CN112833919B - Management method and system for redundant inertial measurement data - Google Patents
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Abstract
The invention discloses a management method and a system for redundant inertial measurement data, wherein the management method comprises the following steps: acquiring multiple paths of IMU data acquired by multiple IMU units; generating detection data by performing multidimensional fault evaluation on the multiple paths of IMU data; extracting optimal IMU data contained in the detection data; and taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment. According to the invention, the joint voting scoring of the IMU data is carried out through multidimensional fault assessment, so that the fault detection accuracy is effectively improved, meanwhile, the IMU data applied at the current moment is gradually corrected to be the same as the IMU data acquired by the first IMU unit at the next moment according to the linear or nonlinear adjustment rate by simulating the trend of the IMU data with the highest joint voting scoring at the current moment, and the problem that the switching amplitude of the IMU data to be applied is large so as to seriously influence the motion stability of the device is avoided.
Description
Technical Field
The invention relates to the technical field of inertial navigation systems, in particular to a management method and a management system of redundant inertial measurement data.
Background
The IMU full scale Inertial Measurement Unit and the inertial measurement unit enable the rear-end data processing unit to calculate the pose of the measured object by detecting and measuring acceleration and rotation. Currently, IMUs are widely used in devices that require precise displacement estimation with gestures and motion control, such as: inertial navigation devices for automobiles, robots, submarines, airplanes, missiles and spacecraft, and the like.
The IMU is used as a key data acquisition device for motion control of a device, and if a fault occurs in the use process, effective data information cannot be provided, so that serious potential safety hazards of the controlled device can be caused, and huge economic loss is caused. Therefore, most of the existing devices using the IMU carry out IMU redundancy design, and the isolation of the fault IMU and the switching between IMU data to be applied of different channels are realized by continuously carrying out fault detection on each IMU.
However, the conventional fault detection method of the inertial measurement data has the problems of false detection and missing detection due to single criterion, which can cause the error switching of the channels of the subsequent IMU data to be applied, and when the IMU data to be applied of different channels have large difference, the conventional data switching method of the inertial measurement data has the problem of overlarge switching amplitude, and seriously affects the motion stability of the device.
In summary, the conventional inertial measurement data management method has the problems of poor fault detection accuracy and large switching range of the IMU data to be applied.
Disclosure of Invention
In view of this, the invention provides a management method and a system for inertial measurement data of redundancy, which performs joint voting scoring of IMU data through multidimensional fault evaluation, effectively improves fault detection accuracy, and simultaneously gradually corrects IMU data applied at the current moment to be identical with IMU data acquired at the next moment by a first IMU unit according to linear or nonlinear adjustment rate by simulating the trend of IMU data with highest joint voting scoring at the current moment, thereby avoiding the problem that the switching amplitude of the IMU data to be applied is large and seriously affecting the motion stability of the device, and solving the problems of poor fault detection accuracy and large switching amplitude of the IMU data to be applied in the traditional management method for inertial measurement data.
In order to solve the above problems, the technical solution of the present invention is specifically a management method for inertial measurement data using redundancy, including: acquiring multiple paths of IMU data acquired by multiple IMU units; generating detection data by performing multidimensional fault evaluation on the multiple paths of IMU data; extracting optimal IMU data contained in the detection data; and taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, and when a second IMU unit corresponding to the IMU data applied at the current moment is different from the first IMU unit, gradually correcting the IMU data applied at the current moment to coincide with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit.
Optionally, the method for simulating the trend of the IMU data acquired by the first IMU unit by using the IMU data applied at the current moment includes: continuously extracting the optimal IMU data as target adjustment data in a time interval between the current time and the next time, so that the IMU data applied at the current time gradually approaches the optimal IMU data; and gradually correcting the IMU data applied at the current moment according to a linear or nonlinear adjustment rate to be the same as the IMU data acquired by the first IMU unit at the next moment.
Optionally, the method for managing inertial measurement data further includes: and in the process of continuously acquiring the multipath IMU data acquired by a plurality of the IMU units and calculating the IMU units corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, if a plurality of the first IMU units continuously generated in the time interval are the same, gradually correcting the IMU data applied at the current moment to be overlapped with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit, if a plurality of the first IMU units continuously generated in the time interval are different, gradually correcting the IMU data applied at the current moment to be overlapped with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit, and if a plurality of the first IMU units continuously generated in the time interval are different for the first time, taking the moment of generating the first IMU unit as the first IMU unit to be applied at the current moment, and updating the IMU data after the IMU is gradually updated according to the mode of simulating the trend of the IMU data acquired by the first IMU unit after the IMU is gradually overlapped with the first IMU data acquired by the first IMU unit at the next moment.
Optionally, generating detection data based on the multiple paths of IMU data includes: calculating first evaluation data of the multi-path IMU data based on a preset navigation information estimation model; calculating second evaluation data of the plurality of paths of IMU data based on data statistics; and carrying out joint voting on the first evaluation data and the second evaluation data based on a preset fault voting rule, and generating the detection data.
Optionally, extracting the optimal IMU data included in the detection data includes: and extracting the IMU data with the highest score in the joint voting as the optimal IMU data.
Accordingly, the present invention provides a management system for inertial measurement data of redundancy, comprising: the IMU units are used for collecting multiple paths of IMU data; and the upper computer unit is used for generating detection data by performing multidimensional fault evaluation on the multi-path IMU data, extracting optimal IMU data contained in the detection data, and then taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, wherein when a second IMU unit corresponding to the IMU data applied by the upper computer unit at the current moment is different from the first IMU unit, gradually correcting the IMU data applied at the current moment to coincide with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit.
Optionally, the upper computer unit includes a data quality evaluation module, where, when the upper computer unit receives the multiple paths of IMU data, the data quality evaluation module calculates first evaluation data of the multiple paths of IMU data based on a preset navigation information estimation model, and calculates second evaluation data of the multiple paths of IMU data based on a data statistical characteristic, and then the data quality evaluation module performs joint voting on the first evaluation data and the second evaluation data based on a preset fault voting rule, and generates the detection data.
Optionally, the upper computer unit further includes a data switching module, where the data switching module gradually corrects the IMU data applied at the current moment to be the same as the IMU data acquired by the first IMU unit at the next moment according to a linear or nonlinear adjustment rate.
Optionally, in a process that the IMU data applied at the current time is gradually corrected to overlap with the IMU data acquired by the first IMU unit at the next time according to a trend of the IMU data acquired by the first IMU unit in a time interval of the current time and the next time and the data switching module gradually corrects the IMU data applied at the current time to overlap with the IMU data acquired by the first IMU unit at the next time in a mode of simulating the trend of the IMU data acquired by the first IMU unit, the data quality evaluation module continuously calculates the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next time, if the IMU units continuously generated by the data quality evaluation module in the time interval are the same as the first IMU unit to be applied at the next time, the data switching module gradually corrects the IMU data applied at the current time to overlap with the IMU data acquired by the first IMU unit at the next time in a mode of simulating the trend of the IMU data acquired by the first IMU unit, and if the IMU units continuously generated by the data quality evaluation module continuously in the time interval are different from the first IMU unit to the next time to update the IMU data acquired by the data switching module at the next time.
Optionally, the upper computer unit further includes a gesture resolving module, and the gesture resolving module can generate gesture data based on the IMU data applied at the current moment.
The primary improvement of the invention is that the provided management method of the inertial measurement data of redundancy carries out joint voting scoring of the IMU data through multidimensional fault evaluation, effectively improves the fault detection accuracy, and simultaneously gradually corrects the IMU data applied at the current moment to be identical with the IMU data acquired at the next moment by the first IMU unit according to the linear or nonlinear adjustment rate by simulating the trend of the IMU data with the highest joint voting scoring at the current moment, so that the data among different channels are smoothly switched, the problem that the switching amplitude of the IMU data to be applied is large, and the movement stability of the device is seriously influenced is avoided, and the problems of poor fault detection accuracy and large switching amplitude of the IMU data to be applied in the traditional management method of the inertial measurement data are solved.
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FIG. 1 is a simplified flow chart of a method of managing redundant inertial measurement data of the present invention;
fig. 2 is a simplified block diagram of a method of managing redundant inertial measurement data of the present invention.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a method for managing redundant inertial measurement data includes: acquiring multiple paths of IMU data acquired by multiple IMU units; generating detection data by performing multidimensional fault evaluation on the multiple paths of IMU data; extracting optimal IMU data contained in the detection data; and taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, and when a second IMU unit corresponding to the IMU data applied at the current moment is different from the first IMU unit, gradually correcting the IMU data applied at the current moment to coincide with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit. Wherein extracting the optimal IMU data contained in the detection data includes: and extracting the IMU data with the highest score in the joint voting as the optimal IMU data. The present time and the next time are qualitative limits, not quantitative limits, of the method for managing inertial measurement data for describing redundancy according to the present invention. Specifically, the time interval between the current time and the next time may be 0.1s or 0.2s, and the influencing parameters include: the data transmission frame rate between the IMU unit and the upper computer unit, the gesture resolving precision requirement, the IMU unit stability and the like, and a user can set the time interval between the current moment and the next moment according to the actual application scene and the requirement thereof.
According to the invention, the joint voting scoring of the IMU data is carried out through multi-dimensional fault assessment, so that the fault detection accuracy is effectively improved, meanwhile, the IMU data applied at the current moment is gradually corrected to be the same as the IMU data acquired by the first IMU unit at the next moment according to the linear or nonlinear adjustment rate by simulating the trend of the IMU data with the highest joint voting scoring at the current moment, so that the data among different channels are smoothly switched, the problem that the switching amplitude of the IMU data to be applied is large, and the problem that the movement stability of the device is seriously influenced is solved, and the problems of poor fault detection accuracy and large switching amplitude of the IMU data to be applied in the traditional inertia measurement data management method are solved.
Further, the method for simulating the trend of the IMU data acquired by the first IMU unit by using the IMU data applied at the current moment includes: continuously extracting the optimal IMU data as target adjustment data in a time interval between the current time and the next time, so that the IMU data applied at the current time gradually approaches the optimal IMU data; and gradually correcting the IMU data applied at the current moment according to a linear or nonlinear adjustment rate to be the same as the IMU data acquired by the first IMU unit at the next moment.
Furthermore, the method for simulating the trend of the IMU data acquired by the first IMU unit by using the IMU data applied at the current time may further be: extracting a plurality of historical IMU data acquired by the first IMU unit in a last preset time period; after arranging a plurality of historical IMU data according to time sequence information, simulating the trend of the IMU data acquired by the first IMU unit, and calculating the IMU data acquired by the first IMU unit at the next moment; and gradually correcting the IMU data applied at the current moment according to a linear or nonlinear adjustment rate to be identical with the IMU data acquired by the simulated first IMU unit at the next moment. The preset time period can be the same as the time interval between the current time and the next time, and can be increased more accurately and appropriately for simulation, namely, more historical IMU data are called as simulation samples, and a user can set the time period according to actual application scenes and requirements of the actual application scenes. The method for simulating the trend of the IMU data acquired by the first IMU unit may be a preset navigation information estimation model, a pre-trained neural network model, or the like.
Further, the method for managing inertial measurement data further includes: and in the process of continuously acquiring the multipath IMU data acquired by a plurality of the IMU units and calculating the IMU units corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, if a plurality of the first IMU units continuously generated in the time interval are the same, gradually correcting the IMU data applied at the current moment to be overlapped with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit, if a plurality of the first IMU units continuously generated in the time interval are different, gradually correcting the IMU data applied at the current moment to be overlapped with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit, and if a plurality of the first IMU units continuously generated in the time interval are different for the first time, taking the moment of generating the first IMU unit as the first IMU unit to be applied at the current moment, and updating the IMU data after the IMU is gradually updated according to the mode of simulating the trend of the IMU data acquired by the first IMU unit after the IMU is gradually overlapped with the first IMU data acquired by the first IMU unit at the next moment.
According to the invention, in the process of switching the first IMU units to be applied by the data switching module, multi-dimensional fault evaluation is continuously carried out and a plurality of first IMU units are generated, and the problem that the motion stability of the device is seriously affected due to instantaneous faults of the target first IMU units at the initial moment of data switching by the data switching module in the data switching process is effectively avoided by continuously iterating the target first IMU units. Meanwhile, by being as claimed in the present invention: the IMU data applied at the current moment is gradually corrected to be the same as the IMU data acquired by the first IMU unit at the next moment according to the linear or nonlinear adjustment rate, so that the data switching method for smooth data switching among different channels is matched, and the problems that when the data switching is frequent due to the frequent occurrence of transient faults of one or more IMU units, the upper computer unit is prevented from frequent and transient switching and IMU data with larger difference degree are used as data for gesture calculation, so that serious shaking of the device is caused and the like are avoided, and the stability of the motion of the device is ensured to the greatest extent.
Further, when the IMU unit represented by one or more channel IMU data is determined to be a faulty IMU unit, the upper computer unit isolates the IMU data collected by the faulty IMU unit so as not to participate in subsequent gesture resolving, and meanwhile, performs weight degradation on the faulty IMU unit, so that the frame rate of the IMU data collected by the faulty IMU unit subsequently is reduced when the IMU data are transmitted to the data quality evaluation module, until the data quality evaluation module determines that the faulty IMU unit breaks away from the fault when performing multidimensional fault evaluation, and the frame rate of the IMU data collected by the faulty IMU unit subsequently is restored to an initial value when the IMU data collected by the faulty IMU unit subsequently are transmitted to the data quality evaluation module. By setting the weight degradation method, the invention effectively avoids the situation that the upper computer unit continuously has larger and useless computational load caused by the fact that the fault IMU unit continuously outputs invalid IMU data. And meanwhile, the loss of potential optimal IMU data caused by the fact that the upper computer unit directly and completely isolates the IMU data output by the fault IMU unit when the fault IMU unit is recovered after the transient fault is generated is avoided.
Further, generating detection data based on the plurality of paths of IMU data includes: calculating first evaluation data of the multi-path IMU data based on a preset navigation information estimation model; calculating second evaluation data of the plurality of paths of IMU data based on data statistics; and carrying out joint voting on the first evaluation data and the second evaluation data based on a preset fault voting rule, and generating the detection data. The preset fault voting rule may be a fault correspondence table with preset scores.
Further, the predetermined navigation information estimation model may be a model capable of calculating navigation information of the moving object, such as a Kalman Filter (KF) model, an Extended Kalman Filter (EKF) model, etc., which includes a linear system state prediction equation X using the Kalman Filter (KF) model as an example k And the linear system observation equation Z k The overall model expression is as follows:a is an n X n order state transfer coefficient matrix, B is a gain matrix of an optional control input, w is process excitation noise, X k Is the true value of the prediction state at the moment k, X k-1 Is the state true value at the moment k-1, Z k To observe true values (i.e., data from other sensors or processed navigation information, the other sensor type may be gps, barometer, etc.), u k-1 For the real input at time k-1, C is a matrix of measurement coefficients of m×n order, and v is observation noise. And then based on the above-mentioned whole model expression: />Wherein (1)>For the data quality, K, of a certain path of IMU data in the first evaluation data k Is Kalman gain factor, < >>Is based on x k-1 The estimated observed value is expressed as +.>And after the data quality of the IMU data of all channels is completed based on the model traversal, the first evaluation data can be generated in a packing mode.
Further, calculating second evaluation data of the plurality of paths of IMU data based on the data statistics includes: and extracting any type of statistical characteristic index based on the multi-path IMU data, and generating the data quality of the IMU data of all channels based on a preset statistical characteristic index threshold value of the type, so that the second evaluation data can be generated in a packing way. Wherein, the statistical characteristic index is defined as: the difference value between different paths of IMU data is calculated, then the approximate standard deviation data of the difference value data is calculated, and the method for describing the fluctuation condition of the difference value data, for example, the method for calculating the statistical characteristic index can be as follows: firstly, extracting difference data among multiple paths of IMU data, obtaining N (N-1)/2 paths of difference data on the assumption that N paths of IMU data exist, and then carrying out smoothing treatment on each path of difference sequence to obtain a group of statistical indexes of approximate mean values of the difference sequences. And according to a preset threshold value of the preset class index, judging whether IMU data corresponding to the difference value is faulty or not.
Accordingly, as shown in fig. 2, the present invention provides a management system for redundant inertial measurement data, including: the IMU units are used for collecting multiple paths of IMU data; and the upper computer unit is used for generating detection data by performing multidimensional fault evaluation on the multi-path IMU data, extracting optimal IMU data contained in the detection data, and then taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, wherein when a second IMU unit corresponding to the IMU data applied by the upper computer unit at the current moment is different from the first IMU unit, gradually correcting the IMU data applied at the current moment to coincide with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit. The upper computer unit further comprises a gesture resolving module, wherein the gesture resolving module can generate gesture data based on IMU data applied at the current moment so that the upper computer unit can control the motion state of the device based on the gesture data. The upper computer unit may be any kind of data processing unit capable of transmitting control commands/performing data processing, for example: when the invention is applied to an unmanned aerial vehicle, the upper computer unit can be a flight control unit; when the invention is applied to a vehicle, the upper computer unit can be a complete Vehicle Controller (VCU). The invention is not particularly limited to the type of the upper computer unit.
Further, the upper computer unit includes a data quality evaluation module, where, when the upper computer unit receives the multiple paths of IMU data, the data quality evaluation module calculates first evaluation data of the multiple paths of IMU data based on a preset navigation information evaluation model, calculates second evaluation data of the multiple paths of IMU data based on a data statistical characteristic, and then performs joint voting on the first evaluation data and the second evaluation data based on a preset fault voting rule to generate the detection data.
Further, the upper computer unit further comprises a data switching module, wherein the data switching module gradually corrects the IMU data applied at the current moment to be identical with the IMU data acquired by the first IMU unit at the next moment according to a linear or nonlinear adjustment rate.
Further, in a process that the IMU data applied at the current time is gradually corrected to overlap with the IMU data acquired by the first IMU unit at the next time according to a trend of the IMU data acquired by the first IMU unit in a time interval of the current time and the next time and the data switching module gradually corrects the IMU data applied at the current time to overlap with the IMU data acquired by the first IMU unit at the next time in a mode of simulating the trend of the IMU data acquired by the first IMU unit, if the IMU unit corresponding to the optimal IMU data is continuously calculated by the data quality evaluation module as a first IMU unit to be applied at the next time in the time interval, the data switching module gradually corrects the IMU data applied at the current time to overlap with the IMU data acquired by the first IMU unit at the next time in a mode of simulating the trend of the IMU data acquired by the first IMU unit, and if the IMU units corresponding to the optimal IMU data are continuously calculated by the data quality evaluation module in the time interval, the data switching module gradually updates the data acquired by the IMU unit at the next time to the first IMU unit at the next time according to a mode of updating the current trend of the IMU data acquired by the first IMU unit.
The method and the system for managing the redundant inertial measurement data provided by the embodiment of the invention are described in detail. In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the invention can be made without departing from the principles of the invention and these modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Claims (8)
1. A method of managing inertial measurement data for a redundancy, comprising:
acquiring multiple paths of IMU data acquired by multiple IMU units;
generating detection data by performing multidimensional fault evaluation on the multiple paths of IMU data, and particularly calculating first evaluation data of the multiple paths of IMU data based on a preset navigation information estimation model; calculating second evaluation data of the plurality of paths of IMU data based on data statistics; carrying out joint voting on the first evaluation data and the second evaluation data based on a preset fault voting rule and generating the detection data;
extracting optimal IMU data contained in the detection data;
and taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, wherein,
when a second IMU unit corresponding to IMU data applied at the current moment is different from the first IMU unit, gradually correcting the IMU data applied at the current moment to coincide with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit.
2. The method of claim 1, wherein simulating the trend of IMU data acquired by the first IMU unit using IMU data applied at the current time comprises:
continuously extracting the optimal IMU data as target adjustment data in a time interval between the current time and the next time, so that the IMU data applied at the current time gradually approaches the optimal IMU data;
and gradually correcting the IMU data applied at the current moment according to a linear or nonlinear adjustment rate to be the same as the IMU data acquired by the first IMU unit at the next moment.
3. The method of managing inertial measurement data of claim 1, further comprising:
in the process that the IMU data applied at the current moment is gradually corrected to coincide with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit in the time interval between the current moment and the next moment,
continuously acquiring the multi-path IMU data acquired by the plurality of IMU units and calculating the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment,
if the plurality of first IMU units continuously generated in the time interval are the same, the IMU data applied at the current moment is continuously corrected gradually to coincide with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit,
and if the plurality of first IMU units continuously generated in the time interval are different for the first time, taking the moment of generating the first IMU units which are different for the first time as the current moment, and gradually correcting the IMU data applied at the current moment to be overlapped with the IMU data acquired by the updated first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the updated first IMU unit.
4. The method of managing inertial measurement data of claim 1, wherein extracting the optimal IMU data contained in the detection data comprises:
and extracting the IMU data with the highest score in the joint voting as the optimal IMU data.
5. A system for managing inertial measurement data for a plurality of devices, comprising:
the IMU units are used for collecting multiple paths of IMU data;
the upper computer unit is used for generating detection data by carrying out multidimensional fault evaluation on the multi-path IMU data and extracting optimal IMU data contained in the detection data, and then taking the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next moment, wherein when a second IMU unit corresponding to the IMU data applied by the upper computer unit at the current moment is different from the first IMU unit, the IMU data applied at the current moment is gradually corrected to be overlapped with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit, and the upper computer unit further comprises a data quality evaluation module which is used for calculating the first evaluation data of the multi-path IMU data based on a preset navigation information evaluation model and voting the first evaluation data and the second evaluation data based on the data statistics characteristic, and then carrying out voting on the detection data and the first evaluation data and the second evaluation data based on a preset fault rule.
6. The inertial measurement data management system of claim 5, wherein the host computer unit further comprises a data switching module, wherein,
and the data switching module gradually corrects the IMU data applied at the current moment to be identical with the IMU data acquired by the first IMU unit at the next moment according to a linear or nonlinear adjustment rate.
7. The inertial measurement data management system according to claim 6, wherein in a process of gradually correcting IMU data applied at the current time to overlap with IMU data acquired by the first IMU unit at a next time in a manner of simulating a trend of the IMU data acquired by the first IMU unit in a time interval between the current time and the next time and the data switching module, a plurality of IMU units continuously acquire the plurality of paths of IMU data, the data quality evaluation module continuously calculates the IMU unit corresponding to the optimal IMU data as a first IMU unit to be applied at the next time,
if the plurality of first IMU units continuously generated by the data quality evaluation module in the time interval are the same, the data switching module gradually corrects the IMU data applied at the current moment to coincide with the IMU data acquired by the first IMU unit at the next moment in a mode of simulating the trend of the IMU data acquired by the first IMU unit,
and if the plurality of first IMU units continuously generated by the data quality evaluation module in the time interval are different for the first time, taking the moment when the data quality evaluation module generates the first IMU units which are different for the first time as the current moment, and gradually correcting the IMU data applied at the current moment to coincide with the IMU data acquired by the first IMU unit at the next moment after updating in a mode of simulating the trend of the IMU data acquired by the first IMU unit after updating by the data switching module.
8. The inertial measurement data management system of claim 5, wherein the host computer unit further comprises a gesture resolution module capable of generating gesture data based on IMU data applied at a current time.
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