Method and apparatus for determining main coverage cell of 5g private network

Abstract

The present application relates to the field of wireless communication, and proposes a method and apparatus for determining a main coverage cell of a 5G private network. The method comprises: acquiring neighbor cell measurement statistical data and neighbor cell switching statistical data of a first region, and on the basis of the neighbor cell measurement statistical data and the neighbor cell switching statistical data, obtaining a cell association graph; extracting sample feature variables of a main coverage cell of a sample scenario, and mapping the sample feature variables to the cell association graph to generate corresponding mapping data; inputting the mapping data into a graph neural network GraphSAGE model to obtain features of respective sample nodes, on the basis of the features of the respective sample nodes and labels of the respective sample nodes, training a logistic regression model, and after training is finished, obtaining a 5G private network cell identification model; and extracting feature variables of a scenario cell to be identified, and inputting the feature variables into the 5G private network cell identification model to obtain a prediction result.

Description

A method and device for determining the primary coverage cell of a 5G private network

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application No. 202311159469.5 filed in China on September 7, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present application relates to the field of wireless communications, and in particular to a method and device for determining the primary coverage cell of a 5G private network. Background Art

[0004] With the development of technologies such as big data, the Internet of Things, and the Industrial Internet, vertical industries are increasingly demanding higher network transmission speeds, network latency, and security. The fifth-generation mobile communication technology (5G) public network cannot meet the security and other needs of enterprises, which is driving more and more companies to target 5G private networks.

[0005] Based on the principle of "network follows industry and network is built on demand", China Mobile has launched three modes of 5G private network: "Premium, Exclusive, and Premium", and released a number of 5G private network capabilities such as edge computing, super uplink, and network services. It helps industry customers quickly build a customized exclusive network that is secure, reliable, stable in performance, and has visible services, meeting customers' needs for data non-existence, ultra-low latency, and ultra-large bandwidth, and realizing operational scenarios such as high-definition video backhaul and remote control.

[0006] The "Premium" product solution is at the ordinary level and uses China Mobile's large-scale network resources. Among them, the wireless, transmission, and bearer functions are consistent with China Mobile's large-scale network. The "Exclusive" product solution also uses existing cells, but will reserve dedicated private network resources in the cells. The "Premium" product solution will add new cells and exclusively use the cell resources. The "Premium and Exclusive" solutions require the main coverage cells within the customer scenario to carry private network services, and the "Exclusive" solution requires understanding the main coverage cells within the customer scenario to plan new exclusive sites. Therefore, the output of specific 5G private network "Premium, Exclusive, and Exclusive" model solutions is based on a precise survey of the 5G cells covering the user's needs.

[0007] The methods for determining the main coverage cells of 5G private networks in related technologies include: on-site testing method, which has high accuracy, but requires test engineers to conduct a full range of tests on site, and the time and economic costs of the survey are extremely high; the basic data judgment method, which does not utilize existing precise positioning big data resources and related intelligent algorithms for identification, and the accuracy is too low, resulting in poor accuracy of the solution; and the method of determining the main coverage by modeling from a single cell perspective ignores the network structure, which will reduce the integrity and accuracy of the main coverage cells of the private network.

[0008] Summary of the Invention

[0009] In response to the above problems, a method and device for determining the primary coverage cell of a 5G private network are proposed.

[0010] In a first aspect, the present application proposes a method for determining a primary coverage cell of a 5G private network, comprising:

[0011] Obtaining neighboring cell measurement statistics and neighboring cell handover statistics of the first area, and obtaining a cell association map based on the neighboring cell measurement statistics and the neighboring cell handover statistics;

[0012] Extracting sample characteristic variables of the primary coverage cell of the sample scenario, mapping the sample characteristic variables onto the cell association map, and generating corresponding mapping data;

[0013] The mapping data is input into the graph neural network GraphSAGE model to obtain the features of each sample node, and a logistic regression model is trained based on the features and labels of each sample node. After the training is completed, a 5G private network cell identification model is obtained;

[0014] Extract the characteristic variables of the scene cell to be identified, input the characteristic variables into the 5G private network cell identification model, and obtain the prediction results.

[0015] Optionally, obtaining a cell association map according to the neighboring cell measurement statistics and the neighboring cell handover statistics includes:

[0016] Obtaining a correlation coefficient between any two cells in the first area based on the neighboring cell measurement statistics, and constructing an initial cell correlation map based on the correlation coefficient;

[0017] The average daily number of handovers between any two cells in the first area is obtained according to the neighboring cell handover statistics, and the redundant edges in the initial cell association map are trimmed according to the average daily number of handovers and preset trimming regulations to obtain the cell association map.

[0018] Optionally, obtaining a correlation coefficient between any two cells in the first area according to the neighboring cell measurement statistical data, and constructing an initial cell correlation map according to the correlation coefficient includes:

[0019] For the cell i and the cell j in the first area, the correlation coefficient is: H i,j =(X i +X j ) / Y i +Y j )

[0020] Among them, X i Y is the number of sampling points where cell i is measured as a serving cell to cell j in the neighboring cell measurement statistics. i is the total number of sampling points of cell i as a serving cell, X j Y is the number of sampling points measured from cell j as the serving cell to cell i, j is the total number of sampling points of cell j as a serving cell;

[0021] If the relationship coefficient H between cell i and cell j is i,j >a, then there is a connection between cell i and cell j, and the cell i and cell j are connected by an edge, and the correlation coefficient H i,j As the characteristic value of the edge connecting cell i and cell j, where a is the preset correlation coefficient threshold.

[0022] Optionally, obtaining the daily average number of handovers between any two cells in the first area according to the neighboring cell handover statistics, and pruning redundant edges in the initial cell association map according to the daily average number of handovers and a preset pruning rule to obtain the cell association map includes:

[0023] For cell a and cell b within the first area, obtaining the daily average number of handovers of each connected edge of cell a and cell b according to the neighboring cell handover statistics;

[0024] The connected edges whose average daily switching times are lower than the preset switching times are regarded as pre-pruned edges;

[0025] For pre-cut edges A1 a,b , if edge A1 is pre-cut a,b It is not the last edge between cells a and b, and the pre-pruned edge A1 is pruned a,b If it does not cause the split of the initial cell association map, the pre-pruned edge A1 is pruned. a,b ;

[0026] Repeat the above steps to remove the redundant edges between the remaining cells in turn to obtain the cell association graph.

[0027] Optionally, extracting the sample characteristic variables of the primary coverage cell of the sample scenario includes:

[0028] Extracting basic information features of the primary coverage cell of the sample scenario, wherein the basic information features include coverage distance, coverage angle, antenna height, longitude, latitude, overlap area, frequency band, and location;

[0029] Extracting the Minimization of Drive-Test (MDT) coverage features of the primary coverage cell of the sample scenario, wherein the MDT coverage features include coverage signal strength, number of coverage sampling points per grid, and average coverage area of ​​the scenario.

[0030] Optionally, mapping the sample characteristic variables onto the cell association map to generate corresponding mapping data includes:

[0031] The sample feature variables are mapped to the nodes and connected edges of the cell association graph to obtain corresponding sample nodes and sample connected edges.

[0032] Optionally, inputting the mapping data into a graph neural network GraphSAGE model to obtain features of each sample node includes:

[0033] For each sample node, use the K neighbor nodes around it to sample information until each sample node contains the characteristics of the adjacent nodes;

[0034] Through the mean aggregation function, the features of each sample node and its neighboring nodes are aggregated one by one to obtain the features of each sample node.

[0035] Optionally, after inputting the characteristic variables into the 5G private network cell identification model to obtain a prediction result, the method further includes:

[0036] Compare the prediction result with the preset prediction threshold to determine whether the scene cell to be identified is the main coverage cell of the 5G private network.

[0037] Optionally, comparing the prediction result with a preset prediction threshold to determine whether the scene cell to be identified is a 5G private network primary coverage cell includes:

[0038] If the probability of the prediction result is not less than the preset prediction threshold, determining that the scene cell to be identified is a 5G private network primary coverage cell;

[0039] Otherwise, it is determined that the scene cell to be identified is a non-5G private network main coverage cell.

[0040] In a second aspect, the present application provides a device for determining a primary coverage cell of a 5G private network, comprising:

[0041] an association map construction module, configured to obtain neighboring cell measurement statistics and neighboring cell handover statistics of the first area, and obtain a cell association map based on the neighboring cell measurement statistics and the neighboring cell handover statistics;

[0042] A feature extraction module is used to extract sample feature variables of the primary coverage cell of the sample scenario, map the sample feature variables onto the cell association map, and generate corresponding mapping data;

[0043] A training module is used to input the mapping data into the graph neural network GraphSAGE model to obtain the characteristics of each sample node, and train a logistic regression model based on the characteristics and labels of each sample node. After the training is completed, a 5G private network cell identification model is obtained;

[0044] The prediction module is used to extract the characteristic variables of the scene cell to be identified, input the characteristic variables into the 5G private network cell identification model, and obtain the prediction results.

[0045] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0046] By proposing a method for identifying correlation graphs between communication cells, a wireless communication graph network is constructed, and the network structure is reflected by the graph structure. The physical coverage characteristics of the cell and the MDT minimized drive test data are used as the analysis basis. The analysis is based on massive user drive test data, which is more accurate and saves time and economic costs. A 5G private network cell identification model based on a two-layer stack of graph neural networks and logistic regression is constructed. In view of the particularity of 5G private network coverage scenarios, inter-cell correlation features are introduced to greatly improve the comprehensiveness and accuracy of identification, ensuring the quality of service (Quality of Service, QoS) of private network users.

[0047] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0049] FIG1 is a flowchart of a method for determining a primary coverage cell of a 5G private network according to an embodiment of the present application;

[0050] Figure 2 is a block diagram of a device for determining a primary coverage cell of a 5G private network according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0052] There are certain differences between identifying a primary coverage cell for a private network and identifying a general primary coverage cell. Private networks require ensuring the quality of service (QoS) for private network users and need to consider user perception throughout the entire service process. However, identifying a general primary coverage cell primarily considers the coverage accuracy of a single cell. Due to this difference, identifying a primary coverage cell for a private network requires further consideration of the wireless network structure. When a private network user occupies private network cell A, A and B are strongly associated cells on the association map, and B is a non-private network cell. The private network user may briefly switch to cell B due to various reasons, such as signal reflection or signal fluctuation. Since cell B does not reserve resources for the private network user, user perception may deteriorate.

[0053] FIG1 is a flowchart of a method for determining a primary coverage cell of a 5G private network according to an embodiment of the present application, including:

[0054] Step 101: Obtain neighboring cell measurement statistics and neighboring cell handover statistics of a first area, and obtain a cell association map based on the neighboring cell measurement statistics and neighboring cell handover statistics.

[0055] In an embodiment of the present application, the first area can be the total network data of a city or the total network data of a district. This application does not impose any restrictions on this, but the amount of data cannot be too small, otherwise there will be major limitations in the subsequent training process.

[0056] Additionally, neighboring cell handover statistics refer to situations where, in traditional network optimization, coverage may differ significantly from the original planned data due to construction or errors in basic data collection. This is typically detected through drive testing or abnormal handover success rates, but this approach is inefficient and relies on a mechanical cell-by-cell comparison. Currently, handover reports lack effective integration with underlying data. Neighboring cell handover statistics typically report information such as the number of handovers from the source cell to the target cell and the number of successful handovers. Various tools can import these reports into graphical tools for analysis.

[0057] Neighboring cell measurement statistics mean that in a wireless network, the terminal first needs to measure the signal level or signal quality of the cell to perform cell selection or cell reselection. When in the RRC_Connected state, the user terminal (User Equipment, UE) needs to report the measurement results to the 5G base station (gNodeB, gNB). That is, the UE continuously sends measurement reports to the base station at regular intervals. The measurement reports include the measurement results of the serving cell and the measurement results of the neighboring cells.

[0058] In the embodiment of the present application, the correlation coefficient between any two cells in the first area is obtained based on the neighboring cell measurement statistical data, and an initial cell correlation map is constructed based on the correlation coefficient.

[0059] Specifically, for cell i and cell j in the first area, the correlation coefficient is: H i,j =(X i +X j ) / Y i +Y j )

[0060] Among them, X i Y is the number of sampling points from cell i as the serving cell to cell j in the neighboring cell measurement statistics. i is the total number of sampling points of cell i as a serving cell, X j Y is the number of sampling points measured from cell j as the serving cell to cell i, j is the total number of sampling points of cell j as a serving cell;

[0061] If the relationship coefficient H between cell i and cell j is i,j >a, then there is a connection between cell i and cell j, connect cell i and cell j with an edge, and calculate the correlation coefficient H i,j As the characteristic value of the edge connecting cell i and cell j, where a is the preset correlation coefficient threshold.

[0062] In the embodiment of the present application, there is no specific limitation on the value of the preset correlation coefficient threshold.

[0063] In an embodiment of the present application, the average daily number of handovers between any two cells in the first area is obtained based on the neighboring cell handover statistics, and the redundant edges in the initial cell association map are trimmed based on the average daily number of handovers and preset clipping regulations to obtain a cell association map.

[0064] Specifically, for cell a and cell b in the first area, obtain the daily average number of handovers of each connected edge of cell a and cell b based on neighboring cell handover statistics;

[0065] The connected edges whose average daily switching times are lower than the preset switching times are regarded as pre-pruned edges;

[0066] For pre-cut edges A1 a,b , if edge A1 is pre-cut a,b It is not the last edge between cells a and b, and the pre-pruned edge A1 is pruned a,b If it does not cause the initial cell association graph to split, then the pre-pruned edge A1 is pruned a,b ;

[0067] Repeat the above steps and remove the redundant edges between the remaining cells in turn to obtain the cell association graph.

[0068] It should be noted that the priority order of edge cutting is that the smaller the number of handovers between cell a and cell b, the higher the priority.

[0069] Step 102: extract sample characteristic variables of the primary coverage cell of the sample scenario, map the sample characteristic variables to the cell association map, and generate corresponding mapping data.

[0070] In the embodiment of the present application, the feature variables include basic information features and minimization of drive-test (MDT) coverage features.

[0071] It should be noted that basic information features include coverage distance, coverage angle, antenna height, longitude, latitude, overlap area, frequency band and location, and Minimization of Drive-Test (MDT) coverage features include coverage signal strength, number of coverage sampling points per grid and average coverage area of ​​the scene.

[0072] For example, Table 1 is used to introduce the sample characteristic variables in detail.

[0073] Table 1

[0074] In the embodiment of the present application, the sample scene main coverage cell is an existing manually selected scene main coverage cell, such as an existing school or hospital scene main coverage cell.

[0075] Since private network users have high QOS requirements for the communication service process, it is necessary to integrate and analyze the characteristics of the communication service cell and its surrounding related cells to ensure that the private network main coverage cell selected by the model can reflect the network structure of the existing network.

[0076] In a possible embodiment, a school in a primary coverage area of ​​a sample scenario is taken as an example.

[0077] First, we obtain the spatial information field of the school and the information of the primary coverage area. We then use the spatial information field to obtain the cells within this area and the cells within 500 meters of this area, and extract the characteristic variables of these cells.

[0078] After each cell has features, it is associated with the second column of main coverage cells to determine whether these cells are main coverage or non-main coverage, forming training data containing positive and negative samples.

[0079] In the embodiment of the present application, it is necessary to fuse the sample feature variables with the cell association map features.

[0080] Specifically, the sample feature variables are mapped to the nodes and connected edges of the cell association graph to obtain the corresponding sample nodes and sample connected edges.

[0081] In step 103, the mapping data is input into the graph neural network GraphSAGE model to obtain the characteristics of each sample node, and a logistic regression model is trained based on the characteristics of each sample node and the label of each sample node. After the training is completed, a 5G private network cell identification model is obtained.

[0082] Among the related technologies, Graph Neural Networks (GNN) is a framework that has emerged in recent years that uses deep learning to directly learn graph-structured data. By extracting and exploring features and patterns in graph-structured data, it meets the needs of graph learning tasks such as clustering, classification, prediction, segmentation, and generation. The GraphSAGE algorithm is optimized compared to the traditional GNN graph neural network: first, the acquisition algorithm is optimized to greatly improve the modeling efficiency; second, the neighbor aggregation method is optimized to improve the accuracy of the aggregated features. Logistic regression, also known as logistic regression analysis, is a generalized linear regression analysis model. It belongs to supervised learning in machine learning. Its derivation process and calculation method are similar to the regression process, but it is actually mainly used to solve binary classification problems. It is one of the most commonly used and most stable binary classification models.

[0083] In the embodiment of the present application, after the mapping data is obtained, GraphSAGE sampling and aggregation are performed on the cells of the associated graph.

[0084] Specifically, information is sampled from each sample node using its surrounding K neighbor nodes until each sample node contains adjacent node features, where K≤2.

[0085] Subsequently, since the cell node location already contains the characteristics of the adjacent nodes, the characteristics of the node and the adjacent nodes are further aggregated for each node one by one.

[0086] Specifically, the mean aggregation function is used to aggregate the features of each sample node and its neighboring nodes one by one to obtain the features of each sample node, and the logistic regression model is trained in combination with the labels of each sample node.

[0087] It should be noted that GraphSAGE can choose a variety of mean aggregation functions, sum aggregation functions, convolution aggregation functions, long short-term memory (LSTM) aggregation functions, etc. This application uses the sum aggregation function.

[0088] It can be understood that model training is an iterative process, which is carried out by continuously adjusting the network parameters of the model until the overall loss function value of the model is less than the preset value, or the overall loss function value of the model no longer changes or changes slowly, the model converges, and a trained model is obtained.

[0089] Optionally, the training may be considered completed when a preset number of training times is reached.

[0090] Optionally, the training may be considered to be finished when a preset training time is reached.

[0091] Step 104: extract the characteristic variables of the scene cell to be identified, input the characteristic variables into the 5G private network cell identification model, and obtain the prediction results.

[0092] In an embodiment of the present application, as shown in step 102, basic information features of the cell in the scene to be identified and minimization of drive tests (MDT) coverage features are extracted. The basic information features include coverage distance, coverage angle, antenna height, longitude, latitude, overlap area, frequency band and location. The MDT coverage features include coverage signal strength, number of coverage sampling points per grid and average coverage area of ​​the scene.

[0093] In an embodiment of the present application, after obtaining the prediction result, it is also used to compare the prediction result with a preset prediction threshold to determine whether the scene cell to be identified is the main coverage cell of the 5G private network.

[0094] Specifically, if the probability of the prediction result is not less than the preset prediction threshold, the scene cell to be identified is determined to be the 5G private network main coverage cell; otherwise, the scene cell to be identified is determined to be a non-5G private network main coverage cell.

[0095] In this way, the main coverage cell of the 5G private network is identified.

[0096] This application proposes a method for identifying association graphs between communication cells, constructs a wireless communication graph network, reflects the network structure with a graph structure, uses the physical coverage characteristics of the cell and MDT minimized drive test data as the analysis basis, and uses massive user drive test data as the analysis basis, which is more accurate and saves time and economic costs. It constructs a 5G private network cell identification model based on a two-layer stack of graph neural networks and logistic regression. In view of the particularity of 5G private network coverage scenarios, it introduces association features between cells to greatly improve the comprehensiveness and accuracy of identification, and ensure the service quality (QOS) of private network users.

[0097] FIG2 is a block diagram of a device 200 for determining a primary coverage cell of a 5G private network according to an embodiment of the present application, including:

[0098] The association map construction module 210 is configured to obtain neighboring cell measurement statistics and neighboring cell handover statistics in the first area, and obtain a cell association map based on the neighboring cell measurement statistics and the neighboring cell handover statistics;

[0099] The feature extraction module 220 is used to extract sample feature variables of the primary coverage cell of the sample scenario, map the sample feature variables to the cell association map, and generate corresponding mapping data;

[0100] The training module 230 is used to input the mapping data into the graph neural network GraphSAGE model to obtain the characteristics of each sample node, and train a logistic regression model based on the characteristics and labels of each sample node. After the training is completed, a 5G private network cell identification model is obtained;

[0101] The prediction module 240 is used to extract the characteristic variables of the scene cell to be identified, input the characteristic variables into the 5G private network cell identification model, and obtain the prediction results.

[0102] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0103] 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 described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this disclosure can be achieved, and this document is not limited here.

[0104] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for determining a primary coverage cell of a 5G private network, comprising: Obtaining neighboring cell measurement statistics and neighboring cell handover statistics of the first area, and obtaining a cell association map according to the neighboring cell measurement statistics and the neighboring cell handover statistics; Extracting sample characteristic variables of the main coverage cell of the sample scenario, mapping the sample characteristic variables to the cell association map, and generating corresponding mapping data; The mapping data is input into the graph neural network GraphSAGE model to obtain the characteristics of each sample node, and a logistic regression model is trained according to the characteristics of each sample node and the label of each sample node. After the training, a 5G private network cell identification model is obtained; Extract the characteristic variables of the scene cell to be identified, input the characteristic variables into the 5G private network cell identification model, and obtain the prediction result.

2. The method according to claim 1, wherein: The obtaining a cell association map according to the neighboring cell measurement statistical data and the neighboring cell handover statistical data includes: Obtaining a correlation coefficient between any two cells in the first area according to the neighboring cell measurement statistical data, and constructing an initial cell correlation map according to the correlation coefficient; The average daily number of switching between any two cells in the first area is obtained according to the neighboring cell switching statistics, and the redundant edges in the initial cell association map are trimmed according to the average daily number of switching and preset trimming regulations to obtain the cell association map.

3. The method according to claim 2, wherein: The obtaining, according to the neighboring cell measurement statistical data, a correlation coefficient between any two cells in the first area, and constructing an initial cell correlation map according to the correlation coefficient, comprises: For the i cell and the j cell in the first area, the correlation coefficient is: H i,j =(X i +X j ) / Y i +Y j ) Among them, X i Y is the number of sampling points from the neighboring cell measurement statistics where the i cell is used as the serving cell to measure the j cell, i is the total number of sampling points of cell i as a service cell, X j Y is the number of sampling points measured by cell j as the serving cell to cell i, j is the total number of sampling points of cell j as a service cell; If the relationship coefficient H between cell i and cell j is i,j >a, then there is a connection between cell i and cell j, and the cell i and cell j are connected by an edge, and the correlation coefficient H i,j As the edge connecting cell i and cell j The characteristic value of , where a is the preset correlation coefficient threshold.

4. The method according to claim 2, wherein: The step of obtaining the daily average number of handovers between any two cells in the first area according to the neighboring cell handover statistics, and removing redundant edges in the initial cell association map according to the daily average number of handovers and a preset clipping rule to obtain the cell association map includes: For cell a and cell b in the first area, obtaining the daily average number of handovers of each connected edge of cell a and cell b according to the neighboring cell handover statistics; The connected edges whose daily average switching times are lower than the preset switching times are taken as pre-pruned edges; For pre-cut edges A1 a,b , if edge A1 is pre-cut a,b It is not the last edge of cell a and cell b, and the pre-pruned edge A1 is pruned a,b If the initial cell association graph will not be split, the pre-pruned edge A1 is pruned. a,b ; Repeat the above steps to successively remove the redundant edges between the remaining cells to obtain the cell association graph.

5. The method according to claim 1, wherein: The extracting of sample characteristic variables of the main coverage area of ​​the sample scene includes: Extracting basic information features of the main coverage cell of the sample scenario, wherein the basic information features include coverage distance, coverage angle, antenna height, longitude, latitude, overlap area, frequency band and location; Extract the minimization of drive test (MDT) coverage features of the main coverage cell in the sample scenario, wherein the MDT coverage features include coverage signal strength, number of coverage sampling points per grid and average coverage area of ​​the scenario.

6. The method according to claim 1, wherein: The step of mapping the sample characteristic variables onto the cell association map to generate corresponding mapping data includes: The sample feature variables are mapped to the nodes and connected edges of the cell association graph to obtain corresponding sample nodes and sample connected edges.

7. The method according to claim 1, wherein: The mapping data is input into the graph neural network GraphSAGE model to obtain the characteristics of each sample node, including: For each sample node, use the K neighboring nodes around it to sample information until each sample node contains the characteristics of the adjacent nodes; Through the mean aggregation function, each sample node and its neighboring nodes are integrated one by one. The features are aggregated to obtain the features of each sample node.

8. The method according to claim 1, wherein: After inputting the characteristic variables into the 5G private network cell identification model to obtain the prediction result, the method further includes: Compare the prediction result with the preset prediction threshold to determine whether the scene cell to be identified is the main coverage cell of the 5G private network.

9. The method according to claim 8, wherein: The comparing the prediction result with a preset prediction threshold to determine whether the scene cell to be identified is a 5G private network primary coverage cell includes: If the probability of the prediction result is not less than the preset prediction threshold, the scene cell to be identified is determined to be a 5G private network primary coverage cell; Otherwise, it is determined that the scene cell to be identified is a non-5G private network main coverage cell.

10. A device for determining a primary coverage cell of a 5G private network, comprising: An association map construction module, configured to obtain neighboring cell measurement statistics and neighboring cell handover statistics of the first area, and obtain a cell association map according to the neighboring cell measurement statistics and the neighboring cell handover statistics; A feature extraction module, used to extract sample feature variables of the main coverage cell of the sample scenario, map the sample feature variables to the cell association map, and generate corresponding mapping data; A training module is used to input the mapping data into the graph neural network GraphSAGE model to obtain the characteristics of each sample node, and train a logistic regression model according to the characteristics of each sample node and the label of each sample node. After the training, a 5G private network cell identification model is obtained; The prediction module is used to extract the characteristic variables of the scene cell to be identified, input the characteristic variables into the 5G private network cell identification model, and obtain the prediction results.

Images