CN110750536A - Vibration noise smoothing method and system for attitude time series data - Google Patents
Vibration noise smoothing method and system for attitude time series data Download PDFInfo
- Publication number
- CN110750536A CN110750536A CN201910964404.5A CN201910964404A CN110750536A CN 110750536 A CN110750536 A CN 110750536A CN 201910964404 A CN201910964404 A CN 201910964404A CN 110750536 A CN110750536 A CN 110750536A
- Authority
- CN
- China
- Prior art keywords
- attitude
- data
- time
- sequence data
- storage space
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2477—Temporal data queries
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Probability & Statistics with Applications (AREA)
- Fuzzy Systems (AREA)
- Position Input By Displaying (AREA)
Abstract
The invention provides a method and a system for smoothing vibration noise of attitude time sequence data, which are used for dividing the original attitude time sequence data according to a preset time window to form a plurality of sections of attitude time sub-sequence data, can simultaneously carry out vibration noise smoothing on each section of attitude time sub-sequence data, can improve the processing efficiency and have higher real-time performance.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a vibration noise smoothing method and system for attitude time series data.
Background
And acquiring attitude time sequence data of the measured object by adopting the three-axis acceleration data of the gyroscope according to the force synthesis and decomposition. The object to be measured is generally a complex mechanical device, and each part of the complex mechanical device generates coupling vibration during working, so that the detected attitude time series data of the object to be measured is superposed with vibration noise interference, and the attitude time series data of the object to be measured cannot accurately reflect the attitude of the object to be measured.
The traditional mode is to carry out vibration noise smoothing processing on attitude time series data of a measured object in batch, and because the data volume of the attitude time series data is large, the processing time is long, the real-time performance is not high, and the hysteresis is long when the attitude time series data is processed in batch.
Disclosure of Invention
To overcome the above existing problems or at least partially solve the above problems, embodiments of the present invention provide a method and a system for smoothing vibration noise of pose time-series data.
According to a first aspect of the embodiments of the present invention, there is provided a vibration noise smoothing method for attitude time series data, including:
intercepting original attitude time sequence data according to preset time windows to form attitude time sub-sequence data corresponding to each preset time window, wherein the attitude time sub-sequence data comprises attitude data corresponding to a plurality of time points;
performing vibration noise smoothing processing on each attitude data in each attitude time sub-sequence data to obtain smoothed attitude time sub-sequence data;
and combining each smoothed attitude time sub-sequence data, and replacing and updating the original attitude time sub-sequence data.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, before intercepting the original pose time-series data according to a preset time window, the method further includes:
creating a first array in a memory for storing original posture time series data;
creating a second array in the memory for storing a copy of the original posture time series data;
correspondingly, the intercepting the original attitude time series data according to a preset time window comprises:
and intercepting the copy of the original posture time sequence data stored in the second array according to a preset time window.
Further, the intercepting the copy of the original posture time series data stored in the second array according to a preset time window further includes:
the method comprises the steps of forming a first matrix by a plurality of attitude time sub-sequence data, and storing the first matrix in a first queue created in a memory, wherein each attitude time sub-sequence data in the first matrix occupies one storage space in the first queue.
Further, after the storing the first matrix in the first queue created in the memory, the method further includes:
creating a second queue, forming a second matrix by indexes of each attitude data in the first matrix in the second array, and storing the second matrix in the second queue, wherein the indexes corresponding to a plurality of attitude data belonging to the same attitude time sub-sequence data occupy one storage space of the second queue.
Further, each attitude data in the first matrix and each attitude data in the second array are in one-to-one correspondence through an index of each attitude data in the second matrix.
Further, the smoothing processing of the vibration noise in each attitude time sub-sequence data to obtain the attitude time sub-sequence data after smoothing processing includes:
for any one storage space in the first queue, calculating an average value of a plurality of attitude data in the any one storage space;
and replacing the plurality of attitude data in any storage space by adopting the average value to form a plurality of attitude data after the replacement of any storage space.
Further, the method also comprises the following steps:
creating a third array in the memory, and storing the calculated average value corresponding to any storage space in the third array;
correspondingly, replacing the plurality of posture data in any storage space with the average value comprises:
and replacing each attitude data in any storage space by using the average value corresponding to any storage space in the third array.
According to a second aspect of the embodiments of the present invention, there is provided a vibration noise smoothing processing system for attitude time series data, including:
the system comprises an intercepting module, a processing module and a processing module, wherein the intercepting module is used for intercepting original attitude time sequence data according to preset time windows to form attitude time sub-sequence data corresponding to each preset time window, and the attitude time sub-sequence data comprises attitude data corresponding to a plurality of time points;
the smoothing module is used for smoothing vibration noise of each attitude data in each attitude time sub-sequence data to obtain smoothed attitude time sub-sequence data;
and the replacing and updating module is used for combining each smoothed gesture time sub-sequence data and replacing and updating the original gesture time sub-sequence data.
Further, the smoothing processing module comprises a computing unit and a mean value replacing unit;
the calculation unit is used for calculating an average value of a plurality of attitude data in any storage space in the first queue;
the mean value replacing unit is used for replacing the plurality of attitude data in any storage space by adopting the mean value to form a plurality of attitude data after the replacement of any storage space;
the method comprises the steps of creating a first matrix by a plurality of attitude time sub-sequence data, and storing the first matrix in a first queue created in a memory, wherein each attitude time sub-sequence data in the first matrix occupies one storage space in the first queue.
According to a third aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor calls the program instruction to execute a vibration noise smoothing processing method for posture time series data provided in any one of various possible implementations of the first aspect.
According to a fourth aspect of embodiments of the present invention, there is also provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute a vibration noise smoothing processing method for pose time-series data provided in any one of various possible implementations of the first aspect.
The embodiment of the invention provides a method and a system for smoothing vibration noise of attitude time sequence data, which are used for dividing the original attitude time sequence data according to a preset time window to form a plurality of sections of attitude time sub-sequence data, can simultaneously carry out vibration noise smoothing on each section of attitude time sub-sequence data, can improve the processing efficiency and have higher real-time performance, and can inhibit the vibration noise in the attitude time sequence data and support the smooth transition of continuous data with the same working condition and different attitudes.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic overall flow chart of a vibration noise smoothing processing method for attitude time series data according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a vibration noise smoothing system for attitude time series data according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a vibration noise smoothing system for attitude time series data according to an embodiment of the present invention
FIG. 4 is a block diagram of the internal connections of the smoothing module of FIG. 3;
fig. 5 is a schematic view of an overall structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
In an embodiment of the present invention, a method for smoothing vibration noise of attitude time series data is provided, and fig. 1 is a schematic overall flow chart of the method for smoothing vibration noise provided in the embodiment of the present invention, where the method includes:
intercepting original attitude time sequence data according to preset time windows to form attitude time sub-sequence data corresponding to each preset time window, wherein the attitude time sub-sequence data comprises attitude data corresponding to a plurality of time points;
performing vibration noise smoothing processing on each attitude data in each attitude time sub-sequence data to obtain smoothed attitude time sub-sequence data;
and combining each smoothed attitude time sub-sequence data, and replacing and updating the original attitude time sub-sequence data.
It can be understood that, when acquiring attitude data of an object to be measured, the gyroscope triaxial acceleration sensor is used to measure triaxial acceleration data of the object to be measured, and attitude time series data of the object to be measured, that is, data of time variation of an inclination angle of the object to be measured with a horizontal direction, is obtained by calculation according to the gyroscope triaxial acceleration data.
In practical application, the measured object is generally complex mechanical equipment, and each part of the measured object generates coupling vibration during working, so that the time series data of the posture of the measured object is superposed with vibration noise interference, and the real posture of the measured object cannot be accurately reflected. In the embodiment of the invention, when a batch of gyroscope attitude time sequence data (called as original attitude time sequence data) arrives, the original attitude time sequence data is intercepted according to the preset time window to form a plurality of sections of attitude time sub-sequence data corresponding to each preset time window.
Wherein, each time point corresponds to an attitude data, and the attitude data corresponding to a plurality of time points form the time sequence data of the original attitude. In addition, for example, the preset time window is 10s, each second corresponds to the posture data of the measured object at a time point, the original posture time series data is intercepted according to the time window of every 10s, it should be noted that in the embodiment of the present invention, multiple pieces of posture time series data formed after the interception are overlapped, for example, the first time window intercepts 10 posture data from 1s to 10s of the original posture time series data, and the second time window intercepts 10 posture data from 2s to 11s of the original posture time series data.
Smoothing the vibration noise in each attitude time sub-sequence data to obtain attitude time sub-sequence data after the vibration noise is smoothed; and combining each smoothed attitude time sub-sequence data, and replacing and updating the original attitude time sequence data to obtain the attitude time sequence data after vibration smoothing corresponding to the whole original attitude time sequence data.
The embodiment of the invention provides a method and a system for smoothing vibration noise of attitude time sequence data, which are used for dividing the original attitude time sequence data according to a preset time window to form a plurality of sections of attitude time sub-sequence data, can simultaneously carry out vibration noise smoothing on each section of attitude time sub-sequence data, can improve the processing efficiency and have higher real-time performance, and can inhibit the vibration noise in the attitude time sequence data and support the smooth transition of continuous data with the same working condition and different attitudes.
On the basis of the foregoing embodiments, in an embodiment of the present invention, before intercepting the original pose time-series data according to a preset time window, the method further includes:
creating a first array in a memory for storing original posture time series data;
creating a second array in the memory for storing a copy of the original posture time series data;
correspondingly, the intercepting the original attitude time series data according to a preset time window comprises:
and intercepting the copy of the original posture time sequence data stored in the second array according to a preset time window.
It will be appreciated that a first array, in which the original pose time series data is stored, and a second array, in which a copy of the original pose time series data is stored, are created separately in memory. When the original attitude time series data are intercepted, the copies of the original attitude time series data stored in the second array are intercepted according to preset time windows to form attitude time sub-series data corresponding to each preset time window, and each attitude time sub-series data comprises a plurality of attitude data.
On the basis of the foregoing embodiments, in an embodiment of the present invention, after intercepting the copy of the original pose time-series data stored in the second array according to a preset time window, the method further includes:
the method comprises the steps of forming a first matrix by a plurality of attitude time sub-sequence data, and storing the first matrix in a first queue created in a memory, wherein each attitude time sub-sequence data in the first matrix occupies a storage space in the first queue.
It can be understood that, after the original pose time series data in the second array are intercepted according to the preset time window, a plurality of pose time sub-series data are formed, in the embodiment of the present invention, the plurality of pose time sub-series data form a first matrix, a first queue is opened up in the memory, and the first matrix is stored in the first queue, where each pose time sub-series data in the first matrix occupies one storage space in the first queue, that is, each storage space in the first queue stores a plurality of pose data.
On the basis of the foregoing embodiments, in an embodiment of the present invention, after storing the first matrix in the first queue created in the memory, the method further includes:
and creating a second queue, forming a second matrix by indexes of each attitude data in the first matrix in the second array, and storing the second matrix in the second queue, wherein the indexes corresponding to a plurality of attitude data belonging to the same attitude time sub-sequence data occupy one storage space of the second queue.
It can be understood that the original posture time sequence data is stored in the second array, and each posture time sequence data obtained after interception is stored in the first matrix in the first queue. The embodiment of the invention opens up a second queue in the memory, wherein the index of each attitude data in the first matrix in the second array forms a second matrix, and the second matrix is stored in the second queue, namely the index of each attitude data is stored in the second matrix. The indexes corresponding to a plurality of posture data belonging to the same posture time sub-sequence data occupy a storage space of the second queue, namely, the indexes corresponding to a plurality of posture data in the same posture time sub-sequence data occupy a storage space of the second queue.
After the index processing, each attitude data in the first matrix and each attitude data in the second array are in one-to-one correspondence through the index of each attitude data in the second matrix. Therefore, when the attitude data needs to be processed subsequently, the attitude data in the second array can be in one-to-one correspondence with the attitude data in the first matrix.
On the basis of the foregoing embodiments, in an embodiment of the present invention, smoothing the vibration noise in each posture time sub-sequence data, and obtaining posture time sub-sequence data after smoothing includes:
for any one storage space in the first queue, calculating an average value of a plurality of attitude data in the any one storage space;
and replacing the plurality of attitude data in any storage space with the average value to form a plurality of attitude data after replacement of any storage space.
It can be understood that the intercepted attitude time sub-sequence data is stored in the first queue, and each attitude time sub-sequence data occupies one storage space in the first queue. Therefore, in the embodiment of the present invention, for any storage space in the first queue, an average value of the plurality of posture data in the any storage space is calculated, and the average value is used to replace each posture data in the storage space, so as to form a plurality of posture data after any storage space is replaced.
On the basis of the above embodiments, in an embodiment of the present invention, the method further includes:
creating a third array in the memory, and storing the calculated average value corresponding to any storage space in the third array;
correspondingly, replacing the plurality of posture data in any storage space with the average value comprises:
and replacing each attitude data in any storage space by using the average value corresponding to any storage space in the third array.
It is understood that, in the embodiment of the present invention, a third array is created in the memory, wherein the calculated average value corresponding to any one of the storage spaces is stored in the third array.
When numerical value replacement is carried out, the average value corresponding to any storage space in the third array is used for replacing a plurality of attitude data in the any storage space, and then for any storage space, the plurality of attitude data are all normal attitude data after vibration noise smoothing processing.
And after the smoothing processing, the plurality of attitude data in each attitude time subsequence are normal data, namely the plurality of attitude data in each storage space are subjected to the smoothing processing of vibration noise. Writing each posture time sub-sequence data after smoothing back to an original position in the second array, replacing and updating a copy of the original posture time sequence data in the first array, and processing vibration noise in the original posture time sequence data.
In another embodiment of the present invention, a vibration noise smoothing system for attitude time series data is provided, which is used for implementing the method in the foregoing embodiments. Therefore, the description and definition in each embodiment of the vibration noise smoothing processing method of the aforementioned attitude time series data can be used for understanding each execution module in the embodiment of the present invention. Fig. 2 is a schematic diagram of an overall structure of a vibration noise smoothing processing system for attitude time series data according to an embodiment of the present invention, where the system includes a truncation module 21, a smoothing module 22, and a replacement update module 23.
The intercepting module 21 is configured to intercept the original posture time sequence data according to preset time windows to form posture time sub-sequence data corresponding to each preset time window, where the posture time sub-sequence data includes posture data corresponding to multiple time points;
the smoothing module 22 is configured to perform vibration noise smoothing on each attitude data in each attitude time sub-sequence data to obtain smoothed attitude time sub-sequence data;
and a replacement updating module 23, configured to combine each smoothed pose time sub-sequence data, and perform replacement updating on the original pose time sub-sequence data.
Referring to fig. 3, the system for smoothing vibration noise of time-series data of posture according to the embodiment of the present invention further includes a storage module 24;
the storage module 24 is configured to store the original posture time series data in the first array, and store a copy of the original posture time series data in the second array;
correspondingly, the intercepting module 21 is specifically configured to intercept the copy of the original pose time-series data stored in the second group according to a preset time window.
The storage module 24 is further configured to store the first matrix in a first queue created in the memory, where the plurality of posture time sub-sequence data form the first matrix, and each posture time sub-sequence data in the first matrix occupies one storage space in the first queue; and a second matrix formed by indexes of each attitude data in the first matrix in the second array is stored in a second queue, wherein the indexes corresponding to a plurality of attitude data belonging to the same attitude time sub-sequence data occupy one storage space of the second queue.
And each attitude data in the first matrix and each attitude data in the second array are in one-to-one correspondence through the index of each attitude data in the second matrix.
Referring to fig. 4, the smoothing processing module 22 includes a calculation unit 221 and a mean value replacement unit 222.
A calculation unit 221, configured to calculate, for any one of the storage spaces in the first queue, an average value of a plurality of posture data in the any one of the storage spaces;
a mean value replacing unit 222, configured to replace the multiple pieces of pose data in any one storage space with the mean value, and form multiple pieces of pose data after replacement in any one storage space;
the method comprises the steps of creating a first matrix by a plurality of attitude time sub-sequence data, and storing the first matrix in a first queue created in a memory, wherein each attitude time sub-sequence data in the first matrix occupies one storage space in the first queue.
The storage module 24 is further configured to store the calculated average value corresponding to any storage space in the third array;
accordingly, the mean value replacing unit 222 is configured to replace each pose data in any storage space with the mean value corresponding to any storage space in the third array.
Fig. 5 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 5: a processor (processor)510, a communication Interface (Communications Interface)520, a memory (memory)530 and a communication bus 540, wherein the processor 510, the communication Interface 520 and the memory 530 communicate with each other via the communication bus 540. Processor 510 may call logic instructions in memory 530 to perform the following method: intercepting original attitude time sequence data according to preset time windows to form attitude time sub-sequence data corresponding to each preset time window, wherein the attitude time sub-sequence data comprises attitude data corresponding to a plurality of time points; performing vibration noise smoothing processing on each attitude data in each attitude time sub-sequence data to obtain smoothed attitude time sub-sequence data; and combining each smoothed attitude time sub-sequence data, and replacing and updating the original attitude time sub-sequence data.
Furthermore, the logic instructions in the memory 530 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the methods provided by the above method embodiments, for example, including: intercepting original attitude time sequence data according to preset time windows to form attitude time sub-sequence data corresponding to each preset time window, wherein the attitude time sub-sequence data comprises attitude data corresponding to a plurality of time points; performing vibration noise smoothing processing on each attitude data in each attitude time sub-sequence data to obtain smoothed attitude time sub-sequence data; and combining each smoothed attitude time sub-sequence data, and replacing and updating the original attitude time sub-sequence data.
The embodiment of the invention provides a method and a system for smoothing vibration noise of attitude time sequence data, which are used for dividing the original attitude time sequence data according to a preset time window to form a plurality of sections of attitude time sub-sequence data, can simultaneously carry out vibration noise smoothing on each section of attitude time sub-sequence data, can improve the processing efficiency and have higher real-time performance, and can inhibit the vibration noise in the attitude time sequence data and support the smooth transition of continuous data with the same working condition and different attitudes.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A vibration noise smoothing method for attitude time series data is characterized by comprising the following steps:
intercepting original attitude time sequence data according to preset time windows to form attitude time sub-sequence data corresponding to each preset time window, wherein the attitude time sub-sequence data comprises attitude data corresponding to a plurality of time points;
performing vibration noise smoothing processing on each attitude data in each attitude time sub-sequence data to obtain smoothed attitude time sub-sequence data;
and combining each smoothed attitude time sub-sequence data, and replacing and updating the original attitude time sub-sequence data.
2. The vibration noise smoothing method according to claim 1, further comprising, before the truncating the raw pose time-series data according to a preset time window:
creating a first array in a memory for storing original posture time series data;
creating a second array in the memory for storing a copy of the original posture time series data;
correspondingly, the intercepting the original attitude time series data according to a preset time window comprises:
and intercepting the copy of the original posture time sequence data stored in the second array according to a preset time window.
3. The vibration noise smoothing method according to claim 2, wherein after intercepting the copy of the original pose time-series data stored in the second array according to a preset time window, the method further comprises:
the method comprises the steps of forming a first matrix by a plurality of attitude time sub-sequence data, and storing the first matrix in a first queue created in a memory, wherein each attitude time sub-sequence data in the first matrix occupies one storage space in the first queue.
4. The vibration noise smoothing method according to claim 3, further comprising, after storing the first matrix in a first queue created in a memory:
creating a second queue, forming a second matrix by indexes of each attitude data in the first matrix in the second array, and storing the second matrix in the second queue, wherein the indexes corresponding to a plurality of attitude data belonging to the same attitude time sub-sequence data occupy one storage space of the second queue.
5. The vibration noise smoothing method according to claim 4, wherein each attitude data in the first matrix corresponds to each attitude data in the second array in one-to-one correspondence by an index of each attitude data in the second matrix.
6. The vibration noise smoothing method according to claim 5, wherein the performing vibration noise smoothing processing on each posture data in each posture time sub-sequence data to obtain smoothed posture time sub-sequence data comprises:
for any one storage space in the first queue, calculating an average value of a plurality of attitude data in the any one storage space;
and replacing the plurality of attitude data in any storage space by adopting the average value to form a plurality of attitude data after the replacement of any storage space.
7. The vibration noise smoothing method according to claim 6, further comprising:
creating a third array in the memory, and storing the calculated average value corresponding to any storage space in the third array;
correspondingly, replacing the plurality of posture data in any storage space with the average value comprises:
and replacing each attitude data in any storage space by using the average value corresponding to any storage space in the third array.
8. A vibration noise smoothing system for attitude time-series data, comprising:
the system comprises an intercepting module, a processing module and a processing module, wherein the intercepting module is used for intercepting original attitude time sequence data according to preset time windows to form attitude time sub-sequence data corresponding to each preset time window, and the attitude time sub-sequence data comprises attitude data corresponding to a plurality of time points;
the smoothing module is used for smoothing vibration noise of each attitude data in each attitude time sub-sequence data to obtain smoothed attitude time sub-sequence data;
and the replacing and updating module is used for combining each smoothed gesture time sub-sequence data and replacing and updating the original gesture time sub-sequence data.
9. The vibration noise smoothing processing system according to claim 8, wherein the smoothing processing module includes a calculation unit and a mean value replacement unit;
the calculation unit is used for calculating an average value of a plurality of attitude data in any storage space in the first queue;
the mean value replacing unit is used for replacing the plurality of attitude data in any storage space by adopting the mean value to form a plurality of attitude data after the replacement of any storage space;
the method comprises the steps of creating a first matrix by a plurality of attitude time sub-sequence data, and storing the first matrix in a first queue created in a memory, wherein each attitude time sub-sequence data in the first matrix occupies one storage space in the first queue.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the vibration noise smoothing method of attitude time-series data according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910964404.5A CN110750536B (en) | 2019-10-11 | 2019-10-11 | Vibration noise smoothing method and system for attitude time series data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910964404.5A CN110750536B (en) | 2019-10-11 | 2019-10-11 | Vibration noise smoothing method and system for attitude time series data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110750536A true CN110750536A (en) | 2020-02-04 |
| CN110750536B CN110750536B (en) | 2020-06-23 |
Family
ID=69278030
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910964404.5A Active CN110750536B (en) | 2019-10-11 | 2019-10-11 | Vibration noise smoothing method and system for attitude time series data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110750536B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116441031A (en) * | 2023-06-20 | 2023-07-18 | 广东绿富域资源再生科技有限公司 | Intelligent crushing system for garbage incineration slag |
| CN117786346A (en) * | 2023-12-18 | 2024-03-29 | 深圳市悦融易数据科技有限公司 | Enterprise portrait generation method, device, equipment and storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6735550B1 (en) * | 2001-01-16 | 2004-05-11 | University Corporation For Atmospheric Research | Feature classification for time series data |
| CN102509001A (en) * | 2011-10-18 | 2012-06-20 | 西安交通大学 | Method for automatically removing time sequence data outlier point |
| CN103617373A (en) * | 2013-12-10 | 2014-03-05 | 西南大学 | Method for inspecting nonlinear characteristics of time sequence |
| CN104679834A (en) * | 2015-02-06 | 2015-06-03 | 北京工商大学 | Time series data cleaning method and system |
| CN105205112A (en) * | 2015-09-01 | 2015-12-30 | 西安交通大学 | System and method for excavating abnormal features of time series data |
| CN109063145A (en) * | 2018-08-07 | 2018-12-21 | 哈尔滨工业大学 | A kind of exceptional value restorative procedure and device for time series |
| CN109684311A (en) * | 2018-12-06 | 2019-04-26 | 中科恒运股份有限公司 | Abnormal deviation data examination method and device |
| CN109684902A (en) * | 2017-10-19 | 2019-04-26 | 厦门雅迅网络股份有限公司 | Vehicle noise source localization method and computer readable storage medium |
| CN109902703A (en) * | 2018-09-03 | 2019-06-18 | 华为技术有限公司 | A kind of time series method for detecting abnormality and device |
| CN110109923A (en) * | 2019-04-04 | 2019-08-09 | 北京市天元网络技术股份有限公司 | Storage method, analysis method and the device of time series data |
| CN110297832A (en) * | 2019-07-01 | 2019-10-01 | 联想(北京)有限公司 | A kind of time series data storage method and device, time series data querying method and device |
-
2019
- 2019-10-11 CN CN201910964404.5A patent/CN110750536B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6735550B1 (en) * | 2001-01-16 | 2004-05-11 | University Corporation For Atmospheric Research | Feature classification for time series data |
| CN102509001A (en) * | 2011-10-18 | 2012-06-20 | 西安交通大学 | Method for automatically removing time sequence data outlier point |
| CN103617373A (en) * | 2013-12-10 | 2014-03-05 | 西南大学 | Method for inspecting nonlinear characteristics of time sequence |
| CN104679834A (en) * | 2015-02-06 | 2015-06-03 | 北京工商大学 | Time series data cleaning method and system |
| CN105205112A (en) * | 2015-09-01 | 2015-12-30 | 西安交通大学 | System and method for excavating abnormal features of time series data |
| CN109684902A (en) * | 2017-10-19 | 2019-04-26 | 厦门雅迅网络股份有限公司 | Vehicle noise source localization method and computer readable storage medium |
| CN109063145A (en) * | 2018-08-07 | 2018-12-21 | 哈尔滨工业大学 | A kind of exceptional value restorative procedure and device for time series |
| CN109902703A (en) * | 2018-09-03 | 2019-06-18 | 华为技术有限公司 | A kind of time series method for detecting abnormality and device |
| CN109684311A (en) * | 2018-12-06 | 2019-04-26 | 中科恒运股份有限公司 | Abnormal deviation data examination method and device |
| CN110109923A (en) * | 2019-04-04 | 2019-08-09 | 北京市天元网络技术股份有限公司 | Storage method, analysis method and the device of time series data |
| CN110297832A (en) * | 2019-07-01 | 2019-10-01 | 联想(北京)有限公司 | A kind of time series data storage method and device, time series data querying method and device |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116441031A (en) * | 2023-06-20 | 2023-07-18 | 广东绿富域资源再生科技有限公司 | Intelligent crushing system for garbage incineration slag |
| CN116441031B (en) * | 2023-06-20 | 2023-08-25 | 广东绿富域资源再生科技有限公司 | Intelligent crushing system for garbage incineration slag |
| CN117786346A (en) * | 2023-12-18 | 2024-03-29 | 深圳市悦融易数据科技有限公司 | Enterprise portrait generation method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110750536B (en) | 2020-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112561078B (en) | Distributed model training method and related device | |
| EP4120199A1 (en) | Image rendering method and apparatus, and electronic device and storage medium | |
| US11656845B2 (en) | Dot product calculators and methods of operating the same | |
| US10867083B2 (en) | Technique for generating approximate design solutions | |
| US20150206028A1 (en) | Point cloud reduction apparatus, system, and method | |
| CN110750536B (en) | Vibration noise smoothing method and system for attitude time series data | |
| CN108876893A (en) | Method, apparatus, system and the computer storage medium of three-dimensional facial reconstruction | |
| KR20170012019A (en) | Method for optimizing parallel matrix multiplication in a system supporting multiple CPU and multiple GPU | |
| CN115147265B (en) | Avatar generation method, apparatus, electronic device, and storage medium | |
| CN115131476A (en) | Bone binding migration method, device, equipment and storage medium of virtual object | |
| CN112581573A (en) | Avatar driving method, apparatus, device, medium, and program product | |
| CN111833391B (en) | Image depth information estimation method and device | |
| CN111966361A (en) | Method, device and equipment for determining model to be deployed and storage medium thereof | |
| CN114241151A (en) | Three-dimensional model simplification method and device, computer equipment and computer storage medium | |
| CN108961316A (en) | Image processing method, device and server | |
| CN110517345A (en) | A kind of method and device of threedimensional model rendering | |
| CN110763197B (en) | Abnormal value processing method and system for attitude time series data | |
| CN113656891A (en) | Liquid rocket dynamic characteristic modeling analysis method and terminal equipment | |
| CN113987690B (en) | Method, system, equipment and storage medium for analyzing fatigue stress of undercarriage | |
| CN113361380B (en) | Human body key point detection model training method, detection method and device | |
| JP2024508866A (en) | Vibration evaluation method, device, computer equipment, and computer program | |
| CN110415361B (en) | Method and device for splicing broken objects | |
| WO2021087740A1 (en) | Magnetic resonance imaging method and device, server, and storage medium | |
| CN115063299B (en) | Image preprocessing method and device, electronic equipment and storage medium | |
| JP4823928B2 (en) | Parallel solver for simultaneous linear equations |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |