generated in advance and used for representing the relationship between the temperature outside the IMU and the temperature inside the IMU and the error parameter;

step 1022: and according to the calculated first error parameter value, carrying out temperature compensation on IMU data output by the IMU.

First, step 1021 is described in detail with specific examples.

In an exemplary embodiment, before the step 101, the method can further include the following steps 103-104:

step 103: calibrating the IMU to obtain multiple groups of sample data, wherein each group of sample data comprises: a first sample temperature external to the IMU, a second sample temperature internal to the IMU, and a sample error parameter value of the IMU corresponding to the first sample temperature and the second sample temperature;

step 104: and performing curve fitting according to the multiple groups of sample data to generate a fitting curve.

In this way, a fitted curve is obtained that represents the relationship between the temperature outside the IMU, the temperature inside the IMU and the error parameters. So, because the model that the ambient temperature that obtained is combined by the outside temperature of IMU, the inside temperature of IMU formed of the fitting curve who obtains, so, when coming the IMU data to IMU output according to the fitting curve who obtains and carrying out temperature compensation, just can overcome IMU's temperature hysteresis phenomenon to a certain extent, reduce the influence of ambient temperature change to IMU performance, promote IMU's performance to, promote IMU's measurement accuracy.

In the specific implementation process, according to different processing methods for multiple sets of sample data, the step 104 may exist but is not limited to include the following two exemplary embodiments:

in a first exemplary embodiment, the step 104 may include:

firstly, each group of sample data is respectively processed by one of the following processes: calculating the derivative of the second sample temperature in the group of sample data to the time to obtain a first temperature change rate value corresponding to the group of sample data; or calculating the derivative of the first sample temperature in the set of sample data to time to obtain a first temperature change rate value corresponding to the set of sample data; or, performing weighting processing on the derivative of the first sample temperature with respect to time and the derivative of the second sample temperature with respect to time to obtain a first temperature change rate value corresponding to the set of sample data.

Next, after obtaining a plurality of first temperature change rate values, curve fitting is performed on the plurality of sets of sample data and the calculated plurality of first temperature change rate values, a first fitted curve representing a relationship between the temperature outside the IMU, the temperature inside the IMU, and the temperature change rate and the intermediate variable is generated, and a second fitted curve representing a relationship between the intermediate variable and the error parameter is generated. Here, the fitting curve generated by curve fitting from a plurality of sets of sample data includes: a first fitted curve and a second fitted curve.

Therefore, the first fitting curve is a model formed according to the fitting relation between the environmental temperature and the intermediate variable obtained from the temperature outside the IMU, the temperature inside the IMU and the temperature change rate, and the second fitting curve is a model formed according to the fitting relation between the intermediate variable and the error parameter, so that the obtained fitting curve is a model formed according to the fitting relation between the environmental temperature and the error parameter obtained from the temperature outside the IMU, the temperature inside the IMU and the temperature change rate, and when the IMU data output by the IMU is subjected to temperature compensation according to the obtained fitting curve, the temperature hysteresis phenomenon of the IMU can be overcome more effectively, the influence of the environmental temperature change on the performance of the IMU is reduced, the performance of the IMU is improved, and therefore the measurement accuracy of the IMU can be greatly improved.

Accordingly, in the first exemplary embodiment, the step 1021 may include:

first, a second rate of temperature change value is obtained according to one of the following processes: calculating the derivative of the first temperature to the time to obtain a second temperature change rate value; or calculating the derivative of the second temperature to the time to obtain a second temperature change rate value; or, the derivative of the first temperature with respect to time and the derivative of the second temperature with respect to time are weighted to obtain a second temperature change rate value.

Next, after obtaining the second rate of temperature change value, intermediate variable values corresponding to the first temperature, the second temperature, and the second rate of temperature change value are calculated from the first fitted curve.

And finally, calculating a second error parameter value corresponding to the intermediate variable value according to the second fitting curve. Here, the calculated second error parameter value is the first error parameter value.

The manner in which the second rate of temperature change value is obtained before the temperature compensation is performed is the same as the manner in which the first rate of temperature change value is obtained in the process of generating the fitting curve. For example, only the second sample temperature is used to calculate the first rate of temperature change value, and correspondingly, only the second temperature is used to calculate the second rate of temperature change value.

In a second illustrative example, the step 104 may include:

firstly, each group of sample data is respectively processed as follows: and calculating a third sample temperature corresponding to the set of sample data according to the first sample temperature and the second sample temperature in the set of sample data.

And then, performing curve fitting on the plurality of sample error parameter values in the plurality of groups of sample data and the plurality of calculated third sample temperatures to generate a fitting curve.

Accordingly, in a second exemplary embodiment, the step 1021 may include: calculating a third temperature according to the first temperature and the second temperature; and calculating a third error parameter value corresponding to the third temperature according to the fitting curve. Here, the calculated third error parameter value is the first error parameter value.

In an exemplary instance, calculating a third sample temperature corresponding to the set of sample data according to the first sample temperature and the second sample temperature in the set of sample data may include: and weighting the first sample temperature and the corresponding second sample temperature to obtain a corresponding third sample temperature. Or calculating a corresponding fourth temperature change rate value according to at least one of the first sample temperature and the corresponding second sample temperature; and respectively carrying out weighting processing on the first sample temperature, the second sample temperature and the fourth temperature change rate value to obtain a third sample temperature.

Accordingly, from the first temperature and the second temperature, implementations of calculating the third temperature may exist, but are not limited to, including the following two:

the first method is as follows: and weighting the first temperature and the second temperature to obtain a third temperature.

The second method comprises the following steps: first, a third rate of temperature change value is obtained according to one of the following processes: calculating the derivative of the first temperature to the time to obtain a third temperature change rate value; or calculating the derivative of the second temperature to the time to obtain a third temperature change rate value; or weighting the derivative of the first temperature with time and the derivative of the second temperature with time to obtain a third temperature change rate value. And then, weighting the first temperature, the second temperature and the third temperature change rate value to obtain a third temperature.

Second, step 1022 is described in detail with specific examples.

For example, sensors in an IMU include: gyroscopes and accelerometers are examples, then IMU output IMU data may include: the specific force measurement data and the angular velocity measurement data, and then the first error parameter values corresponding to the first temperature and the second temperature calculated by the fitting curve generated in advance, may include: zero offset error of the gyroscope, proportional coefficient error of the gyroscope, zero offset error of the accelerometer and proportional coefficient error of the accelerometer; finally, the temperature compensated IMU data may be calculated based on the acquired IMU data output by the IMU and the first error parameter value through an IMU sensor model represented by the following equations (1) to (2). Here, the calculated temperature compensated IMU data may include: temperature compensated specific force data and temperature compensated angular velocity data.

a_m＝K_aM_aa+b_aFormula (1);

ω_m＝K_gM_gω+GC_ga+b_gformula (2);

wherein, a_mSpecific force measurement data; k_aIs the scale factor error of the accelerometer; m_aIs the orthogonality error of the accelerometer; a is specific force data after temperature compensation; b_aIs the zero offset error of the accelerometer; omega_mMeasuring data for angular velocity; k_gIs the scale factor error of the gyroscope; m_gIs the orthogonality error of the gyroscope; omega is angular velocity data after temperature compensation; g is a G (gravity acceleration) sensitivity coefficient matrix; c_gIs a matrix of mounting angles between the gyroscope and the accelerometer; b_gIs the zero offset error of the gyroscope.

Here, the parameter M in the IMU sensor model described above_a、M_g、G、C_gIs a parameter that is not related to the ambient temperature of the IMU. In practical applications, the parameter M_a、M_g、G、C_gThe calibration method can be used for directly calibrating the IMU in advance through the turntable during the calibration process of the IMU. And the error parameter K in the IMU sensor model_a、K_g、b_a、b_gIs a parameter that is related to the ambient temperature of the IMU and will change as the ambient temperature of the IMU changes. The data processing method provided by the embodiment of the application can be used for calculating in real time according to the first temperature outside the IMU and the second temperature inside the IMU, which are acquired in real time, through the pre-generated fitting curve. Thus, the measurement accuracy of the IMU can be improved based on the error parameters calculated in real time.

In the following, an IMU sensor model in the present application is described in detail by taking an example in which a gyroscope in an IMU is implemented by a three-axis gyroscope and an accelerometer in the IMU is implemented by a three-axis accelerometer.

In one illustrative example, when the sensors in the IMU include: the three-axis gyroscope and the three-axis accelerometer, temperature-related parameters in the IMU sensor model can be respectively shown in the following formulas (3) to (6).

Wherein,

is the scale factor error of the triaxial accelerometer;

for three-axis accelerationScale factor error of the x-axis of the meter;

is the proportionality coefficient error of the y-axis of the tri-axial accelerometer;

is the scale factor error of the z-axis of the tri-axial accelerometer.

Wherein,

is the scale factor error of the three-axis gyroscope;

is the scale factor error of the x-axis of the three-axis gyroscope;

is the scale factor error of the y-axis of the three-axis gyroscope;

is the scale factor error of the z-axis of the three-axis gyroscope.

Wherein,

is the zero offset error of the triaxial accelerometer;

zero offset error for the x-axis of the tri-axial accelerometer;

zero offset error for the y-axis of the tri-axial accelerometer;

is the zero offset error of the z-axis of the tri-axial accelerometer.

Wherein,

zero bias error for a three-axis gyroscope;

is the zero offset error of the x-axis of the three-axis gyroscope;

is the zero offset error of the y-axis of the three-axis gyroscope;

is the zero offset error of the z-axis of the three-axis gyroscope.

Then, based on the above equations (1) to (6), the sensor models of the six-axis IMU can be obtained as shown in the following equations (7) to (8).

Wherein, a_mSpecific force measurement data;

is the scale factor error of the triaxial accelerometer; m_aIs the orthogonality error of the tri-axial accelerometer; a is after temperature compensationSpecific force data;

is the zero offset error of the triaxial accelerometer; omega_mMeasuring data for angular velocity;

is the scale factor error of the three-axis gyroscope; m_gIs the orthogonality error of the three-axis gyroscope; omega is angular velocity data after temperature compensation; g is a G sensitivity coefficient matrix; c_gIs a matrix of mounting angles between the gyroscope and the accelerometer;

is the zero offset error of the three-axis gyroscope.

The fitted curve in the present application is described in detail below with reference to specific examples.

Assuming that the gyroscope in the IMU is implemented by a three-axis gyroscope, the accelerometer in the IMU is implemented by a three-axis accelerometer, the curve fitting is implemented by a polynomial curve fitting and the order of the polynomial is third order, the error parameters include: zero bias error for a three-axis gyroscope

Scale factor error for a three-axis gyroscope

Zero offset error for tri-axial accelerometers

And scale factor error of a three-axis accelerometer

The implementation method for performing curve fitting to generate a fitting curve according to multiple groups of sample data comprises the following steps: calculating a plurality of first temperature change rate values corresponding to the plurality of groups of sample data according to a plurality of second sample temperatures in the plurality of groups of sample data; for multiple groups of sample data and multiple corresponding second groupsPerforming curve fitting on a temperature change rate value to generate a first fitted curve for representing the relation between the intermediate variable and the temperature outside the IMU, the temperature inside the IMU and the temperature change rate and a second fitted curve for representing the relation between the intermediate variable and the error parameter; and determining the first fitted curve and the second fitted curve as fitted curves.

Then, in an exemplary example, the fitted curve may be represented by the following formula (9) to formula (16). Wherein the zero offset error of the three-axis gyroscope

The corresponding first fitting curve is expressed by formula (9), and the zero offset error of the three-axis gyroscope

The corresponding second fitted curve may be represented by equation (10); scale factor error for a three-axis gyroscope

The corresponding first fitted curve can be represented by equation (11), the scale factor error of a three-axis gyroscope

The corresponding second fitted curve may be represented by equation (12); zero offset error for tri-axial accelerometers

The corresponding first fitted curve can be expressed by equation (13), the zero offset error of the tri-axial accelerometer

The corresponding second fitted curve may be represented by equation (14); scale factor error for a three-axis accelerometer

The corresponding first fitted curve can be represented by equation (15), the ratio of the three-axis accelerometerError of example coefficient

The corresponding second fitted curve may be represented by equation (16).

Wherein, T_aRepresenting a first temperature; t is_imuRepresents a second temperature;

is T_imuFirst derivative with respect to time, representing temperature changes inside the IMUA rate;

is an intermediate variable;

is the coefficient of a polynomial in the fitted curve;

zero bias error for a three-axis gyroscope;

is the scale factor error of the three-axis gyroscope;

is the zero offset error of the triaxial accelerometer;

is the scale factor error of the tri-axial accelerometer.

Further, it should be understood that, in the formula (10),

to represent

To the power of 2;

to represent

To the 3 rd power. Similarly, the formula (12), the formula (14), and the formula (16) can be understood with reference to the description of the formula (10), and will not be too much hereThe description is given.

In an exemplary embodiment, the IMU may be calibrated in advance through the transition table, and multiple sets of sample data are obtained, where each set of sample data includes: a first sample temperature external to the IMU, a second sample temperature internal to the IMU, and a sample error parameter value of the IMU corresponding to the first and second sample temperatures. Then, a plurality of sets of sample data (first sample temperature is taken as T)_aAnd the second sample temperature is taken as T_imu) By substituting the above equations (9) to (16), the coefficients in the fitting curve, i.e., the coefficients in the fitting curve can be solved

Thus, a fitted curve is obtained.

Then, in an exemplary embodiment, after the fitting curve is generated in advance, when the temperature compensation needs to be performed on the IMU data output by the IMU, the first temperature T outside the IMU acquired in real time may be used as the first temperature T_aAnd a second temperature T inside the IMU_imuAnd its corresponding rate of temperature change

And calculating corresponding error parameters in real time, namely updating the zero offset error and the scale factor error of the gyroscope of the IMU in real time and updating the zero offset error and the scale factor error of the accelerometer of the IMU in real time. Then, based on the zero offset error and the proportional coefficient error of the gyroscope and the zero offset error and the proportional coefficient error of the accelerometer which are updated in real time according to the first temperature and the second temperature, the angular velocity measurement data and the specific force measurement data output by the IMU are subjected to temperature compensation, and accurate angular velocity data after temperature compensation and specific force data after temperature compensation can be obtained. Therefore, the influence of the ambient temperature on the IMU can be eliminated, and the measurement precision of the IMU is improved.

It should be understood thatThat is, if a fitting curve is generated by a polynomial curve fitting method, then the coefficients of the polynomial in the generated fitting curve

Depending on the order of the polynomial employed for fitting. For example, if a first-order polynomial is used for fitting, the fitted curve can be expressed by the above equation (9), equation (11), equation (13), equation (15), and the following equations (17) to (20).

Wherein,

is an intermediate variable;

is the coefficient of a polynomial in the fitted curve;

zero bias error for a three-axis gyroscope;

is the scale factor error of the three-axis gyroscope;

is the zero offset error of the triaxial accelerometer;

is the scale factor error of the tri-axial accelerometer.

Of course, when curve fitting is performed, besides the first-order polynomial fitting or the third-order polynomial fitting listed above, other fitting methods may be used to implement the curve fitting, such as least square fitting, which can be determined by those skilled in the art according to practical situations, and the embodiments of the present application are not limited specifically herein.

In an exemplary example, after the step 102, the method may further include: and displaying the temperature compensated IMU data.

For example, the displaying of the temperature compensated IMU data may be, for example, broadcasting the temperature compensated IMU data in a voice manner, displaying the temperature compensated IMU data, uploading the temperature compensated IMU data to a user terminal bound to a device where the IMU is located, and the like.

According to the data processing method, the temperature of the IMU is compensated by using the temperature outside the IMU and the temperature inside the IMU, so that the measurement accuracy of the IMU is improved. In addition, the temperature hysteresis phenomenon of the IMU can be effectively overcome, the influence of the change of the ambient temperature on the IMU is reduced, the temperature compensation precision of the IMU can be improved, and the problem of low data precision caused by the IMU along with the temperature drift is effectively solved.

Based on the same inventive concept, the embodiment of the application provides a data processing device. Fig. 2A is a schematic structural diagram of a data processing apparatus according to a first embodiment of the present application, as shown in fig. 2A, the data processing apparatus may include: a housing 201, a processor 202, a first temperature sensor 203, and an IMU 204 disposed within the housing 201; wherein,

a first temperature sensor 203 for acquiring a first temperature external to the IMU 204; outputting the first temperature to the processor 202;

the IMU 204 may include: a housing 2041, a second temperature sensor 2042 disposed within the housing 2041, a gyroscope 2043, and an accelerometer 2044;

a second temperature sensor 2042 for acquiring a second temperature inside the IMU 204; outputting the second temperature to the processor 202;

and the processor 202 is configured to perform temperature compensation on the IMU data output by the IMU according to the first temperature and the second temperature, so as to correct a measurement error caused by a temperature change.

In one illustrative example, as also shown in FIG. 2A, IMU 204 may include: a gyroscope 2043 and an accelerometer 2044;

a gyroscope 2043 for collecting angular velocity measurement data; outputting angular velocity measurement data to processor 202;

an accelerometer 2044 for collecting specific force measurement data; outputting the specific force measurement data to the processor 202;

a processor 202 for calculating a first error parameter value corresponding to the first temperature and the second temperature from a pre-generated fitting curve representing a relationship between a temperature outside the IMU, a temperature inside the IMU, and the error parameter; according to the calculated first error parameter value, performing temperature compensation on the IMU data output by the IMU 204 to obtain temperature-compensated IMU data, wherein the IMU data output by the IMU 204 includes: angular velocity measurement data and specific force measurement data, and correspondingly, the IMU data after temperature compensation comprises: temperature compensated angular velocity data and temperature compensated specific force data.

In one illustrative example, the gyroscope may be, for example, a three-axis gyroscope.

In one illustrative example, the accelerometer can be, for example, a three-axis accelerometer.

In an exemplary example, at least one of the first temperature sensor and the second temperature sensor is a target temperature sensor, and the number of the target temperature sensors to be disposed may be plural. In this way, an accurate IMU temperature can be collected. Therefore, the measurement accuracy of the IMU can be improved better.

For example, a plurality of first temperature sensors may be provided within the apparatus to measure the ambient temperature within the housing, such that an accurate first temperature external to the IMU can be acquired. Furthermore, when the IMU data output by the IMU is subjected to temperature compensation based on the accurate first temperature, the measurement precision of the IMU can be better improved.

For example, a plurality of second temperature sensors may be provided within the apparatus to measure the ambient temperature within the IMU, such that an accurate second temperature within the IMU can be acquired. Furthermore, when the IMU data output by the IMU is subjected to temperature compensation based on the accurate second temperature, the measurement precision of the IMU can be better improved.

In an exemplary embodiment, at least one of the first temperature sensor and the second temperature sensor is a target temperature sensor, and the target temperature sensors are disposed at uniformly distributed or symmetrically distributed positions. In this way, a more accurate IMU temperature can be collected. Therefore, the measurement accuracy of the IMU can be improved better.

For example, when a plurality of first temperature sensors are disposed in the device, the plurality of first temperature sensors may be symmetrically distributed or uniformly distributed in the housing. For example, the plurality of first temperature sensors may be evenly distributed around the IMU, or the plurality of first temperature sensors may be symmetrically distributed on both sides of the IMU as shown in FIG. 2B, or the plurality of first sensor devices may be symmetrically distributed away from the heat generating source (e.g., power supply, etc.) centered within the housing. So, can gather the outside first temperature of more accurate IMU to, can promote IMU's measurement accuracy better.

For example, when a plurality of second temperature sensors are provided within the apparatus, the plurality of first temperature sensors may be symmetrically or uniformly distributed within the IMU. For example, the plurality of first temperature sensors may be symmetrically distributed or uniformly distributed around the gyroscope and the accelerometer, or the plurality of first sensors may be symmetrically distributed centering on the gyroscope and the accelerometer. So, can gather the inside second temperature of more accurate IMU to, can promote IMU's measurement accuracy better.

In one illustrative example, the first temperature sensor may be, for example, a thermocouple, a thermal resistor, or the like.

In one illustrative example, the temperature accuracy of the first temperature sensor may be better than 0.1 degrees Celsius (C.). So, can gather the outside first temperature of more accurate IMU to, just, can promote IMU's measurement accuracy better.

In one illustrative example, the temperature output frequency of the first temperature sensor may be greater than 1 hertz (Hz). So, can gather the outside first temperature of more accurate IMU to, just, can promote IMU's measurement accuracy better.

In an exemplary embodiment, the first temperature sensor is disposed as far away from the heat generating source (e.g., power supply, etc.) within the housing as possible. So, can gather the outside first temperature of more accurate IMU to, just, can promote IMU's measurement accuracy better.

In an exemplary embodiment, as also shown in fig. 2A, a thermal insulation layer 205 may also be disposed between the first temperature sensor 203 and the inner wall of the housing 201. So, can gather more accurate IMU outside first temperature, and then, just can promote IMU's measurement accuracy better.

In one illustrative example, the housing may be a closed structure. So, can reduce the outer air admission of data processing apparatus and disturb the temperature measurement accuracy of first temperature sensor in the casing to, can gather more accurate IMU outside first temperature, and then, just can promote IMU's measurement accuracy better.

In one illustrative example, as also shown in FIG. 2A, the processor 202 is coupled to the first temperature sensor 203 and the IMU 204 via a bus 206.

In an illustrative example, the bus may be, for example, a SPI (Serial Peripheral Interface) bus, an I2C bus, or the like.

In an illustrative example, as also shown in fig. 2A, the apparatus may further include: a communication interface 207; the processor 202 is coupled to the communication interface 207 via the bus 206 and to an external electronic device via the communication interface 207 such that the processor 202 may transmit temperature compensated IMU data to the external electronic device.

In an illustrative example, the communication interface may be, for example, an SPI interface, an I2C interface, or the like.

As can be seen from the above, in the data processing apparatus in the embodiment of the present application, the processor performs temperature compensation on the IMU data output by the IMU by using the first temperature outside the IMU acquired by the first temperature sensor and the second temperature inside the IMU acquired by the second temperature sensor, so as to improve the measurement accuracy of the IMU.

Based on the same inventive concept, the embodiment of the application provides electronic equipment. Fig. 3 is a schematic structural diagram of an electronic device according to the present application, and as shown in fig. 3, the electronic device 30 may include: at least one processor 301; and at least one memory 302, bus 303 connected to processor 301; wherein, the processor 301 and the memory 302 complete the communication with each other through the bus 303; the processor 301 is configured to call program instructions in the memory 302 to perform the steps of the data processing method in one or more of the embodiments described above.

The Processor may be implemented by a Central Processing Unit (CPU), a microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like. The Memory may include volatile Memory in a computer readable medium, Random Access Memory (RAM), and/or nonvolatile Memory such as Read Only Memory (ROM) or Flash Memory (Flash RAM), and the Memory includes at least one Memory chip.

It should be noted that, in the embodiments of the present application, if the data processing method in one or more embodiments described above is implemented in the form of a software functional module and sold or used as a standalone product, it may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof that contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present application.

Accordingly, based on the same inventive concept, embodiments of the present application further provide a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the electronic device where the storage medium is located is controlled to execute the steps of the data processing method in one or more embodiments described above.

Here, it should be noted that: the above description of the apparatus, device or computer-readable storage medium embodiments is similar to the description of the method embodiments above, with similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the apparatus, device or computer-readable storage medium of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.

Based on the same inventive concept, the embodiment of the present application further provides a data processing method, which can be applied to the following scenarios: the IMU can be arranged in a mobile terminal such as a mobile phone, a sports bracelet and a watch carried by a walker, correspondingly, the IMU is used for acquiring IMU data of the walker, and then the exercise step number of the walker can be obtained according to the IMU data after temperature compensation. Thus, the accuracy of the number of steps of the exercise can be improved.

Fig. 4 is a schematic flowchart of a data processing method according to a second embodiment of the present application, and as shown in fig. 4, the data processing method may include:

step 401: obtaining a first temperature outside the IMU and a second temperature inside the IMU;

here, the IMU is used to collect IMU data of the pedestrian.

Step 402: according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation;

step 403: determining the number of the moving steps of the walker according to the IMU data after temperature compensation;

step 404: and showing the number of the motion steps of the walker.

In one illustrative example, step 403 may comprise: calculating the newly added steps of the walker according to the IMU data after temperature compensation; acquiring the historical step number of the walker in a preset time period; and adding the historical step number and the newly added step number to obtain the exercise step number of the walker.

For example, a gait detection algorithm such as a peak detection algorithm, a short-time Fourier transform (STFT), or the like may be used to calculate the number of added steps of the pedestrian from the temperature-compensated IMU data. Therefore, the IMU data after temperature compensation is accurate, and the temperature drift error is overcome, so that the more accurate step number of the walker can be obtained.

In one illustrative example, step 404 may comprise: and when the preset trigger condition is met, displaying the movement steps of the walker in a preset display mode.

In an exemplary instance, the preset trigger condition may be, for example, that the number of exercise steps of the pedestrian exceeds a preset step number threshold, that a preset user operation indicating to show the number of exercise steps is received, that a preset instruction indicating to upload the number of exercise steps is received, or the like.

In an exemplary example, the preset display mode may be, for example, a voice mode, a display mode, a vibration mode, uploading the number of steps of the exercise to the user terminal bound to the device where the IMU is located, and the like.

For example, when the number of moving steps of the walker exceeds a preset step threshold, the mobile terminal carried by the walker can automatically vibrate and display the total number of steps. Or, when the pedestrian performs a preset user operation for indicating the showing of the number of the moving steps on the mobile terminal carried by the pedestrian, the mobile terminal may display the number of the moving steps of the pedestrian to the pedestrian in response to the preset user operation. Or, when the walker performs the uploading operation on the user terminal (i.e., the device bound to the device where the IMU is located), the user terminal may issue a preset instruction for instructing to upload the number of motion steps to the mobile terminal carried by the walker (i.e., the device where the IMU is located), and then the device where the IMU is located may respond to the preset instruction and send the number of motion steps of the walker to the device bound to the device where the IMU is located. Of course, the step 404 can be implemented in other ways besides the three exemplary examples listed above, and the embodiments of the present application are not limited in detail here.

As can be seen from the above, the data processing method according to the present application performs temperature compensation on IMU data output by the IMU using the temperature outside the IMU and the temperature inside the IMU, can obtain accurate temperature-compensated IMU data, and then calculates the number of exercise steps of a walker based on the temperature-compensated IMU data, thereby improving the accuracy of the number of exercise steps.

Based on the same inventive concept, the embodiment of the present application further provides a data processing method, which can be applied to the following scenarios: the IMU can be arranged in a mobile terminal such as a mobile phone, a sport bracelet, a watch and the like carried by an unmanned aerial vehicle, a robot, a vehicle or a walker, correspondingly, the IMU is used for acquiring IMU data of a first object, wherein the first object can be the unmanned aerial vehicle, the robot, the vehicle, the walker and the like, and then the sport track of the first object is obtained according to the IMU data after temperature compensation. Thus, the accuracy of the motion trajectory can be improved.

Fig. 5 is a schematic flowchart of a data processing method according to a third embodiment of the present application, and as shown in fig. 5, the data processing method may include:

step 501: obtaining a first temperature outside the IMU and a second temperature inside the IMU;

here, the IMU is used to acquire IMU data of the first object.

Wherein the first object may be, for example, an unmanned aerial vehicle, a robot, a vehicle, a pedestrian, or the like.

Step 502: according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation;

step 503: determining a motion track of the first object according to the IMU data after temperature compensation and pre-stored initial position information;

here, the initial position information may be, for example, a start coordinate, a posture angle, or the like.

For example, when the first object starts to move, initial position information of the first object may be measured in advance by using a Positioning module, such as a Global Positioning System (GPS) module, in a mobile terminal carried by an unmanned aerial vehicle, a robot, a vehicle, or a pedestrian, and then stored. Thus, initial position information stored in advance is obtained.

Step 504: and displaying the motion trail in the electronic map layer.

In an exemplary embodiment, step 503 may comprise: and performing integral operation on the IMU data after temperature compensation to obtain course angle change and the movement distance of the first object, and then obtaining the movement track of the first object according to the course angle change, the movement distance of the first object and pre-stored initial position information.

In another exemplary embodiment, step 503 may include: and determining the motion trail of the first object by a Pedestrian Dead Reckoning (PDR) algorithm according to the IMU data after temperature compensation and the pre-stored initial position information.

Of course, besides the two exemplary embodiments listed above, step 503 can also be implemented in other ways, and the embodiments of the present application are not limited in particular.

As can be seen from the above, the data processing method according to the present application performs temperature compensation on IMU data output by the IMU by using the temperature outside the IMU and the temperature inside the IMU, so as to obtain accurate temperature-compensated IMU data, and then determines the motion trajectory of the first object based on the temperature-compensated IMU data, so as to improve the accuracy of the motion trajectory.

Based on the same inventive concept, the embodiment of the present application further provides a data processing method, which can be applied to the following scenarios: the IMU can be arranged in an unmanned aerial vehicle, a robot, a vehicle and a head-mounted device (such as Augmented Reality (AR) equipment and Virtual Reality (VR) equipment), correspondingly, the IMU is used for collecting IMU data such as the unmanned aerial vehicle, the robot, the vehicle and the head-mounted device, and then the IMU data after temperature compensation is fused with measurement data collected by other sensor data. Therefore, the deviation can be overcome, and more accurate pose information of the second object can be obtained.

Fig. 6 is a schematic flowchart of a fourth embodiment of the data processing method of the present application, as shown in fig. 6, the method may include:

step 601: obtaining a first temperature outside the IMU and a second temperature inside the IMU;

here, the IMU is used to acquire IMU data of the second object.

Wherein, the second object is for example unmanned aerial vehicle, robot, vehicle, head mounted device etc..

Step 602: according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation;

step 603: acquiring other measurement data;

here, other sensors such as a GPS, a radar, a magnetometer, a camera, and the like may be provided in the second object in addition to the IMU.

Wherein the other measurement data is one or more of GPS data, radar data, magnetometer data, and image data of the second object measured by the other sensor.

Step 604: and performing data fusion on the IMU data subjected to temperature compensation and other measurement data to determine pose information of the second object.

The pose information includes one or more of speed information, position information, attitude angle (also called course angle) and distance information.

In an exemplary embodiment, the method of data fusion may be, for example, a kalman filter algorithm, a complementary filter algorithm, a particle filter algorithm, or the like.

In an exemplary embodiment, after accurate pose information is obtained through data fusion, the driving tracks of the unmanned aerial vehicle, the robot and the vehicle can be tracked and controlled according to the pose information.

In another exemplary embodiment, after obtaining accurate pose information through data fusion, the VR/AR image displayed by the head-mounted device may be controlled according to the pose information.

According to the data processing method, the IMU data output by the IMU are subjected to temperature compensation by utilizing the temperature outside the IMU and the temperature inside the IMU, accurate IMU data after temperature compensation can be obtained, and multi-source sensor data fusion is carried out on the IMU data after temperature compensation and other sensor data to obtain the pose information of the second object, so that the measurement error of a single sensor can be overcome, and the accuracy of the pose information is improved.

Based on the same inventive concept, the embodiment of the present application further provides a data processing method, which can be applied to the following scenarios: in practical application, before an earthquake occurs or a building collapses, the posture of the building changes, so that the IMU can be arranged in the building and correspondingly used for acquiring IMU data of the building, and then whether the building has potential safety hazards or not is conveniently determined according to the IMU data after temperature compensation so as to give an alarm in time.

Fig. 7 is a schematic flowchart of a fifth embodiment of the data processing method of the present application, and as shown in fig. 7, the method may include:

step 701: obtaining a first temperature outside the IMU and a second temperature inside the IMU;

here, the IMU is used to collect IMU data for the building.

Step 702: according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation;

step 703: determining whether potential safety hazards appear in the building or not according to the IMU data after temperature compensation;

step 704: and if the potential safety hazard of the building is determined, outputting preset alarm information for warning the potential safety hazard of the building.

In one illustrative example, step 703 may include: performing integral operation on the IMU data after temperature compensation to obtain attitude information of the building, wherein the attitude information of the building comprises one or more of speed information and attitude angle; and determining whether the potential safety hazard occurs in the building or not based on the attitude information of the building.

For example, when an earthquake occurs, the angle of the building may change, and in an exemplary embodiment, the temperature compensated IMU data may be integrated to obtain the attitude angle of the building, and when it is determined that the attitude angle of the building is greater than the preset attitude angle threshold, it may be indicated that a potential safety hazard occurs in the building, and at this time, preset alarm information may be output.

For another example, when an earthquake occurs, a building may have a speed change, and in an exemplary embodiment, the velocity information of the building may be obtained by performing an integral operation on the temperature compensated IMU data, and when it is determined that the velocity information of the building is greater than a preset velocity threshold, it may be indicated that a potential safety hazard occurs in the building, and at this time, preset alarm information may be output.

In an exemplary embodiment, the manner of outputting the preset alarm information may be one or more of the following implementation manners: sending preset alarm information to a management platform bound with the IMU, controlling sound equipment bound with the IMU and located in the building to play preset alarm audio, and controlling electric lamps bound with the IMU and located in the building to display preset light effects.

According to the data processing method, the IMU data of the building output by the IMU are subjected to temperature compensation by utilizing the temperature outside the IMU and the temperature inside the IMU, accurate IMU data after temperature compensation can be obtained, whether the potential safety hazard occurs to the building or not can be conveniently and accurately determined based on the IMU data after temperature compensation, then an alarm is timely sent out when the potential safety hazard occurs, and the safety problem of a user can be avoided to a certain extent.

The present application describes embodiments, but the description is illustrative rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.

The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive concept as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Further, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.

It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.

Claims

1. A method of data processing, comprising:

acquiring a first temperature outside the inertial measurement unit IMU and a second temperature inside the IMU;

and according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU so as to correct a measurement error caused by temperature change.

2. The data processing method of claim 1, wherein the temperature compensating the IMU data output by the IMU according to the first and second temperatures comprises:

calculating a first error parameter value corresponding to the first temperature and the second temperature according to a fitting curve which is generated in advance and used for representing the relation between the temperature outside the IMU and the temperature inside the IMU and the error parameter;

and according to the calculated first error parameter value, carrying out temperature compensation on IMU data output by the IMU.

3. The data processing method of claim 2, prior to the acquiring a first temperature external to the Inertial Measurement Unit (IMU) and a second temperature internal to the IMU, the method further comprising:

calibrating the IMU to obtain multiple groups of sample data, wherein each group of sample data comprises: a first sample temperature external to the IMU, a second sample temperature internal to the IMU, and a sample error parameter value of the IMU corresponding to the first sample temperature and the second sample temperature;

and performing curve fitting according to the multiple groups of sample data to generate the fitting curve.

4. The data processing method of claim 3, wherein said curve fitting according to the plurality of sets of sample data, generating the fitted curve, comprises:

and respectively processing each group of sample data as follows: calculating the derivative of the second sample temperature in the group of sample data to the time to obtain a first temperature change rate value corresponding to the group of sample data;

performing curve fitting on the multiple groups of sample data and the calculated multiple first temperature change rate values to generate a first fitting curve for representing the relationship among the temperature outside the IMU, the temperature inside the IMU and the temperature change rate and the intermediate variable, and a second fitting curve for representing the relationship between the intermediate variable and the error parameter; wherein the fitting curve comprises: the first fitted curve and the second fitted curve.

5. The data processing method of claim 4, wherein the calculating a first error parameter value corresponding to the first temperature and the second temperature comprises:

calculating the derivative of the second temperature to the time to obtain a second temperature change rate value;

calculating intermediate variable values corresponding to the first temperature, the second temperature and the second temperature change rate value according to the first fitted curve;

calculating a second error parameter value corresponding to the intermediate variable value according to the second fitting curve; wherein the calculated second error parameter value is the first error parameter value.

6. The data processing method of claim 3, wherein said curve fitting according to the plurality of sets of sample data, generating the fitted curve, comprises:

and respectively processing each group of sample data as follows: calculating a third sample temperature corresponding to the set of sample data according to the first sample temperature and the second sample temperature in the set of sample data;

and performing curve fitting on the plurality of sample error parameter values in the plurality of groups of sample data and the plurality of calculated third sample temperatures to generate the fitting curve.

7. The data processing method of claim 6, wherein the calculating a first error parameter value corresponding to the first temperature and the second temperature comprises:

calculating a third temperature according to the first temperature and the second temperature;

calculating a third error parameter value corresponding to a third temperature according to the fitted curve; wherein the calculated third error parameter value is the first error parameter value.

8. The data processing method of claim 7, wherein the calculating a third temperature from the first temperature and the second temperature comprises:

weighting the first temperature and the second temperature to obtain a third temperature;

or calculating a derivative of the second temperature to obtain a third temperature change rate value; and weighting the first temperature, the second temperature and the third temperature change rate value to obtain the third temperature.

9. The data processing method of any of claims 1 to 8, after the temperature compensating the IMU data output by the IMU according to the first and second temperatures, the method further comprising:

and displaying the temperature compensated IMU data.

10. A computer-readable storage medium, comprising: a stored program, wherein the electronic device in which the storage medium is located is controlled to perform the steps of the data processing method according to any one of claims 1 to 9 when the program is run.

11. An electronic device, comprising:

at least one processor;

and at least one memory, bus connected with the processor;

the processor and the memory complete mutual communication through the bus; the processor is arranged to call program instructions in the memory to perform the steps of the data processing method of any of claims 1 to 9.

12. A data processing apparatus comprising: the temperature measurement device comprises a shell, and a processor, a first temperature sensor and an Inertial Measurement Unit (IMU) which are arranged in the shell; wherein,

13. The data processing apparatus according to claim 12, wherein a position where the first temperature sensor is disposed is remote from a position where a heat generation source within the enclosure is disposed.

14. The data processing apparatus of claim 12, wherein a thermal insulation layer is disposed between the first temperature sensor and an inner wall of the enclosure.

15. The data processing apparatus of claim 12, wherein the enclosure is a closed structure.

16. The data processing apparatus according to claim 12, wherein at least one of the first temperature sensor and the second temperature sensor is a target temperature sensor, and the target temperature sensor is provided in plural number.

17. The data processing device of claim 16, wherein the target sensors are disposed at uniformly distributed or symmetrically distributed positions.

18. A method of data processing, comprising:

acquiring a first temperature outside an Inertial Measurement Unit (IMU) and a second temperature inside the IMU, wherein the IMU is used for acquiring IMU data of a walker;

according to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation;

determining the number of the moving steps of the walker according to the IMU data after temperature compensation;

and displaying the number of the motion steps of the walker.

19. The data processing method of claim 18, wherein determining the number of motion steps of the pedestrian from the temperature compensated IMU data comprises:

calculating the newly added steps of the walker according to the IMU data after the temperature compensation;

acquiring the historical step number of the walker in a preset time period;

and adding the historical step number and the newly added step number to obtain the exercise step number of the walker.

20. A method of data processing, comprising:

acquiring a first temperature outside an Inertial Measurement Unit (IMU) and a second temperature inside the IMU, wherein the IMU is used for acquiring IMU data of a first object, and the first object is one of an unmanned aerial vehicle, a robot, a vehicle and a pedestrian;

determining a motion trail of the first object according to the IMU data after temperature compensation and pre-stored initial position information;

and displaying the motion trail in the electronic map layer.

21. A method of data processing, comprising:

acquiring a first temperature outside an Inertial Measurement Unit (IMU) and a second temperature inside the IMU, wherein the IMU is used for acquiring IMU data of a second object, and the second object is one of an unmanned aerial vehicle, a robot, a vehicle and a head-mounted device;

acquiring other measurement data, wherein the other measurement data is one or more of GPS data, radar data, magnetometer data and image data of a second object measured by other sensors;

and performing data fusion on the IMU data subjected to temperature compensation and the other measurement data, and determining the pose information of the second object, wherein the pose information of the second object comprises one or more of speed information, position information, attitude angle and distance information.

22. A method of data processing, comprising:

acquiring a first temperature outside an Inertial Measurement Unit (IMU) and a second temperature inside the IMU, wherein the IMU is used for acquiring IMU data of a building;

determining whether the building has potential safety hazards or not according to the IMU data after temperature compensation;

and if the potential safety hazard of the building is determined, outputting preset warning information for warning the potential safety hazard of the building.

23. The data processing method of claim 22, wherein said determining whether a safety hazard is present in the building from the temperature compensated IMU data comprises:

performing integral operation on the temperature compensated IMU data to obtain attitude information of the building, wherein the attitude information of the building comprises one or more of speed information and attitude angle;

and determining whether the building has potential safety hazards or not based on the attitude information of the building.