CN111818557B - Network coverage problem identification method, device and system - Google Patents

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

Network coverage problem identification method, device and system

technical field

Embodiments of the present invention relate to the field of communication technologies, and in particular, to a method, device and system for identifying network coverage problems.

Background technique

With the development of communication technology and the application of big data, the current network coverage assessment has expanded from community-level KPI and MR index assessment to geographic and grid assessment. Combined with the electronic border of the scene, the network coverage of the scene can be accurately evaluated The situation and the specific location of the coverage problem play an important guiding role in network construction and optimization. But there are a lot of mountains and waters in the actual wireless environment. Most of these areas are inaccessible or uninhabited and do not require mobile network coverage.

In related technologies, commonly used evaluation indicators for network coverage are the proportion of effective grids and the proportion of good grids. Among them, the effective grid ratio refers to the ratio of the grid area with MR sampling points to the scene area; the good grid refers to the ratio of the good grid coverage area to the scene area. For example, according to the statistics of a certain area, mountains and hills account for 74.63%, flat land accounts for 20.32%, and rivers and lakes account for 5.05%. Compared with plain areas, if the area of mountains and waters in this area is not excluded, it will not be able to reflect the real network coverage of each scene in this area.

At present, the test method for the network coverage problem is not perfect, and there is a technical problem that the validity and accuracy of the test for the network coverage problem are low.

Contents of the invention

Embodiments of the present invention provide a method, device and system for identifying network coverage problems, so as to overcome the technical problem of low effectiveness and accuracy in testing network coverage problems.

In a first aspect, an embodiment of the present invention provides a method for identifying a network coverage problem, including:

Obtain Internet map data, the Internet map data includes images to be processed;

Preprocessing the image to be processed;

Input the preprocessed image into a pre-established neural network to obtain the POI boundary information of the map point of interest in the image to be processed, the boundary information of mountains and waters;

Fitting and displaying the POI boundary information of the map interest point, the boundary information of mountains and waters, and the network quality index information of each minimum drive test MDT sampling point on the gridded electronic map to present the network coverage problem identification result .

Optionally, the image to be processed includes a satellite map image; the preprocessing of the image to be processed includes:

determining a preset number of target areas in the satellite map image;

Mark the mountains and waters in the target area;

Cut the marked satellite map image into pictures of preset size;

Perform grayscale-based equalization processing on the segmented satellite map image.

Optionally, the grayscale-based equalization processing of the segmented satellite map image includes:

A grayscale-based equalization process is performed on the regions in the segmented satellite map image except for the marked position.

Optionally, the grayscale-based equalization processing of the segmented satellite map image includes:

Use histogram equalization processing to perform grayscale-based equalization processing on the segmented satellite map image, or directly adjust the contrast and brightness indicators to perform grayscale-based equalization processing on the image to be processed.

Optionally, the neural network includes a first neural network and a second neural network; the pre-processed image is input into the pre-established neural network to obtain the map interest point POI boundary information, the mountain body in the image to be processed and water boundary information, including:

inputting the preprocessed image into the first neural network, and the first neural network is used to extract image boundary information in the preprocessed image;

The image boundary information output by the first neural network is input into the second neural network, and the second neural network is used to identify POI boundary information on the map, boundary information of mountains and waters in the image boundary information.

Optionally, the POI boundary information of the map interest point, the boundary information of mountains and waters, and the network quality index information of each minimum drive test MDT sampling point are fitted and displayed on the gridded electronic map, so as to Presentation of network coverage problem identification results, including:

Fitting the POI boundary information of the map interest point, the boundary information of mountains and waters in combination with the parameter information used to characterize the quality of the network corresponding to each sampling point, and performing global presentation on the electronic map;

The result presented globally includes network coverage information of each area;

Eliminate the area occupied by mountains and waters, and determine the effective grid ratio and good grid ratio according to the network coverage information;

According to the effective grid ratio and the proportion of good grids, the location of the network coverage problem is identified.

In a second aspect, an embodiment of the present invention provides a network coverage problem identification device, including:

A data acquisition module, configured to acquire Internet map data, the Internet map data including images to be processed;

A data processing module, configured to preprocess the image to be processed;

The scene recognition module is used to input the pre-processed image into the pre-established neural network to obtain the POI boundary information of the map point of interest in the image to be processed, the boundary information of mountains and waters;

The problem identification module is used to fit and display the POI boundary information of the map point of interest, the boundary information of mountains and waters, and the network quality index information of each minimum drive test MDT sampling point on the gridded electronic map, to Presents the network coverage problem identification results.

In a third aspect, an embodiment of the present invention provides a network coverage problem identification system, including:

Internet map data collection subsystem, used to obtain Internet map data, the Internet map data includes images to be processed;

An image preprocessing subsystem, configured to preprocess the image to be processed;

Based on the neural network image segmentation subsystem, it is used to input the preprocessed image into the pre-established neural network to obtain the POI boundary information of the map interest point in the image to be processed, the boundary information of mountains and waters;

The network coverage presentation and evaluation subsystem is used to simulate the POI boundary information of the map point of interest, the boundary information of mountains and waters, and the network quality index information of each minimum drive test MDT sampling point on the gridded electronic map. combined display to present the identification results of network coverage problems.

In a fourth aspect, an embodiment of the present invention provides an electronic device, including: at least one processor and a memory;

the memory stores computer-executable instructions;

The at least one processor executes the computer-executed instructions stored in the memory, so that the at least one processor executes the network coverage problem identification method described in the above first aspect and various possible designs of the first aspect.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the processor executes the computer-executable instructions, the above first aspect and the first Aspects of various possible designs of the described network coverage problem identification method.

The network coverage problem identification method, device and system provided by the embodiments of the present invention, the method acquires Internet map data, the Internet map data includes images to be processed; then preprocesses the images to be processed; then obtains based on the neural network The POI boundary information of the map POI in the preprocessing image, the boundary information of mountains and waters such as mountains, rivers, and lakes; finally, the POI boundary information of the map, the boundary information of mountains and waters and each minimized drive test MDT The network quality index information of the sampling points is fitted and displayed on the gridded electronic map, so as to obtain the identification result of the network coverage problem, thereby improving the validity and accuracy of the identification of the network coverage problem.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description These are some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained according to these drawings without any creative effort.

FIG. 1 is a schematic flowchart of a network coverage problem identification method provided by an embodiment of the present invention;

FIG. 2 is a schematic flowchart of a method for identifying network coverage problems provided by another embodiment of the present invention;

FIG. 3 is a schematic diagram of region boundary extraction provided by an embodiment of the present invention;

FIG. 4 is a schematic diagram of region boundary extraction provided by another embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a network coverage problem identification device provided by an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of a network coverage problem identification system provided by an embodiment of the present invention;

FIG. 7 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

At present, with the development of communication technology and the application of big data, the current coverage evaluation for the network has been expanded from the evaluation of community-level KPI and MR indicators to the evaluation of geographic and grid evaluation. Combined with the electronic border of the scene, it can accurately evaluate the coverage of the scene. The network coverage and the specific location of the coverage problem play an important guiding role in network construction and optimization. But there are a lot of mountains and waters in the actual wireless environment. Most of these areas are inaccessible or uninhabited and do not require mobile network coverage. In related technologies, commonly used evaluation indicators for network coverage are the proportion of effective grids and the proportion of good grids. Among them, the effective grid ratio refers to the ratio of the grid area with MR sampling points to the scene area; the good grid refers to the ratio of the good grid coverage area to the scene area. For example, according to the statistics of a certain area, mountains and hills account for 74.63%, flat land accounts for 20.32%, and rivers and lakes account for 5.05%. Compared with plain areas, if the area of mountains and waters in this area is not excluded, it will not be able to reflect the real network coverage of each scene in this area. At present, the test method for the network coverage problem is not perfect, and there is a technical problem that the validity and accuracy of the test for the network coverage problem are low.

In response to this defect, the technical idea provided by this application is: when evaluating the network coverage of a designated area, obtain Internet map data, preprocess the electronic map data, provide the preprocessed images to the neural network, and pass the The neural network outputs the processing result of the image to be processed. Further combine the MDT rasterized data to complete the accurate statistics of effective grid coverage and good grid coverage at the city level and scene level, which are used to guide planning and construction and network optimization.

FIG. 1 is a schematic flowchart of a method for identifying a network coverage problem provided by an embodiment of the present invention.

As shown in FIG. 1 , the method provided in this embodiment may include the following steps.

S101. Acquire Internet map data, where the Internet map data includes images to be processed.

Specifically, the Internet map data includes a 2D plan view, a satellite image, a 3D top view and a panoramic map of the map. It also includes provincial and urban boundary data, map point of interest (Point of Interest, POI) data, POI electronic frame data, and geographic classification data.

S102. Perform preprocessing on the image to be processed.

Wherein, the image to be processed may be an image file in TIF format of a satellite map downloaded by cities on the Internet.

Specifically, part of the area is randomly calculated in the image, and the labeling software LabelMe software is used to label the mountains and waters in the area, and then the marked image is randomly divided into small-sized pictures of 256*256 pixels. Then the image is equalized based on the gray level, and finally the preprocessed image is obtained.

It should be noted that the specific implementation process of this step will be described in detail in the following embodiments.

S103. Input the preprocessed image into the pre-established neural network to obtain boundary information of POIs on the map, boundary information of mountains and waters in the image to be processed.

Among them, the network structure of the neural network can be but not limited to the image semantic segmentation model adopted by the fully convolutional network FCN, U-Net, SegNet, DeepLab, RefineNet, Mask Rcnn and other neural networks.

Specifically, the neural network may include a first neural network and a second neural network, the first neural network is used to extract the image boundary information in the preprocessed image; the second neural network is used to extract the image boundary information in the image boundary information Identify POI boundary information of map points of interest, boundary information of mountains and waters. Input the preprocessed image into the first neural network, and the first neural network outputs image boundary information in the preprocessed image; input the image boundary information output by the first neural network into the first neural network Two neural networks, the second neural network outputs the POI boundary information of the map identified in the image boundary information, the boundary information of mountains and waters.

In this step, the image processed by the neural network is shown in Fig. 3 and Fig. 4 .

S104, fitting and displaying the POI boundary information of the map interest point, the boundary information of mountains and waters, and the network quality index information of each minimum drive test MDT sampling point on the gridded electronic map, so as to present the network coverage problem Recognition results.

Specifically, the POI boundary information of the map interest point, the boundary information of mountains and waters are combined with the parameter information corresponding to each sampling point for representing the quality of the network, and are globally presented on the electronic map; the global The presented results include the network coverage information of each area, for example, the network coverage of a certain road or a certain community or a certain district; then remove the area occupied by mountains and waters, such as mountains, rivers, lakes and other The area occupied by the water area; determine the effective grid ratio (that is, the ratio of the grid area with MR sampling points to the scene area) and the good grid ratio (that is, the good grid coverage area and the scene area) according to the network coverage information area ratio); according to the effective grid ratio and good grid ratio, identify the location of the network coverage problem for subsequent network optimization or other processing.

In this embodiment, by obtaining the Internet map data, the Internet map data includes the image to be processed; then the image to be processed is preprocessed; then the map POI boundary information in the preprocessed image is obtained based on the neural network, mountains and rivers , rivers, lakes and other mountain and water boundary information; finally, the POI boundary information of the map point of interest, the boundary information of mountains and waters, and the network quality index information of each minimum drive test MDT sampling point in the gridded electronic Fitting and displaying on the map, thereby obtaining the identification result of the network coverage problem, thereby improving the validity and accuracy of the identification of the network coverage problem.

FIG. 2 is a schematic flowchart of a network coverage problem identification method provided by another embodiment of the present invention. This embodiment further describes the image preprocessing process in detail on the basis of the embodiment shown in FIG. 1 .

As shown in FIG. 2, the method provided in this embodiment may include the following steps.

S201. Determine a preset number of target areas in the satellite map image.

Specifically, the Internet map data includes provincial, city, county boundary data, map POI data, POI electronic frame data, and geographic classification data. According to these data, the satellite map TIF format image file is downloaded from the Internet by prefecture and city, and an area is randomly selected in the map image as the target area labeling software. Can be set randomly.

S202. Mark mountains and waters in the target area.

Specifically, after determining the target area, use the labeling software LabelMe software to mark the mountains and waters in the area.

Exemplarily, mountains and waters such as mountains and rivers in the target area may be selected by a frame.

S203. Segment the marked satellite map image into pictures of a preset size.

Wherein, the preset size can be set according to actual needs, for example, set to 256*256 pixels.

Specifically, the marked satellite map image is randomly divided into small-sized pictures of 256*256 pixels.

S204. Perform grayscale-based equalization processing on the segmented satellite map image.

In a possible embodiment, the grayscale-based equalization processing of the segmented satellite map image includes: performing grayscale-based equalization processing on regions other than marked positions in the segmented satellite map image. Grayscale equalization.

Specifically, the marked image is randomly divided into small-sized images of 256*256 pixels, and equalization processing based on grayscale is performed, and partial areas of the image to be processed can also be equalized based on grayscale. For example, for the image to be processed Grayscale-based equalization is performed on other areas of the image except for the marked frame.

In a possible embodiment, the grayscale-based equalization processing on the segmented satellite map image includes: using histogram equalization processing to perform grayscale-based equalization processing on the segmented satellite map image. The equalization processing of brightness, or, by directly adjusting the contrast and brightness indicators, the image to be processed is equalized based on grayscale.

Specifically, the marked image is randomly divided into 256*256 pixel small-size pictures, and the histogram equalization processing method can be used to perform grayscale-based equalization processing on the image to be processed, or other methods can be used to process the image to be processed Perform grayscale-based equalization processing, for example, perform grayscale-based equalization processing on the image to be processed by directly adjusting indicators such as contrast and/or brightness.

Further, the labeled image is randomly divided into small-sized images of 256*256 pixels, and the validity of the sample can be increased by processing such as rotation, mirroring, blurring, and adding white noise.

Fig. 5 is a schematic structural diagram of an apparatus for identifying a network coverage problem according to an embodiment of the present invention.

As shown in Figure 5, the device provided by this embodiment includes: a data acquisition module 501, a data processing module 502, a scene identification module 503 and a problem identification module 504; wherein, the data acquisition module is used to acquire Internet map data, and the Internet The map data includes an image to be processed; the data processing module is used to preprocess the image to be processed; the scene recognition module is used to input the preprocessed image into a pre-established neural network to obtain the Map interest point POI boundary information, boundary information of mountains and waters; problem identification module, for the network quality index of the map POI boundary information, mountains and waters boundary information and each minimum drive test MDT sampling point The information is fitted and displayed on a rasterized electronic map to present the identification results of network coverage problems.

Further, the image to be processed includes a satellite map image; the data processing module is specifically used to: determine a preset number of target areas in the satellite map image; mark mountains and waters in the target area; Segment the marked satellite map image into images of a preset size; perform grayscale-based equalization processing on the segmented satellite map image.

Further, the data processing module is specifically configured to: perform grayscale-based equalization processing on regions in the segmented satellite map image except for marked positions.

Further, the data processing module is specifically used to: use histogram equalization processing to perform grayscale-based equalization processing on the segmented satellite map image, or directly adjust the contrast and brightness indicators. The image to be processed is equalized based on the gray level.

Further, the neural network includes a first neural network and a second neural network; the scene recognition module is specifically configured to: input the preprocessed image into the first neural network, and the first neural network uses Extracting the image boundary information in the preprocessed image; inputting the image boundary information output by the first neural network into the second neural network, and the second neural network is used in the image boundary information Identify POI boundary information of map points of interest, boundary information of mountains and waters.

Further, the problem identification module is specifically configured to: combine the POI boundary information of the map interest point, the boundary information of mountains and waters with the parameter information corresponding to each sampling point for characterizing the network quality, and perform the fitting in the electronic A global presentation is performed on the map; the results of the global presentation include network coverage information in each area; the area occupied by mountains and waters is eliminated, and the effective grid ratio and good grid ratio are determined according to the network coverage information; according to the Effective grid ratio and good grid ratio to identify problematic locations of network coverage.

The device provided in this embodiment can be used to implement the technical solutions of the above method embodiments, and its implementation principle and technical effect are similar, so this embodiment will not repeat them here.

Fig. 6 is a schematic structural diagram of a network coverage problem identification system provided by an embodiment of the present invention,

As shown in Figure 6, the system provided by this embodiment includes: Internet map data collection subsystem 601, image preprocessing subsystem 602, neural network-based image segmentation subsystem 603 and network coverage presentation and evaluation subsystem 604, wherein the Internet The map data collection subsystem is used to obtain Internet map data, and the Internet map data includes images to be processed; the image preprocessing subsystem is used to preprocess the images to be processed; the neural network image segmentation subsystem is used to Input the preprocessed image into the pre-established neural network to obtain the POI boundary information of the map interest point in the image to be processed, the boundary information of mountains and waters; the network coverage presentation and evaluation subsystem is used to use the map Point of interest POI boundary information, mountain and water boundary information, and network quality index information of each minimum drive test MDT sampling point are fitted and displayed on the rasterized electronic map to present the identification results of network coverage problems.

Further, the image to be processed includes a satellite map image; the image preprocessing subsystem is specifically used to: determine a preset number of target areas in the satellite map image; Labeling; segmenting the marked satellite map image into pictures of preset sizes; performing grayscale-based equalization processing on the segmented satellite map image.

Further, the image preprocessing subsystem is specifically configured to: perform grayscale-based equalization processing on regions other than marked positions in the segmented satellite map image.

Further, the image preprocessing subsystem is specifically used to: use histogram equalization processing to perform grayscale-based equalization processing on the segmented satellite map image, or directly adjust the contrast and brightness indicators The method is used to perform grayscale-based equalization processing on the image to be processed.

Further, the neural network includes a first neural network and a second neural network; the neural network-based image segmentation subsystem is specifically configured to: input the preprocessed image into the first neural network, and the second neural network A neural network is used to extract the image boundary information in the preprocessed image; the image boundary information output by the first neural network is input into the second neural network, and the second neural network is used in the In the image boundary information, identify the POI boundary information of the map interest point, the boundary information of mountains and waters.

Further, the network coverage presentation and evaluation subsystem is specifically used to: combine the POI boundary information of the map interest point, the boundary information of mountains and waters with the parameter information used to characterize the network quality corresponding to each sampling point for fitting , and perform a global presentation on the electronic map; the result of the global presentation includes network coverage information in each area; remove the area occupied by mountains and waters, and determine the effective grid ratio and good grid ratio according to the network coverage information ; According to the effective grid ratio and the proportion of good grids, identify the location of the network coverage problem.

The system provided in this embodiment can be used to implement the technical solution of the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not repeat them here.

FIG. 7 is a schematic diagram of a hardware structure of an electronic device provided by an embodiment of the present invention. As shown in FIG. 7, the electronic device 70 of this embodiment includes: a processor 701 and a memory 702;

memory 702, for storing computer-executable instructions;

The processor 701 is configured to execute the computer-executable instructions stored in the memory, so as to implement each step performed by the method for identifying a network coverage problem in the foregoing embodiments. For details, refer to the related descriptions in the foregoing method embodiments.

Optionally, the memory 702 can be independent or integrated with the processor 701 .

When the memory 702 is set independently, the electronic device further includes a bus 703 for connecting the memory 702 and the processor 701 .

An embodiment of the present invention also provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when a processor executes the computer-executable instructions, the method for identifying a network coverage problem as described above is implemented.

In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, for example, multiple modules can be combined or integrated. to another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or modules may be in electrical, mechanical or other forms.

The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to implement the solution of this embodiment.

In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each module may physically exist separately, or two or more modules may be integrated into one unit. The units formed by the above modules can be implemented in the form of hardware, or in the form of hardware plus software functional units.

The above-mentioned integrated modules implemented in the form of software function modules may be stored in a computer-readable storage medium. The above-mentioned software function modules are stored in a storage medium, and include several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) or a processor execute some steps of the methods described in various embodiments of the present application.

It should be understood that the above-mentioned processor may be a central processing unit (Central Processing Unit, referred to as CPU), and may also be other general-purpose processors, a digital signal processor (Digital Signal Processor, referred to as DSP), an application specific integrated circuit (Application Specific Integrated Circuit, referred to as ASIC) and so on. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, and the like. The steps of the method disclosed in conjunction with the invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

The storage may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk storage, and may also be a U disk, a mobile hard disk, a read-only memory, a magnetic disk, or an optical disk.

The bus may be an Industry Standard Architecture (Industry Standard Architecture, ISA for short) bus, a Peripheral Component Interconnect (PCI for short) bus, or an Extended Industry Standard Architecture (EISA for short) bus. The bus can be divided into address bus, data bus, control bus and so on. For ease of representation, the buses in the drawings of the present application are not limited to only one bus or one type of bus.

The above-mentioned storage medium can be realized by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable In addition to programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. 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 a component of the processor. The processor and the storage medium may be located in application specific integrated circuits (Application Specific Integrated Circuits, ASIC for short). Of course, the processor and the storage medium can also exist in the electronic device or the main control device as discrete components.

Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by program instructions and related hardware. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above-mentioned method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other various media that can store program codes.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the various embodiments of the present invention. scope.

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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