Method and device for determining optimal landing point of unmanned aerial vehicle based on multi-source remote sensing data

Abstract

The application provides a kind of unmanned aerial vehicle optimal landing point determination method and equipment based on multi-source remote sensing data, comprising: the image information collected by unmanned aerial vehicle is preprocessed and feature extraction, in combination with each sensor data to judge the geographical environment where it is located;The path image collected by unmanned aerial vehicle is analyzed, and the area with spacious and flat and no obstruction above is selected through the overhead view, and the environmental parameters of each alternative landing point are recorded;Each type of environmental parameter will specify the corresponding threshold value, and the data collected above the threshold value will be used as the alternative landing point;The environmental parameters of the alternative landing points along the way are processed;The standard data environmental parameters are substituted into the optimal parameter model determined in the geographical environment, to obtain the score of the point about the landing feasibility, and define the reliability of the point as the landing point;Get the optimal landing point on this path.The application collects data simultaneously through intelligent vehicle chassis and unmanned aerial vehicle at multiple angles, which is convenient for more optimized adjustment and work in later period.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of satellite remote sensing, in particular to a method and device for determining optimal landing point of unmanned aerial vehicle (UAV) based on multi-source remote sensing data. BACKGROUND

[0002] Traditional inspection is mostly in the form of manual patrol, combined with part of UAV inspection test. There are problems such as not reaching the inspection point, missing the inspection point, incomplete data storage, inaccurate data storage, data loss, missing, etc. Hidden danger points cannot be reported in time, the location and situation of hidden danger are not clear, and the hidden danger cannot be handled in time. Small hidden dangers accumulate into big accidents, causing direct harm and property loss.

[0003] On the basis of upgrading the key technology research of UAV mobile airport, the automatic driving robot is creatively combined with the UAV nest, the UAV nest is carried by the full unmanned robot, the maximum distance of single UAV fine inspection is expanded to the driving range of the robot, so that the full unmanned long-distance fine inspection is realized, and the application prospect is wide in forestry, agriculture, transportation, border defense, electric power, geology and other fields. Since the automatic driving robot is in constant motion, the previous fixed airport UAV landing algorithm is obviously no longer applicable, but the environment in which the automatic driving robot is located is also changing, that is, the landing conditions of the UAV are also changing. At this time, a new algorithm is needed. Therefore, developing a method and device for determining optimal landing point of unmanned aerial vehicle based on multi-source remote sensing data can effectively overcome the defects in the related art, and has become a technical problem to be solved in the industry. SUMMARY

[0004] In view of the above problems existing in the prior art, the embodiment of the present application provides a method and device for determining optimal landing point of unmanned aerial vehicle based on multi-source remote sensing data.

[0005] In a first aspect, embodiments of the present application provide a method for determining an optimal landing point of a UAV based on multi-source remote sensing data, comprising: pre-processing and feature extraction of image information collected by the UAV, and determining the geographical environment in combination with sensor data; analyzing path images collected by the UAV, selecting an area that is spacious, flat and unobstructed from above through a bird's eye view, and the UAV travels along the selected path of the candidate landing point and records the environmental parameters of each candidate landing point along the way; a threshold value is set for each type of environmental parameter, and the data collected above the threshold value is used as a candidate landing point; processing the environmental parameters of the candidate landing points along the way, removing non-standard data outside the threshold value, removing non-standard points from the candidate landing point sequence, and increasing the proportion of excellent categories of each type of environmental parameter; substituting the standard data environmental parameters into the optimal parameter model determined under the geographical environment to obtain a score of the point on the landing feasibility, which is defined as the reliability of the point as a landing point, and the higher the score, the higher the reliability of the point as a landing point of the UAV, i.e. the more suitable it is for landing; integrating the scored points on the path and arranging them in descending order to obtain the optimal landing point on the path.

[0006] On the basis of the above method embodiment content, the environmental parameters of each candidate landing point in the method for determining an optimal landing point of a UAV based on multi-source remote sensing data provided in the embodiments of the present application include: slope, humidity, temperature, vegetation coverage, wind speed, light, and gravel conditions.

[0007] On the basis of the above method embodiment content, the method for determining an optimal landing point of a UAV based on multi-source remote sensing data provided in the embodiments of the present application includes increasing the proportion of excellent categories of each type of environmental parameter, which comprises: calculating the average value of all values of each type of environmental parameter, and comparing each type of environmental parameter value of the candidate landing point with the average value, and the candidate landing point with a value greater than or equal to fifty percent of the average value obtains an additional score of thirty percent, thereby increasing the proportion of excellent parameters.

[0008] On the basis of the above method embodiment content, the method for determining an optimal landing point of a UAV based on multi-source remote sensing data provided in the embodiments of the present application includes establishing the optimal parameter model, which comprises: simulating the target landform conditions of the inspection, taking two types of simulation points: suitable landing simulation points and unsuitable landing simulation points, and randomly arranging all simulation points to obtain simulation point data under various geographical conditions as a training set of the model; setting environmental parameters under various geographical conditions, and assigning them discretization or continuous according to their characteristics, and making them change constantly; substituting the simulation training set into the parameter algorithm with variable weights to find the maximum distance separating hyperplane, i.e. the classification effect of the group is the best, the correctness is the highest, and the reliability is higher; the obtained parameter weight combination is the optimal weight combination under the geographical conditions, which is used to establish the optimal parameter model under the landform conditions.

[0009] In a second aspect, embodiments of the present application provide a UAV optimal landing point determination device based on multi-source remote sensing data, comprising: a first main module for realizing pre-processing and feature extraction of image information collected by a UAV, and judging the geographical environment in combination with various sensor data; a second main module for realizing analysis of path images collected by the UAV, selecting an area that is spacious and flat and has no obstruction above from a bird's eye view, and the UAV travels along the selected candidate landing point path and records environmental parameters of each candidate landing point on the way; a third main module for realizing that a corresponding threshold value is specified for each type of environmental parameter, and data higher than the threshold value is taken as a candidate landing point; a fourth main module for realizing processing of environmental parameters of candidate landing points along the way, removing non-standard data other than the threshold value, removing non-standard points from the candidate landing point sequence, and increasing the proportion of optimal classes of each type of environmental parameter; a fifth main module for realizing that the standard data environmental parameters are substituted into an optimal parameter model determined in the geographical environment to obtain a score of the point on the landing feasibility, and the score is defined as the reliability of the point as a landing point, the higher the score, the higher the reliability of the point as a UAV landing point, i.e. the more suitable it is for landing; and a sixth main module for realizing integration of the scored points on the path and arranging them in descending order to obtain the optimal landing point on the path.

[0010] In a third aspect, embodiments of the present application provide an electronic device, comprising:

[0011] at least one processor; and

[0012] at least one memory communicatively connected with the processor, wherein:

[0013] The memory stores program instructions executable by the processor, and the processor invoking the program instructions can execute the UAV optimal landing point determination method based on multi-source remote sensing data provided in any of the various implementation manners of the first aspect.

[0014] In a fourth aspect, embodiments of the present application provide a non-transitory computer readable storage medium, which stores computer instructions, and the computer instructions cause a computer to execute the UAV optimal landing point determination method based on multi-source remote sensing data provided in any of the various implementation manners of the first aspect.

[0015] The UAV optimal landing point determination method and device based on multi-source remote sensing data provided by the embodiments of the present application can dynamically adjust the algorithm according to different environments, and is used for calculating and screening landing points; can realize full-automatic inspection in remote areas such as mountains and plains, and avoid waste of excessive human resources; and can also collect data from multiple angles through the intelligent vehicle chassis and the UAV at the same time during operation, so as to more optimally adjust and work later. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0017] Figure 1 The flow chart of the method for determining the optimal landing point of the unmanned aerial vehicle based on multi-source remote sensing data provided by the embodiment of the present application is shown in the figure.

[0018] Figure 2 The structure diagram of the device for determining the optimal landing point of the unmanned aerial vehicle based on multi-source remote sensing data provided by the embodiment of the present application is shown in the figure.

[0019] Figure 3 The physical structure diagram of the electronic device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the present application. In addition, the technical features in each embodiment or single embodiment provided by the present application can be combined with each other to form a feasible technical solution, and this combination is not restricted by the sequence of steps and / or structure mode, but it must be based on the implementation by those skilled in the art. When the combination of technical solutions appears contradictory or unfeasible, it should be considered that the combination of technical solutions does not exist, and it is not within the protection scope of the present application.

[0021] The embodiment of the present application provides a method for determining the optimal landing point of the unmanned aerial vehicle based on multi-source remote sensing data, which is shown in the figure. Figure 1The method comprises: pre-processing and feature extraction of image information collected by the unmanned aerial vehicle, and judging the geographical environment in combination with sensor data; analyzing path images collected by the unmanned aerial vehicle, selecting a wide and flat area without obstruction above from a bird's eye view, and the unmanned vehicle travels along the selected landing point path and records the environmental parameters of each landing point; a threshold value is set for each type of environmental parameter, and the data higher than the threshold value is used as a candidate landing point; the environmental parameters of the candidate landing points along the path are processed, the non-standard data outside the threshold value is removed, the non-standard points are removed from the candidate landing point sequence, and the proportion of the excellent class of each type of environmental parameter is increased; the standard data environmental parameters are substituted into the optimal parameter model determined under the geographical environment to obtain the score of the point on the landing feasibility, which is defined as the reliability of the point as a landing point, and the higher the score, the higher the reliability of the point as a landing point of the unmanned aerial vehicle, that is, the more suitable for landing; the scoring points on the path are integrated and arranged in descending order to obtain the optimal landing point on the path.

[0022] Based on the content of the above method embodiment, as an optional embodiment, the unmanned aerial vehicle optimal landing point determination method based on multi-source remote sensing data provided in the embodiment of the application, the environmental parameters of each candidate landing point include: slope, humidity, temperature, vegetation coverage, wind speed, illumination and gravel condition.

[0023] Based on the content of the above method embodiment, as an optional embodiment, the unmanned aerial vehicle optimal landing point determination method based on multi-source remote sensing data provided in the embodiment of the application, the increasing the proportion of the excellent class of each type of environmental parameter includes: calculating the average value of all values of each type of environmental parameter, and comparing each type of environmental parameter value of the candidate landing point with the average value, and the candidate landing point with a value greater than or equal to fifty percent of the average value obtains an additional score of thirty percent, thereby increasing the proportion of excellent parameters.

[0024] Based on the content of the above method embodiment, as an optional embodiment, the unmanned aerial vehicle optimal landing point determination method based on multi-source remote sensing data provided in the embodiment of the application, the establishment of the optimal parameter model includes: simulating the target landform condition, taking two types of simulation points: suitable landing simulation points and unsuitable landing simulation points, and randomly arranging all simulation points to obtain simulation point data under various geographical conditions as a training set of the model; setting the environmental parameters under various geographical conditions, and giving them discretization or continuous according to their characteristics, and making them change constantly; substituting the simulation training set into the variable weight parameter algorithm to find the maximum distance separating hyperplane, that is, the classification effect of the group is the best, the correctness is the highest, and the reliability is higher; the obtained parameter weight combination is the optimal weight combination under the geographical condition, which is used to establish the optimal parameter model under the landform condition.

[0025] The optimal landing point determination method based on multi-source remote sensing data provided by the embodiment of the present application can dynamically adjust the algorithm according to different environments, is used for calculating and screening landing points, can realize full-automatic inspection in remote areas such as mountains, plains and the like, and avoids waste of excessive human resources; and in runtime, data can also be collected by the intelligent vehicle chassis and the unmanned aerial vehicle simultaneously and from multiple angles, so as to more optimally adjust and work later.

[0026] The implementation basis of each embodiment of the present application is that the processing of the device with the processor function is programmed. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present application can be packaged into various modules. Based on this actual situation, on the basis of each embodiment described above, an optimal landing point determination device for an unmanned aerial vehicle based on multi-source remote sensing data is provided by the embodiment of the present application, which is used for executing the optimal landing point determination method for an unmanned aerial vehicle based on multi-source remote sensing data in the method embodiment described above. Referring to Figure 2 , the device comprises: a first main module, which is used for realizing pre-processing and feature extraction of image information collected by an unmanned aerial vehicle, and judging a geographical environment in combination with sensor data; a second main module, which is used for realizing analysis of path images collected by the unmanned aerial vehicle, selecting a region that is spacious and flat and has no obstruction above from a top perspective, and recording environmental parameters of each candidate landing point by the unmanned vehicle traveling according to a selected candidate landing point path; a third main module, which is used for realizing that a corresponding threshold is specified for each type of environmental parameter, and a point is taken as a candidate landing point if the collected data is higher than the threshold; a fourth main module, which is used for realizing processing of the environmental parameters of the candidate landing points along the path, removing non-standard data other than the threshold, removing non-standard points from the candidate landing point sequence, and increasing the proportion of optimal classes of each type of environmental parameter; a fifth main module, which is used for realizing that the standard data environmental parameters are substituted into an optimal parameter model determined in the geographical environment, scoring the point in terms of landing feasibility, and defining the reliability of the point as a landing point according to the score, that is, the higher the score, the higher the reliability of the point as a landing point of the unmanned aerial vehicle, and the more suitable the point is for landing; and a sixth main module, which is used for realizing integration of the scored points on the path, and arranging the points in descending order to obtain an optimal landing point on the path.

[0027] The optimal landing point determination device for an unmanned aerial vehicle based on multi-source remote sensing data provided by the embodiment of the present application adopts Figure 2 several modules, can dynamically adjust the algorithm according to different environments, is used for calculating and screening landing points, can realize full-automatic inspection in remote areas such as mountains, plains and the like, and avoids waste of excessive human resources; and in runtime, data can also be collected by the intelligent vehicle chassis and the unmanned aerial vehicle simultaneously and from multiple angles, so as to more optimally adjust and work later.

[0028] It should be noted that the device in the device embodiment provided by the present application can be used to implement the method in the above-mentioned method embodiment, and can also be used to implement the method in other method embodiments provided by the present application, the difference is only that the corresponding function module is set, and the principle is basically the same as that of the above-mentioned device embodiment provided by the present application. As long as the person skilled in the art can obtain the corresponding technical means by combining technical features on the basis of the above-mentioned device embodiment, and the technical solution formed by these technical means, on the premise of ensuring the practicability of the technical solution, the device in the above-mentioned device embodiment can be improved to obtain the corresponding device class embodiment, which is used to implement the method in other method class embodiments. For example:

[0029] Based on the content of the above device embodiment, as an optional embodiment, the unmanned aerial vehicle optimal landing point determination device based on multi-source remote sensing data provided in the embodiment of the present application further comprises: a first sub-module for realizing the environment parameters of the each candidate landing point, including: slope, humidity, temperature, vegetation coverage, wind speed, illumination and gravel condition.

[0030] Based on the content of the above device embodiment, as an optional embodiment, the unmanned aerial vehicle optimal landing point determination device based on multi-source remote sensing data provided in the embodiment of the present application further comprises: a second sub-module for realizing the increase of the excellent class proportion of each type of environment parameter, including: calculating the average value of all values of each type of environment parameter, and then comparing each type of environment parameter value of the candidate landing point with the average value, and the candidate landing point greater than or equal to fifty percent of the average value obtains an additional score of thirty percent, and the proportion of excellent parameters is increased.

[0031] Based on the content of the above device embodiment, as an optional embodiment, the unmanned aerial vehicle optimal landing point determination device based on multi-source remote sensing data provided in the embodiment of the present application further comprises: a third sub-module for realizing the establishment of the optimal parameter model, including: simulating the target landform condition of the inspection, taking two types of simulation points: suitable landing simulation points and unsuitable landing simulation points, and randomly arranging all simulation points to obtain simulation point data under various geographic conditions as the training set of the model; setting the environment parameters under various geographic conditions, giving them discretization or continuous according to their characteristics, and making them change constantly; substituting the simulation training set into the parameter algorithm with variable weight, finding the maximum distance separating hyperplane, that is, the classification effect of the group is the best, the correctness is the highest, and the reliability is higher; the obtained parameter weight combination is the optimal weight combination under the geographic condition, which is used to establish the optimal parameter model under the landform condition.

[0032] The method of the embodiment of the present application is realized by relying on an electronic device, and therefore it is necessary to introduce the related electronic device. For this purpose, the embodiment of the present application provides an electronic device, as shown in the accompanying drawings, which comprises at least one processor, a communications interface, at least one memory and a communications bus, wherein the at least one processor, the communications interface and the at least one memory complete the communications among each other through the communications bus. The at least one processor can invoke the logic instructions in the at least one memory to execute all or part of the steps of the method provided by the foregoing various method embodiments. Figure 3

[0033] In addition, the logic instructions in the at least one memory described above can be realized in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various method embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0034] The device embodiments described above are only schematic, wherein the units shown as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment of the present application according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0035] ​Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and necessary universal hardware platforms through the above description of the embodiments, and the various embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the methods.

[0036] The flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the systems, methods and computer program products according to the various embodiments of the present application. Based on this understanding, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders from that shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0037] It should be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed, or inherent to such processes, methods, articles or devices. Without more limitations, the elements defined by the statement "comprising" do not exclude the presence of additional identical elements in the processes, methods, articles or devices that include the elements.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for determining an optimal landing point of a UAV based on multi-source remote sensing data, characterized in that, The application relates to an unmanned aerial vehicle (UAV) landing point selection method and device. The method comprises the following steps: pre-processing and feature extraction of image information collected by the UAV, and judging the geographical environment in combination with various sensor data; analyzing path images collected by the UAV, selecting a wide and flat area without upper obstructions through a bird's-eye view, and recording environmental parameters of each selected landing point along a path selected by the UAV, wherein a threshold value is set for each type of environmental parameter, and a landing point is selected as a candidate landing point when the collected data is higher than the threshold value; processing the environmental parameters of the candidate landing points along the path, removing non-standard data beyond the threshold value, removing non-standard points from the candidate landing point sequence, and increasing the proportion of excellent parameters of each type of environmental parameter. The method for increasing the proportion of excellent parameters of each type of environmental parameter comprises the following steps: calculating the average value of all values of each type of environmental parameter, comparing the values of each type of environmental parameter of the candidate landing points with the average value, and giving an additional score of 30% to a candidate landing point with a value greater than or equal to 50% of the average value, so as to increase the proportion of excellent parameters; substituting the standard data environmental parameters into an optimal parameter model determined for the geographical environment, obtaining a score of the landing feasibility of the point, and defining the score as the reliability of the point as a landing point, so that the higher the score, the higher the reliability of the point as a landing point of the UAV, and the more suitable the point is for landing; integrating the scored points on the path and arranging the points in descending order to obtain the optimal landing point on the path. The environmental parameters of the candidate landing points comprise slope, humidity, temperature, vegetation coverage, wind speed, illumination and gravel conditions. 2.The method of claim 1, wherein, The establishment of the optimal parameter model comprises the following steps: simulating the target landform conditions, taking two types of simulation points, i.e. suitable landing simulation points and unsuitable landing simulation points, and randomly arranging all the simulation points to obtain simulation point data under various geographical conditions as a training set of the model; setting the environmental parameters under various geographical conditions, giving the parameters discretization or continuous according to their characteristics, and making the parameters change constantly; substituting the simulation training set into a variable-weight parameter algorithm to find a maximum-distance separating hyperplane, i.e. a group with the best classification effect and the highest correctness, so as to have higher reliability; and obtaining a parameter weight combination as the optimal weight combination under the geographical conditions, and using the combination to establish the optimal parameter model under the landform conditions. 3.The method of claim 1, wherein, The application also discloses a device for realizing the method.

4. An unmanned aerial vehicle optimal landing point determination device based on multi-source remote sensing data, characterized in that, The first main module is used for realizing pre-processing and feature extraction of image information collected by the UAV, and judging the geographical environment in combination with various sensor data. The second main module is used for realizing analysis of path images collected by the UAV, selecting a wide and flat area without upper obstructions through a bird's-eye view, and recording environmental parameters of each selected landing point along a path selected by the UAV; the third main module is used for realizing setting of a threshold value for each type of environmental parameter, and selecting a landing point as a candidate landing point when the collected data is higher than the threshold value; and the fourth main module is used for realizing processing of the environmental parameters of the candidate landing points along the path, removing non-standard data beyond the threshold value, removing non-standard points from the candidate landing point sequence, and increasing the proportion of excellent parameters of each type of environmental parameter. ​ The excellent proportion of each type of environmental parameter is increased, including: calculating the average value of all values of each type of environmental parameter, comparing each type of environmental parameter value of the candidate landing point with the average value, and obtaining an additional score of thirty percent for the candidate landing point if the value is greater than or equal to fifty percent of the average value, and increasing the proportion of excellent parameters; The fifth main module is used to realize the substitution of the specification data environmental parameter into the optimal parameter model determined in the geographical environment, to obtain the score of the point on the landing feasibility, and to define the reliability of the point as the landing point. The higher the score is, the higher the reliability of the point as the unmanned aerial vehicle landing point is, that is, the more suitable the point is for landing.

5. An electronic device, comprising: Comprise: At least one processor, at least one memory and a communication interface; wherein, The processor, memory and communication interface communicate with each other; The memory stores program instructions executable by the processor, and the processor invokes the program instructions to execute the method of any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium, comprising, The non-transitory computer readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method of any one of claims 1 to 3. The non-transitory computer readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the method of any one of claims 1 to 3.

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