wherein T is_k ＝(q_k ,p_k )，T₀ ＝(q₀ ,p₀ )，T_k For the pose of the pose data point to be compressed, T₀ Is the pose of the reference pose data point. Delta_q Is pose quaternion variable, delta_p Is a pose three-dimensional vector variable. q₀ Quaternion of reference data point, p₀ Is the three-dimensional vector of the datum point, q_k Is the quaternion of the gesture data point to be compressed, p_k Is a three-dimensional vector of gesture data points to be compressed. C (q)₀ ) Is a rotation matrix corresponding to the quaternion of the reference data points.

With continued reference to the above embodiment, the pose information of one pose data point in the original real data is calculated by the above formula, and the pose variable of 117 bits can be used for expression after compression.

In an embodiment, if the data variable includes a three-dimensional feature point variable, step S30 in the data processing method includes:

Specifically, three-dimensional feature point variables are calculated according to the following formula:

in another embodiment, upon decompression, the three-dimensional feature points are calculated according to the following formula:

wherein delta_pf For three-dimensional feature point variables, T is the orthogonal projection calculated by orthogonal transformation, p_f As a three-dimensional feature point of the gesture data point to be compressed,

is the center of gravity of all three-dimensional feature points in the target data segment.

It will be appreciated that the range of heights in one data segment will not be too large, e.g. in a 100m data segment, the heights may only be expressed in 20m, and thus the heights may be expressed with less data. Here, all three-dimensional feature point information in the original real data is acquired first, then the barycenter thereof is calculated as a reference, and then the three-dimensional feature point variables can be calculated by performing orthogonal transformation on other three-dimensional feature points with respect to the barycenter. With continued reference to the above embodiment, through the above formula, the three-dimensional feature point information of one gesture data point in the original real data may be expressed by using 39bit three-dimensional feature point variables after compression.

In this embodiment, since the actual track characteristics and the distribution of the corresponding reconstruction elements are considered, for example, the height of the track within 100m does not change greatly, the lateral movement and the three-dimensional feature points are not too far apart, so that a new projection direction without compression can be calculated by using a PCA (principal component analysis) method, and then the required data amount can be dynamically calculated in three directions by using different representation ranges, respectively.

Based on the same inventive concept as the previous embodiments, an embodiment of the present invention provides a data processing apparatus, as shown in fig. 3, including: aprocessor 310 and amemory 311 in which a computer program is stored; the number of theprocessors 310 illustrated in fig. 3 is not used to refer to one number of theprocessors 310, but is merely used to refer to a positional relationship of theprocessors 310 with respect to other devices, and in practical applications, the number of theprocessors 310 may be one or more; likewise, thememory 311 illustrated in fig. 3 is also used in the same sense, that is, only to refer to the positional relationship of thememory 311 with respect to other devices, and in practical applications, the number of thememories 311 may be one or more. The data processing method applied to the data processing apparatus described above is implemented when theprocessor 310 runs the computer program.

The data processing apparatus may further include: at least onenetwork interface 312. The various components in the data processing apparatus are coupled together by abus system 313. It is appreciated that thebus system 313 is used to enable connected communication between these components. Thebus system 313 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled asbus system 313 in fig. 3.

Thememory 311 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Wherein the nonvolatile Memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM, programmable Read-Only Memory), erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), electrically erasable programmable Read Only Memory (EEPROM, electrically Erasable Programmable Read-Only Memory), magnetic random access Memory (FRAM, ferromagnetic random access Memory), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk Read Only Memory (CD-ROM, compact Disc Read-Only Memory); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be random access memory (RAM, random Access Memory), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (SRAM, static Random Access Memory), synchronous static random access memory (SSRAM, synchronous Static Random Access Memory), dynamic random access memory (DRAM, dynamic Random Access Memory), synchronous dynamic random access memory (SDRAM, synchronous Dynamic Random Access Memory), double data rate synchronous dynamic random access memory (ddr SDRAM, double Data Rate Synchronous Dynamic Random Access Memory), enhanced synchronous dynamic random access memory (ESDRAM, enhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM, syncLink Dynamic Random Access Memory), direct memory bus random access memory (DRRAM, direct Rambus Random Access Memory). Thememory 311 described in embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.

Thememory 311 in the embodiment of the present invention is used to store various types of data to support the operation of the data processing apparatus. Examples of such data include: any computer program for operating on the data processing apparatus, such as an operating system and application programs; contact data; telephone book data; a message; a picture; video, etc. The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application programs may include various application programs such as a Media Player (Media Player), a Browser (Browser), etc. for implementing various application services. Here, a program for implementing the method of the embodiment of the present invention may be included in an application program.

Based on the same inventive concept as the previous embodiments, the present application further provides a readable storage medium, in particular, a readable storage medium having a computer program stored thereon, where the readable storage medium may be a Memory such as a magnetic random access Memory (FRAM, ferromagnetic random access Memory), a Read Only Memory (ROM), a programmable Read Only Memory (PROM, programmable Read-Only Memory), an erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), an electrically erasable programmable Read Only Memory (EEPROM, electrically Erasable Programmable Read-Only Memory), a Flash Memory (Flash Memory), a magnetic surface Memory, a compact disc, or a compact disc Read Only Memory (CD-ROM, compact Disc Read-Only Memory); but may be a variety of devices including one or any combination of the above-described memories, such as a mobile phone, computer, tablet device, personal digital assistant, or the like. The computer program stored in the readable storage medium, when executed by a processor, implements the steps of the data processing method in the above embodiment. The specific step flow implemented when the computer program is executed by the processor is described with reference to the embodiment shown in fig. 1, and will not be described herein.

The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.

Claims

1. A method of data processing, comprising:

step S10, in response to acquiring driving gesture data comprising a plurality of gesture data points, cutting the driving gesture data into a plurality of target data segments;

step S20, selecting a posture data point for the target data segment as a reference data point;

step S30, calculating a data variable of each attitude data point in the plurality of target data segments according to the reference data points so as to acquire compressed driving attitude data;

wherein, the step S10 includes: determining the segment number with the minimum total data amount according to the driving gesture data, and cutting the driving gesture data based on the segment number; the total amount of data is calculated according to the following formula:

wherein,,

for the total amount of data->

Bit number for each pose data point, +.>

For the number of segments of said target data segment, < >>

The number of gesture data points contained for each target data segment, +.>

The number of bits for each data variable.

2. The data processing method according to claim 1, wherein the step S10 includes:

step S11, sampling the driving gesture data according to preset data precision to obtain sampling gesture data;

and step S12, acquiring the plurality of cut target data segments according to the sampling gesture data.

3. The data processing method according to claim 1 or 2, wherein the step S20 includes:

4. The data processing method according to claim 1, further comprising, after the step S30:

5. The data processing method of claim 1, wherein the gesture data points are selected from at least one of a timestamp, a pose, a three-dimensional feature point; the data variable is selected from at least one of a timestamp variable, a pose variable and a three-dimensional characteristic point variable.

6. The data processing method according to claim 5, wherein if the pose variable includes a pose quaternion variable and a pose three-dimensional vector variable, the step S30 includes:

7. The data processing method according to claim 5, wherein if the data variable includes a three-dimensional feature point variable, the step S30 includes:

8. A data processing apparatus, comprising: a processor and a memory storing a computer program, which, when run by the processor, implements the steps of the data processing method of any one of claims 1 to 7.

9. A readable storage medium, characterized in that the readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the data processing method according to any of claims 1 to 7.