CN113514076B

CN113514076B - Data processing method, device, equipment and storage medium

Info

Publication number: CN113514076B
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: 2024-05-14
Anticipated expiration: 2040-04-09
Also published as: CN113514076A

Abstract

The embodiment of the application provides a data processing method, a device, equipment and a storage medium.

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, inertial Measurement Unit) is a sensor capable of providing navigation information, and is widely used in the fields of aerospace, vehicles, ships, robots, warehousing, logistics, navigation, etc.

In practical application, the IMU is easily affected by 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, apparatus, device, and storage medium, which can improve 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, performing temperature compensation on IMU data output by the IMU so as to correct measurement errors caused by temperature changes.

In an exemplary embodiment, the temperature compensation for IMU data output by the IMU according to the first temperature and the second temperature includes: calculating a first error parameter value corresponding to the first temperature and the second temperature according to a pre-generated fitting curve for representing the relationship 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, performing temperature compensation on the IMU data output by the IMU.

In an exemplary embodiment, before the acquiring the first temperature outside the inertial measurement unit IMU and the second temperature inside the IMU, the method further comprises: and calibrating the IMU to obtain multiple groups of sample data, wherein each group of sample data comprises: a first sample temperature outside the IMU, a second sample temperature inside 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 plurality of groups of sample data to generate the fitting curve. .

In an exemplary embodiment, the performing curve fitting according to the plurality of sets of sample data, and generating the fitting curve includes: the following processing is performed on each group of sample data: calculating a derivative of a second sample temperature in the set of sample data with respect to time to obtain a first temperature rate of change value corresponding to the set of sample data; performing curve fitting on the plurality of groups of sample data and the calculated plurality of first temperature change rate values to generate a first fitting curve for representing the relationship between 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 fitted 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 with respect to 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 fitting 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 embodiment, the performing curve fitting according to the plurality of sets of sample data, and generating the fitting curve includes: the following processing is performed on each group of sample data: 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 a plurality of sample error parameter values in the plurality of groups of sample data and a 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 from 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 an exemplary embodiment, said calculating a third temperature from said first temperature and said second temperature comprises: weighting the first temperature and the second temperature to obtain the third temperature; or calculating the 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 an exemplary embodiment, after the temperature compensating the IMU data output by the IMU according to the first temperature and the second temperature, the method further includes: and displaying the IMU data after temperature compensation.

In a second aspect, embodiments of the present application provide a computer-readable storage medium comprising: a stored program, wherein the program, when executed, controls an electronic device in which the storage medium is located to execute the steps of the data processing method described in any one of the above.

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 to the processor; the processor and the memory complete communication with each other through the bus; the processor is configured to invoke program instructions in the memory to perform the steps of the data processing method of any of the above.

In a fourth aspect, an embodiment of the present application provides a data processing apparatus, including: a housing, a processor disposed within the housing, a first temperature sensor, and an inertial measurement unit IMU; wherein,

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

The IMU includes: a housing and a second temperature sensor disposed within the housing; the second temperature sensor is used for acquiring a second temperature inside the IMU; outputting a 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 measurement errors caused by temperature change.

In one illustrative example, the first temperature sensor is disposed at a location remote from a location of the heat generating source within the housing.

In one illustrative example, 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 an exemplary embodiment, 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 is a plurality.

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

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 pedestrians; according to the first temperature and the second temperature, performing temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation; determining the number of exercise steps of the pedestrian according to the temperature compensated IMU data; showing the number of athletic steps of the walker.

In one illustrative example, the determining the number of steps walked by the walker based on the temperature compensated IMU data includes: calculating the new step number of the walker according to the temperature compensated IMU data; acquiring the historical step number of the pedestrian 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 plane, a robot, a vehicle and a pedestrian; according to the first temperature and the second temperature, performing temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation; determining a motion track of the first object according to the temperature compensated IMU data 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 plane, a robot, a vehicle and head-mounted equipment; according to the first temperature and the second temperature, performing 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 carrying out data fusion on the IMU data subjected to temperature compensation and the other measurement data, and determining pose information of the second object, wherein the pose information of the second object comprises one or more of speed information, position information, pose 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, performing temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation; determining whether potential safety hazards occur to the building according to the temperature compensated IMU data; if the potential safety hazard of the building is determined, outputting preset alarm information for warning the potential safety hazard of the building.

In an exemplary embodiment, the determining whether the building has a safety hazard according to 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 angles; and determining whether the building has potential safety hazards or not based on the attitude information of the building.

According to the application, the temperature of the external part of the IMU and the temperature of the internal part of the IMU are utilized to carry out temperature compensation on the IMU data output by the IMU, so that the measurement accuracy 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 practice of the application. Other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.

Drawings

The accompanying drawings are included to provide an understanding of the principles of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the principles of the application.

FIG. 1 is a flow chart of a first embodiment of a data processing method of 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 second embodiment of a data processing apparatus according to the present application;

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

FIG. 4 is a flow chart of a second embodiment of the data processing method of the present application;

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

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

fig. 7 is a flowchart of 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 accompanying drawings in the embodiments of the present application.

FIG. 1 is a flowchart of a first embodiment of a data processing method according to the present application, as shown in FIG. 1, may include:

step 101: a first temperature outside the IMU and a second temperature inside the IMU are obtained.

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

Here, the first temperature outside the IMU may refer to a temperature of an external environment in which the IMU is located; the second temperature inside the IMU may refer to the temperature of the environment inside the IMU where the sensors inside the IMU are located.

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

The inventors found in the process of implementing the present application that: in practical application, the accuracy of the measured data output by the IMU will be greatly affected by the environmental temperature change, and the temperature measured by the temperature sensor inside the IMU, that is, the second temperature inside the IMU, is greatly different from the actual real environmental temperature of the IMU to a certain extent because the positions of the gyroscope, the accelerometer and the like inside the IMU and the temperature sensor inside the IMU cannot be strictly overlapped. Particularly, when the temperature of the external environment where the IMU is located has a larger temperature rise or temperature reduction rate, the temperature distribution inside the IMU is also changed drastically, and at this time, the influence of the environmental temperature change on the IMU is very large. The inventors therefore creatively propose to temperature compensate the IMU by combining a first temperature outside the IMU with a second temperature inside the IMU to comprehensively correct measurement errors caused by temperature variations. Therefore, the influence of the environmental temperature change on the IMU can be effectively overcome, and the measurement accuracy of the IMU is improved. Moreover, the temperature hysteresis phenomenon caused by temperature compensation of the IMU data output by the IMU by directly using the temperature data measured by the temperature sensor inside the IMU can be overcome.

In an illustrative example, the step 101 may include: acquiring a first temperature through a first temperature sensor outside the IMU; a second temperature is obtained by a second temperature sensor within the IMU.

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

In one illustrative example, the above step 102 may 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 pre-generated fitting curve 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, performing temperature compensation on IMU data output by the IMU.

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

In an illustrative example, the method may further include the following steps 103 to 104 before the step 101:

Step 103: and calibrating the IMU to obtain a plurality of groups of sample data, wherein each group of sample data comprises: a first sample temperature outside the IMU, a second sample temperature inside 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 plurality of groups of sample data to generate a fitting curve.

In this way, a fitted curve is obtained representing the relationship between the temperature outside the IMU, the temperature inside the IMU and the error parameters. Therefore, because the obtained fitting curve is a model formed by combining the temperature outside the IMU and the temperature inside the IMU with the obtained environmental temperature, 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 to a certain extent, the influence of environmental temperature change on the performance of the IMU is reduced, and the performance of the IMU is improved, so that the measurement accuracy of the IMU is improved.

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

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

First, one of the following processes is performed for each set of sample data, respectively: calculating a derivative of a second sample temperature in the set of sample data with respect to time to obtain a first temperature rate of change value corresponding to the set of sample data; or calculating a derivative of the first sample temperature in the set of sample data with respect to time to obtain a first temperature rate of change value corresponding to the set of sample data; or weighting the time derivative of the first sample temperature and the time derivative of the second sample temperature to obtain a first temperature change rate value corresponding to the set of sample data.

Next, after obtaining the plurality of first temperature change rate values, curve-fitting the plurality of sets of sample data and the plurality of calculated first temperature change rate values, generating a first fitted curve representing a relationship between a temperature outside the IMU, a temperature inside the IMU, and a temperature change rate and the intermediate variable, and generating a second fitted curve representing a relationship between the intermediate variable and the error parameter. Here, the fitting curve generated by performing curve fitting based on the 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 environment temperature obtained by the temperature outside the IMU, the temperature inside the IMU and the temperature change rate and the intermediate variable, 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 environment temperature obtained by the temperature outside the IMU, the temperature inside the IMU and the temperature change rate and the error parameter, 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 effectively overcome, the influence of the environment temperature change on the performance of the IMU is reduced, and the performance of the IMU is improved, so that the measurement accuracy of the IMU can be greatly improved.

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

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

Next, after the second temperature change rate value is obtained, intermediate variable values corresponding to the first temperature, the second temperature, and the second temperature change rate 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. The calculated second error parameter value is the first error parameter value.

The second temperature change rate value is obtained before the temperature compensation is performed in the same manner as the first temperature change rate value is obtained during the generation of the fitted curve. For example, only the second sample temperature is used in calculating the first temperature change rate value, and correspondingly, only the second temperature is used in calculating the second temperature change rate value.

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

First, the following processing is performed for each set of sample data, respectively: 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.

Then, curve fitting is performed on a plurality of sample error parameter values in the plurality of groups of sample data and a plurality of calculated third sample temperatures, so as to generate a fitting curve.

Accordingly, in a second illustrative example, 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 one illustrative example, calculating a third sample temperature corresponding to the set of sample data from 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 weighting 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 two of:

mode one: and weighting the first temperature and the second temperature to obtain a third temperature.

Mode two: first, a third temperature change rate value is obtained according to one of the following processes: calculating the derivative of the first temperature with respect to time to obtain a third temperature change rate value; or calculating the derivative of the second temperature with respect to time to obtain a third temperature change rate value; or weighting the derivative of the first temperature with respect to time and the derivative of the second temperature with respect to 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 in terms of specific examples.

For example, with the sensor in the IMU comprising: for example, gyroscopes and accelerometers, then the IMU data output by the IMU may include: the specific force measurement data and the angular velocity measurement data, and then, first error parameter values corresponding to the first temperature and the second temperature calculated by fitting a curve generated in advance may include: zero bias error of gyroscope, proportional coefficient error of gyroscope, zero bias error of accelerometer and proportional coefficient error of accelerometer; finally, the IMU data after temperature compensation can be calculated based on the obtained IMU data output by the IMU and the first error parameter value by using the IMU sensor model expressed by the following formulas (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_a formula (1);

Omega _m＝K_gM_gω+GC_ga+b_g formula (2);

Wherein a _m is specific force measurement data; k _a is the proportional coefficient error of the accelerometer; m _a is the orthogonality error of the accelerometer; a is specific force data after temperature compensation; b _a is the zero offset error of the accelerometer; omega _m is angular velocity measurement data; k _g is the proportionality coefficient error of the gyroscope; m _g is the orthogonality error of the gyroscope; omega is angular velocity data after temperature compensation; g is a G (gravitational acceleration) sensitivity coefficient matrix; c _g is a matrix of mounting angles between the gyroscope and the accelerometer; b _g is zero offset error of the gyroscope.

Here, the parameter M _a、M_g、G、C_g in the above-described IMU sensor model is a parameter that is not related to the ambient temperature of the IMU. In practical application, the parameter M _a、M_g、G、C_g can be obtained by directly calibrating the IMU through the turntable in advance in the process of calibrating the IMU. The error parameter K _a、K_g、b_a、b_g in the IMU sensor model is a parameter related to the environmental temperature of the IMU, and changes with the environmental temperature of the IMU. 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 fitting curve which is generated in advance. Thus, based on the error parameter calculated in real time, the measurement accuracy of the IMU can be improved.

The IMU sensor model of the present application will be described in detail below taking the example that the gyroscope in the IMU is implemented by a three-axis gyroscope and the accelerometer in the IMU is implemented by a three-axis accelerometer.

In one illustrative example, when a sensor in an IMU includes: the temperature-related parameters in the IMU sensor model can be represented by the following formulas (3) to (6), respectively.

Wherein,The proportional coefficient error of the triaxial accelerometer; /(I)The proportional coefficient error of the x axis of the triaxial accelerometer; /(I)A scaling factor error of a y-axis of the triaxial accelerometer; /(I)Is the z-axis scaling factor error of the tri-axis accelerometer.

Wherein,Is the proportionality coefficient error of the triaxial gyroscope; /(I)The scaling factor error of the x axis of the three-axis gyroscope; /(I)The ratio coefficient error of the y axis of the triaxial gyroscope; /(I)Is the z-axis scaling factor error of the tri-axis gyroscope.

Wherein,Zero offset error for a triaxial accelerometer; /(I)Zero offset error for the x-axis of the tri-axis accelerometer; /(I)Zero offset error for the y-axis of the tri-axis accelerometer; /(I)Is the zero offset error of the z-axis of the tri-axis accelerometer.

Wherein,Zero offset error of the triaxial gyroscope; /(I)Zero offset error of the x axis of the three-axis gyroscope; /(I)Zero offset error of the y axis of the three-axis gyroscope; /(I)Is the zero offset error of the z axis of the three-axis gyroscope.

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

Wherein a _m is specific force measurement data; the proportional coefficient error of the triaxial accelerometer; m _a is the orthogonality error of the tri-axial accelerometer; a is specific force data after temperature compensation; /(I) Zero offset error for a triaxial accelerometer; omega _m is angular velocity measurement data; /(I)Is the proportionality coefficient error of the triaxial gyroscope; m _g is the orthogonality error of the tri-axis gyroscope; omega is angular velocity data after temperature compensation; g is a G sensitivity coefficient matrix; c _g is a matrix of mounting angles between the gyroscope and the accelerometer; /(I)Is the zero offset error of the triaxial gyroscope.

The fitting curve in the present application is described in detail below in connection with specific examples.

Assuming that the gyroscopes in the IMU are implemented by tri-axial gyroscopes, the accelerometers in the IMU are implemented by tri-axial accelerometers, the curve fitting is implemented by polynomial curve fitting and the order of the polynomial is third order, the error parameters include: zero offset error of triaxial gyroscopeScaling factor error of triaxial gyroscope/>Zero bias error/>, of a triaxial accelerometerAnd the scaling factor error/>, of a triaxial accelerometerThe implementation method for generating the fitting curve by performing curve fitting according to the plurality of 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; performing curve fitting on a plurality of groups of sample data and a plurality of corresponding first temperature change rate values, and generating a first fitting curve for representing the relationship between the intermediate variable and the temperature outside the IMU and the relationship between the temperature inside the IMU and the temperature change rate, and a second fitting curve for representing the relationship between the intermediate variable and the error parameter; the first fitted curve and the second fitted curve are determined as fitted curves.

Then, in an illustrative example, the fitted curve can be represented by the following formulas (9) to (16). Wherein, zero offset error of triaxial gyroscopeThe corresponding first fitting curve is represented by formula (9), zero bias error/>, of the tri-axis gyroscopeThe corresponding second fitted curve can be represented by formula (10); scaling factor error of triaxial gyroscope/>The corresponding first fitted curve can be represented by formula (11), the scaling factor error/>, of the tri-axis gyroscopeThe corresponding second fitted curve can be represented by formula (12); zero bias error/>, of a triaxial accelerometerThe corresponding first fitted curve can be represented by equation (13), zero bias error/>, of the tri-axis accelerometerThe corresponding second fitted curve can be represented by formula (14); scaling factor error of triaxial accelerometer/>The corresponding first fitted curve can be expressed by equation (15), the scaling factor error/>, of the triaxial accelerometerThe corresponding second fitted curve can be represented by formula (16).

Wherein T _a represents a first temperature; t _imu represents a second temperature; The first derivative of T _imu with respect to time represents the rate of temperature change inside the IMU; /(I) Is an intermediate variable; /(I) Coefficients for a polynomial in the fitted curve; /(I)Zero offset error of the triaxial gyroscope; /(I)Is the proportionality coefficient error of the triaxial gyroscope; zero offset error for a triaxial accelerometer; /(I) Is the scaling factor error of the triaxial accelerometer.

In addition, it should be understood that, in equation (10),Representation/>To the power of 2; /(I)Representation/>To the 3 rd power of (3). Similarly, equations (12), (14) and (16) may be understood with reference to the description of equation (10), and will not be described in detail herein.

In an exemplary embodiment, the IMU may be calibrated in advance by the turntable to obtain multiple sets of sample data, where each set of sample data includes: a first sample temperature outside the IMU, a second sample temperature inside the IMU, and a sample error parameter value of the IMU corresponding to the first sample temperature and the second sample temperature. Then, the coefficients in the fitting curve can be solved by substituting a plurality of groups of sample data (the first sample temperature is taken as T _a, the second sample temperature is taken as T _imu) into the formulas (9) to (16), namely Thus, a fitted curve is obtained.

Then, in an exemplary embodiment, after the fitting curve is generated in advance, when temperature compensation is required to be performed on the IMU data output by the IMU, the first temperature T _a outside the IMU and the second temperature T _imu inside the IMU and the corresponding temperature change rate thereof can be obtained in real timeCorresponding error parameters are calculated in real time, namely, zero offset errors and proportional coefficient errors of gyroscopes of the IMU are updated in real time, and zero offset errors and proportional coefficient errors of accelerometers of the IMU are updated. And then, performing temperature compensation on the angular velocity measurement data and the specific force measurement data output by the IMU 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, so that 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 accuracy of the IMU is improved.

It should be appreciated that if a polynomial curve fitting method is used to generate a fitted curve, then the coefficients of the polynomial in the generated fitted curve Depending on the order of the polynomial used for fitting. For example, if a first order polynomial is used for fitting, the fitted curve can be expressed by the above-described formula (9), formula (11), formula (13), formula (15), and the following formulas (17) to (20).

Wherein,Is an intermediate variable; /(I) Coefficients for a polynomial in the fitted curve; /(I)Zero offset error of the triaxial gyroscope; /(I)Is the proportionality coefficient error of the triaxial gyroscope; /(I)Zero offset error for a triaxial accelerometer; /(I)Is the scaling factor error of the triaxial accelerometer.

Of course, in performing curve fitting, other fitting methods, such as least squares fitting, may be used in addition to the first-order polynomial fitting or third-order polynomial fitting listed above, and may be determined by those skilled in the art according to practical situations, and embodiments of the present application are not limited herein.

In an illustrative example, following step 102 described above, the method described above may further include: and displaying the IMU data after temperature compensation.

For example, the manner of displaying 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 in which the IMU is located, and so on.

As can be seen from the above, the data processing method of the application utilizes the temperature outside the IMU and the temperature inside the IMU to perform temperature compensation on the IMU data output by the IMU, thereby improving the measurement accuracy of the IMU. In addition, the temperature hysteresis phenomenon of the IMU can be effectively overcome, the influence of environmental temperature change on the IMU is reduced, the temperature compensation precision of the IMU can be improved, and the problem that the data precision of the IMU along with temperature drift is low is effectively solved.

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

A first temperature sensor 203 for acquiring a first temperature outside 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 a second temperature to the processor 202;

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

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

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

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

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

In one illustrative example, the gyroscope may be, for example, a tri-axis gyroscope or the like.

In one illustrative example, the accelerometer may be, for example, a tri-axial 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 target temperature sensors may be plural. In this way, an accurate temperature of the IMU can be acquired. Therefore, the measurement accuracy of the IMU can be better improved.

For example, a plurality of first temperature sensors may be provided within the device to measure the ambient temperature within the housing, so that an accurate first temperature outside the IMU can be acquired. Furthermore, when the temperature compensation is performed on the IMU data output by the IMU based on the accurate first temperature, the measurement accuracy of the IMU can be better improved.

For example, a plurality of second temperature sensors may be provided in the device to measure the ambient temperature in the IMU, so that accurate second temperatures inside the IMU can be acquired. Furthermore, when the temperature compensation is performed on the IMU data output by the IMU based on the accurate second temperature, the measurement accuracy 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 setting positions of the target sensors are uniformly distributed or symmetrically distributed. In this way, a more accurate temperature of the IMU can be acquired. Therefore, the measurement accuracy of the IMU can be better improved.

For example, when the plurality of first temperature sensors are disposed in the above 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 uniformly distributed around the IMU, or may be symmetrically distributed on both sides of the IMU as shown in fig. 2B, or may be symmetrically distributed at a position away from the heat generating source (e.g., a power source, etc.) in the housing with the heat generating source in the housing as a center. Therefore, the first temperature outside the IMU can be acquired more accurately, and the measurement accuracy of the IMU can be improved better.

For example, when a plurality of second temperature sensors are disposed in the above device, the plurality of first temperature sensors may be symmetrically or uniformly distributed in the IMU. For example, the plurality of first temperature sensors may be symmetrically or uniformly distributed around the gyroscope and the accelerometer, or the plurality of first sensors may be symmetrically distributed centering around the gyroscope and the accelerometer. Therefore, the second temperature inside the IMU can be acquired more accurately, and the measurement accuracy of the IMU can be improved 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). Therefore, the first temperature outside the IMU can be acquired more accurately, and the measurement accuracy of the IMU can be improved better.

In one illustrative example, the temperature output frequency of the first temperature sensor may be greater than 1 hertz (Hz). Therefore, the first temperature outside the IMU can be acquired more accurately, and the measurement accuracy of the IMU can be improved better.

In one illustrative example, the first temperature sensor is positioned as far as possible from the heat generating source (e.g., power source, etc.) within the housing. Therefore, the first temperature outside the IMU can be acquired more accurately, and the measurement accuracy of the IMU can be improved better.

In an illustrative example, as also shown in fig. 2A, a thermal insulation layer 205 may also be provided between the first temperature sensor 203 and the inner wall of the housing 201. Therefore, the first temperature outside the IMU can be acquired more accurately, and further, the measurement accuracy of the IMU can be improved better.

In one illustrative example, the housing may be a closed structure. Therefore, the temperature measurement accuracy of the first temperature sensor can be reduced when air outside the data processing device enters the shell, so that the first temperature outside the IMU can be acquired more accurately, and the measurement accuracy of the IMU can be improved better.

In one illustrative example, as also shown in fig. 2A, processor 202 connects first temperature sensor 203 and IMU 204 via bus 206.

In one illustrative example, the bus may be, for example, an SPI (SERIAL PERIPHERAL INTERFACE ) bus, an I2C bus, or the like.

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

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 device according to the embodiment of the present application, the processor compensates the temperature of 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, an electronic device 30 may include: at least one processor 301; and at least one memory 302, bus 303 connected to the processor 301; wherein, the processor 301 and the memory 302 complete communication with each other through the bus 303; the processor 301 is operative to invoke program instructions in the memory 302 to perform the steps of the data processing method in one or more embodiments described above.

The Processor may be implemented by a central processing unit (Central Processing Unit, CPU), a microprocessor (Micro Processor Unit, MPU), a digital signal Processor (DIGITAL SIGNAL Processor, DSP), or a field programmable gate array (Field Programmable GATE ARRAY, FPGA). The Memory may include non-volatile Memory, random access Memory (Random Access Memory, RAM) and/or non-volatile Memory in a computer-readable medium, such as Read Only Memory (ROM) or Flash Memory (Flash RAM), the Memory including at least one Memory chip.

It should be noted that, in the embodiment 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 separate product, it may also be stored in a computer readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present application.

Accordingly, based on the same inventive concept, an embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes a stored program, where the program controls an electronic device where the storage medium is located to execute the steps of the data processing method in one or more embodiments.

It should be noted here that: the description of the apparatus, device, or computer-readable storage medium embodiments above is similar to that of the method embodiments described above, with similar benefits 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 should be made to the description of the embodiments of the method of the present application.

Based on the same inventive concept, the embodiment of the application also provides a data processing method, which can be applied to the following scenes: the IMU may be disposed in a mobile terminal carried by a walker, such as a mobile phone, a sport wristband, a watch, etc., and correspondingly, the IMU is configured to collect IMU data of the walker, and then the number of sport steps of the walker may be obtained according to the IMU data after temperature compensation. In this way, the accuracy of the number of moving steps can be improved.

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

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

Here, IMU is used to collect IMU data for pedestrians.

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

Step 403: determining the number of exercise steps of the pedestrian according to the temperature compensated IMU data;

Step 404: showing the number of steps of the walker.

In one illustrative example, step 403 may include: calculating the new step number of the pedestrian according to the temperature compensated IMU data; acquiring the historical step number of a pedestrian in a preset time period; and adding the historical step number and the newly added step number to obtain the movement step number of the walker.

For example, gait detection algorithms such as peak detection algorithms, short-time fourier transforms (STFT, short-time Fourier transform) may be used to calculate the pedestrian's new step count from the temperature compensated IMU data. Therefore, because the IMU data after temperature compensation is accurate, the temperature drift error is overcome, and more accurate steps of the walker can be obtained.

In one illustrative example, step 404 may include: when the preset triggering condition is met, displaying the number of the exercise steps of the walker in a preset display mode.

In one illustrative example, the preset trigger condition may be, for example, the number of steps of the walker exceeding a preset step number threshold, receiving a preset user operation indicating to show the number of steps of the athletic performance, receiving a preset instruction indicating to upload the number of steps of the athletic performance, etc.

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

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

As can be seen from the above, in the data processing method of the present application, the temperature of the outside of the IMU and the temperature of the inside of the IMU are used to perform temperature compensation on the IMU data output by the IMU, so that accurate temperature-compensated IMU data can be obtained, and then the number of steps of the walker is calculated based on the temperature-compensated IMU data, so that the accuracy of the number of steps of the walker can be improved.

Based on the same inventive concept, the embodiment of the application also provides a data processing method, which can be applied to the following scenes: the IMU may be disposed in a mobile terminal carried by an unmanned plane, a robot, a vehicle, or a pedestrian, such as a mobile phone, a sports bracelet, a watch, or the like, and correspondingly, the IMU is configured to collect IMU data of a first object, where the first object may be an unmanned plane, a robot, a vehicle, a pedestrian, or the like, and then obtain a motion trail of the first object according to the IMU data after temperature compensation. In this way, the accuracy of the motion trajectory can be improved.

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

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

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

Wherein the first object may be, for example, an unmanned plane, a robot, a vehicle, a pedestrian, etc.

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

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

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

For example, when the first object starts to move, the initial position information of the first object may be measured in advance using a positioning module in a mobile terminal carried by an unmanned plane, a robot, a vehicle, or a pedestrian, such as a global positioning system (GPS, global Positioning System) module, etc., and then stored. Thus, the 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 include: and performing integral operation on the temperature compensated IMU data 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 the pre-stored initial position information.

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

Of course, in addition to the two exemplary embodiments listed above, step 503 may be implemented in other manners, and embodiments of the present application are not specifically limited herein.

As can be seen from the above, in the data processing method of the present application, the temperature of the IMU and the temperature of the IMU are utilized to perform temperature compensation on the IMU data output by the IMU, so that accurate temperature-compensated IMU data can be obtained, and the motion track of the first object is determined based on the temperature-compensated IMU data, so that the accuracy of the motion track can be improved.

Based on the same inventive concept, the embodiment of the application also provides a data processing method, which can be applied to the following scenes: the IMU may be disposed in an unmanned aerial vehicle, a robot, a vehicle, a head-mounted device (e.g., an augmented Reality (Augmented Reality, AR) device, a Virtual Reality (VR) device, etc.), and correspondingly, the IMU is configured to collect IMU data of, e.g., an unmanned aerial vehicle, a robot, a vehicle, a head-mounted device, etc., and then perform data fusion with measurement data collected from other sensor data according to the temperature compensated IMU data. Thus, the deviation can be overcome, and more accurate pose information of the second object can be obtained.

FIG. 6 is a flowchart of a fourth embodiment of the data processing method according to the present application, as shown in FIG. 6, may include:

Step 601: acquiring a first temperature outside the IMU and a second temperature inside the IMU;

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

Wherein the second object is e.g. an unmanned plane, a robot, a vehicle, a head mounted device, etc.

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

step 603: acquiring other measurement data;

Here, in addition to the IMU, other sensors such as GPS, radar, magnetometer, camera, etc. may be provided in the second object.

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

Step 604: and carrying out data fusion on the IMU data after temperature compensation and other measurement data, and determining pose information of the second object.

Wherein the pose information includes one or more of speed information, position information, attitude angle (which may also be referred to as heading 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, tracking control can be performed on the running tracks of the unmanned aerial vehicle, the robot and the vehicle according to the pose information.

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

As can be seen from the above, according to the data processing method of the present application, the temperature of the external IMU and the temperature of the internal IMU are used to perform temperature compensation on the IMU data output by the IMU, so that accurate temperature-compensated IMU data can be obtained, and then the multi-source sensor data fusion is performed on the temperature-compensated IMU data and other sensor data to obtain pose information of the second object, so that measurement errors of a single sensor can be overcome, and accuracy of the pose information can be improved.

Based on the same inventive concept, the embodiment of the application also provides a data processing method, which can be applied to the following scenes: in practical application, before an earthquake occurs or a building collapses, the posture of the building can be changed, so that the IMU can be arranged in the building, correspondingly, the IMU is used for collecting 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 flowchart of a fifth embodiment of the data processing method according to the present application, as shown in FIG. 7, which may include:

Step 701: acquiring 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, performing temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation;

step 703: determining whether potential safety hazards occur to the building according to the IMU data after temperature compensation;

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

In an illustrative example, step 703 may include: integrating operation is carried out 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 angles; based on the attitude information of the building, whether the building has potential safety hazards or not is determined.

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 the building has a potential safety hazard, and at this time, preset alarm information may be output.

For example, when an earthquake occurs, a speed change may occur in the building, and in an exemplary embodiment, the temperature compensated IMU data may be integrated to obtain speed information of the building, and when it is determined that the speed information of the building is greater than a preset speed threshold, it may indicate 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 alert information may be one or more of the following implementations: the method comprises the steps of sending preset alarm information to a management platform bound with the IMU, controlling audio equipment bound with the IMU and positioned in a building to play preset alarm audio, and controlling electric lamps bound with the IMU and positioned in the building to display preset light effects.

As can be seen from the above, according to the data processing method of the present application, the temperature of the IMU and the temperature of the IMU are utilized to perform temperature compensation on the IMU data of the building output by the IMU, so that accurate temperature-compensated IMU data can be obtained, and whether the building has a potential safety hazard or not can be conveniently and accurately determined based on the temperature-compensated IMU data, so that an alarm can be timely sent when the potential safety hazard occurs, and the safety problem of a user can be avoided to a certain extent.

The present application has been described in terms of several embodiments, but the description is illustrative and not restrictive, 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 described embodiments. 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 in place of any other feature or element of any other embodiment unless specifically limited.

The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The disclosed embodiments, features and elements of the present application may also be combined with any conventional features or elements to form a unique inventive arrangement as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive arrangements to form another unique inventive arrangement as defined in the claims. It is therefore to be understood that any of the features shown and/or discussed in the present application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not to be restricted except in light of the attached claims and their equivalents. Further, various modifications and changes may be made within the scope of the appended claims.

Furthermore, 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 sequences of steps are possible as will be appreciated by those of ordinary skill in the art. Accordingly, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Furthermore, 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.

Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the 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 cooperatively by several physical components. 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 both 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 known to those skilled 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 be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, 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.

Claims

1. A data processing method, comprising:

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

According to the first temperature and the second temperature, carrying out temperature compensation on IMU data output by the IMU to correct measurement errors caused by temperature change, including: calculating a first error parameter value corresponding to the first temperature and the second temperature according to a pre-generated fitting curve for representing the relation between the environment temperature and the error parameter, wherein the environment temperature is obtained by the temperature outside the IMU, the temperature inside the IMU and the temperature change rate; and according to the calculated first error parameter value, performing temperature compensation on the IMU data output by the IMU.

2. The data processing method of claim 1, prior to the acquiring a first temperature outside the inertial measurement unit IMU and a second temperature inside the IMU, the method further comprising:

and calibrating the IMU to obtain multiple groups of sample data, wherein each group of sample data comprises: a first sample temperature outside the IMU, a second sample temperature inside 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 plurality of groups of sample data to generate the fitting curve.

3. The data processing method according to claim 2, wherein the performing curve fitting according to the plurality of sets of sample data to generate the fitting curve includes:

The following processing is performed on each group of sample data: calculating a derivative of a second sample temperature in the set of sample data with respect to time to obtain a first temperature rate of change value corresponding to the set of sample data;

Performing curve fitting on the plurality of groups of sample data and the calculated plurality of first temperature change rate values to generate a first fitting curve for representing the relationship between 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 fitted curve comprises: the first fitted curve and the second fitted curve.

4. A data processing method according to claim 3, wherein said calculating a first error parameter value corresponding to said first temperature and said second temperature comprises:

calculating the derivative of the second temperature with respect to 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 fitting 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.

5. The data processing method according to claim 2, wherein the performing curve fitting according to the plurality of sets of sample data to generate the fitting curve includes:

The following processing is performed on each group of sample data: 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 a plurality of sample error parameter values in the plurality of groups of sample data and a plurality of calculated third sample temperatures to generate the fitting curve.

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

Calculating a third temperature from 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.

7. The data processing method of claim 6, 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 the third temperature;

or calculating the 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.

8. The data processing method according to any one of claims 1 to 7, further comprising, after the temperature compensating the IMU data output from the IMU according to the first temperature and the second temperature:

and displaying the IMU data after temperature compensation.

9. A computer-readable storage medium, comprising: a stored program, wherein the program, when run, controls an electronic device in which the storage medium is located to perform the steps of the data processing method according to any one of claims 1 to 8.

10. An electronic device, comprising:

at least one processor;

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

The processor and the memory complete communication with each other through the bus; the processor being operative to invoke program instructions in the memory to perform the steps of the data processing method as claimed in any of claims 1 to 8.

11. A data processing apparatus comprising: a housing, a processor disposed within the housing, a first temperature sensor, and an inertial measurement unit IMU; wherein,

The processor is configured to perform temperature compensation on IMU data output by the IMU according to the first temperature and the second temperature, so as to correct a measurement error caused by temperature change, and includes: calculating a first error parameter value corresponding to the first temperature and the second temperature according to a pre-generated fitting curve for representing the relation between the environment temperature and the error parameter, wherein the environment temperature is obtained by the temperature outside the IMU, the temperature inside the IMU and the temperature change rate; and according to the calculated first error parameter value, performing temperature compensation on the IMU data output by the IMU.

12. The data processing apparatus of claim 11, wherein the first temperature sensor is disposed at a location remote from a location of a heat generating source within the housing.

13. The data processing apparatus of claim 11, wherein a thermal barrier is disposed between the first temperature sensor and an inner wall of the housing.

14. The data processing apparatus of claim 11, wherein the housing is a closed structure.

15. The data processing apparatus of claim 11, wherein at least one of the first temperature sensor and the second temperature sensor is a target temperature sensor, the target temperature sensor being provided in a plurality of numbers.

16. The data processing apparatus of claim 15, wherein the target temperature sensors are disposed at evenly distributed or symmetrically distributed positions.

17. A data processing method, 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 pedestrians;

According to the first temperature and the second temperature, performing temperature compensation on IMU data output by the IMU to obtain IMU data after temperature compensation, including: calculating a first error parameter value corresponding to the first temperature and the second temperature according to a pre-generated fitting curve for representing the relation between the environment temperature and the error parameter, wherein the environment temperature is obtained by the temperature outside the IMU, the temperature inside the IMU and the temperature change rate; according to the calculated first error parameter value, performing temperature compensation on IMU data output by the IMU;

Determining the number of exercise steps of the pedestrian according to the temperature compensated IMU data;

Showing the number of athletic steps of the walker.

18. The data processing method of claim 17, wherein the determining the number of steps of the walker from the temperature compensated IMU data comprises:

Calculating the new step number of the walker according to the temperature compensated IMU data;

acquiring the historical step number of the pedestrian 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.

19. A data processing method, 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 plane, a robot, a vehicle and a pedestrian;

Determining a motion track of the first object according to the temperature compensated IMU data and pre-stored initial position information;

And displaying the motion trail in the electronic map layer.

20. A data processing method, 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 plane, a robot, a vehicle and head-mounted equipment;

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

21. A data processing method, 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 potential safety hazards occur to the building according to the temperature compensated IMU data;

If the potential safety hazard of the building is determined, outputting preset alarm information for warning the potential safety hazard of the building.

22. The data processing method of claim 21, wherein the determining whether the building has a safety hazard according to 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 angles;

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