Attitude Sensing Analysis Method and System Based on Gyroscope and Accelerometer

Abstract

The present invention discloses a method and system for attitude sensing analysis based on a gyroscope and an accelerometer, which relates to the technical field of attitude analysis. The method includes: obtaining a historical attitude sensing data set, and constructing a prototype set of an attitude sensing early warning model based on the historical attitude sensing data set; obtaining a three-axis data sequence of the angular velocity and a three-axis data sequence of the acceleration of a device within a preset monitoring window; determining a screened data set in the three-axis set of the angular velocity and a screened data set in the three-axis set of the acceleration; obtaining a set of pitch angles, roll angles and yaw angles; matching the set of pitch angles, roll angles and yaw angles with the prototype set of the attitude sensing early warning model, and taking the attitude sensing abnormal type corresponding to the matching result as the target attitude sensing analysis result. The present invention solves the technical problems in the prior art that attitude sensing analysis requires a long period and has low analysis reliability, and achieves the technical effects of improving analysis accuracy and analysis efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of attitude analysis, and particularly relates to an attitude sensing and analysis method and system based on a gyroscope and an accelerometer. Background Art

[0002] In the field of attitude sensing and analysis, traditional methods mainly rely on a single sensor (such as a gyroscope or an accelerometer) for attitude detection, and obtain basic attitude parameters (such as Euler angles) through algorithm processing. A single sensor is easily interfered by noise, resulting in poor stability of the attitude analysis result and inability to perform attitude feedback quickly and timely. There are technical problems in the prior art that attitude sensing and analysis require a long period and the analysis reliability is low. Summary of the Invention

[0003] The present application provides an attitude sensing and analysis method and system based on a gyroscope and an accelerometer, which are used to solve the technical problems in the prior art that attitude sensing and analysis require a long period and the analysis reliability is low.

[0004] In view of the above problems, the present application provides an attitude sensing and analysis method and system based on a gyroscope and an accelerometer.

[0005] In the first aspect of the present application, an attitude sensing and analysis method based on a gyroscope and an accelerometer is provided. The method includes:

[0006] Obtain a historical attitude sensing data set, and construct a prototype set of attitude sensing early warning models based on the historical attitude sensing data set. Each attitude sensing early warning model prototype corresponds to an attitude sensing abnormal type.

[0007] Respectively obtain a three-axis data sequence of angular velocity and a three-axis data sequence of acceleration of the device within a preset monitoring window through a gyroscope and an accelerometer disposed on the device.

[0008] Respectively perform data set screening and analysis on the three-axis data sequence of angular velocity and the three-axis data sequence of acceleration to determine a three-axis concentrated screening data set of angular velocity and a three-axis concentrated screening data set of acceleration.

[0009] Perform attitude analysis based on the three-axis concentrated screening data set of angular velocity and the three-axis concentrated screening data set of acceleration to obtain a set of pitch angle, roll angle and yaw angle.

[0010] Match the set of pitch angle, roll angle and yaw angle with the prototype set of attitude sensing early warning models, and use the attitude sensing abnormal type corresponding to the matching result as the target attitude sensing analysis result.

[0011] In the second aspect of the present application, an attitude sensing and analysis system based on a gyroscope and an accelerometer is provided. The system includes:

[0012] A prototype set construction module, configured to obtain a historical attitude sensing data set, and construct a prototype set of an attitude sensing early warning model based on the historical attitude sensing data set, wherein each attitude sensing early warning model prototype corresponds to an attitude sensing anomaly type.

[0013] A data sequence acquisition module, configured to respectively obtain a three-axis data sequence of the angular velocity of the device and a three-axis data sequence of the acceleration of the device within a preset monitoring window through a gyroscope and an accelerometer disposed on the device.

[0014] A centralized screening data acquisition module, configured to respectively perform centralized screening analysis on the three-axis data sequence of the angular velocity and the three-axis data sequence of the acceleration to determine a three-axis centralized screening data set of the angular velocity and a three-axis centralized screening data set of the acceleration.

[0015] A yaw angle set acquisition module, configured to perform attitude analysis based on the three-axis centralized screening data set of the angular velocity and the three-axis centralized screening data set of the acceleration to obtain a pitch angle, a roll angle, and a yaw angle set.

[0016] A target attitude sensing analysis result acquisition module, configured to match the pitch angle, the roll angle, and the yaw angle set with the prototype set of the attitude sensing early warning model, and use the attitude sensing anomaly type corresponding to the matching result as the target attitude sensing analysis result.

[0017] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0018] In the present application, by obtaining a historical attitude sensing data set and constructing a prototype set of an attitude sensing early warning model based on the historical attitude sensing data set, wherein each attitude sensing early warning model prototype corresponds to an attitude sensing anomaly type, and then respectively obtaining a three-axis data sequence of the angular velocity of the device and a three-axis data sequence of the acceleration of the device within a preset monitoring window through a gyroscope and an accelerometer disposed on the device, and further respectively performing centralized screening analysis on the three-axis data sequence of the angular velocity and the three-axis data sequence of the acceleration to determine a three-axis centralized screening data set of the angular velocity and a three-axis centralized screening data set of the acceleration, and then performing attitude analysis based on the three-axis centralized screening data set of the angular velocity and the three-axis centralized screening data set of the acceleration to obtain a pitch angle, a roll angle, and a yaw angle set, and matching the pitch angle, the roll angle, and the yaw angle set with the prototype set of the attitude sensing early warning model, and using the attitude sensing anomaly type corresponding to the matching result as the target attitude sensing analysis result. The technical effect of improving the reliability of the analysis data and further improving the accuracy of the attitude sensing analysis is achieved by performing data screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 Schematic flowchart of the attitude sensing analysis method based on gyroscope and accelerometer provided by the embodiment of the present application;

[0021] Figure 2 Schematic structural diagram of the attitude sensing analysis system based on gyroscope and accelerometer provided by the embodiment of the present application.

[0022] Explanation of reference numerals: prototype set construction module 11, data sequence acquisition module 12, centralized screening data acquisition module 13, yaw angle set acquisition module 14, target attitude sensing analysis result acquisition module 15. Detailed implementation manners

[0023] The present application provides an attitude sensing analysis method and system based on gyroscope and accelerometer to solve the technical problems of long cycle and low analysis reliability in attitude sensing analysis in the prior art.

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0025] It should be noted that the terms "include" and "have" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0026] Embodiment 1, as Figure 1 shown, the present application provides an attitude sensing analysis method based on gyroscope and accelerometer, wherein the method includes:

[0027] Step S100: Obtain a historical attitude sensing data set, and construct a prototype set of attitude sensing early warning models based on the historical attitude sensing data set, where each attitude sensing early warning model prototype corresponds to an attitude sensing abnormal type.

[0028] In a possible embodiment, the historical attitude sensing data set is the analysis result of the data collected by the gyroscope and accelerometer of the device within the historical time, as well as the obtained attitude sensing result. The historical attitude sensing data set reflects the attitude changes of the device in various environments and working states, and is the basic data for training the attitude warning model. The attitude sensing warning model prototype set is a set containing multiple model prototypes, and each prototype represents a specific type of attitude abnormality, such as excessive vibration, excessive tilt, etc. By performing clustering learning analysis on the historical attitude sensing data set, the constructed attitude sensing warning model prototype set is obtained, achieving the technical effect of providing data support for the rapid attitude sensing analysis of subsequent real-time attitude data. The attitude sensing abnormality type refers to various abnormal states that may occur during the use of the device, such as the tilt angle of the device exceeding a certain threshold, abnormal vibration of the device, etc.

[0029] Preferably, by integrating and analyzing the attitude data situation of a type of attitude sensing abnormality in the historical state, the attitude sensing warning model prototype of this type of attitude sensing abnormality is determined, thereby providing a prototype comparison basis for subsequent feature matching.

[0030] Further, obtaining the historical attitude sensing data set and constructing the attitude sensing warning model prototype set based on the historical attitude sensing data set, step S100 of the embodiment of the present application further includes:

[0031] Aggregating the historical attitude sensing data set for the same type of attitude sensing abnormality to obtain L aggregated historical attitude sensing data sets, where L is a positive integer;

[0032] Extracting the pitch angle, roll angle, and yaw angle sets respectively from the L aggregated historical attitude sensing data sets to obtain a set of L aggregated historical pitch-roll-yaw angle set data groups;

[0033] Using a prototype network to learn from the set of L aggregated historical pitch-roll-yaw angle set data groups respectively to construct an attitude sensing warning model prototype set.

[0034] Further, using a prototype network to learn from the set of L aggregated historical pitch-roll-yaw angle set data groups respectively to construct an attitude sensing warning model prototype set, step S100 of the embodiment of the present application further includes:

[0035] Extracting the first aggregated historical pitch-roll-yaw angle set data group set from the set of L aggregated historical pitch-roll-yaw angle set data groups;

[0036] Input the first aggregated historical pitch-roll-yaw angle set data group set into the prototype network construction function for metric space learning to obtain the prototype of the first attitude sensing warning model;

[0037] Perform metric space learning on the L aggregated historical pitch-roll-yaw angle set data group sets to obtain a set of prototypes of the attitude sensing warning model.

[0038] Furthermore, the prototype network construction function is:

[0039] ;

[0040] Wherein, is the first aggregated historical pitch-roll-yaw angle set data group set, is the number of data groups in the first aggregated historical pitch-roll-yaw angle set data group set, is a positive integer, is the rd first aggregated historical pitch-roll-yaw angle set data group, is the embedding function, is used to map the input first aggregated historical pitch-roll-yaw angle set data group set to a three-dimensional embedded feature space through a prototype network, is the learning parameter of the prototype network model.

[0041] In a possible embodiment, taking the attitude sensing anomaly type as an index, extract the anomaly types existing in each historical attitude sensing data in the historical attitude sensing data set, and divide the historical attitude sensing data belonging to the same attitude sensing type (such as angle offset, excessive tilt, excessive vibration, static drift, etc.) into the same set to obtain L aggregated historical attitude sensing data sets. Wherein, L is the total number of all attitude sensing anomaly types in the historical attitude sensing data set.

[0042] Furthermore, taking the pitch angle, roll angle, and yaw angle set as an index, retrieve the data in the L aggregated historical attitude sensing data sets to determine the attitude situation when an anomaly occurs in each aggregated historical attitude sensing data, and obtain L aggregated historical pitch-roll-yaw angle set data group sets. Wherein, each aggregated historical pitch-roll-yaw angle set data group reflects the overall attitude situation of the device when an anomaly occurs. Through the prototype network, perform feature mean recognition on the L aggregated historical pitch-roll-yaw angle set data group sets to determine the general situation of the attitude features that conform to each attitude sensing anomaly type, thereby constructing the prototype of the attitude sensing warning model for each attitude sensing anomaly type.

[0043] Preferably, the prototype network learns the distance between different first aggregated historical pitch-roll-yaw angle set data groups within the first aggregated historical pitch-roll-yaw angle set data group set, and uses the prototype network construction function to obtain the centroid of the data within the set (the most representative data feature, that is, the prototype). That is, the prototype network construction function is used to learn the attitude features within the first aggregated historical pitch-roll-yaw angle set data group set and balance the similarity between the features, thereby constructing the prototype of the first attitude sensing early warning model. When the real-time feature matches the prototype of the first attitude sensing early warning model successfully, it indicates that the device is in the attitude sensing abnormal type corresponding to the prototype of the first attitude sensing early warning model.

[0044] Preferably, the embedding function is the core of the prototype network and is used to map the historical pitch angle, historical roll angle, and historical yaw angle sets in the first aggregated historical pitch-roll-yaw angle set data group to a three-dimensional feature space, thus facilitating subsequent distance measurement. is the learning parameter automatically obtained during the training process when the prototype network learns the mapping relationship between features and attitude sensing abnormal types. The prototype network construction function is used to learn the general correspondence between the data features and attitude sensing abnormal types in the first aggregated historical pitch-roll-yaw angle set data group set, thereby obtaining the prototype of the first attitude sensing early warning model.

[0045] Based on the same principle as obtaining the prototype of the first attitude sensing early warning model, similar learning is performed on L aggregated historical pitch-roll-yaw angle set data group sets to construct a complete set of prototype of the attitude sensing early warning model. By providing the prototype features of each attitude sensing abnormal type, a benchmark is provided for the real-time attitude data analysis of the device. Through the matching of the real-time attitude data of the device with these prototypes, it can be identified whether the device has abnormalities and early warning information can be sent in a timely manner, thereby improving the operation safety and reliability of the device.

[0046] Step S200: Respectively obtain the angular velocity three-axis data sequence and the acceleration three-axis data sequence of the device within a preset monitoring window through the gyroscope and accelerometer arranged on the device.

[0047] Furthermore, each angular velocity three-axis data includes three angular velocity data of the device on the x-axis, y-axis, and z-axis respectively, and each acceleration three-axis data includes three acceleration data of the device on the x-axis, y-axis, and z-axis respectively.

[0048] In one embodiment, the preset monitoring window is a sampling time period preset by those skilled in the art. Within the preset monitoring window, data of the gyroscope and the accelerometer are collected at a preset sampling frequency, so as to obtain a three-axis angular velocity data sequence and a three-axis acceleration data sequence of the device. Each three-axis angular velocity data includes three angular velocity data of the device on the x-axis, y-axis, and z-axis respectively. Each three-axis acceleration data includes three acceleration data of the device on the x-axis, y-axis, and z-axis respectively. The three-axis angular velocity data sequence reflects the angular velocity change of the device on the three axes. The three-axis acceleration data sequence reflects the acceleration change of the device on the three axes. The technical effect of providing data support for subsequent device attitude analysis is achieved.

[0049] Step S300: Perform data centralized screening and analysis on the three-axis angular velocity data sequence and the three-axis acceleration data sequence respectively to determine a three-axis angular velocity centralized screening data set and a three-axis acceleration centralized screening data set.

[0050] In one embodiment, after obtaining the three-axis angular velocity data sequence and the three-axis acceleration data sequence, it is necessary to perform general situation analysis on the angular velocity data and acceleration data of the device within the preset monitoring window, that is, to screen out the data that can best represent the device attitude during this period, and obtain the three-axis angular velocity centralized screening data set and the three-axis acceleration centralized screening data set. Among them, the three-axis angular velocity centralized screening data set includes a three-axis angular velocity centralized screening data set on the x-axis, a three-axis angular velocity centralized screening data set on the y-axis, and a three-axis angular velocity centralized screening data set on the z-axis. The three-axis acceleration centralized screening data set includes a three-axis acceleration centralized screening data set on the x-axis, a three-axis acceleration centralized screening data set on the y-axis, and a three-axis acceleration centralized screening data set on the z-axis.

[0051] Furthermore, when performing data centralized screening and analysis on the three-axis angular velocity data sequence and the three-axis acceleration data sequence respectively to determine a three-axis angular velocity centralized screening data set and a three-axis acceleration centralized screening data set, step S300 of the embodiment of the present application further includes:

[0052] Extract the mode of the three-axis angular velocity data sequence and the three-axis acceleration data sequence respectively to obtain a three-axis angular velocity mode data set with timestamp identification and a three-axis acceleration mode data set with timestamp identification;

[0053] Extract the angular velocity x-axis mode set from the three-axis angular velocity mode data set with timestamp identification, and extract the angular velocity x-axis data sequence from the three-axis angular velocity data sequence;

[0054] Combined with the timestamp identification, perform data similarity and time approximation two-dimensional constraint centralized screening on the angular velocity x-axis mode set in the angular velocity x-axis data sequence to obtain the angular velocity x-axis centralized screening data;

[0055] In the angular velocity three-axis data sequence and the acceleration three-axis data sequence, perform data similarity and time approximation two-dimensional constraint centralized screening on the angular velocity three-axis mode data set and the acceleration three-axis mode data set with time stamp identifiers, and obtain the angular velocity three-axis centralized screening data set and the acceleration three-axis centralized screening data set.

[0056] Further, in combination with the time stamp identifier, perform data similarity and time approximation two-dimensional constraint centralized screening on the angular velocity x-axis mode set in the angular velocity x-axis data sequence to obtain the angular velocity x-axis centralized screening data. Step S300 of the embodiment of the present application further includes:

[0057] In combination with the time stamp identifier, use the two-dimensional approximation analysis function to traverse and perform approximation analysis on the angular velocity x-axis data in the angular velocity x-axis data sequence and the angular velocity x-axis mode set to obtain a two-dimensional approximation coefficient sequence;

[0058] Add the angular velocity x-axis data in the two-dimensional approximation coefficient sequence that is greater than or equal to the preset two-dimensional approximation coefficient threshold to the x-axis centralized screening set;

[0059] Calculate the mean value of the x-axis centralized screening set to obtain the angular velocity x-axis centralized screening data.

[0060] Further, the two-dimensional approximation analysis function is:

[0061] ;

[0062] Wherein, is the two-dimensional approximation coefficient, is the number of angular velocity x-axis data in the angular velocity x-axis data sequence, is a positive integer, is any angular velocity x-axis data in the angular velocity x-axis data sequence, is the th angular velocity x-axis mode in the angular velocity x-axis mode set, is the time stamp corresponding to any angular velocity x-axis data in the angular velocity x-axis data sequence, is the th time stamp corresponding to the angular velocity x-axis mode in the angular velocity x-axis mode set, is the weight for balancing data similarity and time approximation.

[0063] In a possible embodiment, the mode is the data with the highest frequency of occurrence in a set of data. That is to say, compared with other data in the set of data, the mode is more representative. Therefore, it is necessary to extract the mode from the angular velocity three-axis data sequence and the acceleration three-axis data sequence respectively. And in order to improve the reliability of subsequent nearest neighbor analysis, the time points where the extracted modes are located are synchronously extracted to obtain an angular velocity three-axis mode data set and an acceleration three-axis mode data set with time stamp identifiers. Among them, the time stamp identifier is used to describe the data acquisition time.

[0064] Extract the angular velocity x-axis mode set from the angular velocity three-axis mode data set with time stamp identifier, and extract the angular velocity x-axis data sequence from the angular velocity three-axis data sequence, so as to screen the angular velocity data on the x-axis. In the process of centralized screening, data that is also representative except for the mode is screened and analyzed from two dimensions: data similarity and time approximation, so as to obtain the angular velocity x-axis centralized screening data.

[0065] Preferably, the two-dimensional approximation analysis function is used to analyze the overall approximation of each angular velocity x-axis data in the angular velocity x-axis data sequence to the angular velocity x-axis mode set from two dimensions: data similarity and time approximation, so as to obtain the two-dimensional approximation coefficient sequence. Among them, the two-dimensional approximation coefficient reflects the degree to which the angular velocity x-axis data can be used as representative data. The larger the two-dimensional approximation coefficient, the higher the quality of the corresponding angular velocity x-axis data. After performing approximate analysis on all the angular velocity x-axis data in the angular velocity x-axis data sequence, the two-dimensional approximation coefficient sequence is obtained.

[0066] The preset two-dimensional approximation coefficient threshold is the lowest two-dimensional approximation coefficient that can be used as reliable data preset by those skilled in the art. Add the angular velocity x-axis data in the two-dimensional approximation coefficient sequence that is greater than or equal to the preset two-dimensional approximation coefficient threshold into the x-axis centralized screening set, and perform mean calculation to obtain the angular velocity x-axis centralized screening data.

[0067] Based on the same principle as obtaining the angular velocity x-axis centralized screening data, the angular velocity three-axis data sequence and the acceleration three-axis data sequence are respectively screened to obtain the angular velocity three-axis centralized screening data set and the acceleration three-axis centralized screening data set.

[0068] Step S400: Perform attitude analysis based on the angular velocity three-axis centralized screening data set and the acceleration three-axis centralized screening data set to obtain a set of pitch angles, roll angles and yaw angles.

[0069] Furthermore, step S400 of the embodiment of the present application further includes:

[0070] The pitch angle calculation formula is used to analyze the acceleration three-axis concentrated screening data set to obtain the pitch angle;

[0071] The roll angle calculation formula is used to analyze the acceleration triaxial concentrated screening data set to obtain the roll angle;

[0072] The yaw angle calculation formula is used to analyze the angular velocity three-axis concentrated screening data set to obtain a yaw angle set.

[0073] Preferably, after obtaining the angular velocity three-axis concentrated screening data set and the acceleration three-axis concentrated screening data set, that is, the basic data for posture analysis, the pitch angle calculation formula, the roll angle calculation formula and the yaw angle calculation formula are used to calculate each angle.

[0074] Wherein, the pitch angle calculation formula is:

[0075] ;

[0076] The roll angle calculation formula is:

[0077] ;

[0078] in, is the pitch angle, is the roll angle, To filter the data for the acceleration x-axis, To filter the data for the acceleration y-axis, Filter the data to focus on the acceleration z-axis.

[0079] The yaw angle calculation formula is:

[0080] ;

[0081] in, is the z-axis yaw angle of the device, is the z-axis yaw angle of the device during the last attitude analysis. Filter the data to focus on the gyroscope's angular velocity z-axis.

[0082] Based on the same principle as obtaining the z-axis yaw angle of the device, the x-axis yaw angle and the y-axis yaw angle of the device are calculated respectively. The x-axis yaw angle, the yaw angle, and the z-axis yaw angle are summed up to obtain the yaw angle set.

[0083] Step S500: matching the pitch angle, roll angle and yaw angle set with the attitude sensing warning model prototype set, and taking the attitude sensing abnormality type corresponding to the matching result as the target attitude sensing analysis result.

[0084] Using embedded functions Perform feature mapping on the set of pitch angle, roll angle, and yaw angle to obtain real-time attitude features, calculate the cosine similarity between the real-time attitude features and the prototype set of the attitude sensing warning model respectively to obtain a similarity set. Take the prototype of the attitude sensing warning model corresponding to the maximum value in the similarity set as the matching result, and take the attitude sensing abnormal type corresponding to the matching result as the target attitude sensing analysis result.

[0085] By performing prototype matching, the response speed and accuracy to the change of the device attitude are achieved, ensuring the timely discovery of potential faults or abnormalities and guaranteeing the safe and stable operation of the device.

[0086] In summary, the embodiments of the present application at least have the following technical effects:

[0087] In the present application, by obtaining a historical attitude sensing data set, constructing a prototype set of an attitude sensing warning model based on the historical attitude sensing data set, where each prototype of the attitude sensing warning model corresponds to an attitude sensing abnormal type, and then respectively obtaining the three-axis data sequence of the angular velocity and the three-axis data sequence of the acceleration of the device within a preset monitoring window through a gyroscope and an accelerometer arranged on the device, and then respectively performing screening and analysis on the three-axis data sequence of the angular velocity and the three-axis data sequence of the acceleration to determine the three-axis concentrated screening data set of the angular velocity and the three-axis concentrated screening data set of the acceleration, and then performing attitude analysis based on the three-axis concentrated screening data set of the angular velocity and the three-axis concentrated screening data set of the acceleration to obtain a set of pitch angle, roll angle, and yaw angle, and matching the set of pitch angle, roll angle, and yaw angle with the prototype set of the attitude sensing warning model, and taking the attitude sensing abnormal type corresponding to the matching result as the target attitude sensing analysis result. The technical effect of improving the reliability of the analysis data and further improving the accuracy of the attitude sensing analysis is achieved by performing data screening.

[0088] Embodiment 2, based on the same inventive concept as the attitude sensing analysis method based on a gyroscope and an accelerometer in the foregoing embodiment, as Figure 2 shown, the present application provides an attitude sensing analysis system based on a gyroscope and an accelerometer. The system in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the system includes:

[0089] A prototype set construction module 11, configured to obtain a historical attitude sensing data set and construct a prototype set of an attitude sensing warning model based on the historical attitude sensing data set, where each prototype of the attitude sensing warning model corresponds to an attitude sensing abnormal type;

[0090] A data sequence obtaining module 12, configured to respectively obtain a three-axis data sequence of the angular velocity and a three-axis data sequence of the acceleration of the device within a preset monitoring window through a gyroscope and an accelerometer arranged on the device;

[0091] The centralized screening data acquisition module 13 is used to perform centralized screening analysis on the three-axis angular velocity data sequence and the three-axis acceleration data sequence respectively, and determine the three-axis centralized screening data set of angular velocity and the three-axis centralized screening data set of acceleration;

[0092] The yaw angle set acquisition module 14 is used to perform attitude analysis based on the three-axis centralized screening data set of angular velocity and the three-axis centralized screening data set of acceleration, and obtain the pitch angle, roll angle and yaw angle set;

[0093] The target attitude sensing analysis result acquisition module 15 is used to match the pitch angle, roll angle and yaw angle set with the attitude sensing early warning model prototype set, and use the attitude sensing abnormal type corresponding to the matching result as the target attitude sensing analysis result.

[0094] Furthermore, the prototype set construction module 11 is used to execute the following steps:

[0095] Aggregate the historical attitude sensing data sets by the same type of attitude sensing abnormal type to obtain L aggregated historical attitude sensing data sets, where L is a positive integer;

[0096] Extract the pitch angle, roll angle and yaw angle set from the L aggregated historical attitude sensing data sets respectively to obtain a set of L aggregated historical pitch angle-roll angle-yaw angle set data groups;

[0097] Use the prototype network to learn the set of L aggregated historical pitch angle-roll angle-yaw angle set data groups respectively, and construct the attitude sensing early warning model prototype set.

[0098] Furthermore, the prototype set construction module 11 is used to execute the following steps:

[0099] Extract the first aggregated historical pitch angle-roll angle-yaw angle set data group set from the set of L aggregated historical pitch angle-roll angle-yaw angle set data groups;

[0100] Input the first aggregated historical pitch angle-roll angle-yaw angle set data group set into the prototype network construction function for metric space learning to obtain the first attitude sensing early warning model prototype;

[0101] Perform metric space learning on the set of L aggregated historical pitch angle-roll angle-yaw angle set data groups to obtain the attitude sensing early warning model prototype set.

[0102] Furthermore, the prototype network construction function is:

[0103] ;

[0104] Among them, is the first aggregated historical pitch-roll-yaw angle set data group set, is the number of data groups in the first aggregated historical pitch-roll-yaw angle set data group set, is a positive integer, is the th first aggregated historical pitch-roll-yaw angle set data group, is an embedding function, which is used to map the input first aggregated historical pitch-roll-yaw angle set data group set to a three-dimensional embedded feature space through a prototype network, is the learning parameter of the prototype network model.

[0105] Furthermore, the centralized screening data acquisition module 13 is used to perform the following steps:

[0106] Extract the mode of the angular velocity three-axis data sequence and the acceleration three-axis data sequence respectively to obtain an angular velocity three-axis mode data set and an acceleration three-axis mode data set with timestamp identifiers;

[0107] Extract the angular velocity x-axis mode set from the angular velocity three-axis mode data set with timestamp identifier, and extract the angular velocity x-axis data sequence from the angular velocity three-axis data sequence;

[0108] Combined with the timestamp identifier, perform data similarity and time approximation two-dimensional constraint centralized screening on the angular velocity x-axis mode set in the angular velocity x-axis data sequence to obtain angular velocity x-axis centralized screening data;

[0109] Perform data similarity and time approximation two-dimensional constraint centralized screening on the angular velocity three-axis mode data set and the acceleration three-axis mode data set with timestamp identifier in the angular velocity three-axis data sequence and the acceleration three-axis data sequence to obtain the angular velocity three-axis centralized screening data set and the acceleration three-axis centralized screening data set.

[0110] Furthermore, the centralized screening data acquisition module 13 is used to perform the following steps:

[0111] Combined with the timestamp identifier, use the two-dimensional approximation analysis function to traverse and perform approximation analysis on the angular velocity x-axis data in the angular velocity x-axis data sequence and the angular velocity x-axis mode set to obtain a two-dimensional approximation coefficient sequence;

[0112] Add the angular velocity x-axis data in the two-dimensional approximation coefficient sequence that is greater than or equal to the preset two-dimensional approximation coefficient threshold to the x-axis centralized screening set;

[0113] Calculate the mean value of the x-axis concentrated screening set to obtain the angular velocity x-axis concentrated screening data.

[0114] Further, the two-dimensional approximation analysis function is:

[0115] ;

[0116] Where is the two-dimensional approximation coefficient, is the number of angular velocity x-axis data in the angular velocity x-axis data sequence, is a positive integer, is any angular velocity x-axis data in the angular velocity x-axis data sequence, is the th angular velocity x-axis mode in the angular velocity x-axis mode set, is the timestamp corresponding to any angular velocity x-axis data in the angular velocity x-axis data sequence, is the th timestamp corresponding to the angular velocity x-axis mode in the angular velocity x-axis mode set, is the weight for balancing data similarity and time approximation.

[0117] Further, the yaw angle set obtaining module 14 is used to perform the following steps:

[0118] Analyze the acceleration three-axis concentrated screening data set using the pitch angle calculation formula to obtain the pitch angle;

[0119] Analyze the acceleration three-axis concentrated screening data set using the roll angle calculation formula to obtain the roll angle;

[0120] Analyze the angular velocity three-axis concentrated screening data set using the yaw angle calculation formula to obtain the yaw angle set.

[0121] Further, each angular velocity three-axis data includes three angular velocity data of the device on the x-axis, y-axis, and z-axis respectively, and each acceleration three-axis data includes three acceleration data of the device on the x-axis, y-axis, and z-axis respectively.

[0122] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the advantages or disadvantages of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0124] This specification and the drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. An attitude sensing and analysis method based on a gyroscope and an accelerometer, characterized in that, The method includes: Obtaining a historical posture sensing data set, and constructing a posture sensing early warning model prototype set based on the historical posture sensing data set, wherein each posture sensing early warning model prototype corresponds to a posture sensing anomaly type; Respectively obtaining a three-axis data sequence of angular velocity and a three-axis data sequence of acceleration of the device within a preset monitoring window through a gyroscope and an accelerometer disposed on the device; Respectively performing data set screening and analysis on the three-axis data sequence of angular velocity and the three-axis data sequence of acceleration to determine a three-axis concentrated screening data set of angular velocity and a three-axis concentrated screening data set of acceleration; Performing posture analysis based on the three-axis concentrated screening data set of angular velocity and the three-axis concentrated screening data set of acceleration to obtain a set of pitch angles, roll angles and yaw angles; Matching the set of pitch angles, roll angles and yaw angles with the posture sensing early warning model prototype set, and using the posture sensing anomaly type corresponding to the matching result as the target posture sensing analysis result; Among them, respectively performing data set screening and analysis on the three-axis data sequence of angular velocity and the three-axis data sequence of acceleration to determine a three-axis concentrated screening data set of angular velocity and a three-axis concentrated screening data set of acceleration, including: Respectively performing mode extraction on the three-axis data sequence of angular velocity and the three-axis data sequence of acceleration to obtain a three-axis mode data set of angular velocity with timestamp identification and a three-axis mode data set of acceleration with timestamp identification; Extracting an x-axis mode set of angular velocity from the three-axis mode data set of angular velocity with timestamp identification, and extracting an x-axis data sequence of angular velocity from the three-axis data sequence of angular velocity; Combining the timestamp identification, performing data similarity and time approximation two-dimensional constraint concentrated screening on the x-axis mode set of angular velocity in the x-axis data sequence of angular velocity to obtain the x-axis concentrated screening data of angular velocity; Based on the same principle as obtaining the x-axis concentrated screening data of angular velocity, performing data similarity and time approximation two-dimensional constraint concentrated screening on the three-axis mode data set of angular velocity with timestamp identification and the three-axis mode data set of acceleration with timestamp identification in the three-axis data sequence of angular velocity and the three-axis data sequence of acceleration to obtain the three-axis concentrated screening data set of angular velocity and the three-axis concentrated screening data set of acceleration.

2. The attitude sensing and analysis method based on gyroscope and accelerometer according to claim 1, wherein Obtaining a historical posture sensing data set, and constructing a posture sensing early warning model prototype set based on the historical posture sensing data set, including: Aggregating the historical posture sensing data set by the same type of posture sensing anomaly type to obtain L aggregated historical posture sensing data sets, where L is a positive integer; Respectively extracting a set of pitch angles, roll angles and yaw angles from the L aggregated historical posture sensing data sets to obtain a set of L aggregated historical pitch angle-roll angle-yaw angle set data groups; Using a prototype network to respectively learn the set of L aggregated historical pitch angle-roll angle-yaw angle set data groups to construct a posture sensing early warning model prototype set.

3. The attitude sensing and analysis method based on a gyroscope and an accelerometer according to claim 2, wherein Using a prototype network to respectively learn the set of L aggregated historical pitch angle-roll angle-yaw angle set data groups to construct a posture sensing early warning model prototype set, including: Extract the first set of aggregated historical pitch-roll-yaw angle set data groups from the set of the L sets of aggregated historical pitch-roll-yaw angle set data groups; Input the first set of aggregated historical pitch-roll-yaw angle set data groups into the prototype network construction function for metric space learning to obtain the prototype of the first attitude sensing and warning model; Perform metric space learning on the set of the L sets of aggregated historical pitch-roll-yaw angle set data groups to obtain a set of prototypes of the attitude sensing and warning model.

4. The attitude sensing and analysis method based on a gyroscope and an accelerometer according to claim 3, wherein, The prototype network construction function is: ； Among them, is the first aggregated historical pitch-roll-yaw angle set data group set, is the number of data groups in the first aggregated historical pitch-roll-yaw angle set data group set, is a positive integer, is the th first aggregated historical pitch-roll-yaw angle set data group, is an embedding function, used to map the input first aggregated historical pitch-roll-yaw angle set data group set to a three-dimensional embedded feature space through a prototype network, are the learning parameters of the prototype network model.

5. The attitude sensing and analysis method based on a gyroscope and an accelerometer according to claim 1, characterized in that Combined with the timestamp identifier, perform data similarity and time approximation two-dimensional constraint concentration screening on the set of angular velocity x-axis modes in the angular velocity x-axis data sequence to obtain the angular velocity x-axis concentrated screening data, including: Combined with the timestamp identifier, use the two-dimensional approximation analysis function to traverse and perform approximation analysis on the angular velocity x-axis data in the angular velocity x-axis data sequence and the set of angular velocity x-axis modes to obtain a two-dimensional approximation coefficient sequence; Add the angular velocity x-axis data in the two-dimensional approximation coefficient sequence that is greater than or equal to the preset two-dimensional approximation coefficient threshold into the x-axis concentrated screening set; Calculate the mean value of the x-axis concentrated screening set to obtain the angular velocity x-axis concentrated screening data.

6. The attitude sensing and analysis method based on a gyroscope and an accelerometer according to claim 5, characterized in that, The two-dimensional approximation analysis function is: ； Wherein, is the two-dimensional approximation coefficient, is the number of angular velocity x-axis data in the angular velocity x-axis data sequence, is a positive integer, is any angular velocity x-axis data in the angular velocity x-axis data sequence, is the th angular velocity x-axis mode in the angular velocity x-axis mode set, is the timestamp corresponding to any angular velocity x-axis data in the angular velocity x-axis data sequence, is the th timestamp corresponding to the angular velocity x-axis mode in the angular velocity x-axis mode set, is the weight of balance data similarity and time approximation.

7. The attitude sensing and analysis method based on a gyroscope and an accelerometer according to claim 1, wherein Including: Use the pitch angle calculation formula to analyze the set of acceleration three-axis concentrated screening data to obtain the pitch angle; Use the roll angle calculation formula to analyze the set of acceleration three-axis concentrated screening data to obtain the roll angle; Use the yaw angle calculation formula to analyze the set of angular velocity three-axis concentrated screening data to obtain the set of yaw angles.

8. The attitude sensing and analysis method based on a gyroscope and an accelerometer according to claim 1, characterized in that Each angular velocity three-axis data includes three angular velocity data of the device on the x-axis, y-axis, and z-axis respectively, and each acceleration three-axis data includes three acceleration data of the device on the x-axis, y-axis, and z-axis respectively.

9. An attitude sensing and analysis system based on a gyroscope and an accelerometer, characterized in that, For implementing the attitude sensing analysis method based on gyroscope and accelerometer according to any one of claims 1-8, the system includes: A prototype set construction module, configured to obtain a historical attitude sensing data set, and construct a set of prototypes of the attitude sensing and warning model based on the historical attitude sensing data set, wherein each prototype of the attitude sensing and warning model corresponds to an attitude sensing abnormal type; A data sequence obtaining module, configured to respectively obtain an angular velocity three-axis data sequence and an acceleration three-axis data sequence of the device within a preset monitoring window through a gyroscope and an accelerometer disposed on the device; A concentrated screening data obtaining module, configured to respectively perform data concentrated screening analysis on the angular velocity three-axis data sequence and the acceleration three-axis data sequence to determine an angular velocity three-axis concentrated screening data set and an acceleration three-axis concentrated screening data set; A yaw angle set obtaining module, configured to perform attitude analysis based on the angular velocity three-axis concentrated screening data set and the acceleration three-axis concentrated screening data set to obtain a pitch angle, a roll angle, and a set of yaw angles; A target attitude sensing analysis result obtaining module, configured to match the pitch angle, roll angle, and set of yaw angles with the set of prototypes of the attitude sensing and warning model, and use the attitude sensing abnormal type corresponding to the matching result as the target attitude sensing analysis result.

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