阅读说明：本技术 一种姿态时间序列数据的异常值处理方法及系统 (Abnormal value processing method and system for attitude time series data ) 是由 *** 马再超 张力 马昕 刘英博 孙家广 于 2019-10-11 设计创作，主要内容包括：本发明提供一种姿态时间序列数据的异常值处理方法及系统,将原始姿态时间序列数据按照预设时间窗口进行划分,形成多段姿态时间子序列数据,可以同时对每一段姿态时间子序列数据进行异常值的处理,能够提高处理效率,具有较高的实时性,本发明的方法能够抑制姿态时间序列数据中的异常数据,支持连续同工况、不同姿态数据的平滑过度。(The invention provides an abnormal value processing method and system 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 process the abnormal value of each section of attitude time sub-sequence data, can improve the processing efficiency and have higher real-time performance.)

1. An abnormal value processing method of 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;

processing abnormal attitude data in each attitude time sub-sequence data to obtain attitude time sub-sequence data after abnormal processing;

and combining each abnormal posture time sub-sequence data, and replacing and updating the original posture time sequence data.

2. The outlier processing method according to claim 1, further comprising, before the intercepting 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 abnormal value processing method according to claim 2, wherein the intercepting a copy of the original pose time-series data stored in the second array according to a preset time window 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 outlier processing method of claim 3, wherein said storing said first matrix in a first queue created in memory further comprises:

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 outlier processing method of claim 4, wherein each pose data in the first matrix is in one-to-one correspondence with each pose data in the second array by an index of each pose data in the second matrix.

6. The abnormal value processing method according to claim 5, wherein the processing the abnormal attitude data in each attitude time sub-sequence data to obtain the attitude time sub-sequence data after abnormal processing comprises:

for any one storage space in the first queue, calculating a standard deviation of a plurality of attitude data in the any one storage space;

recording each attitude data exceeding three times the standard deviation in any one storage space, and calculating the average value of other attitude data;

and replacing each attitude data exceeding three times of the standard deviation by adopting the average value to form a plurality of attitude data after any storage space is replaced.

7. The outlier processing method according to claim 6, further comprising:

creating a third array in the memory, and storing the calculated standard deviation corresponding to any storage space in the third array;

creating a fourth array in the memory, and storing an index of each attitude data exceeding three times the standard deviation in any one storage space in the fourth array;

and creating a fifth array in the memory, and storing the calculated average value corresponding to any storage space in the fifth array.

8. An outlier processing system for pose 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 abnormal processing module is used for processing abnormal attitude data in each attitude time sub-sequence data to obtain attitude time sub-sequence data after abnormal processing;

and the replacement updating module is used for combining each abnormal posture time sub-sequence data and performing replacement updating on the original posture time sequence data.

9. The outlier processing system according to claim 8, wherein the abnormality processing module includes a first calculating unit, a recording unit, a second calculating unit, and a mean value replacing unit;

the first calculation unit is used for calculating the standard deviation of a plurality of attitude data in any storage space in the first queue;

a recording unit for recording each attitude data exceeding three times the standard deviation in any one of the storage spaces;

a second calculation unit configured to calculate an average value of a plurality of attitude data other than the attitude data exceeding three times the standard deviation in the any one storage space;

the mean value replacing unit is used for replacing each attitude data exceeding three times of the standard deviation by adopting the mean value to form a plurality of attitude data after any storage space is replaced;

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 implements the steps of the method for processing abnormal values of pose time-series data according to any one of claims 1 to 7 when executing the program.

Technical Field

The invention belongs to the technical field of data processing, and particularly relates to an abnormal value processing 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. However, in practical applications, the posture time-series data of the object to be measured generally has a data quality problem of large intermittent abnormal values, and therefore, it is necessary to perform abnormal processing on the posture time-series data of the object to be measured.

The conventional method is to perform exception processing on attitude time series data of a measured object in batch, and because the amount 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 during batch processing.

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 processing an abnormal value of pose time-series data.

According to a first aspect of the embodiments of the present invention, there is provided a method for processing an abnormal value of pose time-series data, including:

intercepting the 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;

processing abnormal attitude data in each attitude time sub-sequence data to obtain attitude time sub-sequence data after abnormal processing;

and combining each abnormal posture time sub-sequence data, and replacing and updating the original posture time 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 processing the abnormal posture data in each posture time sub-sequence data to obtain the posture time sub-sequence data after abnormal processing includes:

for any one storage space in the first queue, calculating a standard deviation of a plurality of attitude data in the any one storage space;

recording each attitude data exceeding three times the standard deviation in any one storage space, and calculating the average value of other attitude data;

and replacing each attitude data exceeding three times of the standard deviation by adopting the average value to form a plurality of attitude data after any storage space is replaced.

Further, the method also comprises the following steps:

creating a third array in the memory, and storing the calculated standard deviation corresponding to any storage space in the third array;

creating a fourth array in the memory, and storing an index of each attitude data exceeding three times the standard deviation in any one storage space in the fourth array;

and creating a fifth array in the memory, and storing the calculated average value corresponding to any storage space in the fifth array.

According to a second aspect of the embodiments of the present invention, there is provided an abnormal value processing system for pose 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 abnormal processing module is used for processing abnormal attitude data in each attitude time sub-sequence data to obtain attitude time sub-sequence data after abnormal processing;

and the replacement updating module is used for combining each abnormal posture time sub-sequence data and performing replacement updating on the original posture time sequence data.

Further, the exception handling module comprises a first calculating unit, a recording unit, a second calculating unit and a mean value replacing unit;

the first calculation unit is used for calculating the standard deviation of a plurality of attitude data in any storage space in the first queue;

a recording unit for recording each attitude data exceeding three times the standard deviation in any one of the storage spaces;

a second calculation unit configured to calculate an average value of a plurality of attitude data other than the attitude data exceeding three times the standard deviation in the any one storage space;

the mean value replacing unit is used for replacing each attitude data exceeding three times of the standard deviation by adopting the mean value to form a plurality of attitude data after any storage space is replaced;

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 an abnormal value processing method of the pose time-series data, which is capable of being performed by any one of the 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 an outlier processing method for pose time-series data provided in any of various possible implementations of the first aspect.

The invention provides an abnormal value processing method and system of attitude time sequence data, which are used for dividing 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 process an abnormal value of each section of attitude time sub-sequence data, can improve the processing efficiency and have higher real-time performance.

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 flowchart of an abnormal value processing method for pose time series data according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of an outlier processing system for pose time-series data according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an outlier processing system for pose time series data according to another embodiment of the present invention;

FIG. 4 is a block diagram of the internal connections of the exception handling module of FIGS. 2 and 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 processing an abnormal value of pose time-series data is provided, and fig. 1 is a schematic flowchart of an entire abnormal value processing method provided in an embodiment of the present invention, where the method includes: intercepting the 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; processing abnormal attitude data in each attitude time sub-sequence data to obtain attitude time sub-sequence data after abnormal processing; and combining each abnormal posture time sub-sequence data, and replacing and updating the original posture time 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 the embodiment of the invention, when a batch of gyroscope attitude time series data (called as original attitude time series data) arrives, the original attitude time series data is intercepted according to a preset time window to form a plurality of pieces of attitude time sub-series 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 original attitude time series data. In addition, for example, the preset time window is 10s, each second corresponds to one piece of gyroscope attitude data, the original attitude 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 attitude time series data formed after the interception are overlapped, for example, the first time window intercepts 10 attitude data from 1s to 10s of the original attitude time series data, and the second time window intercepts 10 attitude data from 2s to 11s of the original attitude time series data.

Processing abnormal attitude data in each attitude time sub-sequence data to obtain attitude time sub-sequence data after abnormal processing; and combining the attitude time sub-sequence data after each abnormal processing, and replacing and updating the original attitude time sub-sequence data.

The invention provides an abnormal value processing method and system of attitude time sequence data, which are used for dividing 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 process an abnormal value of each section of attitude time sub-sequence data, can improve the processing efficiency and have higher real-time performance.

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, processing abnormal posture data in each posture time sub-sequence data to obtain posture time sub-sequence data after abnormal processing includes:

for any one storage space in the first queue, calculating a standard deviation of a plurality of attitude data in any one storage space;

recording each attitude data exceeding three times of standard deviation in any storage space, and calculating the average value of other attitude data;

and replacing each attitude data exceeding three times of the standard deviation by adopting an average value to form a plurality of attitude data after any storage space is replaced.

It can be understood that the intercepted pose time sub-sequence data is stored in the first queue, and each pose time sub-sequence data occupies one storage space in the first queue, in the embodiment of the present invention, for any storage space in the first queue, a plurality of pose data are stored in any one storage space, and the standard deviation of the plurality of pose data in any one storage space is calculated; recording each attitude data exceeding three times of standard deviation in any storage space, and calculating the average value of other attitude data; and replacing each attitude data exceeding three times of standard deviation by adopting an average value to form a plurality of attitude data after any storage space is replaced.

In the process of exception processing of a plurality of attitude data in any storage space, the standard deviation sigma of the plurality of attitude data is calculated firstly, and attitude data exceeding 3 sigma or less than-3 sigma in any storage space is recorded according to the standard deviation sigma, and the attitude data is exception data.

And for the plurality of attitude data in any storage space, excluding the abnormal attitude data, calculating an average value for other normal attitude data, replacing the abnormal attitude data with the average value to form each replaced attitude data, and forming a plurality of attitude data after replacing any storage space, wherein the plurality of attitude data after replacing are normal attitude data.

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 standard deviation corresponding to any calculated storage space in the third array;

creating a fourth array in the memory, and storing the index of each attitude data exceeding three times of standard deviation in any storage space in the fourth array;

and creating a fifth array in the memory, and storing the calculated average value corresponding to any storage space in the fifth array.

It can be understood that, in the embodiment of the present invention, a third array, a fourth array and a fifth array are created in the memory, where the calculated standard deviation corresponding to any storage space is stored in the third array, the index of each attitude data exceeding three times the standard deviation in any storage space is stored in the fourth array, and the calculated average value corresponding to any storage space is stored in the fifth array, where the fifth array stores the calculated average values according to the storage space, the attitude time subsequence and the average value.

And replacing the attitude data corresponding to the index in any storage space stored in the fourth array by using the average value corresponding to any storage space in the fifth array to obtain a plurality of replaced attitude data in any storage space, wherein the plurality of attitude data in any storage space are normal attitude data after exception processing.

And (3) adopting the same exception processing mode for the plurality of attitude data in each storage space, namely for each intercepted attitude time subsequence, and eliminating the abnormal values, wherein the plurality of attitude data in each attitude time subsequence are normal data after processing. And writing each attitude time sub-sequence data after exception processing back to the original position in the second data, replacing and updating the copy of the original attitude time sequence data in the first queue, and processing the abnormal value in the original attitude time sequence data.

In another embodiment of the present invention, an outlier processing system for pose time-series data is provided, which is used to implement the method in the foregoing embodiments. Therefore, the description and definition in each embodiment of the above-described abnormal value processing method of pose time-series data can be used for understanding of each execution module in the embodiment of the present invention. Fig. 2 is a schematic diagram of an overall structure of an abnormal value processing system for pose time-series data according to an embodiment of the present invention, where the system includes an intercept module 21, an exception processing 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 anomaly processing module 22 is configured to process the anomaly attitude data in each attitude time sub-sequence data to obtain attitude time sub-sequence data after anomaly processing;

and a replacement updating module 23, configured to combine each abnormal posture time sub-sequence data, and perform replacement updating on the original posture time sub-sequence data.

Referring to fig. 3, the system for processing an abnormal value of pose time-series data 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 exception handling module 22 includes a first calculation unit 221, a recording unit 222, a second calculation unit 223, and a mean value replacement unit 224;

a first calculation unit 221 configured to calculate, for any one of the storage spaces in the first queue, a standard deviation of the plurality of posture data in any one of the storage spaces;

a recording unit 222 for recording each attitude data exceeding three times the standard deviation in any one storage space;

a second calculation unit 223 for calculating an average value of a plurality of attitude data other than the attitude data exceeding three times the standard deviation in the any one storage space;

and a mean value replacing unit 224, configured to replace each of the pose data exceeding three times the standard deviation with the mean value, to form a plurality of pose data after replacement of any one of the storage spaces.

The storage module 24 is further configured to store the calculated standard deviation corresponding to any storage space in a third array; storing the index of each attitude data exceeding three times the standard deviation in any one storage space in a fourth array; and storing the calculated average value corresponding to any storage space in a fifth 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 (communication 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 the 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; processing abnormal attitude data in each attitude time sub-sequence data to obtain attitude time sub-sequence data after abnormal processing; and combining each abnormal posture time sub-sequence data, and replacing and updating the original posture time 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 the 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; processing abnormal attitude data in each attitude time sub-sequence data to obtain attitude time sub-sequence data after abnormal processing; and combining each abnormal posture time sub-sequence data, and replacing and updating the original posture time sequence data.

The invention provides an abnormal value processing method and system of attitude time sequence data, which are used for dividing 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 process an abnormal value of each section of attitude time sub-sequence data, can improve the processing efficiency and have higher real-time performance.

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.