阅读说明：本技术 基于多线激光雷达的路沿检测方法及装置 (Road edge detection method and device based on multi-line laser radar ) 是由 杨潇潇 汪涛 熊琪 张放 李晓飞 张德兆 王肖 霍舒豪 于 2019-08-09 设计创作，主要内容包括：本发明提供了一种基于多线激光雷达的路沿检测方法,包括：对每个路沿点的第一位置信息进行坐标转换,得到第二坐标系下,每个路沿点的第二位置信息；获取当前帧的激光点云信息；根据路沿点的第二位置信息和当前帧的激光点云信息,确定原始路沿点云信息；根据激光雷达的探头信息,对原始路沿点云信息中的路沿点进行聚类,得到聚类路沿点云信息；根据聚类点云信息中的路沿点的中心点坐标和路沿点的第二位置信息,对聚类路沿点云信息进行过滤,得到过滤的路沿点云信息；计算过滤的路沿点云信息的局部特征；根据局部特征,对过滤的路沿点云信息进行处理,得到目标路沿点；对多个目标路沿点进行拟合,得到路沿信息。该方法对环境要求第、鲁棒性高。(The invention provides a road edge detection method based on a multi-line laser radar, which comprises the following steps: performing coordinate conversion on the first position information of each road edge point to obtain second position information of each road edge point in a second coordinate system; acquiring laser point cloud information of a current frame; determining original road edge point cloud information according to the second position information of the road edge point and the laser point cloud information of the current frame; according to probe information of the laser radar, carrying out clustering on the road edge points in the original road edge point cloud information to obtain clustered road edge point cloud information; filtering the clustered road edge point cloud information according to the central point coordinates of the road edge points in the clustered point cloud information and the second position information of the road edge points to obtain filtered road edge point cloud information; calculating local characteristics of the filtered road edge point cloud information; processing the filtered road edge point cloud information according to the local characteristics to obtain a target road edge point; and fitting the plurality of target road edge points to obtain road edge information. The method has the first requirement on the environment and high robustness.)

1. A road edge detection method based on a multiline laser radar is characterized by comprising the following steps:

acquiring first position information of each road edge point in a plurality of road edge points in a preset map in a first coordinate system according to the position information of the vehicle;

performing coordinate conversion on the first position information of each road edge point to obtain second position information of each road edge point in a second coordinate system;

acquiring laser point cloud information of a current frame;

determining original road edge point cloud information according to the second position information of the road edge point and the laser point cloud information of the current frame;

according to probe information of the laser radar, carrying out clustering on the road edge points in the original road edge point cloud information to obtain clustered road edge point cloud information;

respectively calculating the coordinates of the central point of each path point in the clustered point cloud information;

filtering the clustered road edge point cloud information according to the central point coordinates of the road edge points in the clustered point cloud information and the second position information of the road edge points to obtain filtered road edge point cloud information;

calculating local characteristics of the filtered road edge point cloud information;

processing the filtered road edge point cloud information according to the local features to obtain a target road edge point;

and fitting the plurality of target road edge points to obtain road edge information.

2. The method according to claim 1, wherein the coordinate conversion of the first position information of each road edge point to obtain the second position information of each road edge point in the second coordinate system specifically includes:

using formulasCalculating second position information of the road edge point;

wherein the content of the first and second substances,

3. The method according to claim 1, wherein the determining the original road edge point cloud information according to the second position information of the road edge point and the laser point cloud information of the current frame specifically comprises:

extracting laser point cloud information with the distance from the road edge point within a first preset range according to the overlapping area of the second position information of the road edge point and the laser point cloud information of the current frame to obtain original laser point cloud information; wherein the original laser point cloud information comprises n₀Dot

4. The method according to claim 1, wherein the local features comprise radial gradients, and wherein the calculating local features of the filtered road edge point cloud information comprises:

using formulas

wherein the content of the first and second substances,

5. The method according to claim 4, wherein the processing the filtered road edge point cloud information according to the local features to obtain a target road edge point specifically comprises:

when the radial gradient of the road edge point cloud in the filtered road edge point cloud information is larger than a preset radial gradient threshold value, retaining the road edge point cloud;

sorting all the retained road edge point clouds according to heights, and taking height median points;

and taking the height median point as a center, and selecting the road edge point cloud with the distance from the median point in a second preset range as a target road edge point.

6. The method according to claim 1, wherein the fitting a plurality of target road edge points to obtain road edge information specifically comprises:

filtering the target road edge point by a random sampling consistency algorithm;

and fitting the filtered target road edge points by a least square method to obtain road edge information.

7. A road edge detection device based on a multiline laser radar, characterized in that the device comprises:

the acquisition unit is used for acquiring first position information of each road edge point in a plurality of road edge points in a preset map under a first coordinate system according to the position information of the vehicle;

the coordinate conversion unit is used for carrying out coordinate conversion on the first position information of each road edge point to obtain second position information of each road edge point in a second coordinate system;

the acquisition unit is also used for acquiring laser point cloud information of the current frame;

the determining unit is used for determining original road edge point cloud information according to the second position information of the road edge point and the laser point cloud information of the current frame;

the clustering unit is used for clustering the road edge points in the original road edge point cloud information according to the probe information of the laser radar to obtain clustered road edge point cloud information;

the computing unit is used for respectively computing the center point coordinates of each path point in the clustered point cloud information;

the filtering unit is used for filtering the clustered road edge point cloud information according to the central point coordinates of the road edge points in the clustered point cloud information and the second position information of the road edge points to obtain filtered road edge point cloud information;

the computing unit is also used for computing the local features of the filtered road edge point cloud information;

the processing unit is used for processing the filtered road edge point cloud information according to the local features to obtain a target road edge point;

and the fitting unit is used for fitting the target road edge points to obtain road edge information.

8. An apparatus, comprising a memory for storing a program and a processor for performing the method of any of claims 1-6.

9. A computer program product comprising instructions for causing a computer to perform the method of any one of claims 1 to 6 when the computer program product is run on the computer.

10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method of any one of claims 1-6.

Technical Field

The invention relates to the technical field of data processing, in particular to a road edge detection method and device based on a multi-line laser radar.

Background

In recent years, with rapid development of intelligent driving technology, environmental perception is an important component in an intelligent driving system, road information extraction is important research content of environmental perception, a road edge is typical road information, and detection of the road edge can assist the intelligent driving system to realize various complex functions and tasks.

In order to realize the function of road edge detection, researchers have proposed various solutions, which can be divided into two categories, camera-based road edge detection and laser radar-based road edge detection, according to the difference of using sensors.

In the research on road detection algorithm in intelligent vehicle visual navigation, Liu Hua Sheng et al propose a road edge detection algorithm based on a camera, but the road edge detection algorithm based on the camera is easily affected by factors such as weather, illumination, cracks, water marks and the like.

In the real-time road edge detection algorithm based on the 3D laser radar, Liu cata proposes a road edge detection algorithm based on the laser radar, and the algorithm does not consider obstacles existing in the road and has poor anti-interference capability. Rho provides a road edge detection algorithm based on Hough transformation, and road edge information is extracted by extracting break points of line segments.

The existing road edge detection algorithm based on visual information and multi-line laser radar information has high requirements on the road environment and poor robustness, and cannot adapt to the condition that obstacles exist on the road. And the calculation complexity is higher, the requirement on the system performance is higher, and a plurality of algorithms cannot meet the requirement on real-time performance.

Disclosure of Invention

The embodiment of the invention aims to provide a road edge detection method based on a multi-line laser radar, and aims to solve the problems that the road edge detection method in the prior art is easily influenced by factors such as weather, illumination, cracks and water traces, has poor robustness and high calculation complexity, and cannot meet the requirement of real-time performance.

In order to solve the above problem, the present invention provides a road edge detection method based on a multiline laser radar, wherein the method comprises:

acquiring first position information of each road edge point in a plurality of road edge points in a preset map in a first coordinate system according to the position information of the vehicle;

performing coordinate conversion on the first position information of each road edge point to obtain second position information of each road edge point in a second coordinate system;

acquiring laser point cloud information of a current frame;

determining original road edge point cloud information according to the second position information of the road edge point and the laser point cloud information of the current frame;

according to probe information of the laser radar, carrying out clustering on the road edge points in the original road edge point cloud information to obtain clustered road edge point cloud information;

respectively calculating the coordinates of the central point of each path point in the clustered point cloud information;

filtering the clustered road edge point cloud information according to the central point coordinates of the road edge points in the clustered point cloud information and the second position information of the road edge points to obtain filtered road edge point cloud information;

calculating local characteristics of the filtered road edge point cloud information;

processing the filtered road edge point cloud information according to the local features to obtain target road edge points;

and fitting the plurality of target road edge points to obtain road edge information.

In a possible implementation manner, the performing coordinate conversion on the first position information of each road edge point to obtain the second position information of each road edge point in the second coordinate system specifically includes:

using formulas

Calculating second position information of the road edge point;

wherein the content of the first and second substances,

and

respectively a rotation matrix and a translation matrix under a first coordinate system; c_MThe position information of the vehicle under the first coordinate system; u shape_MThe first position information of the road edge point under the first coordinate system;

and second position information of the road edge point in a second coordinate system.

In a possible implementation manner, the determining the original road edge point cloud information according to the second position information of the road edge point and the laser point cloud information of the current frame specifically includes:

extracting laser point cloud information with the distance from the road edge point within a first preset range according to the second position information of the road edge point and the overlapping area of the laser point cloud information of the current frame to obtain original laser point cloud information; wherein the original laser point cloud information comprises n₀Dot

p_i＝(x_i，y_i，z_i，r_i)，(x_i，y_i，z_i) Is the spatial coordinate of a point, r_iThe laser probe information of the current point.

In a possible implementation manner, the local feature includes a radial gradient, and the calculating the local feature of the filtered road edge point cloud information specifically includes:

using formulasCalculating a radial gradient;

wherein the content of the first and second substances,

in order to be a radial gradient, the gradient is,

and

the point cloud three-dimensional space coordinates x and y components, k ═ 2, 2, 2, 2, 0, -2, -2, -2, -2, -2. In a possible implementation manner, the processing the filtered road edge point cloud information according to the local feature to obtain a target road edge point specifically includes:

when the radial gradient of the road edge point cloud in the filtered road edge point cloud information is greater than a preset radial gradient threshold value, retaining the road edge point cloud;

sorting all the retained road edge point clouds according to heights, and taking height median points;

and taking the height median point as a center, and selecting the road edge point cloud with the distance from the median point in a second preset range as a target road edge point.

In a possible implementation manner, the fitting a plurality of target road edge points to obtain road edge information specifically includes:

filtering the target road edge point by a random sampling consistency algorithm;

and fitting the filtered target road edge points by a least square method to obtain road edge information.

In a second aspect, the present invention provides a road edge detection device based on a multiline laser radar, including:

the acquisition unit is used for acquiring first position information of each road edge point in a plurality of road edge points in a preset map under a first coordinate system according to the position information of the vehicle;

the coordinate conversion unit is used for carrying out coordinate conversion on the first position information of each road edge point to obtain second position information of each road edge point in a second coordinate system;

the acquisition unit is also used for acquiring laser point cloud information of the current frame;

the determining unit is used for determining original road edge point cloud information according to the second position information of the road edge point and the laser point cloud information of the current frame;

the clustering unit is used for clustering the road edge points in the original road edge point cloud information according to the probe information of the laser radar to obtain clustered road edge point cloud information;

the computing unit is used for respectively computing the center point coordinates of each path point in the clustered point cloud information;

the filtering unit is used for filtering the clustered road edge point cloud information according to the central point coordinates of the road edge points in the clustered point cloud information and the second position information of the road edge points to obtain filtered road edge point cloud information;

the computing unit is also used for computing the local features of the filtered road edge point cloud information;

the processing unit is used for processing the filtered road edge point cloud information according to the local features to obtain a target road edge point;

and the fitting unit is used for fitting the target road edge points to obtain road edge information.

In a third aspect, the invention provides an apparatus comprising a memory for storing a program and a processor for performing the method of any of the first aspects.

In a fourth aspect, the present invention provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method according to any one of the first aspect.

In a fifth aspect, the invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of the first aspects.

The method and the device for detecting the road edge based on the multi-line laser radar can accurately extract the geometric information of the road edge, have low requirement on the environment, high robustness and low calculation complexity, can meet the requirement on real-time performance, and can assist an intelligent driving system to finish complex tasks, such as local track calculation, welt cleaning and the like.

Drawings

Fig. 1 is a flowchart of a method for detecting a road edge based on a multi-line lidar according to an embodiment of the present invention;

FIG. 2 is a rear view of a multi-line lidar beam distribution;

FIG. 3 is a top view of a multiline lidar projected onto the ground;

FIG. 4 is a schematic diagram illustrating a reduced detection range of the road edge point cloud using the second position information of the road edge point;

FIG. 5A is a rear view of a multiline lidar projected onto a waypoint;

FIG. 5B is a rear view of the multiline lidar projected onto a waypoint;

fig. 6 is a schematic structural diagram of a multi-line lidar-based road edge detection apparatus according to a second embodiment of the present invention.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for the convenience of description, only the portions related to the present invention are shown in the drawings.

It should be noted that the embodiments and features in the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 is a flowchart of a method for detecting a road edge based on a multi-line lidar according to an embodiment of the present invention. The method can be applied to an unmanned vehicle or a robot equipped with a multiline laser radar, and the method is described below as applied to an unmanned vehicle as an example. When the method is applied to an unmanned driving vehicle, the execution subject of the method is a processor of the vehicle. As shown in fig. 1, the method comprises the steps of:

step 101, acquiring first position information of each road edge point in a plurality of road edge points in a preset map in a first coordinate system according to the position information of the vehicle.

Specifically, the first coordinate System is a map coordinate System, the vehicle is equipped with a multi-line laser radar and other sensors, for example, the vehicle is equipped with a Global Positioning System (GPS), and the vehicle position information in the map coordinate System can be obtained through the GPS. In a preset map, the position and angle of the lower road edge of the map coordinate system are marked. Each of the road edge points in the road edge has first position information.

Therefore, through a processor of the intelligent driving system in the vehicle, second position information of the road edge point under a vehicle coordinate system can be calculated according to the first position information of the road edge point of the preset high-precision map and the position information of the current vehicle, and through the second position information, the detection range of the road edge detection can be narrowed, and the detection precision and robustness are improved.

And 102, performing coordinate conversion on the first position information of each road edge point to obtain second position information of each road edge point in a second coordinate system.

In particular, a formula may be utilized

Calculating second position information of the road edge point;

wherein the content of the first and second substances,

and

respectively a rotation matrix and a translation matrix under a first coordinate system; c_MThe position information of the vehicle under the first coordinate system; u shape_MThe first position information of the road edge point under the first coordinate system;

and second position information of the road edge point in a second coordinate system.

The second position information may be a three-dimensional spatial coordinate of the road edge point.

And 103, acquiring laser point cloud information of the current frame.

Specifically, referring to fig. 2, the multi-line lidar has a plurality of laser probes vertically arranged, the laser probes are uniformly distributed in the vertical direction, the laser probes measure distance information by using a time of flight (ToF) technique, the lidar rotates around a vertical shaft at a high speed in the operation process, and each probe measures environment information at a high frequency according to a certain sequence during rotation. The radar can acquire environmental data around a circle by rotating the radar for one circle, and the surrounding environmental information is provided in the form of discrete sparse three-dimensional space coordinates and is called as original laser point cloud information.

As shown in fig. 3, the point cloud projected onto the plane by the multiline lidar point cloud is theoretically a complete circle when viewed from the top down.

For the original laser point cloud information, the original laser point cloud information under the laser radar coordinate system can be converted into the laser point cloud information under the vehicle coordinate system according to the installation position of the multi-line laser radar in the vehicle.

And step 104, determining original road edge point cloud information according to the second position information of the road edge point and the laser point cloud information of the current frame.

Specifically, referring to fig. 4, according to a superposition area of the second position information of the road edge point and the laser point cloud information of the current frame, extracting the laser point cloud information of which the distance from the road edge point is within a first preset range to obtain original laser point cloud information; wherein, the original laser point cloud information contains n₀Dot

p_i＝(x_i，y_i，z_i，r_i)，(x_i，y_i，z_i) Is the spatial coordinate of a point, r_iFor laser probe information at the current point, e.g. a 16-line lidar comprising 16 laser heads, r_i1, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 }. Number n of extracted original laser point cloud information₀Is far less than the quantity N of the laser point cloud information. Therefore, the calculation range of the laser point cloud information is narrowed through the map, and the calculation speed is improved.

And 105, clustering the road edge points in the original road edge point cloud information according to the probe information of the laser radar to obtain the clustered road edge point cloud information.

And step 106, respectively calculating the center point coordinates of the road edge points in the clustered point cloud information.

And 107, filtering the clustered road edge point cloud information according to the central point coordinates of the road edge points and the second position information of the road edge points in the clustered point cloud information to obtain filtered road edge point cloud information.

Specifically, r is calculated according to the original road edge point cloud information_iInformation, will r_iAnd clustering the road edge point cloud information with the same value, respectively calculating a central point coordinate of the clustered road edge point cloud information, and deleting the clustered road edge point cloud information from the road edge points if the distance between the central point coordinate and the road edge line segment in the map is too far, namely not within a preset second distance range. Therefore, most of obstacle point clouds and the obstacle sheltered points can be filteredThe point cloud of (2).

And step 108, calculating local characteristics of the filtered road edge point cloud information.

And step 109, processing the filtered road edge point cloud information according to the local characteristics to obtain a target road edge point.

Specifically, referring to fig. 5A and 5B, when the road edge beside the road is higher than the road surface, and the laser radar projects on the ground and the road edge with different heights, as shown in fig. 5A and 5B, the radial distance between adjacent point clouds is jumped, and the height of the point cloud on the road edge is higher than that of the point cloud on the ground. Therefore, the point cloud projected on the road edge can be extracted by using the local characteristics of the point cloud, such as the radial distance gradient or the height gradient.

Using formulas

Calculating a radial gradient;

wherein the content of the first and second substances,

in order to be a radial gradient, the gradient is,and

the point cloud three-dimensional space coordinates x and y components, k ═ 2, 2, 2, 2, 0, -2, -2, -2, -2, -2.

Then, when the radial gradient of the road edge point cloud in the filtered road edge point cloud information is larger than a preset radial gradient threshold value, retaining the road edge point cloud;

sorting all the retained road edge point clouds according to heights, and taking height median points;

and taking the height median point as a center, and selecting the road edge point cloud with the distance from the median point in a second preset range as a target road edge point.

Specifically, a radial gradient threshold G is set_threshIf radial gradient

The point cloud is retained, otherwise, the point cloud is discarded. Recording the stored point cloud

For the saved points

Sorting according to height, selecting height middle position point

At the midpoint p_mAs the center, selecting the points near the mid-point as the target road edge points

And step 110, fitting the multiple target road edge points to obtain a road edge curve.

In particular, for a target road edge point

The curve fitting may be performed using a Random sample consensus (RANSAC) algorithm.

Specifically, the RANSAC algorithm is explained:

(1) some of the original data combinations were randomly selected as the original points for curve fitting. Selected raw data, laser probe information r of each point_iAre all inconsistent.

(2) Extracting a group from the combination as original data to establish a curve model;

(3) verifying the model by using other data, and calculating the distances from other points to the curve;

(4) extracting points with the curve distance smaller than a threshold value, setting the points as interior points, and calculating the number of the interior points and an original starting point set;

(5) cycling the second step to the fourth step until all combinations have been processed;

(6) and selecting the original point set with the maximum number of inner points as a best fitting point set, and fitting the road edge curve by a least square method according to the inner points corresponding to the original point set.

Projecting the road edge linear equation to an x-y plane to generate a two-dimensional linear equation, and calculating an intersection point p of the road edge linear equation and a y axis of a vehicle coordinate system according to the two-dimensional linear equation_yThe road-following direction n is a direction representing the road-following information (p) in a point-wise manner_y，n)。

When RANSAC curve fitting sampling points are carried out, prior information of each point and laser probe information r of each point are considered_i. All points in the combination of sampling points, r_iMust not be the same. The limiting condition ensures that the distance between each point is maximum when RANSAC fitting is carried out, and the fitted straight line is more accurate. And selecting an optimal sampling point according to the number of the interior points, extracting all interior points of the optimal sampling point, and calculating a linear equation of the road edge by using the interior points by adopting a least square method.

By applying the road edge detection method based on the multi-line laser radar provided by the embodiment of the invention, the geometric information of the road edge can be accurately extracted, the requirement on the environment is lower, the robustness is high, the calculation complexity is low, the real-time requirement can be met, and the extracted road edge information can assist an intelligent driving system to complete complex tasks, such as local track calculation, welt cleaning and the like.

Fig. 6 is a schematic structural diagram of a multi-line lidar-based road edge detection apparatus according to a second embodiment of the present invention. The apparatus is applied to the first embodiment of the method for detecting a road edge based on a multiline laser radar, as shown in fig. 6, the apparatus 600 for detecting a road edge based on a multiline laser radar includes: the system comprises an acquisition unit 601, a coordinate conversion unit 602, a determination unit 603, a clustering unit 604, a calculation unit 605, a filtering unit 606, a processing unit 607 and a fitting unit 608.

The obtaining unit 601 is configured to obtain first position information of each of a plurality of road edge points in a preset map in a first coordinate system according to position information of a vehicle.

The coordinate conversion unit 602 is configured to perform coordinate conversion on the first position information of each road edge point to obtain second position information of each road edge point in a second coordinate system.

The obtaining unit 601 is further configured to obtain laser point cloud information of the current frame.

The determining unit 603 is configured to determine original road edge point cloud information according to the second position information of the road edge point and the laser point cloud information of the current frame.

The clustering unit 604 is configured to cluster road edge points in the original road edge point cloud information according to probe information of the laser radar, so as to obtain clustered road edge point cloud information.

The calculating unit 605 is configured to calculate the center point coordinates of each edge point in the clustered point cloud information.

The filtering unit 606 is configured to filter the clustered road edge point cloud information according to the center point coordinates of the road edge points in the clustered point cloud information and the second position information of the road edge points, so as to obtain filtered road edge point cloud information.

The calculation unit 605 is further configured to calculate local features of the filtered road edge point cloud information.

The processing unit 607 is configured to process the filtered road edge point cloud information according to the local feature to obtain a target road edge point.

The fitting unit 608 is configured to fit a plurality of target road edge points to obtain road edge information.

The specific functions of each unit and the technical effects of the device for detecting a road edge based on a multi-line lidar are similar to those of the first embodiment, and are not described herein again.

The third embodiment of the invention provides equipment, which comprises a memory and a processor, wherein the memory is used for storing programs, and the memory can be connected with the processor through a bus. The memory may be a non-volatile memory such as a hard disk drive and a flash memory, in which a software program and a device driver are stored. The software program is capable of performing various functions of the above-described methods provided by embodiments of the present invention; the device drivers may be network and interface drivers. The processor is used for executing a software program, and the software program can realize the method provided by the embodiment of the invention when being executed.

A fourth embodiment of the present invention provides a computer program product including instructions, which, when the computer program product runs on a computer, causes the computer to execute the method provided in the first embodiment of the present invention.

The fifth embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method provided in the first embodiment of the present invention is implemented.

Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.