CN113724418A - Data processing method and device and readable storage medium - Google Patents
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Abstract
The application provides a data processing method, a device and a readable storage medium, wherein the data processing method comprises the following steps: step S10, in response to the fact that the driving posture data comprising a plurality of posture data points are obtained, the driving posture data are cut into a plurality of target data segments; step S20, selecting a posture data point as a reference data point for the target data segment; step S30: and calculating the data variable of each attitude data point in the target data segments according to the reference data point so as to obtain the compressed driving attitude data. The data processing method, the data processing device and the readable storage medium can effectively compress the driving attitude data according to requirements, effectively reduce the data volume and facilitate transmission and/or storage.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a data processing method, apparatus, and readable storage medium.
Background
The driving posture data generally refers to the real-time position, the driving track and the like of the vehicle, the problems in the running process of the vehicle can be found and found according to the driving posture data, and meanwhile, the driving habits, the personal information and the like of the user can be obtained. In addition, after the automobile has an accident, the reason of the accident can be analyzed according to the operation data.
The driving posture data is very important, and the data size is strictly limited in some transmission or storage application environments, so that the data is usually compressed by using a lossless compression method. However, due to the limitation of compression ratio, it is impossible to solve all the problems of storage and transmission of images and digital video using only the lossless compression method.
Disclosure of Invention
The application provides a data processing method, a data processing device and a readable storage medium, which are used for solving the problem that the size of data in driving data application is strictly limited and improving the convenience of transmission and storage.
In one aspect, the present application provides a data processing method, specifically, the data processing method includes: step S10, in response to the fact that the driving posture data comprising a plurality of posture data points are obtained, the driving posture data are cut into a plurality of target data segments; step S20, selecting a posture data point as a reference data point for the target data segment; s30: and calculating the data variable of each attitude data point in the target data segments according to the reference data point so as to obtain the compressed driving attitude data.
Optionally, the step S10 in the data processing method includes: step S11, sampling the driving attitude data according to the preset data precision to obtain sampled attitude data; and step S12, acquiring the plurality of cut target data segments according to the sampling attitude data.
Optionally, the step S20 in the data processing method includes: and selecting the first attitude data point of each target data segment as a reference data point corresponding to the target data segment.
Optionally, the step S10 in the data processing method includes:
determining the segment number with the minimum total data amount according to the driving posture data, and cutting the driving posture data based on the segment number;
the total amount of data is calculated according to the following formula:
M＝x[a+(y-1)×b]
wherein, M is the total data amount, a is the bit number of each attitude data point, x is the number of segments of the target data segment, y is the number of attitude data points contained in each target data segment, and b is the bit number of each data variable.
Optionally, after the step S30 in the data processing method, the method further includes: and according to the reference data points and the data variables, performing reverse calculation to restore the driving posture data.
Optionally, the pose data point in the data processing method is selected from at least one of a time stamp, a pose, and a three-dimensional feature point; and/or the data variable is selected from at least one of a timestamp variable, a pose variable and a three-dimensional feature point variable.
Optionally, if the pose variables include pose quaternion variables and pose three-dimensional vector variables, the step S30 in the data processing method includes:
calculating a pose quaternion variable of the attitude data point to be compressed according to the quaternion of the attitude data point to be compressed and the quaternion of the reference data point;
and calculating the pose three-dimensional vector variable of the attitude data point to be compressed according to the three-dimensional vector of the attitude data point to be compressed, the rotation matrix corresponding to the quaternion of the datum data point and the three-dimensional vector of the datum data point.
Optionally, if the data variable includes a three-dimensional feature point variable, the step S30 in the data processing method includes:
and calculating the three-dimensional characteristic point variable of the attitude data point to be compressed according to the difference between the three-dimensional characteristic point of the attitude data point to be compressed and the gravity center of the three-dimensional characteristic points of all the attitude data points in the target data segment where the attitude data point to be compressed is located.
In another aspect, the present application provides a data processing apparatus comprising: a processor and a memory storing a computer program which, when executed by the processor, implement the steps of the data processing method as described above.
In another aspect, the present application provides a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the data processing method as described above.
As described above, according to the data processing method, the data processing device and the readable storage medium provided by the application, the driving attitude data can be effectively compressed by cutting the driving attitude data, selecting the reference data point of the cut data segment, calculating the data variable of the attitude data point in the data segment and the like, so that the data volume is effectively reduced, and the driving attitude data is more convenient to transmit and/or store.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating an embodiment of the step S10 shown in the embodiment of FIG. 1;
fig. 3 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings. With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the recitation of an element by the phrase "comprising an … …" does not exclude the presence of additional like elements in the process, method, article, or apparatus that comprises the element, and further, where similarly-named elements, features, or elements in different embodiments of the disclosure may have the same meaning, or may have different meanings, that particular meaning should be determined by their interpretation in the embodiment or further by context with the embodiment.
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one aspect, the present application provides a data processing method, and fig. 1 is a schematic flow chart of the data processing method according to an embodiment of the present application.
Referring to fig. 1, in an embodiment, the data processing method includes:
step S10: responsive to obtaining the driving posture data comprising a plurality of posture data points, the driving posture data is sliced into a plurality of target data segments.
It will be appreciated that the cutting of data into smaller units of data segments of a certain length is the basis for data compression. If the driving attitude data can be uniformly cut, the compression calculation can be more conveniently carried out.
Step S20: and selecting a posture data point as a reference data point for the target data segment.
Optionally, the reference data point refers to a data point used for recording complete accuracy as a basis, for example, a first attitude data point, a last attitude data point, or any one intermediate attitude data point of the driving attitude data may be selected as the reference data point, or a plurality of target data segments may be selected to share one reference data point. In this embodiment, for each target data segment, the first attitude data point of each target data segment may be selected as the reference data point corresponding to the target data segment, or the first attitude data point of the driving attitude data may be selected as the reference data point corresponding to the target data segment.
Step S30: and calculating the data variable of each attitude data point in the plurality of target data segments according to the reference data point so as to obtain the compressed driving attitude data.
For each target data segment, if the first attitude data point is selected as a reference data point, the subsequently calculated data variables of other attitude data points are data increments; if the last attitude data point is selected as a reference data point, the data variables of other attitude data points calculated subsequently are data decrement; if any attitude data point between the first attitude data point and the last attitude data point is selected as the reference data point, the data increment and the data decrement exist in the data variables of other attitude data points in the subsequent calculation. After the data variable of each attitude data point in the plurality of target data segments is calculated, the compressed driving attitude data can be obtained based on the reference data point and the data variable of each attitude data point in the target data segments.
In summary, in the data processing method provided in this embodiment, by performing operations such as cutting the driving posture data, selecting the reference data point of the cut data segment, and calculating the data variable of the posture data point in the data segment, the driving posture data can be effectively compressed, the data amount is effectively reduced, and transmission and/or storage are more convenient.
In an embodiment, the pose data points in the data processing method are selected from at least one of time stamps, poses and three-dimensional feature points. In another embodiment, the data variable is selected from at least one of a timestamp variable, a pose variable, and a three-dimensional feature point variable.
In the present embodiment, the pose includes a position and a posture. In three-dimensional space, there are 3 degrees of freedom for rotation and 3 degrees of freedom for position. Since the rotation is relatively complex, it is generally expressed by redundancy over the degree of freedom, which is expressed by normalized 4 parameters, i.e. quaternion, in the literature, i.e. by 4 parameters, 3 rotational degrees of freedom. Therefore, the pose variables are expressed in terms of quaternion plus three-dimensional vector for the 6-degree-of-freedom 7-parameter.
Fig. 2 is a schematic flowchart of an embodiment of step S10 shown in the embodiment of fig. 1.
Referring to fig. 2, in an embodiment, the step S10 in the data processing method includes:
step S11: and sampling the driving attitude data according to the preset data precision to obtain sampling attitude data.
Optionally, different data accuracies may be preset for the timestamp, the pose, the position, and the three-dimensional feature point, respectively, for example, the timestamp may be set to an accuracy of 0.1ms, the pose in the pose may be set to an accuracy of 0.01 degrees, the position may be set to an accuracy of a centimeter level, and the three-dimensional feature point may be set to an accuracy of a centimeter level.
It should be noted that if the pose is to be used for coordinate system transformation (for example, converting a point in one coordinate system to another coordinate system), the transformed point should meet the requirement of accuracy in the centimeter level. Therefore, if the pose is used as a transformation of the coordinate system, the accuracy of the pose needs to be set in combination with the accuracy of the three-dimensional feature points.
Step S12: and acquiring a plurality of cut target data segments according to the sampling attitude data.
Alternatively, the acquired sampling attitude data is cut into data segments of a certain length and smaller units, and the optimal cutting length is obtained by using a solution method of a planning problem, so that the used bit number data variable can be minimized under the condition of meeting the precision requirement.
In one embodiment, step S20 in the data processing method includes:
and selecting the first attitude data point of each target data segment as a reference data point corresponding to the target data segment.
Alternatively, if the first pose data point is used as the reference data point, then only the data increment needs to be calculated. In this case, if one reference data point is selected for each target data segment, the reference data point corresponding to each target data segment is the first pose data point of each target data segment. If the intermediate attitude data point is selected as the reference data point, the data increment and the data decrement need to be calculated. If the tail-end attitude data point is selected as the reference data point, only data decrement needs to be calculated. It will be appreciated that the further a pose data point is from a reference data point, the more data is required for the data variable to represent the pose data point.
In one embodiment, step S10 in the data processing method includes:
and determining the segment number with the minimum total data amount according to the driving posture data so as to cut the driving posture data based on the segment number. In other embodiments, the total amount of data that meets the usage requirements may also be selected to determine the number of segments, as desired. In this embodiment, each of the target data segments includes one reference data point.
The total amount of data can be calculated according to the following formula:
M＝x[a+(y-1)×b]
wherein, M is the total data amount, a is the bit number of each attitude data point, x is the segment number of the target data segment, y is the number of the attitude data points contained in each target data segment, and b is the bit number of each data variable.
For example, assuming that 1000 attitude data points are generated after the vehicle continuously travels for 1km, the raw data size of the traveling attitude data is 512 × 103bit, i.e., each pose data point is a 512 bit. Since the compression calculation can be more conveniently performed by uniform cutting, for 1000 attitude data points within 1km, uniform cutting is performed according to the cutting accuracy requirement of taking y as 100m, namely, every 100mSetting a reference to be expressed with the original precision, cut x ═ 10 segments of data, 5120 bits are needed for all 10 reference data points. For the data variable part, only the information of each pose data point and the information of the reference data point are considered for operation to obtain increments, under the representation range of 100m and the respective precision requirements, the pose information is finally expressed by 117 bits, the timestamp is expressed by 21 bits, and the three-dimensional feature point is expressed by 39 bits, namely b is 177 bits to represent the compression information of one pose data point. Therefore, the whole 1km of the driving posture data is calculated by using about 180X 103bit, i.e. M10 + 512+ (100-1) 177]Expressing, thereby realizing the compression of the data and compressing the data to the original data 512 multiplied by 103About 35% of bit. In other embodiments, due to application engineering constraints, the selected one of the cut segments is 72 meters.
It can be understood that there is a loss of accuracy for the compressed data, and in the development engineering of algorithm prototyping, the original real data may be needed, or in the analysis of the influence of the compressed data on the application result, the original data may also need to be referred to, for example, in the map development work, the information of the posture data points with high frequency may need to be transmitted, but if we only use the data to do some map examinations, only the data density is very low. The total amount of data required is further determined by the number of segments to be cut, depending on the particular application.
In an embodiment, after executing step S30, the data processing method further includes:
and reversely calculating to restore the driving attitude data according to the reference data points and the data variables.
In this embodiment, the compressed data can be restored to the pose timestamp data before compression by performing an inverse operation, that is, performing an inverse operation on the data variable and the reference data point.
In one embodiment, step S30 in the data processing method includes:
the timestamp variable is calculated according to the following formula:
δt＝tk-t0
wherein, deltatAs a variable of the timestamp, tkAt the time to be calculated, t0The time of the reference data point.
In another embodiment, at decompression time, the timestamp is calculated according to the following formula:
tk＝δt+t0
the time stamp information of the pose data point is expressed by the difference of the pose data point relative to the reference data point, and the required data amount can be effectively compressed. For example, when the precision requirement of the timestamp is set to 0.1ms, the timestamp information in the original real data is expressed by 64 bits, and the compressed timestamp variable is only expressed by 21 bits.
In an embodiment, if the pose variables include a pose quaternion variable and a pose three-dimensional vector variable, step S30 in the data processing method includes:
calculating a pose quaternion variable of the attitude data point to be compressed according to the quaternion of the attitude data point to be compressed and the quaternion of the reference data point;
and calculating the pose three-dimensional vector variable of the attitude data point to be compressed according to the three-dimensional vector of the attitude data point to be compressed, the rotation matrix corresponding to the quaternion of the datum data point and the three-dimensional vector of the datum data point.
Specifically, pose variables are calculated according to the following formula:
in another embodiment, at decompression, pose is calculated according to the following formula:
wherein, Tk＝(qk,pk)，T0＝(q0,p0)，TkPose, T, of the pose data point to be compressed0The pose of the reference pose data point. DeltaqAs a quaternion variable of pose, deltapAnd obtaining pose three-dimensional vector variables. q. q.s0Is a quaternion, p, of the reference data point0Is a three-dimensional vector of reference data points, qkQuaternion, p, of the attitude data points to be compressedkIs a three-dimensional vector of the pose data points to be compressed. C (q)0) A rotation matrix corresponding to a quaternion of the reference data points.
With continued reference to the above embodiment, by the above formula calculation, the pose information of a pose data point in the original real data can be expressed by using a pose variable of 117 bits after compression.
In an embodiment, if the data variable includes a three-dimensional feature point variable, step S30 in the data processing method includes:
and calculating the three-dimensional characteristic point variable of the attitude data point to be compressed according to the difference between the three-dimensional characteristic point of the attitude data point to be compressed and the gravity center of the three-dimensional characteristic points of all the attitude data points in the target data segment where the attitude data point to be compressed is located.
Specifically, the three-dimensional feature point variable is calculated according to the following formula:
in another embodiment, the three-dimensional feature points are calculated at decompression according to the following formula:
wherein, deltapfFor three-dimensional characteristic point variables, T is the orthogonal projection calculated by the orthogonal transformation, pfThree-dimensional characteristic points of the posture data points to be compressed,
for all three dimensions in the target data segmentThe center of gravity of the feature points.
It will be appreciated that the range of heights in a data segment is not too large, for example, in a 100m data segment, heights may only need to be expressed in 20m, and thus heights may be expressed with less data. All three-dimensional feature point information in original real data is acquired, then the gravity center of the three-dimensional feature point information is calculated to serve as a reference, and then other three-dimensional feature points are subjected to orthogonal transformation relative to the gravity center to calculate three-dimensional feature point variables. With continued reference to the above embodiment, with the above formula, the three-dimensional feature point information of one pose data point in the original real data can be expressed by a 39-bit three-dimensional feature point variable after compression.
In this embodiment, considering the characteristics of the actual driving track and the distribution of the corresponding reconstruction elements, for example, the height of the driving distance within 100m does not change greatly, and the lateral movement and the three-dimensional feature points are not distributed too much, so a new projection direction without compression can be calculated by using a PCA (principal component analysis) method, and then the required data amount is dynamically calculated by using different representation ranges in three directions.
Based on the same inventive concept as the foregoing embodiment, an embodiment of the present invention provides a data processing apparatus, as shown in fig. 3, including: a processor 310 and a memory 311 storing computer programs; the processor 310 illustrated in fig. 3 is not used to refer to the number of the processors 310 as one, but is only used to refer to the position relationship of the processor 310 relative to other devices, and in practical applications, the number of the processors 310 may be one or more; similarly, the memory 311 shown in fig. 3 is also used in the same sense, i.e. it is only used to refer to the position relationship of the memory 311 with respect to other devices, and in practical applications, the number of the memory 311 may be one or more. The data processing method applied to the data processing apparatus described above is implemented when the processor 310 runs the computer program.
The data processing apparatus may further include: at least one network interface 312. The various components in the data processing apparatus are coupled together by a bus system 313. It will be appreciated that the bus system 313 is used to enable communications among the components connected. The bus system 313 includes a power bus, a control bus, and a status signal bus in addition to the data bus. For clarity of illustration, however, the various buses are labeled as bus system 313 in FIG. 3.
The memory 311 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory 311 described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.
The memory 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 operation on the data processing apparatus, such as operating systems 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, and is used 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, the program that implements the method of the embodiment of the present invention may be included in an application program.
Based on the same inventive concept of the foregoing embodiments, the present application further provides a readable storage medium, and in particular, the readable storage medium stores a computer program thereon, where the readable storage medium may be a Memory such as a magnetic random access Memory (FRAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read Only Memory (CD-ROM), and the like; or may be a variety of devices including one or any combination of the above memories, such as a mobile phone, computer, tablet device, personal digital assistant, etc. The computer program stored in the readable storage medium, when executed by a processor, implements the steps of the data processing method as in the above embodiments. Please refer to the description of the embodiment shown in fig. 1 for a specific step flow realized when the computer program is executed by the processor, which is not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A data processing method, comprising:
step S10, in response to the fact that the driving posture data comprising a plurality of posture data points are obtained, the driving posture data are cut into a plurality of target data segments;
step S20, selecting a posture data point as a reference data point for the target data segment;
step S30, calculating a data variable of each attitude data point in the plurality of target data segments according to the reference data point, so as to obtain the compressed driving attitude data.
2. The data processing method according to claim 1, wherein the step S10 includes:
step S11, sampling the driving attitude data according to the preset data precision to obtain sampled attitude data;
and step S12, acquiring the plurality of cut target data segments according to the sampling attitude data.
3. The data processing method according to claim 1 or 2, wherein the step S20 includes:
and selecting the first attitude data point of each target data segment as a reference data point corresponding to the target data segment.
4. The data processing method according to claim 1, wherein the step S10 includes:
determining the segment number with the minimum total data amount according to the driving posture data, and cutting the driving posture data based on the segment number; the total amount of data is calculated according to the following formula:
M＝x[a+(y-1)×b]
wherein, M is the total data amount, a is the bit number of each attitude data point, x is the number of segments of the target data segment, y is the number of attitude data points contained in each target data segment, and b is the bit number of each data variable.
5. The data processing method according to claim 1, wherein after the step S30, further comprising:
and according to the reference data points and the data variables, performing reverse calculation to restore the driving posture data.
6. The data processing method of claim 1, wherein the pose data points are selected from at least one of a timestamp, a pose, a three-dimensional feature point; and/or the data variable is selected from at least one of a timestamp variable, a pose variable and a three-dimensional feature point variable.
7. The data processing method of claim 6, wherein if the pose variables include pose quaternion variables and pose three-dimensional vector variables, the step S30 includes:
calculating a pose quaternion variable of the attitude data point to be compressed according to the quaternion of the attitude data point to be compressed and the quaternion of the reference data point;
and calculating the pose three-dimensional vector variable of the attitude data point to be compressed according to the three-dimensional vector of the attitude data point to be compressed, the rotation matrix corresponding to the quaternion of the datum data point and the three-dimensional vector of the datum data point.
8. The data processing method of claim 6, wherein if the data variable includes a three-dimensional feature point variable, the step S30 includes:
and calculating the three-dimensional characteristic point variable of the attitude data point to be compressed according to the difference between the three-dimensional characteristic point of the attitude data point to be compressed and the gravity center of the three-dimensional characteristic points of all the attitude data points in the target data segment where the attitude data point to be compressed is located.
9. A data processing apparatus, comprising: a processor and a memory storing a computer program which, when executed by the processor, implement the steps of the data processing method of any one of claims 1 to 8.
10. A readable storage medium, characterized in that the readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the data processing method according to any one of claims 1 to 8.
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