Network topology generation method and device, electronic equipment and storage medium

Abstract

According to the network topology generation method and device, the electronic equipment and the storage medium provided by the invention, the data identification processing is performed on the acquired target base station data to obtain the newly added base station data; placing the newly added base station data on a preset network topology for topology generation processing to obtain a first network topology; inputting the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result; the topology generation model comprises topology layout characteristics of a preset network topology; and under the condition of determining that any new base station data in the first network topology does not conform to the topology layout characteristics, performing position adjustment processing on the new base station data which does not conform to the topology layout characteristics to obtain a target network topology. Compared with the prior art, the embodiment of the invention has the advantages that the layout of the network topology can be locally adjusted through the topology generation model, and the layout of the network topology of other nodes is not influenced, so that the network topology which is correct and attractive in layout and better in user experience is generated.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method and device for generating a network topology, an electronic device, and a storage medium. Background Art

[0002] With the rapid expansion of the operator's network scale and the number of wireless base stations, network management for wireless base stations has become increasingly important. As an important component of network management element information, the topology structure provides the main basic data information for wireless network management, and also provides important support for intuitively displaying wireless network relationships. Wireless network topology generally needs to view all subordinate wireless base stations according to districts, counties, core network element granularity, etc., involving hundreds or even thousands of topological nodes. The existing network topology generation technologies mainly include: when users need to display the network topology of wireless base stations, use layout algorithms based on force-directed models, ring layout algorithms, tree layout algorithms, etc. to automatically layout the network topology.

[0003] Since there are many network elements in wireless base stations and their relationships are complex, the network topology layout directly calculated using the above algorithm has problems such as overlapping network element nodes of wireless base stations and wireless base stations with physically adjacent locations being placed far apart in the network topology. At this time, the user needs to manually drag the network element nodes of the wireless base station on the network topology layout diagram and perform manual adjustments to make the network topology beautiful and scientific.

[0004] At the same time, the frequency of network element changes of wireless base stations is relatively fast. For example, at the county level, there are new base station changes every one or two days, and the scope of each change is relatively small, generally not exceeding 1% of the total. If the network element nodes of the wireless base station change, the layout of the network topology will be recalculated using the algorithm based on the force-directed model, which will cause the layout after manual adjustment to be cleared. If the layout of the wireless topology is not recalculated, the changed network element nodes of the wireless base station cannot be presented well.

[0005] Therefore, although the network topology generation technology in the prior art has solved the problem of network topology layout of wireless base stations to a certain extent, when the number of wireless base stations is large and a new network element node of a wireless base station is added, the layout of all nodes of the network topology will be regenerated, resulting in layout errors, unsightly appearance, and poor user experience. Summary of the invention

[0006] The present disclosure provides a method, device, electronic device and storage medium for generating a network topology. The main purpose is to solve the problem of network topology generation technology in the prior art. Although it solves the problem of network topology layout of wireless base stations to a certain extent, when there are a large number of wireless base stations, if a network element node of a wireless base station is newly added, the layout of all nodes of the network topology will be regenerated, resulting in layout errors, unsightly appearance and poor user experience.

[0007] According to a first aspect of the present disclosure, a method for generating a network topology is provided, comprising:

[0008] Performing data identification processing on the acquired target base station data to obtain newly added base station data in the target base station data; wherein the target base station data includes historical base station data and the newly added base station data;

[0009] Placing the newly added base station data on a preset network topology for topology generation processing to obtain a first network topology; wherein the preset network topology is a network topology corresponding to the historical base station data;

[0010] Inputting the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes topology layout features of the preset network topology;

[0011] In the first network topology determined according to the first comparison result, if any of the newly added base station data does not conform to the topology layout characteristics, position adjustment processing is performed on the newly added base station data that does not conform to the topology layout characteristics to obtain the target network topology.

[0012] Optionally, after inputting the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result, the method further includes:

[0013] In the first network topology determined according to the first comparison result, if each of the newly added base station data meets the topology layout characteristics, the first network topology is used as the target network topology.

[0014] Optionally, the step of inputting the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result includes:

[0015] In the first network topology, data interception processing is performed with each of the newly added base station data as the center to obtain first image data corresponding to each of the newly added base station data;

[0016] Performing a preprocessing operation on the first image data by using a preset processing algorithm to obtain processed image data;

[0017] Perform feature comparison processing on each of the processed image data and the topological layout features respectively to obtain the first comparison result, wherein the first comparison result includes the comparison result corresponding to each of the newly added base station data.

[0018] Optionally, performing feature comparison processing on each of the processed image data and the topological layout feature to obtain the first comparison result includes:

[0019] Input each of the processed image data into the first convolution layer for data convolution processing to obtain a first feature vector corresponding to each of the processed image data; wherein the topology generation model at least includes a convolution layer, a pooling layer, a fully connected layer and an output layer;

[0020] Inputting the first feature vector into a first pooling layer for data pooling processing to obtain a pooled first feature vector, and activating the pooled first feature vector according to a first preset activation function to obtain a first pooled vector;

[0021] Inputting the first pooled vector into a second convolutional layer for data convolution processing to obtain a second feature vector;

[0022] Inputting the second feature vector into the second pooling layer for data pooling processing to obtain a pooled second feature vector, and activating the pooled second feature vector according to the first preset activation function to obtain a second pooled vector;

[0023] Inputting the second pooled vector into the third convolution layer for data convolution processing to obtain a third feature vector, and inputting the third feature vector into the full connection layer for full connection processing to obtain a target feature vector corresponding to each processed image data;

[0024] The target feature vector is input into the output layer, and activated by a second preset activation function to obtain the similarity probability between each of the newly added base station data and the topological layout feature, and the first comparison result is determined according to the similarity probability.

[0025] Optionally, performing position adjustment processing on the newly added base station data that does not conform to the topology layout characteristics to obtain the target network topology includes:

[0026] In the first network topology, position adjustment processing is performed on the newly added base station data that does not conform to the topology layout characteristics to obtain a second network topology, and the second network topology is input into the topology generation model for feature comparison processing to obtain a second comparison result;

[0027] Determining, according to the second comparison result, whether each of the newly added base station data in the second network topology conforms to the topology layout characteristics;

[0028] In the case where any of the newly added base station data does not conform to the topology layout characteristics in the second network topology determined according to the second comparison result, position adjustment processing is performed on the newly added base station data that does not conform to the topology layout characteristics to obtain a third network topology;

[0029] Inputting the third network topology into the topology generation model for feature comparison processing to obtain a third comparison result, and determining whether each of the newly added base station data in the third network topology conforms to the topology layout feature according to the third comparison result;

[0030] The target network topology is obtained until it is determined that each of the newly added base station data meets the topology layout characteristics.

[0031] Optionally, the training method of the topology generation model includes:

[0032] In the preset network topology, data interception processing is performed with each of the historical base station data as the center to obtain second image data corresponding to each of the historical base station data;

[0033] Performing a preprocessing operation on the second image data by using a preset processing algorithm to obtain training image data;

[0034] The training image data is input into the model to be trained for model training processing to obtain the topology generation model.

[0035] Optionally, the step of inputting the training image data into a model to be trained for model training to obtain the topology generation model comprises:

[0036] Input each of the training picture data into the first convolution layer for data convolution processing to obtain a first training feature vector corresponding to each of the training picture data;

[0037] Inputting the first training feature vector into a first pooling layer for data pooling processing to obtain a pooled first training feature vector, and activating the pooled first training feature vector according to a first preset activation function to obtain a first training pooling vector;

[0038] Inputting the first training pooling vector into the second convolutional layer for data convolution processing to obtain a second training feature vector;

[0039] Inputting the second training feature vector into the second pooling layer for data pooling processing to obtain a pooled second training feature vector, and activating the pooled second training feature vector according to the first preset activation function to obtain a second training pooling vector;

[0040] Inputting the second training pooling vector into the third convolutional layer for data convolution processing to obtain a third training feature vector, and inputting the third training feature vector into the full connection layer for full connection processing to obtain a training target feature vector corresponding to each of the training picture data;

[0041] Inputting the training target feature vector into the output layer, performing activation processing through a second preset activation function, and obtaining a training similarity probability between each of the newly added base station data and the topology layout feature;

[0042] According to the similarity probability for training, the model to be trained is optimized by using a preset loss function to obtain the topology generation model.

[0043] According to a second aspect of the present disclosure, there is provided a device for generating a network topology, comprising:

[0044] An identification unit, configured to perform data identification processing on the acquired target base station data to obtain newly added base station data in the target base station data; wherein the target base station data includes historical base station data and the newly added base station data;

[0045] A generating unit, configured to place the newly added base station data on a preset network topology for topology generation processing to obtain a first network topology; wherein the preset network topology is a network topology corresponding to the historical base station data;

[0046] A comparison unit, used for inputting the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes topology layout features of the preset network topology;

[0047] An adjustment unit is used to, when it is determined in accordance with the first comparison result that any of the newly added base station data in the first network topology does not conform to the topology layout characteristics, perform position adjustment processing on the newly added base station data that does not conform to the topology layout characteristics to obtain a target network topology.

[0048] Optionally, the generating unit is further used to, when it is determined according to the first comparison result that in the first network topology, each of the newly added base station data meets the topology layout characteristics, use the first network topology as the target network topology.

[0049] Optionally, the comparison unit includes:

[0050] An interception module, configured to perform data interception processing in the first network topology with each of the newly added base station data as the center, to obtain first image data corresponding to each of the newly added base station data;

[0051] A processing module, used for performing a preprocessing operation on the first image data by using a preset processing algorithm to obtain processed image data;

[0052] The comparison module is used to perform feature comparison processing on each of the processed image data and the topological layout features to obtain the first comparison result, wherein the first comparison result includes the comparison result corresponding to each of the newly added base station data.

[0053] Optionally, the comparison module is further used for:

[0054] Input each of the processed image data into the first convolution layer for data convolution processing to obtain a first feature vector corresponding to each of the processed image data; wherein the topology generation model at least includes a convolution layer, a pooling layer, a fully connected layer and an output layer;

[0055] Inputting the first feature vector into a first pooling layer for data pooling processing to obtain a pooled first feature vector, and activating the pooled first feature vector according to a first preset activation function to obtain a first pooled vector;

[0056] Inputting the first pooled vector into a second convolutional layer for data convolution processing to obtain a second feature vector;

[0057] Inputting the second feature vector into the second pooling layer for data pooling processing to obtain a pooled second feature vector, and activating the pooled second feature vector according to the first preset activation function to obtain a second pooled vector;

[0058] Inputting the second pooled vector into the third convolution layer for data convolution processing to obtain a third feature vector, and inputting the third feature vector into the full connection layer for full connection processing to obtain a target feature vector corresponding to each processed image data;

[0059] The target feature vector is input into the output layer, and activated by a second preset activation function to obtain the similarity probability between each of the newly added base station data and the topological layout feature, and the first comparison result is determined according to the similarity probability.

[0060] Optionally, the adjusting unit includes:

[0061] An adjustment module is used to perform position adjustment processing on the newly added base station data that does not conform to the topology layout characteristics in the first network topology to obtain a second network topology, and input the second network topology into a topology generation model for feature comparison processing to obtain a second comparison result;

[0062] A determination module, configured to determine, according to the second comparison result, whether each of the newly added base station data in the second network topology conforms to the topology layout feature;

[0063] The adjustment module is further configured to, when it is determined according to the second comparison result that any of the newly added base station data in the second network topology does not conform to the topology layout feature, perform position adjustment processing on the newly added base station data that does not conform to the topology layout feature to obtain a third network topology;

[0064] A comparison module, used for inputting the third network topology into a topology generation model for feature comparison processing to obtain a third comparison result, and determining whether each of the newly added base station data in the third network topology conforms to the topology layout feature according to the third comparison result;

[0065] The determination module is further configured to obtain the target network topology until it is determined that each of the newly added base station data meets the topology layout characteristics.

[0066] Optionally, the device further comprises:

[0067] An interception unit, configured to perform data interception processing in the preset network topology with each of the historical base station data as the center, to obtain second image data corresponding to each of the historical base station data;

[0068] A processing unit, configured to perform a preprocessing operation on the second image data by using a preset processing algorithm to obtain training image data;

[0069] The training unit is used to input the training image data into the model to be trained to perform model training processing to obtain the topology generation model.

[0070] Optionally, the training unit includes:

[0071] A convolution module, used for inputting each of the training picture data into a first convolution layer for data convolution processing to obtain a first training feature vector corresponding to each of the training picture data;

[0072] A pooling module, used for inputting the first training feature vector into a first pooling layer for data pooling processing to obtain a pooled first training feature vector, and performing activation processing on the pooled first training feature vector according to a first preset activation function to obtain a first training pooling vector;

[0073] The convolution module is further used to input the first training pooling vector into the second convolution layer for data convolution processing to obtain a second training feature vector;

[0074] The pooling module is further used to input the second training feature vector into the second pooling layer for data pooling processing to obtain a pooled second training feature vector, and activate the pooled second training feature vector according to the first preset activation function to obtain a second training pooling vector;

[0075] The convolution module is further used to input the second training pooling vector into the third convolution layer for data convolution processing to obtain a third training feature vector, and input the third training feature vector into the full connection layer for full connection processing to obtain a training target feature vector corresponding to each training picture data;

[0076] An activation module, used to input the training target feature vector into the output layer, perform activation processing through a second preset activation function, and obtain a training similarity probability between each of the newly added base station data and the topology layout feature;

[0077] The optimization module is used to optimize the model to be trained according to the similarity probability for training through a preset loss function to obtain the topology generation model.

[0078] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0079] at least one processor; and

[0080] a memory communicatively connected to the at least one processor; wherein,

[0081] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0082] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0083] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0084] The network topology generation method and device, electronic device and storage medium provided in the present disclosure perform data identification processing on the acquired target base station data to obtain the newly added base station data in the target base station data; wherein the target base station data includes historical base station data and the newly added base station data; the newly added base station data is placed on a preset network topology for topology generation processing to obtain a first network topology; wherein the preset network topology is the network topology corresponding to the historical base station data; the first network topology is input into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes the topological layout features of the preset network topology; when it is determined according to the first comparison result that any of the newly added base station data in the first network topology does not conform to the topological layout features, position adjustment processing is performed on the newly added base station data that does not conform to the topological layout features to obtain the target network topology. Compared with the related art, the disclosed embodiment can intelligently identify the network element nodes of the newly added wireless base station, and when generating the network topology, it can use the topology generation model to locally adjust the layout of the network topology only according to the network element nodes of the newly added wireless base station, without the need to regenerate the layout of all nodes of the network topology, thereby ensuring that the layout of the network topology of other nodes is not affected, so as to generate a network topology with a correct layout, beautiful appearance, and good user experience.

[0085] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0087] Figure 1 A schematic diagram of a flow chart of a method for generating a network topology provided in an embodiment of the present disclosure;

[0088] Figure 2 A schematic diagram of a process flow of feature comparison processing of a first network topology provided by an embodiment of the present disclosure;

[0089] Figure 3 A schematic diagram of a target network topology generation process provided by an embodiment of the present disclosure;

[0090] Figure 4 A schematic diagram of a flow chart of a training method for a topology generation model provided in an embodiment of the present disclosure;

[0091] Figure 5 A schematic diagram of the structure of a topology generation model provided by an embodiment of the present disclosure;

[0092] Figure 6 An overall flow chart of network topology generation provided by an embodiment of the present disclosure;

[0093] Figure 7 A schematic diagram of the structure of a device for generating a network topology provided in an embodiment of the present disclosure;

[0094] Figure 8 A schematic diagram of the structure of another device for generating a network topology provided by an embodiment of the present disclosure;

[0095] Fig. 9 A schematic block diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0096] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0097] The following describes a method, device, electronic device, and storage medium for generating a network topology according to an embodiment of the present disclosure with reference to the accompanying drawings.

[0098] Figure 1 A schematic diagram of a flow chart of a method for generating a network topology provided in an embodiment of the present disclosure.

[0099] like Figure 1 As shown, the method comprises the following steps:

[0100] Step 101 , performing data identification processing on the acquired target base station data to obtain newly added base station data in the target base station data; wherein the target base station data includes historical base station data and the newly added base station data.

[0101] In the embodiment of the present disclosure, the target base station data is the base station data obtained after the historical base station data is updated. It can be understood that the historical base station data plus the newly added base station data is the target base station data. Therefore, the historical base station data can be all base station data in the target base station data except the newly added base station data. If defined by time scale, the historical base station data can be the base station data at the previous moment of the target base station data, and the target base station data can be the base station data at the next moment of the historical base station data.

[0102] The acquisition of the target base station data only requires directly reading the currently saved base station data. When acquiring the newly added base station data, it can be acquired by comparing the target base station data with the historical base station data. Therefore, the data identification processing of the acquired target base station data to obtain the newly added base station data in the target base station data includes: performing data search processing in the historical base station data according to the designated base station data to obtain a data search result; wherein the designated base station data is any base station data in the target base station data; if it is determined according to the data search result that the designated base station data is found in the historical base station data, then it is determined that the designated base station data is the historical base station data; if it is determined according to the data search result that the designated base station data is not found in the historical base station data, then it is determined that the designated base station data is the newly added base station data.

[0103] Specifically, regarding the acquisition of the newly added base station data, the embodiment of the present disclosure further provides a programming algorithm for illustration, for example:

[0104] for each v in V{

[0105] / / Traverse the nodes that need to display the topology

[0106] for each e in E{

[0107] / / Traverse the nodes of the original topology

[0108] if(v=e){

[0109] exit;

[0110] / / If v finds any corresponding node in E, it means it is not a new node

[0111] }

[0112] }

[0113] output v

[0114] / / If no corresponding node is found after traversing E, it means v is a new node

[0115] }

[0116] Among them, V is the designated base station data in the target base station data, and E is the historical base station data.

[0117] Step 102: placing the newly added base station data on a preset network topology to perform topology generation processing to obtain a first network topology; wherein the preset network topology is the network topology corresponding to the historical base station data.

[0118] In the disclosed embodiment, the preset network topology is the network topology of the wireless base station currently in use, that is, the network topology corresponding to the historical base station data. When acquiring the preset network topology, it is only necessary to directly read the currently saved wireless network topology layout data.

[0119] Among them, when placing the newly added base station data on the preset network topology, each of the newly added base station data is placed on the preset network topology. At the same time, each of the newly added base station data is in a different position on the preset network topology, that is, the newly added wireless nodes (the newly added base station data) are placed at different positions on the topology layout diagram (the preset network topology), and the corresponding network topology (the first network topology) can be pre-generated.

[0120] It should be noted that when placing the newly added base station data on the preset network topology, the position of each of the newly added base station data can be placed randomly, or can be placed regularly according to the topological layout characteristics of the preset network topology. Specifically, the embodiments of the present disclosure are not limited.

[0121] Step 103: input the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes topology layout features of the preset network topology.

[0122] In the embodiment of the present disclosure, the purpose of comparing the features of the first network topology is to determine the feature conformity of the newly added base station data with the topology layout features when the newly added base station data is at different positions on the first network topology, and to determine the newly added base station data at the position with higher feature conformity and the newly added base station data at the position with lower feature conformity, that is, the first comparison result.

[0123] Among them, the topology generation model is obtained by model training through the preset network topology. Therefore, the topology generation model contains the topological layout characteristics of the preset network topology. The structure of the topology generation model is a custom selected model structure, for example: Convolutional Neural Network (CNN) model, etc. Specifically, the structure of the topology generation model is not limited in the embodiments of the present disclosure.

[0124] Step 104: When it is determined in accordance with the first comparison result that any of the newly added base station data in the first network topology does not conform to the topology layout characteristics, position adjustment processing is performed on the newly added base station data that does not conform to the topology layout characteristics to obtain the target network topology.

[0125] In an embodiment of the present disclosure, when it is determined that in the first network topology, any of the newly added base station data does not conform to the topological layout characteristics, that is, when it is determined that the newly added base station data exists at a location with a low characteristic conformity in the first network topology, it is necessary to adjust the position of the newly added base station data at the location with a low characteristic conformity until each of the newly added base station data conforms to the topological layout characteristics, and the target network topology can be obtained.

[0126] Among them, when adjusting the position of the newly added base station data, the position adjustment can be performed by moving a preset pixel distance each time, for example: moving at a distance of 10 pixels on the first network topology in sequence until the position that best meets the topological layout characteristics is found.

[0127] The network topology generation method provided by the present disclosure performs data identification processing on the acquired target base station data to obtain the newly added base station data in the target base station data; wherein the target base station data includes historical base station data and the newly added base station data; the newly added base station data is placed on a preset network topology for topology generation processing to obtain a first network topology; wherein the preset network topology is the network topology corresponding to the historical base station data; the first network topology is input into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes the topological layout features of the preset network topology; in the case where it is determined according to the first comparison result that any of the newly added base station data in the first network topology does not conform to the topological layout features, position adjustment processing is performed on the newly added base station data that does not conform to the topological layout features to obtain the target network topology. Compared with the related art, the disclosed embodiment can intelligently identify the network element nodes of the newly added wireless base station, and when generating the network topology, it can use the topology generation model to locally adjust the layout of the network topology only according to the network element nodes of the newly added wireless base station, without the need to regenerate the layout of all nodes of the network topology, thereby ensuring that the layout of the network topology of other nodes is not affected, so as to generate a network topology with a correct layout, beautiful appearance, and good user experience.

[0128] In one implementable manner of the embodiment of the present disclosure, when the first network topology is subjected to feature comparison processing, it means that feature comparison processing is performed on each of the newly added base station data in the first network topology to determine whether the location of the newly added base station data in the first network topology conforms to the topology layout characteristics. Therefore, regarding the feature comparison processing of the first network topology, the embodiment of the present disclosure provides a flow chart of the feature comparison processing of the first network topology, such as Figure 2 As shown, including:

[0129] Step 201: In the first network topology, data interception processing is performed with each of the newly added base station data as the center to obtain first image data corresponding to each of the newly added base station data.

[0130] In an embodiment of the present disclosure, each of the newly added base station data is taken as the center of the picture data, and a picture of a preset size is captured in the first network topology to obtain the first picture data corresponding to each of the newly added base station data. The picture size of each of the first picture data is a custom setting, for example: 32*32, 64*64, etc. Specifically, the embodiment of the present disclosure does not limit the picture size of the first picture data.

[0131] It should also be noted that the image size of the first image data is the same as the image size of the second image data, that is, the image size captured with each of the newly added base station data as the center needs to be consistent with the image size in the training image data used when training the topology generation model. For example: when training the topology generation model, the image size in the training image data is 32*32. When data is captured with each of the newly added base station data as the center, it is also necessary to capture an image of size 32*32.

[0132] Step 202: pre-process the first image data using a preset processing algorithm to obtain processed image data.

[0133] In the embodiment of the present disclosure, the preset processing algorithm is a user-selected algorithm, such as grayscale processing, etc. Specifically, the embodiment of the present disclosure does not limit the preset processing algorithm.

[0134] By preprocessing the first image data, on the one hand, the image lines are made clearer, reducing interference from other factors, making it easier for the topology generation model to extract the topology layout features of the wireless base station. On the other hand, the size of the image data is effectively reduced, reducing the calculation time and improving the calculation efficiency.

[0135] It should also be noted that the preprocessing operation of the first image data is the same as the preprocessing operation of the second image data. For example, when training the topology generation model, it is necessary to perform grayscale processing on the second image data to obtain training image data. Then, when performing feature comparison processing, it is also necessary to perform grayscale processing on the first image data to obtain processed image data for feature comparison.

[0136] Step 203 , respectively performing feature comparison processing on each of the processed image data and the topological layout features to obtain the first comparison result, wherein the first comparison result includes the comparison result corresponding to each of the newly added base station data.

[0137] In the embodiment of the present disclosure, when performing feature comparison, it is necessary to determine the comparison result of each of the newly added base station data. Therefore, it is necessary to perform feature comparison processing on each of the processed image data and the topological layout feature respectively. Each of the processed image data can represent the position of each of the newly added base station data on the first network topology. The comparison result of each of the processed image data and the topological layout feature can determine whether each of the newly added base station data is located in a position that meets the topological layout feature.

[0138] In one implementable manner of the embodiment of the present disclosure, in addition to the situation where any of the newly added base station data in the first network topology does not conform to the topological layout characteristics, there may also be a situation where each of the newly added base station data in the first network topology conforms to the topological layout characteristics. When each of the newly added base station data conforms to the topological layout characteristics, it means that each of the newly added base station data is located at a position that conforms to the topological layout characteristics. At this time, the first network topology does not need to be adjusted. Therefore, after obtaining the first comparison result, the following method may also be used but is not limited to: when it is determined according to the first comparison result that each of the newly added base station data in the first network topology conforms to the topological layout characteristics, the first network topology is used as the target network topology.

[0139] In one implementable manner of the embodiment of the present disclosure, as a refinement of the above step 104, when there is any newly added base station data in the first network topology that does not conform to the topological layout characteristics, it is necessary to adjust the position of the newly added base station data that does not conform to the topological layout characteristics. However, when adjusting the position, it may happen that after one adjustment, it still does not conform to the topological layout characteristics. Therefore, it is necessary to repeatedly adjust the position of the newly added base station data that does not conform to the topological layout characteristics until each of the newly added base station data conforms to the topological layout characteristics. Therefore, when performing the position adjustment processing on the newly added base station data that does not conform to the topological layout characteristics, it can also be implemented in but not limited to the following manner:

[0140] In the first network topology, the newly added base station data that does not conform to the topology layout characteristics is processed for position adjustment to obtain a second network topology, and the second network topology is input into a topology generation model for feature comparison processing to obtain a second comparison result; according to the second comparison result, it is determined whether each of the newly added base station data in the second network topology conforms to the topology layout characteristics; when it is determined according to the second comparison result that any of the newly added base station data in the second network topology does not conform to the topology layout characteristics, the newly added base station data that does not conform to the topology layout characteristics is processed for position adjustment to obtain a third network topology; the third network topology is input into a topology generation model for feature comparison processing to obtain a third comparison result, and according to the third comparison result, it is determined whether each of the newly added base station data in the third network topology conforms to the topology layout characteristics; until it is determined that each of the newly added base station data conforms to the topology layout characteristics, the target network topology is obtained.

[0141] In the embodiment of the present disclosure, it is necessary to repeatedly adjust the position of the newly added base station data that does not conform to the topological layout characteristics until each of the newly added base station data conforms to the topological layout characteristics. This can ensure that each newly added base station data in the target network topology is located at a position that conforms to the topological layout characteristics, achieve an ideal topological display effect, and make the layout of the target network topology correct and beautiful.

[0142] In summary of the above embodiments, in order to facilitate understanding of the process of generating the target network topology, the present disclosure provides a schematic diagram of the process of generating the target network topology, as shown in FIG. Figure 3 As shown, the topology map is the preset network topology, the newly added wireless base station node topology is the first network topology, the 32*32 grayscale image is the processed image data, the wireless base station topology requirement is the topology layout feature, and adjusting the coordinates of the newly added wireless base station nodes refers to performing position adjustment processing on the newly added base station data that does not conform to the topology layout features.

[0143] In one implementable method of the embodiment of the present disclosure, as a refinement of the above-mentioned step 203, when performing feature comparison processing on each of the processed image data and the topological layout features respectively, it can also be implemented in but not limited to the following manner: input each of the processed image data into the first convolution layer for data convolution processing to obtain a first feature vector corresponding to each of the processed image data; wherein the topological generation model at least includes a convolution layer, a pooling layer, a fully connected layer and an output layer; input the first feature vector into the first pooling layer for data pooling processing to obtain a pooled first feature vector, and activate the pooled first feature vector according to a first preset activation function to obtain a first pooled vector; input the first pooled vector into the second convolution layer Perform data convolution processing to obtain a second feature vector; input the second feature vector to the second pooling layer for data pooling processing to obtain a pooled second feature vector, and activate the pooled second feature vector according to the first preset activation function to obtain a second pooled vector; input the second pooling vector to the third convolution layer for data convolution processing to obtain a third feature vector, and input the third feature vector to the fully connected layer for full connection processing to obtain a target feature vector corresponding to each of the processed image data; input the target feature vector to the output layer, perform activation processing through the second preset activation function, obtain the similarity probability between each of the newly added base station data and the topological layout features, and determine the first comparison result according to the similarity probability.

[0144] In the disclosed embodiment, the topology generation model includes multiple layers of convolutional layers, multiple layers of pooling layers, a fully connected layer and an output layer, wherein the convolutional layer is used to filter data noise and extract key features, the pooling layer utilizes the principle of local correlation of the image to sub-sample the image, which can reduce the amount of data processing while retaining useful information, the fully connected layer is used to perform classification or regression tasks based on the features extracted by the previous layer, and the output layer is used to output data.

[0145] The first preset activation function is an activation function of a custom setting, such as: reLU function, etc., and the second activation function is also an activation function of a custom setting, such as: Softmax function, etc. Specifically, the embodiment of the present disclosure does not limit the first activation function and the second activation function.

[0146] Among them, when determining the first comparison result according to the similarity probability, it can also be implemented by but not limited to the following methods: when the similarity probability is greater than a preset probability threshold, determining that the newly added base station data with the similarity probability greater than the preset probability threshold meets the topological layout characteristics; when the similarity probability is less than or equal to the preset probability threshold, determining that the newly added base station data with the similarity probability less than or equal to the preset probability threshold does not meet the topological layout characteristics.

[0147] In one possible implementation of the embodiment of the present disclosure, the topology generation model is required to complete the generation of the network topology, and the topology generation model needs to be trained. Therefore, in order to obtain the topology generation model, the embodiment of the present disclosure provides a flow chart of a training method for a topology generation model, such as Figure 4 As shown, including:

[0148] Step 401, in the preset network topology, data interception processing is performed with each of the historical base station data as the center to obtain second image data corresponding to each of the historical base station data.

[0149] In an embodiment of the present disclosure, each of the historical base station data is taken as the center of the picture data, and a picture of a preset size is intercepted in the preset network topology to obtain second picture data corresponding to each of the historical base station data. The picture size of each of the second picture data is a custom setting, for example: 32*32, 64*64, etc. Specifically, the embodiment of the present disclosure does not limit the picture size of the second picture data.

[0150] Step 402: pre-process the second image data using a preset processing algorithm to obtain training image data.

[0151] In the embodiment of the present disclosure, the preset processing algorithm is a user-selected algorithm, such as grayscale processing, etc. Specifically, the embodiment of the present disclosure does not limit the preset processing algorithm.

[0152] By preprocessing the second image data, on the one hand, the image lines are made clearer, reducing interference from other factors, making it easier for the topology generation model to extract the topology layout features of the wireless base station. On the other hand, the size of the image data is effectively reduced, reducing the calculation time and improving the calculation efficiency.

[0153] Step 403: input the training image data into the model to be trained to perform model training processing to obtain the topology generation model.

[0154] In the embodiment of the present disclosure, the model to be trained is an initial topology generation model. By performing model training, the topology layout characteristics of the current wireless base station node (the preset network topology) are trained to obtain the topology generation model. The structure of the model to be trained is a custom selected model structure, for example: a convolutional neural network (CNN) model, etc. Specifically, the embodiment of the present disclosure does not limit the structure of the model to be trained.

[0155] Among them, the topology generation model is used to generate a network topology for the newly added base station data based on the existing network topology (the preset network topology). The topology generation models corresponding to the existing network topologies with different topological layout characteristics are different. That is, in actual applications, different topology generation models can be trained according to the existing network topologies with different topological layout characteristics.

[0156] In one implementable manner of the disclosed embodiment, as a refinement of the above-mentioned step 403, when the training image data is input into the model to be trained for model training processing, it can also be implemented in but not limited to the following manner: input each of the training image data into the first convolution layer for data convolution processing to obtain a first training feature vector corresponding to each of the training image data; input the first training feature vector into the first pooling layer for data pooling processing to obtain a pooled first training feature vector, and activate the pooled first training feature vector according to a first preset activation function to obtain a first training pooling vector; input the first training pooling vector into the second convolution layer for data convolution processing to obtain a second training feature vector; input the second training feature vector into the second pooling layer Perform data pooling processing to obtain a second training feature vector after pooling, and activate the second training feature vector after pooling according to the first preset activation function to obtain a second training pooling vector; input the second training pooling vector to the third convolution layer for data convolution processing to obtain a third training feature vector, and input the third training feature vector to the full connection layer for full connection processing to obtain a training target feature vector corresponding to each training image data; input the training target feature vector to the output layer, activate it through the second preset activation function, and obtain a training similarity probability between each of the newly added base station data and the topology layout features; according to the training similarity probability, optimize the model to be trained through a preset loss function to obtain the topology generation model.

[0157] In an embodiment of the present disclosure, the topology generation model can classify input data into two categories: data that conforms to the topology layout characteristics and data that does not conform to the topology layout characteristics. In addition, in the process of training the neural network, a loss function is required to measure the performance of the topology generation model. The loss function is a custom selected loss function, such as a cross entropy loss function, etc. Specifically, the embodiment of the present disclosure does not limit the loss function.

[0158] Among them, the first preset activation function is an activation function of custom setting, such as: reLU function, etc., and the second activation function is also an activation function of custom setting, such as: Softmax function, etc. Specifically, the embodiment of the present disclosure does not limit the first activation function and the second activation function.

[0159] The topology generation model includes a multi-layer structure. In order to intuitively understand the structure of the topology generation model, the embodiment of the present disclosure provides a structural diagram of the topology generation model, such as Figure 5As shown, it includes multiple layers of convolutional layers, multiple layers of pooling layers, a fully connected layer and an output layer, wherein the convolutional layer is used to filter data noise and extract key features, and the pooling layer utilizes the principle of local correlation of the image to sub-sample the image, which can reduce the amount of data processing while retaining useful information, reduce the overfitting degree of network training parameters and the model, and the fully connected layer is used to perform classification or regression tasks based on the features extracted by the previous layer, and the output layer is used to output the data.

[0160] It should be noted that the number of the convolutional layers and the pooling layers included in the topology generation model is custom set. The setting of the number of the convolutional layers and the pooling layers is related to the size of the image data used when training the topology generation model. If the size of the second image data is larger, the number of the convolutional layers and the pooling layers included in the topology generation model is larger. If the size of the second image data is smaller, the number of the convolutional layers and the pooling layers included in the topology generation model is smaller.

[0161] For example: if the second image data is a 32*32 image, the topology generation model may include 3 convolution layers and 2 pooling layers, and the topology generation model has a 7-layer structure; if the second image data is a 64*64 image, the topology generation model may include 4 convolution layers and 3 pooling layers, and the topology generation model has a 9-layer structure. Specifically, the image size of the second image data and the number of layers of the topology generation model can be determined according to actual conditions, and the embodiments of the present disclosure do not limit this.

[0162] In order to facilitate the understanding of the training process of the topology generation model, the topology generation model with a 7-layer structure is used as an example for explanation. The specific method of constructing the CNN model (the topology generation model) in the embodiment of the present disclosure is as follows: the CNN model includes 7 layers, the first layer is the first convolutional layer, which is used to filter noise and extract key features. Use 6 filters of 5*5 size, step size s=1, padding=0; the second layer is the average pooling layer (first pooling layer), which uses the principle of local correlation of the image to sub-sample the image, which can reduce the amount of data processing while retaining useful information, and reduce the overfitting degree of network training parameters and models. In the embodiment of the present disclosure, a 2*2 filter is used, the step size is s=2, padding=0, and the activation function of the second layer is "reLU", and the value is taken as average pooling (Average Pooling); the third layer is the second convolutional layer, and 16 5*5 filters are used, the step size is s=1, and padding=0; the fourth layer is the second pooling layer, and a 2*2 filter is used, the step size is s=2, and padding=0. The activation function of the fourth layer is still "reLU", but the value is taken as maximum pooling (MaxPooling); the fifth layer is the third convolutional layer, and 120 5*5 units are set, the step size is s=1, and padding=0; the sixth layer is the fully connected layer, and the third convolutional layer of the fifth layer is flattened into a one-dimensional vector; the seventh layer is the output layer, and the Softmax function is used to output the probability of each classification. In the embodiment of the present disclosure, the classification is into two categories: compliance and non-compliance of the wireless base station node topology layout diagram. In addition, in the process of training a neural network, a loss function is also needed to measure the performance of the model. In the embodiment of the present disclosure, the cross entropy loss is used to measure the difference between the predicted probability distribution and the true distribution.

[0163] At the same time, based on the above embodiments, when generating the network topology, it is necessary to pre-train the topology generation model according to the preset network topology corresponding to the historical base station data, and then obtain the newly added base station data, and adjust the position of the newly added base station data based on the topology generation model to generate a target network topology that meets the topology layout characteristics.

[0164] For ease of understanding, the present disclosure provides an overall flow chart of network topology generation, as shown in FIG. Figure 6As shown, step s1 indicates: obtaining the network topology corresponding to the historical base station data, that is, the preset network topology; step s2 indicates: in the preset network topology, data interception processing is performed with each of the historical base station data as the center to obtain the second image data corresponding to each of the historical base station data; the second image data is preprocessed by a preset processing algorithm to obtain training image data; s3 indicates: inputting the training image data into the model to be trained for model training processing to obtain the topology generation model; s4 indicates: performing data recognition processing on the acquired target base station data to obtain the newly added base station data in the target base station data; wherein the target base station data includes historical base station data and the newly added base station data; s5 indicates: placing the newly added base station data in the preset network A topology generation process is performed on the topology to obtain a first network topology; wherein the preset network topology is the network topology corresponding to the historical base station data; the first network topology is input into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes the topological layout characteristics of the preset network topology; s6 indicates: in the case where any of the newly added base station data does not conform to the topological layout characteristics in the first network topology determined according to the first comparison result, a position adjustment process is performed on the newly added base station data that does not conform to the topological layout characteristics to obtain a target network topology; in the case where each of the newly added base station data conforms to the topological layout characteristics in the first network topology determined according to the first comparison result, the first network topology is used as the target network topology.

[0165] In summary, the embodiments of the present disclosure can achieve the following effects:

[0166] 1. The disclosed embodiments can intelligently identify the network element nodes of the newly added wireless base stations, and when generating the network topology, can locally adjust the layout of the network topology only according to the network element nodes of the newly added wireless base stations through the topology generation model, without the need to regenerate the layout of all nodes of the network topology, thereby ensuring that the layout of the network topology of other nodes is not affected, so as to generate a network topology with a correct layout, beautiful appearance, and good user experience.

[0167] 2. The disclosed embodiment utilizes a topology generation model (CNN algorithm) to automatically select the optimal topology layout location for newly added wireless base station nodes. It can refer to the overall topology style to achieve an ideal topology display effect, greatly reducing the time for manual intervention and adjustment.

[0168] 3. The disclosed embodiment generates a wireless network topology and only partially adjusts the topology layout of the nodes that have changed, which effectively improves the targetedness of the topology layout change. The topology layout of the nodes that have not changed is not affected, and the wireless network topology generation has memory and growth. The overall topology is adaptively adjusted as the network is adjusted, solving the problem that the overall topology layout needs to be regenerated after a small change in the network.

[0169] 4. The disclosed embodiment can intelligently identify newly added wireless base station nodes, and only needs to adjust and adapt the topological layout of the newly added nodes, which greatly reduces the computing resources for generating the wireless topology.

[0170] Corresponding to the above network topology generation method, the present invention also provides a network topology generation device. Since the device embodiment of the present invention corresponds to the above method embodiment, details not disclosed in the device embodiment can be referred to the above method embodiment, and will not be repeated in the present invention.

[0171] Figure 7 A schematic diagram of a network topology generation device provided by an embodiment of the present disclosure is shown in FIG. Figure 7 As shown, including:

[0172] The identification unit 71 is used to perform data identification processing on the acquired target base station data to obtain the newly added base station data in the target base station data; wherein the target base station data includes the historical base station data and the newly added base station data;

[0173] A generating unit 72 is used to place the newly added base station data on a preset network topology to perform topology generation processing to obtain a first network topology; wherein the preset network topology is a network topology corresponding to the historical base station data;

[0174] A comparison unit 73 is used to input the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes topology layout features of the preset network topology;

[0175] The adjustment unit 74 is used to adjust the position of any newly added base station data in the first network topology that does not conform to the topology layout characteristics, so as to obtain the target network topology.

[0176] The network topology device provided by the present disclosure performs data identification processing on the acquired target base station data to obtain the newly added base station data in the target base station data; wherein the target base station data includes historical base station data and the newly added base station data; the newly added base station data is placed on a preset network topology for topology generation processing to obtain a first network topology; wherein the preset network topology is the network topology corresponding to the historical base station data; the first network topology is input into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes the topological layout features of the preset network topology; in the case where it is determined according to the first comparison result that any of the newly added base station data in the first network topology does not conform to the topological layout features, the newly added base station data that does not conform to the topological layout features is position adjusted to obtain the target network topology. Compared with the related art, the disclosed embodiment can intelligently identify the network element nodes of the newly added wireless base station, and when generating the network topology, it can use the topology generation model to locally adjust the layout of the network topology only according to the network element nodes of the newly added wireless base station, without the need to regenerate the layout of all nodes of the network topology, thereby ensuring that the layout of the network topology of other nodes is not affected, so as to generate a network topology with a correct layout, beautiful appearance, and good user experience.

[0177] Furthermore, in a possible implementation of the embodiment of the present disclosure, the generation unit 72 is also used to, when it is determined according to the first comparison result that in the first network topology, each of the newly added base station data meets the topology layout characteristics, use the first network topology as the target network topology.

[0178] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 8 As shown, the comparison unit 73 includes:

[0179] The interception module 731 is used to perform data interception processing in the first network topology with each of the newly added base station data as the center to obtain the first picture data corresponding to each of the newly added base station data;

[0180] A processing module 732, configured to perform a preprocessing operation on the first image data using a preset processing algorithm to obtain processed image data;

[0181] The comparison module 733 is used to perform feature comparison processing on each of the processed image data and the topological layout features to obtain the first comparison result, wherein the first comparison result includes the comparison result corresponding to each of the newly added base station data.

[0182] Furthermore, in a possible implementation of the embodiment of the present disclosure, the comparison module 733 is further used to:

[0183] Input each of the processed image data into the first convolution layer for data convolution processing to obtain a first feature vector corresponding to each of the processed image data; wherein the topology generation model at least includes a convolution layer, a pooling layer, a fully connected layer and an output layer;

[0184] Inputting the first feature vector into a first pooling layer for data pooling processing to obtain a pooled first feature vector, and activating the pooled first feature vector according to a first preset activation function to obtain a first pooled vector;

[0185] Inputting the first pooled vector into a second convolutional layer for data convolution processing to obtain a second feature vector;

[0186] Inputting the second feature vector into the second pooling layer for data pooling processing to obtain a pooled second feature vector, and activating the pooled second feature vector according to the first preset activation function to obtain a second pooled vector;

[0187] Inputting the second pooled vector into the third convolution layer for data convolution processing to obtain a third feature vector, and inputting the third feature vector into the full connection layer for full connection processing to obtain a target feature vector corresponding to each processed image data;

[0188] The target feature vector is input into the output layer, and activated by a second preset activation function to obtain the similarity probability between each of the newly added base station data and the topological layout feature, and the first comparison result is determined according to the similarity probability.

[0189] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 8 As shown, the adjustment unit 74 includes:

[0190] The adjustment module 741 is used to perform position adjustment processing on the newly added base station data that does not conform to the topology layout characteristics in the first network topology to obtain a second network topology, and input the second network topology into the topology generation model for feature comparison processing to obtain a second comparison result;

[0191] A determination module 742 is used to determine whether each of the newly added base station data in the second network topology meets the topology layout characteristics according to the second comparison result;

[0192] The adjustment module 741 is further configured to, when it is determined according to the second comparison result that any of the newly added base station data in the second network topology does not conform to the topology layout feature, perform position adjustment processing on the newly added base station data that does not conform to the topology layout feature to obtain a third network topology;

[0193] The comparison module 743 is used to input the third network topology into the topology generation model for feature comparison processing to obtain a third comparison result, and determine whether each of the newly added base station data in the third network topology meets the topology layout characteristics according to the third comparison result;

[0194] The determination module 742 is further configured to obtain the target network topology until it is determined that each of the newly added base station data meets the topology layout characteristics.

[0195] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 8 As shown, the device also includes:

[0196] The interception unit 75 is used to perform data interception processing with each of the historical base station data as the center in the preset network topology to obtain the second image data corresponding to each of the historical base station data;

[0197] A processing unit 76, configured to perform a preprocessing operation on the second image data using a preset processing algorithm to obtain training image data;

[0198] The training unit 77 is used to input the training image data into the model to be trained to perform model training processing to obtain the topology generation model.

[0199] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 8 As shown, the training unit 77 includes:

[0200] The convolution module 771 is used to input each of the training picture data into the first convolution layer for data convolution processing to obtain a first training feature vector corresponding to each of the training picture data;

[0201] A pooling module 772 is used to input the first training feature vector into a first pooling layer for data pooling processing to obtain a pooled first training feature vector, and activate the pooled first training feature vector according to a first preset activation function to obtain a first training pooling vector;

[0202] The convolution module 771 is further used to input the first training pooling vector into the second convolution layer for data convolution processing to obtain a second training feature vector;

[0203] The pooling module 772 is further used to input the second training feature vector into the second pooling layer for data pooling processing to obtain a pooled second training feature vector, and activate the pooled second training feature vector according to the first preset activation function to obtain a second training pooling vector;

[0204] The convolution module 771 is further used to input the second training pooling vector into the third convolution layer for data convolution processing to obtain a third training feature vector, and input the third training feature vector into the full connection layer for full connection processing to obtain a training target feature vector corresponding to each training picture data;

[0205] An activation module 773 is used to input the training target feature vector into the output layer, perform activation processing through a second preset activation function, and obtain a training similarity probability between each of the newly added base station data and the topology layout feature;

[0206] The optimization module 774 is used to optimize the model to be trained according to the similarity probability for training through a preset loss function to obtain the topology generation model.

[0207] It should be noted that the above explanation of the method embodiment is also applicable to the device of the embodiment of the present disclosure, and the principle is the same, which is no longer limited in the embodiment of the present disclosure.

[0208] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0209] Fig. 9 A schematic block diagram of an example electronic device 900 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0210] like Fig. 9As shown, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 902 or a computer program loaded from a storage unit 908 to a RAM (Random Access Memory) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An I / O (Input / Output) interface 905 is also connected to the bus 904.

[0211] A number of components in the device 900 are connected to the I / O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0212] The computing unit 901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as a method for generating a network topology. For example, in some embodiments, the method for generating a network topology may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to execute the aforementioned network topology generation method in any other appropriate manner (for example, by means of firmware).

[0213] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general-purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0214] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0215] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0216] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0217] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0218] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0219] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0220] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0221] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for generating a network topology, characterized in that: include: Performing data identification processing on the acquired target base station data to obtain newly added base station data in the target base station data; wherein the target base station data includes historical base station data and the newly added base station data; Placing the newly added base station data on a preset network topology for topology generation processing to obtain a first network topology; wherein the preset network topology is a network topology corresponding to the historical base station data; Inputting the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes topology layout features of the preset network topology; In the first network topology determined according to the first comparison result, if any of the newly added base station data does not conform to the topology layout characteristics, position adjustment processing is performed on the newly added base station data that does not conform to the topology layout characteristics to obtain the target network topology.

2. The method according to claim 1, characterized in that After inputting the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result, the method further includes: In the first network topology determined according to the first comparison result, if each of the newly added base station data meets the topology layout characteristics, the first network topology is used as the target network topology.

3. The method according to claim 1, characterized in that The inputting the first network topology into the topology generation model for feature comparison processing to obtain a first comparison result includes: In the first network topology, data interception processing is performed with each of the newly added base station data as the center to obtain first image data corresponding to each of the newly added base station data; Performing a preprocessing operation on the first image data by using a preset processing algorithm to obtain processed image data; Perform feature comparison processing on each of the processed image data and the topological layout features respectively to obtain the first comparison result, wherein the first comparison result includes the comparison result corresponding to each of the newly added base station data.

4. The method according to claim 3, characterized in that The performing feature comparison processing on each of the processed image data and the topological layout feature to obtain the first comparison result comprises: Input each of the processed image data into the first convolution layer for data convolution processing to obtain a first feature vector corresponding to each of the processed image data; wherein the topology generation model at least includes a convolution layer, a pooling layer, a fully connected layer and an output layer; Inputting the first feature vector into a first pooling layer for data pooling processing to obtain a pooled first feature vector, and activating the pooled first feature vector according to a first preset activation function to obtain a first pooled vector; Inputting the first pooled vector into a second convolutional layer for data convolution processing to obtain a second feature vector; Inputting the second feature vector into the second pooling layer for data pooling processing to obtain a pooled second feature vector, and activating the pooled second feature vector according to the first preset activation function to obtain a second pooled vector; Inputting the second pooled vector into the third convolution layer for data convolution processing to obtain a third feature vector, and inputting the third feature vector into the full connection layer for full connection processing to obtain a target feature vector corresponding to each processed image data; The target feature vector is input into the output layer, and activated by a second preset activation function to obtain the similarity probability between each of the newly added base station data and the topological layout feature, and the first comparison result is determined according to the similarity probability.

5. The method according to claim 1, characterized in that: The performing position adjustment processing on the newly added base station data that does not conform to the topology layout characteristics to obtain the target network topology includes: In the first network topology, position adjustment processing is performed on the newly added base station data that does not conform to the topology layout characteristics to obtain a second network topology, and the second network topology is input into the topology generation model for feature comparison processing to obtain a second comparison result; Determining, according to the second comparison result, whether each of the newly added base station data in the second network topology conforms to the topology layout characteristics; In the case where any of the newly added base station data does not conform to the topology layout characteristics in the second network topology determined according to the second comparison result, position adjustment processing is performed on the newly added base station data that does not conform to the topology layout characteristics to obtain a third network topology; Inputting the third network topology into the topology generation model for feature comparison processing to obtain a third comparison result, and determining whether each of the newly added base station data in the third network topology conforms to the topology layout feature according to the third comparison result; The target network topology is obtained until it is determined that each of the newly added base station data meets the topology layout characteristics.

6. The method according to claim 1, characterized in that The training method of the topology generation model includes: In the preset network topology, data interception processing is performed with each of the historical base station data as the center to obtain second image data corresponding to each of the historical base station data; Performing a preprocessing operation on the second image data by using a preset processing algorithm to obtain training image data; The training image data is input into the model to be trained for model training processing to obtain the topology generation model.

7. The method according to claim 6, characterized in that The step of inputting the training image data into the model to be trained to perform model training processing to obtain the topology generation model comprises: Input each of the training picture data into the first convolution layer for data convolution processing to obtain a first training feature vector corresponding to each of the training picture data; Inputting the first training feature vector into a first pooling layer for data pooling processing to obtain a pooled first training feature vector, and activating the pooled first training feature vector according to a first preset activation function to obtain a first training pooling vector; Inputting the first training pooling vector into the second convolutional layer for data convolution processing to obtain a second training feature vector; Inputting the second training feature vector into the second pooling layer for data pooling processing to obtain a pooled second training feature vector, and activating the pooled second training feature vector according to the first preset activation function to obtain a second training pooling vector; Inputting the second training pooling vector into the third convolutional layer for data convolution processing to obtain a third training feature vector, and inputting the third training feature vector into the full connection layer for full connection processing to obtain a training target feature vector corresponding to each of the training picture data; Inputting the training target feature vector into the output layer, performing activation processing through a second preset activation function, and obtaining a training similarity probability between each of the newly added base station data and the topological layout feature; According to the similarity probability for training, the model to be trained is optimized by using a preset loss function to obtain the topology generation model.

8. A device for generating a network topology, characterized in that: include: An identification unit, configured to perform data identification processing on the acquired target base station data to obtain newly added base station data in the target base station data; wherein the target base station data includes historical base station data and the newly added base station data; A generating unit, configured to place the newly added base station data on a preset network topology for topology generation processing to obtain a first network topology; wherein the preset network topology is a network topology corresponding to the historical base station data; A comparison unit, used for inputting the first network topology into a topology generation model for feature comparison processing to obtain a first comparison result; wherein the topology generation model includes topology layout features of the preset network topology; An adjustment unit is used to, when it is determined in accordance with the first comparison result that any of the newly added base station data in the first network topology does not conform to the topology layout characteristics, perform position adjustment processing on the newly added base station data that does not conform to the topology layout characteristics to obtain a target network topology.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

11. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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