CN111328099B - Mobile network signal testing method, device, storage medium and signal testing system - Google Patents
Mobile network signal testing method, device, storage medium and signal testing system Download PDFInfo
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Abstract
The invention provides a mobile network signal testing method, equipment, a storage medium and a signal testing system, which are characterized in that a testing instruction sent by testing control equipment is received; responding to a test instruction, and controlling the unmanned aerial vehicle to acquire an image of a test environment so as to obtain image information; determining the scene type of the test environment according to the image information; determining a signal test scheme matched with the scene category; testing a mobile network signal corresponding to a test environment according to a signal test scheme; the test result of the mobile network signal is sent to the test control equipment, the flight through the unmanned aerial vehicle is not limited by the geographical position, the test result can enter an area where a tester can not enter, and the mobile network signal test scheme most suitable for the test environment is automatically determined by acquiring image information, so that the problems of low efficiency, poor precision and high cost caused by the traditional manual test are avoided, the test efficiency and the test effect are improved, and the test cost is reduced.
Description
Technical Field
The present invention relates to the field of communication testing technologies, and in particular, to a mobile network signal testing method, device, storage medium, and signal testing system.
Background
The existing mobile communication network signal testing generally uses a drive test (abbreviated as DT) and a dotting test (abbreviated as CQT). DT is to know the signal quality, level, coverage of the network, utilize the specialized test equipment to test the road, it drives and carries on the wireless test equipment to drive and run along certain road to measure the performance of the wireless network; CQT refers to testing wireless data network performance at a fixed location.
In the prior art, the traditional driving test and the dotting test need manual operation, different test schemes are designed according to different environments, the efficiency is low, the cost is high, and meanwhile, due to the fact that the driving test and the dotting test are limited by geographical positions, some limited areas are difficult to enter or approach, so that the area cannot be tested, or test results are inaccurate.
Disclosure of Invention
The invention provides a mobile network signal testing method, mobile network signal testing equipment, a storage medium and a signal testing system, which are used for solving the problems of low efficiency and poor effect of testing mobile network signals in a limited area.
According to a first aspect of the embodiments of the present disclosure, the present disclosure provides a mobile network signal testing method, which is applied to a mobile network signal testing device, where the mobile network signal testing device is in communication connection with a test control device and an unmanned aerial vehicle, respectively, and the method includes:
receiving a test instruction sent by the test control equipment;
responding to the test instruction, controlling the unmanned aerial vehicle to acquire an image of a test environment so as to obtain image information;
determining the scene type of the test environment according to the image information;
determining a signal test scheme matching the scene category;
testing the mobile network signal corresponding to the test environment according to the signal test scheme;
and sending the test result of the mobile network signal to test control equipment.
Optionally, the determining the scene type of the test environment according to the image information includes:
performing Gist feature extraction on the image information to obtain a first image feature;
performing PHOG feature extraction on the image information to obtain a second image feature;
fusing the first image feature and the second image feature to form an image combination feature;
and carrying out scene classification on the shot images according to the image combination characteristics to obtain the scene category.
Optionally, the controlling the drone to perform image acquisition on a test environment to obtain image information includes:
controlling the unmanned aerial vehicle to acquire images of a test environment so as to obtain original image information;
and carrying out normalization preprocessing on the original image information to obtain the image information.
Optionally, the determining a signal test scheme matching the scene category includes:
acquiring a first test item and a first test parameter corresponding to the scene category;
and determining a signal test scheme according to the first test item and the first test parameter.
Optionally, after the determining a signal testing scheme according to the first test item and the first test parameter, the method further includes:
acquiring the position information of the unmanned aerial vehicle;
correcting the first test item and the first test parameter according to the position information to obtain a second test item and a second test parameter;
and determining a signal test scheme according to the second test item and the second test parameter.
Optionally, after the controlling the drone to perform image acquisition on a test environment to obtain image information, the method further includes:
if the image information does not meet the test requirements, adjusting the position of the unmanned aerial vehicle and/or the image acquisition angle according to the image information;
and controlling the unmanned aerial vehicle to carry out image acquisition on the test environment again so as to obtain image information.
Optionally, after the controlling the drone to perform image acquisition on the test environment to obtain image information, the method further includes:
acquiring the network signal strength of the mobile network signal test equipment;
if the network signal intensity is larger than a preset signal intensity threshold value, sending the image information to test control equipment;
and if the network signal intensity is less than or equal to a preset signal intensity threshold value, storing the image information to the local.
According to a second aspect of the embodiments of the present disclosure, the present invention provides a mobile network signal testing device, including:
the test instruction receiving module is used for receiving the test instruction sent by the test control equipment;
the image acquisition control module is used for responding to the test instruction and controlling the unmanned aerial vehicle to acquire images of the test environment so as to obtain image information;
the scene type determining module is used for determining the scene type of the test environment according to the image information;
the test scheme determining module is used for determining a signal test scheme matched with the scene category;
and the network signal testing module is used for testing the mobile network signal corresponding to the testing environment according to the signal testing scheme.
And the sending module is used for sending the test result of the mobile network signal to the test control equipment.
Optionally, the scene category determining module is specifically configured to:
performing Gist feature extraction on the image information to obtain a first image feature;
performing PHOG feature extraction on the image information to obtain a second image feature;
fusing the first image feature and the second image feature to form an image combination feature;
and carrying out scene classification on the shot images according to the image combination characteristics to obtain the scene category.
Optionally, the image acquisition control module is specifically configured to:
controlling the unmanned aerial vehicle to acquire images of a test environment so as to obtain original image information;
and carrying out normalization preprocessing on the original image information to obtain the image information.
Optionally, the test scheme determining module is specifically configured to:
acquiring a first test item and a first test parameter corresponding to the scene category;
and determining a signal test scheme according to the first test item and the first test parameter.
Optionally, after the determining the signal test scheme according to the first test item and the first test parameter, the test scheme determining module is specifically configured to:
acquiring the position information of the unmanned aerial vehicle;
correcting the first test item and the first test parameter according to the position information to obtain a second test item and a second test parameter;
and determining a signal test scheme according to the second test item and the second test parameter.
Optionally, the image acquisition control module is further configured to:
if the image information does not meet the test requirements, adjusting the position of the unmanned aerial vehicle and/or the image acquisition angle according to the image information;
and controlling the unmanned aerial vehicle to carry out image acquisition on the test environment again so as to obtain image information.
Optionally, the image acquisition control module is further configured to:
acquiring the network signal strength of the mobile network signal test equipment;
if the network signal intensity is larger than a preset signal intensity threshold value, sending the image information to test control equipment;
and if the network signal intensity is less than or equal to a preset signal intensity threshold value, storing the image information to the local.
According to a third aspect of the embodiments of the present disclosure, the present invention provides a mobile network signal testing device, including: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to perform the mobile network signal testing method according to any one of the first aspect of the embodiments of the present disclosure.
According to a fourth aspect of the embodiments of the present disclosure, the present disclosure provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the computer-readable storage medium is configured to implement the mobile network signal testing method according to any one of the first aspect of the embodiments of the present disclosure.
According to a fifth aspect of the embodiments of the present disclosure, the present invention provides a signal testing system, including: a drone, a test control device, and a mobile network signal testing device as described above, wherein,
the mobile network signal testing equipment is in communication connection with the testing control equipment and the unmanned aerial vehicle respectively.
The mobile network signal testing method, the mobile network signal testing equipment, the storage medium and the mobile network signal testing system provided by the invention receive the testing instruction sent by the testing control equipment; responding to the test instruction, controlling the unmanned aerial vehicle to acquire an image of a test environment so as to obtain image information; determining the scene type of the test environment according to the image information; determining a signal test scheme matching the scene category; testing the mobile network signal corresponding to the test environment according to the signal test scheme; will the test result of mobile network signal sends test control device, because the flight through unmanned aerial vehicle does not receive geographical position's restriction, can get into the region that the tester can't get into to through gathering image information, confirm automatically with the most suitable mobile network signal test scheme of test environment, avoided traditional reliance artifical test to lead to inefficiency, the precision is poor, problem with high costs, improved efficiency of software testing and test effect, reduced test cost.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is an application scenario diagram of a mobile network signal testing method according to an embodiment of the present invention;
fig. 2 is a flowchart of a mobile network signal testing method according to an embodiment of the present invention;
fig. 3 is a flowchart of a mobile network signal testing method according to another embodiment of the present invention;
FIG. 4 is a flowchart of a mobile network signal testing method according to another embodiment of the present invention;
fig. 5 is a schematic structural diagram of a mobile network signal testing device according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a mobile network signal testing device according to an embodiment of the present invention.
While specific embodiments of the disclosure have been shown and described in detail in the drawings and foregoing description, such drawings and description are not intended to limit the scope of the disclosed concepts in any way, but rather to explain the concepts of the disclosure to those skilled in the art by reference to the particular 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 implementations described in the exemplary embodiments below do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The following explains an application scenario of the embodiment of the present invention:
fig. 1 is an application scenario diagram of the mobile network signal testing method according to the embodiment of the present invention, as shown in fig. 1, a mobile network signal testing device 11 is mounted on an unmanned aerial vehicle 12, a tester controls the unmanned aerial vehicle 12 to fly to a testing area through a testing control device 13 to perform a mobile network signal test, when the unmanned aerial vehicle 12 mounted with the mobile network signal testing device 11 flies to the testing area, the mobile network signal testing device 11 collects image information around the testing area, determines a testing scheme according to the collected image information, completes a subsequent unmanned test, and transmits a test result and the image information back to the testing control device, and a user views the test result through the testing control device.
The following describes the technical solution of the present invention and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of a mobile network signal testing method according to an embodiment of the present invention, which is applied to a mobile network signal testing device, where the mobile network signal testing device is in communication connection with a test control device and an unmanned aerial vehicle, as shown in fig. 2, the mobile network signal testing method according to the embodiment includes the following steps:
and step S101, receiving a test instruction sent by the test control equipment.
The mobile network signal testing equipment receives a testing instruction sent by the testing control equipment, and the testing instruction is used for instructing the mobile network signal testing equipment to test the mobile network signal. Optionally, data exchange is directly performed between the test control device and the mobile network signal test device in a wireless communication manner, for example, bluetooth, wifi, zigbee and the like, and the test instruction is directly sent to the mobile network signal test device by the test control device; for example, the test control device and the mobile network signal testing device complete data exchange after passing through relay points such as a base station and a data server through wireless communication technologies such as 2G, 3G, 4G, or 5G.
And S102, responding to the test instruction, and controlling the unmanned aerial vehicle to acquire images of the test environment so as to obtain image information.
Optionally, the type of the test instruction may be an instant execution instruction or a delayed execution instruction, and correspondingly, according to the type of the test instruction, the mobile network information test device may respond to the content of the test instruction in an instant manner, for example, after receiving the test instruction, the mobile network information test device immediately controls the unmanned aerial vehicle to perform image acquisition on the test environment; the mobile network information testing device can also delay the response of the content of the testing instruction, for example, after the mobile network information testing device receives the testing instruction and delays for a period of time, the unmanned aerial vehicle is controlled to acquire images of the testing environment; or controlling the unmanned aerial vehicle to acquire images of the test environment at the specified time.
Optionally, an image acquisition module is arranged on the unmanned aerial vehicle, images or videos in the test environment can be acquired, the unmanned aerial vehicle is controlled to acquire the images of the test environment, and acquired image information can be stored in a memory of the unmanned aerial vehicle or a memory of the mobile network information test device.
And step S103, determining the scene type of the test environment according to the image information.
The contents in the image information have different scenes and objects, and are classified into different scene categories, such as highways, urban areas, streets, high buildings, beaches, forests, mountains, and the like, according to the positions, sizes, and configurations of main objects in the scenes. Through the image information of the test environment, the scene type of the test environment can be determined through an image recognition technology.
And step S104, determining a signal test scheme matched with the scene type.
For different scene types, different scene characteristics are corresponding to the different scene types, so that different influences can be caused on the reception of the mobile network signals, for example, the mobile network signals can be influenced by electromagnetic shielding in a high-rise building with a reinforced concrete structure; on the expressway, the terminal equipment needs to receive mobile network signals in a fast moving state, so that a signal test scheme matched with a scene type is determined according to different scene types.
And step S105, testing the mobile network signal corresponding to the test environment according to the signal test scheme.
And after the signal test scheme is determined, testing the mobile network signal according to the signal test scheme to obtain a test result matched with the test environment. Further, the signal testing scheme includes a testing mode and a corresponding testing result. Because the testing environments are different, when the testing environments are different, the testing is performed by adopting a corresponding testing mode, a corresponding testing result is obtained, and the state of the mobile network information is evaluated according to the testing result. And for different scene categories, the test results are different due to different test environments.
And step S106, sending the test result of the mobile network signal to the test control equipment.
And the mobile network signal testing equipment sends the test result to the test control equipment for a tester to check the test result. Optionally, the mobile network signal testing device further sends the signal testing scheme to the testing control device, and a tester can check the signal testing scheme and the testing result through the testing control device.
Optionally, the mobile network signal testing device further sends image information to the test control device, where the image information is used to help a tester to know the test environment, and check whether the signal testing scheme and the testing result are correct according to the image information.
In the embodiment, the test instruction sent by the test control equipment is received; responding to a test instruction, and controlling the unmanned aerial vehicle to acquire an image of a test environment so as to obtain image information; determining the scene type of the test environment according to the image information; determining a signal test scheme matched with the scene category; testing a mobile network signal corresponding to a test environment according to a signal test scheme; the test result of the mobile network signal is sent to the test control equipment, the flight through the unmanned aerial vehicle is not limited by the geographical position, the test result can enter an area where a tester can not enter, and the mobile network signal test scheme most suitable for the test environment is automatically determined by acquiring image information, so that the problems of low efficiency, poor precision and high cost caused by the traditional manual test are avoided, the test efficiency and the test effect are improved, and the test cost is reduced.
Fig. 3 is a flowchart of a mobile network signal testing method according to another embodiment of the present invention, and as shown in fig. 3, the mobile network signal testing method according to this embodiment further refines steps S102 to S104 on the basis of the mobile network signal testing method according to the embodiment shown in fig. 2, and then the mobile network signal testing method according to this embodiment includes the following steps:
step S201, receiving a test instruction sent by the test control device.
And S202, responding to the test instruction, controlling the unmanned aerial vehicle to acquire images of the test environment so as to obtain original image information.
Optionally, in response to the test instruction, the unmanned aerial vehicle performs image shooting on the test environment through the image acquisition module to obtain a color test environment image, and the color image has a larger information content, so that the test environment can be more accurately represented, and the accuracy of subsequent signal test scheme evaluation is improved.
Step S203, performing normalization preprocessing on the original image information to obtain image information.
Optionally, the normalization preprocessing includes pixel value normalization and Gamma correction.
Specifically, the pixel value normalization is to perform graying processing on the color picture and convert the pixel value into a real number between 0 and 1, and the Gamma correction is to compensate the pixel value so as to improve the representation performance of the image. The specific implementation methods of pixel value normalization and Gamma correction are the prior art, and are not described herein again.
The image information obtained after the normalization preprocessing is a grayscale image f (x, y).
Step S204, Gist characteristic extraction is carried out on the image information, and first image characteristics are obtained.
Performing convolution operation on h multiplied by w gray scale image f (x, y) by using m scales and n directions of Gabor filter sets, wherein the number of channels n c Multiplying m by n, and then cascading convolution results to obtain a feature vector called image Gist feature, as shown in formula (1):
wherein the content of the first and second substances,
for concatenated symbols, g mn (x, y) is a filter bank, a convolution operator, image Gist feature G I Has a feature dimension of n c ×h×w。
Dividing the preprocessed gray image f (x, y) with h x w pixels into n p ×n p The number of the grid blocks is n g ＝n p ×n p Each grid block is sequentially marked as P according to rows i Wherein i is 1 to n g The size of the grid block is h ' × w ', h ' ═ h/n p ，w’＝w/n p 。
N for filters respectively c And carrying out convolution filtering operation on the image by each channel, and cascading convolution results after each grid block is filtered by each channel to obtain the Gist characteristic of the gray image. As shown in formula (2):
wherein G is p Has a dimension of n c ×h’×w’。
For G p Averaging the filtered results of each channel, then combining the results in rows to construct a weight matrix w, and referring the combined result to be the global Gist characteristic, as shown in fig. 3:
wherein the content of the first and second substances,
G G has a dimension of n c ×n g 。
And S205, performing PHOG feature extraction on the image information to obtain a second image feature.
Firstly, detecting edge contour information of an image by using a canny operator, and taking the contour as a reference map for subsequently calculating the boundary position of the image.
Secondly, pyramid segmentation: dividing the image into four layers, the first layer being the entire image; the second layer is that the whole image is divided into 2 x 2 uniform image blocks; the third layer is to divide the image into 4 × 4 uniform image blocks; the fourth layer is to divide the image into 8 x 8 uniform image blocks.
Thirdly, calculating a Histogram Of Gradient (HOG) Of each image block at each pyramid resolution level, wherein the extraction process is as follows:
taking the pixel processed by the preprocessing module as an image f (x, y) of h × w, and adopting two one-dimensional templates as shown in formulas (4) and (5):
h x ＝[-1 0 1] (4)
h y ＝[1 0 -1] T (5)
and respectively carrying out convolution operation in the vertical direction and the horizontal return on the image. In the cell unit, according to the gradient direction of each pixel, in a well-defined direction interval, the HOG is counted by taking the gradient value as a weight. The gradient direction of the pixel point is defined as shown in formula (6):
where m and n are the number of rows and columns, respectively, in the coordinates of the pixel; g x ，G y The gradients of the images in the x and y directions respectively; g 0 And (m, n) is the gradient direction of the image at (m, n), and the essence of the gradient direction is the included angle between the gradient direction and the x-axis direction.
According to the set interval, in the region called block, G is paired 0 The histogram h (i) is obtained by voting, i.e., the number of votes for the ith interval, and the histogram h (i) of directions within the block is normalized by the equation (7). As shown in formula (7):
wherein H' (i) is a normalized histogram; i is 1, 2, …, n, which is the interval number of the histogram; ε is a small positive constant.
And truncating the obtained histogram by using a truncation threshold th, as shown in equation (8):
and normalizing the cut histogram to obtain a final gradient direction histogram. The normalized histograms of the respective cells are connected in a general image scanning manner, i.e., from top to bottom and from left to right, to form a feature vector. And finally, cascading all HOG feature vectors to obtain a PHOG feature vector descriptor of the shot image.
And step S206, fusing the first image characteristic and the second image characteristic to form an image combination characteristic.
Specifically, the first image feature and the second image feature of the captured image information are subjected to equal weight fusion, that is, the weight of each feature is equal, so that the global feature and the local feature in the captured image information are completely reserved.
Optionally, in this embodiment, Gist feature extraction and PHOG feature extraction are performed on the image information, and the obtained first image feature and the second image feature are fused to obtain an image combination feature, so that the image combination feature has both global features and local features in the image information, the data volume of the image information is reduced, the efficiency of subsequently identifying and classifying the image information is improved, meanwhile, key information in the image information is retained, and the accuracy of image identification is improved.
And step S207, carrying out scene classification on the shot image according to the image combination characteristics to obtain a scene category.
And classifying the image combination characteristics according to the trained classifier to obtain the scene category. Optionally, the classifier further includes a training process before use, and the image samples classified according to the scene categories are trained to converge, which is the prior art in the field and is not described herein again.
Step S208, a first test item and a first test parameter corresponding to the scene category are obtained.
Under different scenes, the items to be tested on the signals are different, and the parameters used in the test of the items are also different. For example, when the scene category is a building, nearby base stations are relatively dense, users using mobile network services are more, the requirements of the users on the network signal quality are higher, and various interference situations may exist, so that the first test item for testing the scene category is a "video call" test; when the scene type is forest, generally, nearby base stations are relatively few, and the user here also uses basic voice call as a main requirement, so that the first test items for testing the scene type are a dial test and a voice call test. Furthermore, for different test items, first test parameters, such as network type, cycle number, dialing mode, server address and the like, which are consistent with the scene type are set so as to match the mobile network signal test under different test environments.
Step S209 determines a signal testing scheme according to the first test item and the first test parameter.
Optionally, a strategy is formed according to a preset test scheme according to the first test item and the first test parameter, and a signal test scheme is determined. For example, if the first test item is "dial test" and "voice test", the "dial test" is performed first and then the "voice test" is performed according to the first test parameter. The test scheme forming strategy is a mapping relation of the test scheme formed by the test items and the test parameters determined according to specific test contents and requirements, and the mapping relation can be flexibly set for meeting different test requirements, and is not specifically limited here.
In the step of this embodiment, the signal testing scheme is determined by obtaining the first testing item and the first testing parameter, and since the first testing item and the first testing parameter both correspond to the scene type, the first testing item and the first testing parameter can achieve the testing purpose matched with the scene type, manual judgment is not needed, and the application range and the testing efficiency of the method are improved.
Step S210, testing the mobile network signal corresponding to the test environment according to the signal test scheme.
Step S211, sending the test result of the mobile network signal to the test control device.
In this embodiment, the implementation manners of steps S201 and steps S210 to S211 are the same as the implementation manners of steps S101 and steps S105 to S106 in the embodiment shown in fig. 2 of the present invention, and are not described again.
Fig. 4 is a flowchart of a mobile network signal testing method according to still another embodiment of the present invention, as shown in fig. 4, based on the mobile network signal testing method provided in the embodiment shown in fig. 3, a step of correcting a first test item and a first test parameter is added after step S208, and a step of adjusting a position of an unmanned aerial vehicle and determining a network signal strength is added after step S202, so that the mobile network signal testing method provided in this embodiment includes the following steps:
step S301, receiving a test instruction sent by the test control device.
And S302, responding to the test instruction, controlling the unmanned aerial vehicle to acquire images of the test environment so as to obtain original image information.
Step S303, performing normalization preprocessing on the original image information to obtain image information.
And S304, if the image information does not meet the test requirements, adjusting the position of the unmanned aerial vehicle and/or the image acquisition angle according to the image information.
Specifically, in some areas with complex environments, when the unmanned aerial vehicle collects image information, the unmanned aerial vehicle can not meet the test requirements due to receiving some accidental influences, for example, a camera of the unmanned aerial vehicle used for collecting the image information is shielded, or the collected images are overexposed due to reflection of light of a building. When detecting that image information can not satisfy the test requirement, mobile network signal test equipment can be according to among the image information, adjust unmanned aerial vehicle's position, and/or image acquisition angle, for example, among the image information that unmanned aerial vehicle gathered, the left half of photo is sheltered from by the leaf, then mobile network signal test equipment then controls unmanned aerial vehicle and moves right, for example again, among the image information that unmanned aerial vehicle gathered, the photo is all blockked by the wall of high building, mobile network signal test equipment can't be according to image information, classify test environment, therefore, mobile network signal test equipment controls unmanned aerial vehicle's image acquisition angle and selects 90 degrees.
And step S305, controlling the unmanned aerial vehicle to perform image acquisition again on the test environment so as to obtain original image information.
After the position of the unmanned aerial vehicle and/or the image acquisition angle are/is adjusted, image acquisition is carried out again to obtain image information with better effect, and the scene category can be accurately determined according to the image information in the follow-up process. Alternatively, steps S304-S305 are repeated a plurality of times until the image information meets the test requirements.
In the step of the embodiment, whether the image information meets the test requirement or not is evaluated, and when the test requirement is not met, the position and/or the image acquisition angle of the unmanned aerial vehicle are/is adjusted, so that the problem that the scene category cannot be identified subsequently due to the fact that the image information does not meet the requirement can be avoided, and the success rate and the accuracy rate of scene category identification are improved.
Step S306, acquiring the network signal strength of the mobile network signal testing device.
Optionally, the network signal strength may be a signal strength of a wireless signal between the mobile network signal testing device and the test control device, and when data exchange is directly achieved between the mobile network signal testing device and the test control device through the wireless signal, the network signal strength may embody a capability of data transmission between the mobile network signal testing device and the test control device.
Optionally, the network signal strength may also be a signal strength of a wireless signal between the mobile network signal testing device and a network signal relay device such as a base station, and when data exchange is implemented between the mobile network signal testing device and the test control device through the relay device, the network signal strength may also be a signal strength of a wireless signal between the mobile network signal testing device and a network signal relay device such as a base station, which may reflect data transmission capability between the mobile network signal testing device and the test control device.
Step S307, if the network signal strength is greater than the preset signal strength threshold, sending the original image information or the image information to the test control device.
Because the image information collected by the unmanned aerial vehicle generally has a large data volume, especially when the image information is a high-definition picture or video, the original image information or the image information is forcibly sent to the test control equipment, network congestion between the mobile network signal test equipment and the test control equipment can be caused, and transmission of other control instructions is influenced.
Step S308, if the network signal strength is less than or equal to the preset signal strength threshold, the original image information or the image information is saved locally.
If the network signal intensity is smaller than or equal to the preset signal intensity threshold value, it is indicated that network blockage may be caused by image information transmission, in order to avoid influencing normal receiving of other control instructions, the original image information or the image information is stored locally, and after the test process is completed, the original image information or the image information is sent back to the test control equipment, so that the risk of blocking of the control instructions in the test process caused by the network blockage is reduced, and the safety and the stability in the signal test process are improved.
Step S309, Gist characteristic extraction is carried out on the image information, and first image characteristics are obtained.
Step S310, performing PHOG feature extraction on the image information to obtain a second image feature.
And step 311, fusing the first image characteristic and the second image characteristic to form an image combination characteristic.
In step S312, the captured image is subjected to scene classification according to the image combination features to obtain a scene classification.
Step S313, a first test item and a first test parameter corresponding to the scene type are acquired.
Step S314, position information of the drone is acquired.
Specifically, the location information of the drone includes an identifier or a number of an area where the drone is located, and the location information may be location information represented by longitude and latitude, or may be a preset area number, for example, a 03 area. There are various ways to obtain the position information of the drone, for example, to obtain the position information from a GPS positioning module of the drone or a device in the mobile network signal testing device, and to obtain a position coordinate input in advance through the testing control device as the position information of the drone, where a method of obtaining the position information of the drone is not particularly limited.
And step S315, correcting the first test item and the first test parameter according to the position information to obtain a second test item and a second test parameter.
In the process of providing mobile network services by operators, network transmission devices for transmitting mobile network signals have great differences in performance and quantity due to differences in geographic locations and user gathering places, for example, in a large city with large population density, the quality of mobile network signals is better in the city, and in a small city with small population density, the quality of mobile network signals is poorer in the city.
After the position information is obtained, the first test item and the first test parameter are corrected according to specific position information of a mobile network signal test, namely the position information of the unmanned aerial vehicle, and a second test item and a second test parameter which take geographical position factors into consideration are obtained.
In a specific embodiment, the scene type of the test environment is determined to be a city through image information acquired by the unmanned aerial vehicle, so that a first test item and a first test parameter are determined, and meanwhile, according to the position information of the unmanned aerial vehicle, it is determined that an area where the unmanned aerial vehicle is located does not cover a 5G network signal currently, therefore, the mobile network signal test equipment modifies the first test item and the first test parameter, and modifies test items and parameters related to the 5G network into test items and parameters using a 4G network, namely a second test item and a second test parameter.
And step S316, determining a signal test scheme according to the second test item and the second test parameter.
And determining the signal test scheme according to the modified second test item and the second test parameter, wherein the specific implementation manner is the same as the implementation manner of determining the signal test scheme according to the first test item and the first test parameter, and details are not repeated here.
In the step of the embodiment, the first test item and the first test parameter are corrected by acquiring the position information of the unmanned aerial vehicle, so that a second test item and a second test parameter related to the position information are obtained, and because the position information of the unmanned aerial vehicle is considered, the same scene types of different positions are correspondingly provided with test schemes matched with the position information, the accuracy of the automatically determined test schemes is improved, the application scenes of the method are enriched, and the accuracy and the test efficiency of the mobile network signal test are improved.
Step S317, testing the mobile network signal corresponding to the test environment according to the signal test scheme.
Step S318, sending the test result of the mobile network signal to the test control device.
In this embodiment, the implementation manners of step S301 to step S303, step 309 to step 313, and step S317 to step S318 are the same as the implementation manners of step S201 to step S203, step S204 to step S208, and step S210 to step S211 in the embodiment shown in fig. 3 of the present invention, and are not repeated herein.
Fig. 5 is a schematic structural diagram of a mobile network signal testing device according to an embodiment of the present invention, and as shown in fig. 5, the mobile network signal testing device 4 according to the embodiment includes:
and the test instruction receiving module 41 is configured to receive a test instruction sent by the test control device.
And the image acquisition control module 42 is used for responding to the test instruction and controlling the unmanned aerial vehicle to acquire images of the test environment so as to obtain image information.
And a scene type determining module 43, configured to determine a scene type of the test environment according to the image information.
And a test scheme determining module 44, configured to determine a signal test scheme matching the scene type.
And the network signal testing module 45 is configured to test the mobile network signal corresponding to the testing environment according to the signal testing scheme.
A sending module 46, configured to send a test result of the mobile network signal to the test control device.
Optionally, the scene category determining module 43 is specifically configured to:
and performing Gist feature extraction on the image information to obtain a first image feature.
And performing PHOG feature extraction on the image information to obtain a second image feature.
And fusing the first image characteristic and the second image characteristic to form an image combination characteristic.
And carrying out scene classification on the shot images according to the image combination characteristics to obtain scene categories.
Optionally, the image acquisition control module 42 is specifically configured to:
and controlling the unmanned aerial vehicle to acquire images of the test environment so as to obtain original image information.
And carrying out normalization preprocessing on the original image information to obtain image information.
Optionally, the test scenario determining module 44 is specifically configured to:
and acquiring a first test item and a first test parameter corresponding to the scene category.
And determining a signal test scheme according to the first test item and the first test parameter.
Optionally, after determining the signal test scheme according to the first test item and the first test parameter, the test scheme determining module 44 is specifically configured to:
and acquiring the position information of the unmanned aerial vehicle.
And correcting the first test item and the first test parameter according to the position information to obtain a second test item and a second test parameter.
And determining a signal test scheme according to the second test item and the second test parameter.
Optionally, the image acquisition control module 42 is further configured to:
and if the image information does not meet the test requirements, adjusting the position of the unmanned aerial vehicle and/or the image acquisition angle according to the image information.
And controlling the unmanned aerial vehicle to carry out image acquisition on the test environment again so as to obtain image information.
Optionally, the image acquisition control module 42 is further configured to:
and acquiring the network signal strength of the mobile network signal testing equipment.
And if the network signal intensity is greater than the preset signal intensity threshold value, sending the image information to the test control equipment.
And if the network signal intensity is less than or equal to the preset signal intensity threshold value, storing the image information to the local.
The system comprises a test instruction receiving module 41, an image acquisition control module 42, a scene type determining module 43, a test scheme determining module 44, a network signal testing module 45 and a sending module 46, which are connected in sequence. The mobile network signal testing device 4 provided in this embodiment may execute the technical solution of the method embodiment shown in fig. 2, and the implementation principle and the technical effect are similar, which are not described herein again.
Fig. 6 is a schematic diagram of a mobile network signal testing device according to an embodiment of the present invention, and as shown in fig. 6, the mobile network signal testing device according to the embodiment includes: a memory 51, a processor 52 and a computer program.
The computer program is stored in the memory 51 and configured to be executed by the processor 52 to implement the mobile network signal testing method according to any one of the embodiments corresponding to fig. 2 to 4.
The memory 51 and the processor 52 are connected by a bus 53.
The relevant description may be understood by referring to the relevant description and effect corresponding to the steps in fig. 2 to fig. 4, and redundant description is not repeated here.
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method for testing a mobile network signal according to any one of the embodiments of fig. 2 to 4.
The computer readable storage medium may be, among others, ROM, Random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (9)
1. A mobile network signal testing method is applied to mobile network signal testing equipment which is in communication connection with a test control device and an unmanned aerial vehicle respectively, and comprises the following steps:
receiving a test instruction sent by the test control equipment;
responding to the test instruction, controlling the unmanned aerial vehicle to acquire an image of a test environment so as to obtain image information;
determining the scene type of the test environment according to the image information;
determining a signal test scheme matching the scene category;
testing the mobile network signal corresponding to the test environment according to the signal test scheme;
sending the test result of the mobile network signal to test control equipment;
the determining a signal test scheme matching the scene category includes:
acquiring a first test item and a first test parameter corresponding to the scene category;
acquiring the position information of the unmanned aerial vehicle;
correcting the first test item and the first test parameter according to the position information to obtain a second test item and a second test parameter;
and determining a signal test scheme according to the second test item and the second test parameter.
2. The method of claim 1, wherein determining the scene type of the test environment from the image information comprises:
performing Gist feature extraction on the image information to obtain a first image feature;
performing PHOG feature extraction on the image information to obtain a second image feature;
fusing the first image feature and the second image feature to form an image combination feature;
and carrying out scene classification on the shot images according to the image combination characteristics to obtain the scene category.
3. The method of claim 1, wherein the controlling the drone to image capture a test environment to obtain image information comprises:
controlling the unmanned aerial vehicle to acquire images of a test environment so as to obtain original image information;
and carrying out normalization preprocessing on the original image information to obtain the image information.
4. The method of claim 1, wherein after said controlling the drone to image capture the test environment to obtain image information, further comprising:
if the image information does not meet the test requirements, adjusting the position of the unmanned aerial vehicle and/or the image acquisition angle according to the image information;
and controlling the unmanned aerial vehicle to carry out image acquisition on the test environment again so as to obtain image information.
5. The method of claim 1, wherein after said controlling said drone to perform image acquisition on a test environment to obtain image information, further comprising:
acquiring the network signal strength of the mobile network signal test equipment;
if the network signal intensity is larger than a preset signal intensity threshold value, sending the image information to test control equipment;
and if the network signal intensity is less than or equal to a preset signal intensity threshold value, storing the image information to the local.
6. A mobile network signal testing device in communicative connection with a test control device and an unmanned aerial vehicle, respectively, the device comprising:
the test instruction receiving module is used for receiving the test instruction sent by the test control equipment;
the image acquisition control module is used for responding to the test instruction and controlling the unmanned aerial vehicle to acquire images of the test environment so as to obtain image information;
the scene type determining module is used for determining the scene type of the test environment according to the image information;
the test scheme determining module is used for determining a signal test scheme matched with the scene category;
the network signal testing module is used for testing the mobile network signal corresponding to the testing environment according to the signal testing scheme;
the sending module is used for sending the test result of the mobile network signal to the test control equipment;
the test scheme determination module is specifically configured to:
acquiring a first test item and a first test parameter corresponding to the scene category;
acquiring the position information of the unmanned aerial vehicle;
correcting the first test item and the first test parameter according to the position information to obtain a second test item and a second test parameter;
and determining a signal test scheme according to the second test item and the second test parameter.
7. A mobile network signal testing device, comprising: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the mobile network signal testing method of any one of claims 1-5.
8. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, are configured to implement the mobile network signal testing method according to any one of claims 1 to 5.
9. An unmanned aerial vehicle signal test system, comprising: a drone, a test control device, and a mobile network signal testing device according to claim 7,
the mobile network signal testing equipment is in communication connection with the testing control equipment and the unmanned aerial vehicle respectively.
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| CN110398984A (en) * | 2019-07-22 | 2019-11-01 | 国网湖南省电力有限公司 | A kind of wireless test system and its test method |
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| TW201008174A (en) * | 2008-08-13 | 2010-02-16 | Chunghwa Telecom Co Ltd | Real-time monitoring and analyzing system for the wireless network communication quality |
| CN106162700A (en) * | 2015-04-27 | 2016-11-23 | 中国移动通信集团公司 | Method of testing, device and the terminal of a kind of terminal speech quality |
| CN110398984A (en) * | 2019-07-22 | 2019-11-01 | 国网湖南省电力有限公司 | A kind of wireless test system and its test method |
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