CN111818557B - Network coverage problem identification method, device and system - Google Patents
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Abstract
The embodiment of the invention provides a method, a device and a system for identifying network coverage problems, wherein the method comprises the following steps: acquiring internet map data, wherein the internet map data comprises an image to be processed; preprocessing the image to be processed; acquiring map interest Point (POI) boundary information in the preprocessed image and boundary information of mountains and water areas such as mountains, rivers and lakes on the basis of a neural network; and fitting and displaying the boundary information of the POI, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map, thereby obtaining a network coverage problem identification result and further improving the effectiveness and the accuracy of network coverage problem identification.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a method, a device and a system for identifying network coverage problems.
Background
With the development of communication technology and the application of big data, currently, coverage evaluation aiming at a network is expanded from cell-level KPI and MR index evaluation to geography, rasterization evaluation, and network coverage conditions of a scene and specific positions of coverage problems can be accurately evaluated by combining an electronic frame of the scene, so that the method has an important guiding function on network construction and optimization. However, in a practical wireless environment, there are a large number of mountains and waters. Most of these areas are rare or unmanned and do not require mobile network coverage.
In the related art, the network coverage evaluation indexes commonly used are an effective grid ratio and a good grid ratio. Wherein, the effective grid occupation ratio refers to the ratio of the grid area of the MR sampling points to the scene area; a good grid refers to the ratio of the good grid coverage area to the scene area. For example, according to statistical data of a certain area, mountains and hills of the area account for 74.63%, flat land accounts for 20.32%, and rivers and lakes account for 5.05%. Compared with a plain area, if the areas of the mountain and the water area of the area are not removed, the real network coverage condition of each scene in the area cannot be reflected.
The existing testing mode for the network coverage problem is not perfect enough, and the technical problem that the effectiveness and the accuracy of the testing for the network coverage problem are low exists.
Disclosure of Invention
The embodiment of the invention provides a method, a device and a system for identifying a network coverage problem, which aim to solve the technical problem of lower validity and accuracy of a test on the network coverage problem.
In a first aspect, an embodiment of the present invention provides a method for identifying a network coverage problem, including:
acquiring internet map data, wherein the internet map data comprises an image to be processed;
preprocessing the image to be processed;
inputting the preprocessed image into a pre-established neural network to obtain map interest Point (POI) boundary information and boundary information of a mountain and a water area in the image to be processed;
and fitting and displaying the boundary information of the POI, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map so as to present a network coverage problem identification result.
Optionally, the image to be processed includes a satellite map image; the preprocessing the image to be processed comprises the following steps:
determining a preset number of target areas in the satellite map image;
marking the mountain and the water area in the target area;
cutting the marked satellite map image into pictures with preset sizes;
and carrying out gray-scale-based equalization processing on the satellite map image after segmentation.
Optionally, the performing gray-level-based equalization processing on the segmented satellite map image includes:
and carrying out gray-scale-based equalization processing on the areas except the marked positions in the segmented satellite map image.
Optionally, the performing gray-level-based equalization processing on the segmented satellite map image includes:
and performing gray-scale-based equalization processing on the divided satellite map image by adopting a histogram equalization processing mode, or performing gray-scale-based equalization processing on the image to be processed by directly adjusting contrast and brightness indexes.
Optionally, the neural network comprises a first neural network and a second neural network; inputting the preprocessed image into a pre-established neural network to obtain map interest point POI boundary information and boundary information of a mountain and a water area in the image to be processed, wherein the map interest point POI boundary information comprises the following steps:
inputting the preprocessed image into the first neural network, wherein the first neural network is used for extracting image boundary information in the preprocessed image;
inputting the image boundary information output by the first neural network into the second neural network, wherein the second neural network is used for identifying the boundary information of the POI (point of interest) of the map, and the boundary information of the mountain and the water area in the image boundary information.
Optionally, the fitting and displaying the map interest point POI boundary information, the boundary information of the mountain and the water, and the network quality indicator information of each MDT sampling point on the rasterized electronic map to present a network coverage problem identification result, including:
fitting the boundary information of the POI (point of interest) of the map, the boundary information of the mountain and the water area by combining the parameter information which corresponds to each sampling point and is used for representing the network quality, and performing global presentation on an electronic map;
the result of the global presentation comprises network coverage information of each area;
removing areas occupied by mountains and water areas, and determining an effective grid ratio and a good grid ratio according to the network coverage information;
and identifying the problem position of network coverage according to the effective grid ratio and the good grid ratio.
In a second aspect, an embodiment of the present invention provides an apparatus for identifying a network coverage problem, including:
the system comprises a data acquisition module, a processing module and a processing module, wherein the data acquisition module is used for acquiring Internet map data which comprises an image to be processed;
the data processing module is used for preprocessing the image to be processed;
the scene recognition module is used for inputting the preprocessed image into a pre-established neural network to obtain map interest Point (POI) boundary information and boundary information of mountain bodies and water areas in the image to be processed;
and the problem identification module is used for fitting and displaying the boundary information of the map interest points POI, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map so as to present a network coverage problem identification result.
In a third aspect, an embodiment of the present invention provides a system for identifying a network coverage problem, including:
the system comprises an internet map data collection subsystem, a processing subsystem and a processing subsystem, wherein the internet map data collection subsystem is used for acquiring internet map data which comprises an image to be processed;
the image preprocessing subsystem is used for preprocessing the image to be processed;
the image segmentation subsystem based on the neural network is used for inputting the preprocessed image into a pre-established neural network to obtain the boundary information of a map interest Point (POI) and the boundary information of a mountain and a water area in the image to be processed;
and the network coverage presenting and evaluating subsystem is used for fitting and displaying the POI boundary information of the map interest points, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map so as to present a network coverage problem identification result.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the network coverage problem identification method as described above in the first aspect and in various possible designs of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer executable instruction is stored, and when a processor executes the computer executable instruction, the network coverage problem identification method according to the first aspect and various possible designs of the first aspect is implemented.
The embodiment of the invention provides a method, a device and a system for identifying network coverage problems, wherein the method comprises the steps of obtaining internet map data, wherein the internet map data comprises an image to be processed; then preprocessing the image to be processed; then obtaining map interest point POI boundary information in the preprocessed image and boundary information of mountains and water areas such as mountains, rivers, lakes and the like based on a neural network; and finally, fitting and displaying the boundary information of the POI, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map, thereby obtaining a network coverage problem identification result and further improving the effectiveness and the accuracy of network coverage problem identification.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on the drawings without inventive labor.
Fig. 1 is a schematic flowchart of a network coverage problem identification method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a network coverage problem identification method according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of region boundary extraction according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating region boundary extraction according to another embodiment of the present invention;
fig. 5 is a schematic structural diagram of an apparatus for identifying network coverage problems according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a network coverage problem identification system according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Currently, with the development of communication technology and the application of big data, coverage evaluation of a network is expanded from cell-level KPI and MR index evaluation to physicochemical and rasterization evaluation, and a scene electronic frame is combined to accurately evaluate the network coverage condition of the scene and the specific position of a coverage problem, so that the method has an important guiding function on network construction and optimization. However, in a practical wireless environment, there are a large number of mountains and waters. Most of these areas are rare or unmanned and do not require mobile network coverage. In the related art, the network coverage evaluation indexes commonly used are an effective grid ratio and a good grid ratio. Wherein, the effective grid occupation ratio refers to the ratio of the grid area of the MR sampling points to the scene area; a good grid refers to the ratio of the good grid coverage area to the scene area. For example, according to statistical data of a certain area, mountains and hills of the area account for 74.63%, flat land accounts for 20.32%, and rivers and lakes account for 5.05%. Compared with a plain area, if the areas of the mountain and the water area of the area are not removed, the real network coverage condition of each scene in the area cannot be reflected. The existing testing mode for the network coverage problem is not perfect enough, and the technical problem that the effectiveness and the accuracy of the testing for the network coverage problem are low exists.
Aiming at the defect, the technical concept provided by the application is as follows: when network coverage evaluation is carried out on a designated area, internet map data are obtained, electronic map data are preprocessed, preprocessed images to be processed are provided for a neural network, and processing results of the images to be processed are output through the neural network. And further combining MDT rasterization data to complete accurate statistics of effective grid coverage and good grid coverage of city level and scene level, and the statistics is used for guiding planning construction and network optimization.
Fig. 1 is a flowchart illustrating a network coverage problem identification method according to an embodiment of the present invention.
As shown in fig. 1, the method provided by the present embodiment may include the following steps.
S101, internet map data are obtained, and the internet map data comprise images to be processed.
Specifically, the internet map data includes a 2D plan view, a satellite view, a 3D top view, and a panoramic map of the map. The system also comprises provincial and urban area boundary data, point of Interest (POI) data, POI electronic frame data and geographical classification data.
S102, preprocessing the image to be processed.
The image to be processed can be an image file in a TIF format of a satellite map downloaded from a local city on the Internet.
Specifically, a partial region is randomly calculated in the image, a mountain and a water area in the region are labeled by using label software LabelMe software, and then the labeled image is randomly cut into 256 × 256 pixel small-size pictures. And then carrying out gray-level-based equalization processing on the image to finally obtain a preprocessed image.
It should be noted that the specific implementation process of this step will be described in detail in the following embodiments.
S103, inputting the preprocessed image into a pre-established neural network to obtain map interest Point (POI) boundary information and boundary information of a mountain and a water area in the image to be processed.
The network structure of the neural network can be, but is not limited to, an image semantic segmentation model adopted by the neural network such as a full convolution network FCN, U-Net, segNet, deepLab, refineNet, mask Rcnn and the like.
Specifically, the neural network may include a first neural network and a second neural network, the first neural network is used to extract image boundary information in the preprocessed image; and the second neural network is used for identifying the boundary information of the POI (point of interest) of the map, the boundary information of the mountain and the water area in the image boundary information. Inputting the preprocessed image into the first neural network, wherein the first neural network outputs image boundary information in the preprocessed image; and inputting the image boundary information output by the first neural network into the second neural network, wherein the second neural network outputs the map interest point POI boundary information identified in the image boundary information and the boundary information of the mountain and the water area.
In this step, the images processed by the neural network are shown in fig. 3 and 4.
And S104, fitting and displaying the POI boundary information of the map interest points, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map so as to present a network coverage problem identification result.
Specifically, fitting the boundary information of the map interest points POI, the boundary information of the mountain and the water area by combining the parameter information which corresponds to each sampling point and is used for representing the network quality, and performing global presentation on an electronic map; the result of the global presentation includes network coverage information of each area, for example, network coverage of a certain road, a certain cell or a certain county; then removing the areas occupied by the mountains and the water areas, such as mountains, rivers, lakes and the like; determining an effective grid ratio (i.e., a ratio of grid area with MR sample points to scene area) and a good grid ratio (i.e., a ratio of good grid coverage area to scene area) from the network coverage information; and according to the effective grid ratio and the good grid ratio, identifying the problem position of network coverage for subsequent network optimization or other processing.
In the embodiment, the internet map data is acquired, and the internet map data comprises an image to be processed; then preprocessing the image to be processed; then, map interest point POI boundary information in the preprocessed image and boundary information of mountains and water areas such as mountains, rivers and lakes are obtained on the basis of a neural network; and finally, fitting and displaying the boundary information of the map interest points POI, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map, thereby obtaining a network coverage problem identification result and further improving the effectiveness and the accuracy of network coverage problem identification.
Fig. 2 is a schematic flowchart of a network coverage problem identification method according to another embodiment of the present invention, and this embodiment further describes in detail a preprocessing process of an image based on the embodiment shown in fig. 1.
As shown in fig. 2, the method provided by the present embodiment may include the following steps.
S201, determining a preset number of target areas in the satellite map image.
Specifically, the internet map data includes province, city, district and county boundary data, map POI data, POI electronic frame data, and geographical classification data. According to the data, image files in the TIF format of the satellite map are downloaded from the city of the internet, an area is randomly selected from the map image and used as target area labeling software LabelMe software to label mountains and water areas in the area, wherein the number of the target areas can be randomly set.
S202, marking the mountain and the water area in the target area.
Specifically, after the target area is determined, marking the mountain and the water area in the area by using LabelMe software.
For example, mountain and water areas such as mountains and rivers in the target area may be framed by the frame.
And S203, cutting the marked satellite map image into pictures with preset sizes.
The preset size may be set according to actual requirements, for example, 256 × 256 pixels.
Specifically, the labeled satellite map image is randomly cut into 256 × 256 pixel small-size pictures.
And S204, carrying out gray-scale-based equalization processing on the divided satellite map image.
In a possible embodiment, the performing gray-based equalization processing on the satellite map image after the segmentation includes: and carrying out gray-scale-based equalization processing on the areas except the marked positions in the segmented satellite map image.
Specifically, the marked image is randomly divided into 256 × 256 small-sized pictures, and the gray-scale-based equalization processing is performed, or the gray-scale-based equalization processing may be performed on a partial region of the image to be processed, for example, the gray-scale-based equalization processing is performed on other regions of the image to be processed except for the marked frame.
In a possible embodiment, the performing gray-based equalization processing on the satellite map image after the segmentation includes: and performing gray-scale-based equalization processing on the divided satellite map image by adopting a histogram equalization processing mode, or performing gray-scale-based equalization processing on the image to be processed by directly adjusting contrast and brightness indexes.
Specifically, for the labeled image, the image is randomly divided into 256 × 256 small-size pictures, a histogram equalization process may be used to perform a gray-based equalization process on the image to be processed, and other methods may also be used to perform a gray-based equalization process on the image to be processed, for example, by directly adjusting indexes such as contrast and/or brightness.
Further, the marked image is randomly cut into 256 × 256-pixel small-size pictures, and the sample effectiveness can be increased through processes of rotation, mirror image, blurring, white noise adding and the like.
Fig. 5 is a schematic structural diagram of a network coverage problem identification apparatus according to an embodiment of the present invention.
As shown in fig. 5, the apparatus provided in this embodiment includes: a data acquisition module 501, a data processing module 502, a scene recognition module 503 and a problem recognition module 504; the system comprises a data acquisition module, a processing module and a processing module, wherein the data acquisition module is used for acquiring Internet map data which comprises an image to be processed; the data processing module is used for preprocessing the image to be processed; the scene recognition module is used for inputting the preprocessed image into a pre-established neural network to obtain map interest Point (POI) boundary information and boundary information of mountain bodies and water areas in the image to be processed; and the problem identification module is used for fitting and displaying the boundary information of the map interest points POI, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map so as to present a network coverage problem identification result.
Further, the image to be processed comprises a satellite map image; the data processing module is specifically configured to: determining a preset number of target areas in the satellite map image; marking the mountain and the water area in the target area; cutting the marked satellite map image into pictures with preset sizes; and carrying out gray-level-based equalization processing on the segmented satellite map image.
Further, the data processing module is specifically configured to: and carrying out gray-scale-based equalization processing on the areas except the marked positions in the segmented satellite map image.
Further, the data processing module is specifically configured to: and performing gray-scale-based equalization processing on the divided satellite map image by adopting a histogram equalization processing mode, or performing gray-scale-based equalization processing on the image to be processed by directly adjusting contrast and brightness indexes.
Further, the neural network comprises a first neural network and a second neural network; the scene recognition module is specifically configured to: inputting the preprocessed image into the first neural network, wherein the first neural network is used for extracting image boundary information in the preprocessed image; and inputting the image boundary information output by the first neural network into the second neural network, wherein the second neural network is used for identifying the POI (point of interest) boundary information of the map, and the boundary information of mountains and waters in the image boundary information.
Further, the problem identification module is specifically configured to: fitting the boundary information of the POI (point of interest) of the map, the boundary information of the mountain and the water area by combining the parameter information which corresponds to each sampling point and is used for representing the network quality, and performing global presentation on an electronic map; the result of the global presentation comprises network coverage information of each area; removing areas occupied by mountains and water areas, and determining an effective grid ratio and a good grid ratio according to the network coverage information; and identifying the problem position of network coverage according to the effective grid ratio and the good grid ratio.
The apparatus provided in this embodiment may be configured to implement the technical solutions of the method embodiments, and the implementation principles and technical effects are similar, which are not described herein again.
Figure 6 is a schematic structural diagram of a network coverage problem identification system according to an embodiment of the present invention,
as shown in fig. 6, the system provided by the present embodiment includes: the system comprises an internet map data collection subsystem 601, an image preprocessing subsystem 602, a neural network-based image segmentation subsystem 603 and a network coverage presentation and evaluation subsystem 604, wherein the internet map data collection subsystem is used for acquiring internet map data, and the internet map data comprises an image to be processed; the image preprocessing subsystem is used for preprocessing the image to be processed; the image segmentation subsystem based on the neural network is used for inputting the preprocessed image into a pre-established neural network to obtain the boundary information of a map interest Point (POI) and the boundary information of a mountain and a water area in the image to be processed; and the network coverage presenting and evaluating subsystem is used for fitting and displaying the POI boundary information of the map interest points, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map so as to present a network coverage problem identification result.
Further, the image to be processed comprises a satellite map image; the image pre-processing subsystem is specifically configured to: determining a preset number of target areas in the satellite map image; marking the mountain and the water area in the target area; cutting the marked satellite map image into pictures with preset sizes; and carrying out gray-level-based equalization processing on the segmented satellite map image.
Further, the image preprocessing subsystem is specifically configured to: and carrying out gray-scale-based equalization processing on the areas except the marked positions in the segmented satellite map image.
Further, the image preprocessing subsystem is specifically configured to: and performing gray-scale-based equalization processing on the divided satellite map image by adopting a histogram equalization processing mode, or performing gray-scale-based equalization processing on the image to be processed by directly adjusting contrast and brightness indexes.
Further, the neural network comprises a first neural network and a second neural network; the neural network based image segmentation subsystem is specifically configured to: inputting the preprocessed image into the first neural network, wherein the first neural network is used for extracting image boundary information in the preprocessed image; inputting the image boundary information output by the first neural network into the second neural network, wherein the second neural network is used for identifying the boundary information of the POI (point of interest) of the map, and the boundary information of the mountain and the water area in the image boundary information.
Further, the network coverage presenting and evaluating subsystem is specifically configured to: fitting the boundary information of the POI (point of interest) of the map, the boundary information of the mountain and the water area by combining the parameter information which corresponds to each sampling point and is used for representing the network quality, and performing global presentation on an electronic map; the result of the global presentation comprises network coverage information of each area; removing areas occupied by mountains and water areas, and determining an effective grid ratio and a good grid ratio according to the network coverage information; and identifying the problem position of network coverage according to the effective grid ratio and the good grid ratio.
The system provided in this embodiment may be used to implement the technical solutions of the above method embodiments, and the implementation principles and technical effects are similar, which are not described herein again.
Fig. 7 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention. As shown in fig. 7, the electronic apparatus 70 of the present embodiment includes: a processor 701 and a memory 702; wherein
A memory 702 for storing computer-executable instructions;
the processor 701 is configured to execute the computer-executable instructions stored in the memory to implement the steps performed by the network coverage problem identification method in the above embodiments. Reference may be made in particular to the description relating to the method embodiments described above.
Alternatively, the memory 702 may be separate or integrated with the processor 701.
When the memory 702 is provided separately, the electronic device further includes a bus 703 for connecting the memory 702 and the processor 701.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer execution instruction is stored in the computer-readable storage medium, and when a processor executes the computer execution instruction, the network coverage problem identification method as described above is implemented.
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, the division of the modules is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of modules 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.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of the embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit. The unit formed by the modules can be realized in a hardware mode, and can also be realized in a mode of hardware and a software functional unit.
The integrated module implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a processor to execute some steps of the methods described in the embodiments of the present application.
It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The storage medium may be implemented by any type or combination of volatile and non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the storage medium may reside as discrete components in an electronic device or host device.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A method for identifying network coverage problems, comprising:
acquiring internet map data, wherein the internet map data comprises an image to be processed;
preprocessing the image to be processed;
inputting the preprocessed image into a first neural network, wherein the first neural network is used for extracting image boundary information in the preprocessed image;
inputting the image boundary information output by the first neural network into a second neural network, wherein the second neural network is used for identifying the boundary information of a map interest Point (POI) and boundary information of a mountain and a water area in the image boundary information;
fitting and displaying the boundary information of the POI, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map so as to present a network coverage problem identification result;
the image to be processed comprises a satellite map image; the preprocessing the image to be processed comprises the following steps:
determining a preset number of target areas in the satellite map image;
marking the mountain and the water area in the target area;
cutting the marked satellite map image into pictures with preset sizes;
and carrying out gray-level-based equalization processing on the areas except the annotation position in the segmented satellite map image.
2. The method according to claim 1, wherein the performing gray-based equalization processing on the satellite map image after the slicing comprises:
and performing gray-scale-based equalization processing on the divided satellite map image by adopting a histogram equalization processing mode, or performing gray-scale-based equalization processing on the image to be processed by directly adjusting contrast and brightness indexes.
3. The method according to claim 1 or 2, wherein the step of fitting and displaying the map interest point POI boundary information, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map to present a network coverage problem identification result comprises the following steps:
fitting the boundary information of the POI (point of interest) of the map, the boundary information of the mountain and the water area by combining the parameter information which corresponds to each sampling point and is used for representing the network quality, and globally presenting the parameter information on the electronic map;
the result of the global presentation comprises network coverage information of each area;
removing areas occupied by mountains and water areas, and determining an effective grid ratio and a good grid ratio according to the network coverage information;
and identifying the problem position of network coverage according to the effective grid ratio and the good grid ratio.
4. An apparatus for identifying network coverage problems, comprising:
the system comprises a data acquisition module, a processing module and a processing module, wherein the data acquisition module is used for acquiring Internet map data which comprises an image to be processed;
the data processing module is used for preprocessing the image to be processed;
a neural network based image segmentation subsystem for: inputting the preprocessed image into a first neural network, wherein the first neural network is used for extracting image boundary information in the preprocessed image; inputting the image boundary information output by the first neural network into a second neural network, wherein the second neural network is used for identifying the boundary information of a map interest Point (POI) and boundary information of a mountain and a water area in the image boundary information;
the problem identification module is used for fitting and displaying the boundary information of the map interest points POI, the boundary information of the mountain and the water area and the network quality index information of each MDT sampling point on a rasterized electronic map so as to present a network coverage problem identification result; the image to be processed includes a satellite map image, and the data processing module is specifically configured to: determining a preset number of target areas in the satellite map image; marking the mountain and the water area in the target area; cutting the marked satellite map image into pictures with preset sizes; and carrying out gray-level-based equalization processing on the areas except the annotation position in the segmented satellite map image.
5. A network coverage problem identification system, comprising:
the system comprises an Internet map data collection subsystem, a processing subsystem and a processing subsystem, wherein the Internet map data collection subsystem is used for acquiring Internet map data which comprises an image to be processed;
the image preprocessing subsystem is used for preprocessing the image to be processed;
the image segmentation subsystem based on the neural network is used for inputting the preprocessed image into a pre-established neural network to obtain the boundary information of a map interest Point (POI) and the boundary information of a mountain and a water area in the image to be processed;
and the network coverage presenting and evaluating subsystem is used for fitting and displaying the boundary information of the POI (point of interest) of the map, the boundary information of the mountain and the water area and the network quality index information of each MDT (minimization drive test) sampling point on the rasterized electronic map so as to present a network coverage problem identification result.
6. An electronic device, comprising: at least one processor and memory;
the memory stores computer execution instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the network coverage problem identification method of any one of claims 1 to 3.
7. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, implement the network coverage problem identification method of any one of claims 1 to 3.
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