Paging area configuration method and apparatus, computer device, storage medium, and product

Abstract

The application relates to a paging area configuration method and device, computer equipment, a storage medium and a product. The method comprises the following steps: determining a seed cell and a high-traffic candidate cell set from each cell according to busy-time network operation data of each cell in a target network area; determining a plurality of high-traffic areas in the target network area according to the correlation degree between each high-traffic candidate cell in the high-traffic candidate cell set and the seed cell; and performing paging area configuration on the target network area according to the plurality of high-traffic areas in the target network area. The above method can first determine the seed cell and each high-traffic candidate cell in the target network area, then determine the plurality of high-traffic areas in the target network area according to the correlation degree between each high-traffic candidate cell and the seed cell, and finally accurately configure the paging area through the plurality of high-traffic areas, thereby improving the rationality of the paging area configuration.

Description

Technical Field

[0001] This application relates to the field of mobile communication technology, and in particular to a paging area configuration method, apparatus, computer equipment, storage medium and product. Background Technology

[0002] With the development of mobile communication services, the proper configuration of paging areas has become particularly important. In practical applications, access network equipment sends paging messages through paging areas to notify terminal equipment to receive data or signaling.

[0003] In related technologies, the paging area within the target network area is configured mainly by using historical experience data and historical network operation data of each cell, and the paging area is adjusted when the network side issues an alarm message.

[0004] However, the relevant technologies suffer from the problem of inefficient paging area configuration, leading to low network resource utilization. Summary of the Invention

[0005] Therefore, it is necessary to provide a paging area configuration method, apparatus, computer equipment, storage medium, and product to address the aforementioned technical problems.

[0006] In a first aspect, embodiments of this application provide a paging area configuration method, the method comprising:

[0007] Based on the busy-hour network operation data of each cell in the target network area, seed cells and high-traffic candidate cell sets are determined from each cell; the seed cell represents the cell with the highest service activity among all cells.

[0008] Based on the correlation between each high traffic candidate cell and the seed cell in the high traffic candidate cell set, multiple high traffic areas in the target network area are determined;

[0009] Configure paging areas for the target network area based on multiple high-traffic areas within the target network area.

[0010] In one embodiment, based on busy-hour network operation data of each cell within the target network area, seed cells and a set of high-traffic candidate cells are determined from each cell, including:

[0011] Based on the busy-hour network operation data of each community, the activity level of community services in each community is determined;

[0012] For any cell, if the cell's service activity level is greater than the preset activity level threshold, the cell will be identified as a call traffic candidate cell.

[0013] The candidate cell with the highest service activity among the identified candidate cells is designated as the seed cell, and a high-traffic candidate cell set is constructed based on the other candidate cells besides the seed cell.

[0014] In one embodiment, the busy-hour network operation data includes the number of cell connections; based on the busy-hour network operation data of each cell, the cell service activity level of each cell is determined, including:

[0015] Determine the total number of cell connections for all cells based on the number of cell connections in each cell.

[0016] The activity level of each community's services is determined based on the number of community connections and the total number of community connections.

[0017] In one embodiment, before determining multiple high-traffic areas in the target network area based on the correlation between each high-traffic candidate cell and the seed cell in the high-traffic candidate cell set, the method further includes:

[0018] Acquire the first signal quality measurement data of the seed cell relative to each high traffic candidate cell, and the second signal quality measurement data of each high traffic candidate cell relative to the seed cell;

[0019] Based on the first signal quality measurement data and the second signal quality measurement data, the correlation between each high traffic candidate cell and the seed cell is determined.

[0020] In one embodiment, multiple high-traffic areas in the target network area are determined based on the correlation between each high-traffic candidate cell and the seed cell in the high-traffic candidate cell set, including:

[0021] Based on the correlation between each high traffic candidate cell and the seed cell, the target high traffic candidate cell with a correlation greater than or equal to the first correlation threshold is identified as a high traffic area;

[0022] The target high-traffic candidate cell is removed from the high-traffic candidate cell set to obtain an updated high-traffic candidate cell set;

[0023] Based on the updated high traffic candidate cell set, high traffic areas are further determined until the total number of high traffic candidate cells in the updated high traffic candidate cell set is less than or equal to a preset threshold, resulting in multiple high traffic areas.

[0024] In one embodiment, the high-traffic area is further determined based on the updated high-traffic candidate cell set, including:

[0025] Based on the total number of high-traffic candidate cells in the updated high-traffic candidate cell set, the first correlation threshold is adjusted to obtain the second correlation threshold; the second correlation threshold is less than the first correlation threshold.

[0026] A high-traffic candidate cell whose cluster correlation is greater than or equal to the second correlation threshold is identified as a high-traffic area.

[0027] In one embodiment, paging area configuration is performed on the target network area based on multiple high-traffic areas within the target network area, including:

[0028] At least two high-traffic areas are obtained from multiple high-traffic areas and merged. The merged high-traffic area is configured as a first-level paging area. Each of the multiple high-traffic areas is treated as an independent paging area and configured as a second-level paging area.

[0029] In one embodiment, the method further includes:

[0030] Once the paging area configuration for the target network area is completed, a paging area identifier is configured for each paging area according to the level to which each paging area belongs; the paging area identifiers are different for paging areas at different levels.

[0031] In one embodiment, the method further includes:

[0032] Once the paging area configuration for the target network area is completed, the paging priority is configured for each paging area according to its level. The paging priority indicates the order in which the network sends terminal paging messages to different paging areas. The paging priority of the first-level paging area is higher than that of the second-level paging area.

[0033] Secondly, embodiments of this application provide a paging area configuration device, the device comprising:

[0034] The first determining module is used to determine the seed cell and the high traffic candidate cell set from each cell based on the busy-hour network operation data of each cell in the target network area; the seed cell represents the cell with the highest service activity among all cells.

[0035] The second determining module is used to determine multiple high-traffic areas in the target network area based on the correlation between each high-traffic candidate cell and the seed cell in the high-traffic candidate cell set.

[0036] The configuration module is used to configure the paging area of ​​the target network area based on multiple high-traffic areas in the target network area.

[0037] Thirdly, embodiments of this application also provide a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the embodiments of the first aspect.

[0038] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described in any of the embodiments of the first aspect.

[0039] Fifthly, embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the method described in any of the embodiments of the first aspect.

[0040] The paging area configuration method, apparatus, computer equipment, storage medium, and product provided in this application embodiment include: determining a seed cell and a high-traffic candidate cell set from each cell based on the busy-hour network operation data of each cell in the target network area; determining multiple high-traffic areas in the target network area based on the correlation between each high-traffic candidate cell in the high-traffic candidate cell set and the seed cell; and configuring the paging area in the target network area based on the multiple high-traffic areas in the target network area. The above method first identifies seed cells and high-traffic candidate cells within the target network area. Then, based on the correlation between the high-traffic candidate cells and seed cells, it first identifies multiple high-traffic areas within the target network area. Subsequently, paging areas can be accurately configured using these multiple high-traffic areas, improving the rationality of paging area configuration. Furthermore, this method does not require consideration of historical experience data or historical network operation data of each cell; it can configure paging areas in real-time using the busy-hour network operation data of each cell, further enhancing the rationality of paging area configuration. Based on a rational paging area configuration, network resource overhead can be reduced, minimizing disruption to normal network operation. Even with fixed network resources, it can effectively improve network resource utilization during paging, extend standby time, and improve user experience. Moreover, this method allows for rational paging area configuration, making the real-time configured paging areas adaptable to the complexity and rapid changes in wireless communication environments and service development, thereby improving the versatility of the paging area configuration method. Based on a rationally configured paging area, network-side paging of terminals can be more accurate, reducing unnecessary network overhead during paging, improving network resource utilization, and enhancing service reliability. Attached Figure Description

[0041] Figure 1 This is an application environment diagram of a paging area configuration method in one embodiment;

[0042] Figure 2 This is a flowchart illustrating a paging area configuration method in one embodiment;

[0043] Figure 3 This is a flowchart illustrating the paging area configuration method in another embodiment;

[0044] Figure 4 This is a flowchart illustrating the paging area configuration method in another embodiment;

[0045] Figure 5 This is a flowchart illustrating the paging area configuration method in another embodiment;

[0046] Figure 6 This is a flowchart illustrating the paging area configuration method in another embodiment;

[0047] Figure 7 This is a flowchart illustrating the paging area configuration method in another embodiment;

[0048] Figure 8 This is a flowchart illustrating the paging area configuration method in another embodiment;

[0049] Figure 9 This is a structural block diagram of a paging area configuration device in one embodiment;

[0050] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] In the field of wireless communication, with the development of communication services, the rational configuration of paging areas has become particularly important. Related technologies mainly configure paging areas within a target network area using historical experience data and historical network operation data of each cell, and adjust the paging areas when the network side issues alarm information (i.e., network problems or network optimization needs). However, these technologies do not configure paging areas based on real-time data and cannot automatically adjust them, resulting in inefficient paging area configuration and low network resource utilization. Therefore, this application provides a paging area configuration method that can rationally configure paging areas and improve network resource utilization.

[0053] The paging area configuration method provided in this application embodiment can be applied to, for example, Figure 1The paging area configuration system shown includes core network equipment and computer equipment. The core network equipment and computer equipment communicate with each other via Bluetooth, Wi-Fi, mobile network connection, etc. The computer equipment can be, but is not limited to, various personal computers, laptops, smartphones, and tablets; this embodiment does not limit the specific form of the computer equipment. Figure 1 This example illustrates a paging area configuration system using a personal computer as the computer device. The following embodiments will detail the specific process of the paging area configuration method, using a computer device as the executing entity.

[0054] like Figure 2 The diagram shown is a flowchart illustrating a paging area configuration method provided in an embodiment of this application. This method may include the following steps:

[0055] S100. Based on the busy-hour network operation data of each cell within the target network area, determine the seed cell and the high-traffic candidate cell set from each cell. The seed cell represents the cell with the highest service activity among all cells.

[0056] The target network area can be a large area covered by the wireless communication network or a small area covered by the wireless communication network. In this embodiment, the size of the target network area is not limited. Optionally, multiple base stations can be deployed within the target network area, and each base station can cover multiple cells. That is, the target network area can include multiple cells.

[0057] In practical applications, the busy-hour network operation data of a cell can be understood as the network operation data corresponding to the time period with the highest traffic volume in the cell. This traffic can be data traffic, control traffic, communication traffic, messaging traffic, etc. The busy hours can include morning busy hours (e.g., 8:30 am to 9:30 am) and evening busy hours (e.g., 8 pm to 9 pm).

[0058] Optionally, the aforementioned network operation data may include base station information, cell configuration information, cell load, and measurement reports of neighboring cells; the aforementioned base station information may include the location of the base station, the configuration information of the base station, the number of cells covered by the base station, the carrier configuration of the base station, etc.; the aforementioned cell configuration information may include the name, direction, identifier, and other attribute parameters of the cell; the aforementioned cell load may include the traffic volume of the cell, etc.; and the aforementioned measurement reports of neighboring cells may include the signal quality of neighboring cells, etc.

[0059] In this embodiment, the seed cell represents the cell with the highest service activity among all cells in the target network area; the service activity of a cell can be understood as the proportion of service volume of that cell among all cells in the target network area; the high-traffic candidate cell set may include multiple high-traffic candidate cells, which can be understood as candidate cells with a large volume of traffic. Optionally, the total number of all high-traffic candidate cells in the seed cell and high-traffic candidate cell sets may be equal to the total number of all cells in the target network area.

[0060] Specifically, the computer equipment can first obtain the busy-hour network operation data of each cell in the target network area, and then, for any cell, retrieve the busy-hour network operation data of the cell in the mapping table, and obtain the cell identifier corresponding to the busy-hour network operation data of the cell found in the mapping table. Then, based on the cell identifier of the cell, determine whether the cell is a seed cell or a high-traffic candidate cell, and then determine all high-traffic candidate cells as a high-traffic candidate cell set.

[0061] Optionally, the mapping table may include busy-hour network operation data for different cells, cell identifiers for different cells, and the correspondence between the two. The cell identifier can be used to distinguish the type of cell, which can be a seed cell or a high-traffic candidate cell. The cell identifier can be composed of at least one of numbers, letters, and symbols.

[0062] In addition, the computer equipment can first obtain the busy-hour network operation data of each cell in the target network area, and then perform analysis, processing and comparison of the busy-hour network operation data of any cell to determine whether the cell is a seed cell or a high-traffic candidate cell. After that, all high-traffic candidate cells are determined as a high-traffic candidate cell set.

[0063] For example, the way to obtain the busy-hour network operation data of each cell in the target network area is to have the core network equipment collect the network operation data of each cell in the target network area at different time periods, then filter out the busy-hour network operation data of each cell from the network operation data of each cell at different time periods, and then send the busy-hour network operation data of each cell to the computer equipment.

[0064] For example, another way to obtain the busy-hour network operation data of each cell in the target network area is for computer devices to directly obtain the busy-hour network operation data of each cell in the target network area from storage locations such as disks, hard drives, and the cloud.

[0065] For example, another way to obtain the busy-hour network operation data of each cell in the target network area is to first obtain the network operation data of each cell in the target network area, and then use statistical methods to perform busy-hour statistics on the network operation data of each cell in the target network area to obtain the busy-hour network operation data of each cell.

[0066] S200. Based on the correlation between each high traffic candidate cell and the seed cell in the high traffic candidate cell set, determine multiple high traffic areas in the target network area.

[0067] In practical applications, computer equipment can directly obtain the correlation between each high traffic candidate cell and the seed cell in a pre-stored high traffic candidate cell set. Then, it compares and processes the correlation between each high traffic candidate cell and the seed cell, and then selects multiple high traffic regions from all high traffic candidate cells based on the comparison results, thus obtaining multiple high traffic regions in the target network region.

[0068] The total number of high-traffic candidate cells in the high-traffic candidate cell set can be less than K. K represents the minimum number of high-traffic cells that each paging area should include when configuring the paging area. The value of K can be determined based on the environment of the high-traffic area, the actual traffic volume and traffic distribution when using the network, etc. Optionally, the value of K cannot be too small (e.g., greater than or equal to 24). If the value of K is too small, it means that the coverage area of ​​multiple high-traffic areas is not large, and there is no need to perform paging stratification, which will increase network resource consumption and reduce network resource utilization.

[0069] Optionally, the total number of all high-traffic areas in the target network area may be less than or equal to the total number of all high-traffic candidate cells in the high-traffic candidate cell set.

[0070] In addition, the computer equipment can also use the correlation calculation method to process the correlation between the busy-hour network operation data of the high-traffic candidate cell and the busy-hour network operation data of the seed cell for any high-traffic candidate cell, and obtain the correlation between the high-traffic candidate cell and the seed cell. Then, the correlation between the high-traffic candidate cell and the seed cell is input into a pre-trained algorithm model. The algorithm model outputs an identifier of whether the high-traffic candidate cell is a high-traffic area. If the identifier of a high-traffic area is yes, it means that the high-traffic candidate cell is a high-traffic area. If the identifier of a high-traffic area is no, it means that the high-traffic candidate cell is not a high-traffic area.

[0071] Optionally, the above-mentioned correlation calculation method can be the cross-entropy method, the correlation coefficient method, or the spatial autocorrelation coefficient method, etc., and this embodiment of the application does not limit this. Optionally, the high traffic area can be identified by 1 or 0, where 1 indicates that the high traffic area is identified as yes, and 0 indicates that the high traffic area is identified as no.

[0072] S300: Configure paging areas for the target network area based on multiple high-traffic areas within the target network area.

[0073] Among them, based on multiple high-traffic areas in the target network area, the paging area can be configured in layers within multiple high-traffic areas. That is, based on multiple high-traffic areas, different levels of paging areas are divided in a balanced manner, and independent paging areas are set up in each level of paging area. This allows the network side to save network resource overhead during the paging process and further effectively improve the utilization rate of network resources.

[0074] In one embodiment, the method for configuring the paging area of ​​the target network area in multiple high traffic areas can be to perform paging layering of multiple high traffic areas according to a preset paging layering strategy, and then divide the paging area according to different high traffic areas in each paging layer according to the preset paging area division strategy, so as to obtain the paging area configuration result of the target network area.

[0075] In another embodiment, the paging area configuration for the target network area in multiple high-traffic areas can be achieved by obtaining the location of different high-traffic areas and the cell traffic volume corresponding to different high-traffic areas, and then configuring the paging area for multiple high-traffic areas according to the location of each high-traffic area and the cell traffic volume corresponding to different high-traffic areas, thereby obtaining the paging area configuration result of the target network area.

[0076] It should be noted that, since the busy-hour network operation data of each cell in the target network area is dynamically changing, in this embodiment of the application, the busy-hour network operation data of each cell in the target network area can be actively acquired periodically. This allows for timely updates to the configuration of high-traffic areas and paging areas based on the latest busy-hour network operation data of each cell, enabling the dynamically configured paging areas to adapt to changes in the wireless communication environment and business development.

[0077] The technical solution in this application embodiment determines seed cells and a set of high-traffic candidate cells from each cell based on busy-hour network operation data of each cell within the target network area. Based on the correlation between each high-traffic candidate cell and the seed cell in the high-traffic candidate cell set, multiple high-traffic areas are determined within the target network area. Then, paging areas are configured within the target network area based on these multiple high-traffic areas. This method first determines seed cells and high-traffic candidate cells within the target network area, then determines multiple high-traffic areas based on the correlation between the high-traffic candidate cells and the seed cells. Subsequently, paging areas can be accurately configured through these multiple high-traffic areas, improving the rationality of paging area configuration. Furthermore, this method does not require consideration of historical experience data or the historical network operation data of each cell. This method allows for real-time configuration of paging areas based on busy network operation data from each cell, improving the rationality of paging area configuration. Furthermore, with proper paging area configuration, network resource overhead can be reduced, minimizing disruption to normal network operation. Given fixed network resources, it can also effectively improve network resource utilization during paging, enhancing user experience. Moreover, this method allows for the rational configuration of paging areas, ensuring that each real-time configured paging area is adaptable to the complexity and rapid changes in wireless communication environments and service developments. This improves the versatility of the paging area configuration method. Furthermore, based on a rationally configured paging area, network-side paging of terminals can be more accurate, reducing unnecessary network overhead during paging and improving network resource utilization and service reliability.

[0078] The process of determining seed cells and a set of high-traffic candidate cells from each cell based on busy-hour network operation data of each cell within the target network area is described below. In one embodiment, as... Figure 3 As shown, the steps in S100 above can be implemented in the following ways:

[0079] S110. Determine the activity level of community services in each community based on the busy-hour network operation data of each community.

[0080] Specifically, for any given cell, the computer equipment can perform arithmetic operations on the busy-hour network operation data of the cell according to a preset cell service activity calculation strategy to obtain the cell service activity level.

[0081] Optionally, the above arithmetic operations can be implemented by at least one of addition, subtraction, multiplication, division, logarithmic operations, exponential operations, etc.

[0082] In one embodiment, busy-hour network operation data includes the number of cell connections; such as Figure 4As shown, the step in S110 above, which determines the activity level of each cell's services based on the busy-hour network operation data of each cell, can be implemented in the following way:

[0083] S111. Determine the total number of cell connections for all cells based on the number of cell connections in each cell.

[0084] In this embodiment of the application, the busy-hour network operation data includes the number of cell connections; the number of cell connections can be the service volume of the cell.

[0085] Specifically, the computer device can obtain the number of cell connections in each cell within the target network area, and then add up the number of cell connections in each cell to obtain the total number of cell connections in all cells.

[0086] S112. Determine the activity level of each cell's services based on the number of cell connections and the total number of cell connections in each cell.

[0087] For any given cell, the computer equipment can perform arithmetic operations on the number of cell connections and the total number of cell connections to obtain the cell's service activity level.

[0088] In this embodiment of the application, for any cell, the cell service activity level can be obtained by dividing the cell's cell connection count by the total number of cell connections, and then multiplying by 100%.

[0089] For example, the cell service activity level of cell i within the target network area is represented by δ. celli Let CONNcelli represent the number of cell connections in cell i, and CONNtotal represent the total number of cell connections in all cells within the target network area. Then, the cell service activity δ of cell i is... celli It can be represented by the following formula (1).

[0090]

[0091] S120. For any cell, if the cell's service activity level is greater than the preset activity level threshold, then the cell is identified as a candidate cell for traffic.

[0092] Optionally, the preset activity threshold can be a user-defined value or a value determined based on historical experience; this embodiment does not limit this. In practical applications, the activity threshold is determined based on the traffic volume of standard high-traffic cells, where the traffic volume of high-traffic cells typically accounts for more than 80% of the total network traffic. However, in this embodiment, the activity threshold can be specifically determined based on the network optimization objectives (e.g., resource adjustment amount, performance achievement, handover success rate, and / or resource utilization) and cell load.

[0093] Specifically, for any given cell, the computer equipment can determine whether the cell's service activity level is greater than a preset activity threshold. If the cell's service activity level is greater than the preset activity threshold, the cell can be identified as a candidate cell for traffic.

[0094] S130. The candidate cell with the highest cell service activity among the identified candidate cells is identified as the seed cell, and a high-volume candidate cell set is constructed based on the other candidate cells in each candidate cell besides the seed cell.

[0095] In practical applications, computer equipment can obtain the cell service activity of each identified traffic candidate cell, and then compare and process the cell service activity of each traffic candidate cell or take the extreme value to obtain the traffic candidate cell with the highest cell service activity. The traffic candidate cell with the highest cell service activity is determined as the seed cell. At the same time, the other traffic candidate cells in all traffic candidate cells except the seed cell can be combined into a high traffic candidate cell set.

[0096] In this embodiment of the application, the cell service activity of each traffic candidate cell can be sorted in order of activity from high to low or from low to high. Then, the traffic candidate cell with the highest cell service activity is obtained from the sorting results and determined as the seed cell.

[0097] In this embodiment of the application, each traffic candidate cell in the high traffic candidate cell set is determined as a high traffic candidate cell.

[0098] The technical solution in this application embodiment determines the cell service activity of each cell based on the busy-hour network operation data of each cell. For any cell, when the cell service activity is greater than a preset activity threshold, the cell is identified as a traffic candidate cell. The traffic candidate cell with the highest cell service activity among the identified traffic candidate cells is identified as the seed cell. A high traffic candidate cell set is constructed based on the other traffic candidate cells besides the seed cell. This method can determine the seed cell and the high traffic candidate cell set from the target network area through the cell service activity of each cell, in order to prepare for the subsequent determination of the high traffic area. This allows the high traffic area to be determined based on the seed cell and the high traffic candidate cells in the high traffic candidate cell set, thereby improving the speed of determining the high traffic area.

[0099] In practical applications, before executing the steps in S200 above, the correlation between each high-traffic candidate cell and the seed cell is first obtained. The process of obtaining the correlation between each high-traffic candidate cell and the seed cell is described below. In one embodiment, before executing the steps in S200 above, as follows... Figure 5 As shown, the above method may further include the following steps:

[0100] S400: Obtain the first signal quality measurement data of the seed cell relative to each high traffic candidate cell, and the second signal quality measurement data of each high traffic candidate cell relative to the seed cell.

[0101] Specifically, signal quality measurement data is sent to the base station only when the signal quality of the high-traffic candidate cell is measured in the seed cell and the signal quality is greater than or equal to a preset signal quality threshold. In this case, the signal quality measurement data can be referred to as the first signal quality measurement data of the seed cell relative to the high-traffic candidate cell.

[0102] Meanwhile, signal quality measurement data is sent to the base station only when the signal quality of the seed cell is measured in a high-traffic candidate cell and is greater than or equal to a preset signal quality threshold. In this case, the signal quality measurement data can be called the second signal quality measurement data of the high-traffic candidate cell relative to the seed cell.

[0103] In practical applications, the signal quality measurement data sent to the base station at different times is different. The signal quality measurement data sent for the first time can be equal to 1, the signal quality measurement data sent for the second time can be equal to 2, and so on. The signal quality measurement data can be the cumulative number of signal quality that meets the standard.

[0104] Optionally, both the first signal quality measurement data and the second signal quality measurement data mentioned above can be signal quality measurement data corresponding to busy times.

[0105] Specifically, the computer equipment can acquire in real time the first signal quality measurement data of the seed cell relative to each high traffic candidate cell, and the second signal quality measurement data of each high traffic candidate cell relative to the seed cell.

[0106] S500. Based on the first signal quality measurement data and the second signal quality measurement data, determine the correlation between each high traffic candidate cell and the seed cell.

[0107] For any high-traffic candidate cell, the computer equipment can perform arithmetic operations on the first signal quality measurement data and the second signal quality measurement data corresponding to the high-traffic candidate cell to obtain the correlation between the high-traffic candidate cell and the seed cell.

[0108] In one embodiment, the steps in S220 above can be used to calculate the correlation between each high traffic candidate cell i and the seed cell S using the following formula (2).

[0109]

[0110] Wherein, MR in equation (2) i→s The second signal quality measurement data, MR, represents the high traffic candidate cell i relative to the seed cell S. s→i M-1 represents the first signal quality measurement data of seed cell S relative to high traffic candidate cell i, and M-1 represents the total number of all high traffic candidate cells in the high traffic candidate cell set.

[0111] The technical solution in this application embodiment can determine the correlation between each high-traffic candidate cell and the seed cell by acquiring the first signal quality measurement data of the seed cell relative to each high-traffic candidate cell, and the second signal quality measurement data of each high-traffic candidate cell relative to the seed cell, so as to prepare for the subsequent determination of high-traffic areas. This allows for the continuous screening of high-traffic areas from the high-traffic candidate cells based on the correlation between each high-traffic candidate cell and the seed cell. At the same time, this method can determine the correlation between each high-traffic candidate cell and the seed cell without the participation of deep learning algorithms, and the implementation process is relatively simple, which can improve the speed of determining the correlation.

[0112] In one embodiment, such as Figure 6 As shown, the step in S200 above, which determines multiple high-traffic areas in the target network area based on the correlation between each high-traffic candidate cell in the high-traffic candidate cell set and the seed cell, can be implemented in the following way:

[0113] S210. Based on the correlation between each high traffic candidate cell and the seed cell, the target high traffic candidate cell with a correlation greater than or equal to the first correlation threshold is determined as a high traffic area.

[0114] Based on the correlation between each high-traffic candidate cell and the seed cell determined in the preceding steps, for any high-traffic candidate cell, it can be determined whether the correlation between the high-traffic candidate cell and the seed cell is greater than or equal to a first correlation threshold. High-traffic candidate cells with a correlation greater than or equal to the first correlation threshold are then identified as target high-traffic candidate cells. Finally, all currently identified target high-traffic candidate cells are identified as high-traffic areas. Optionally, a high-traffic area can be composed of at least one high-traffic candidate cell.

[0115] Optionally, the first correlation threshold can be user-defined or determined based on historical experience. However, in this embodiment, the first correlation threshold can be determined based on the total number of all high traffic candidate cells in the high traffic candidate cell set.

[0116] S220. Remove the target high traffic candidate cell from the high traffic candidate cell set to obtain the updated high traffic candidate cell set.

[0117] Furthermore, all target high-traffic candidate cells identified in the above steps can be removed from the high-traffic candidate cell set to obtain an updated high-traffic candidate cell set.

[0118] S230. Based on the updated high traffic candidate cell set, continue to determine high traffic areas until the total number of high traffic candidate cells in the updated high traffic candidate cell set is less than or equal to a preset threshold, thus obtaining multiple high traffic areas.

[0119] In this embodiment, high-traffic areas can be further determined using the updated high-traffic candidate cell set until the total number of remaining high-traffic candidate cells in the updated high-traffic candidate cell set is less than or equal to a preset threshold, thus obtaining multiple high-traffic areas. Optionally, the preset threshold can be user-defined or determined based on historical experience.

[0120] In this process, after each high traffic area is identified, the previously obtained updated set of high traffic candidate cells can be updated again to obtain an updated set of high traffic candidate cells. In practical applications, each time the set of high traffic candidate cells is updated, the total number of all high traffic candidate cells in the updated set will decrease.

[0121] In one implementation, a computer device can pre-train a screening model, and then input an updated set of high-traffic candidate cells into the screening model. The screening model outputs at least one high-traffic area when the total number of high-traffic candidate cells in the updated set is less than or equal to a preset threshold. Optionally, the screening model can be implemented using at least one of a convolutional neural network model, a fully connected neural network model, a recurrent recurrent neural network model, or a long short-term memory neural network model.

[0122] It should be noted that different high-traffic candidate cells are included in different high-traffic areas; the total number of high-traffic candidate cells included in different high-traffic areas may be the same or different.

[0123] In one embodiment, such as Figure 7 As shown, the step in S230 above, which involves further determining high-traffic areas based on the updated high-traffic candidate cell set, can be implemented in the following way:

[0124] S231. Based on the total number of high-traffic candidate cells in the updated high-traffic candidate cell set, adjust the first correlation threshold to obtain a second correlation threshold. The second correlation threshold is less than the first correlation threshold.

[0125] In this embodiment of the application, the process of continuing to determine high traffic areas based on the updated high traffic candidate cell set can be implemented by repeatedly executing the steps in S210 and S220 above.

[0126] Since the updated high traffic candidate cell set changes dynamically with the number of determined high traffic areas, the first correlation threshold is also continuously updated each time steps S210 and S220 are executed. In other words, in the process of determining high traffic areas for the second time, the first correlation threshold can be replaced by the second correlation threshold.

[0127] Optionally, the second correlation threshold can be determined based on the total number of high-traffic candidate cells in the updated high-traffic candidate cell set obtained after the first high-traffic area is determined, and the second correlation threshold is less than the first correlation threshold.

[0128] S232. The target high traffic candidate cell whose central correlation degree is greater than or equal to the second correlation degree threshold after the update is identified as a high traffic area.

[0129] Each time steps S210 and S220 are executed, it is based on the previously obtained updated high traffic candidate cell set. The target high traffic candidate cell in the updated high traffic candidate cell set with a correlation degree greater than or equal to the second correlation degree threshold is identified as a high traffic area.

[0130] Furthermore, based on the target high-traffic candidate cells in the high-traffic area obtained in the second time, the previously obtained updated high-traffic candidate cell set can be updated to replace the first correlation threshold in S210 and S220 with the third correlation threshold. Then, the next high-traffic area can be determined based on the currently obtained updated high-traffic candidate cell set.

[0131] Optionally, the third correlation threshold can be determined based on the total number of high-traffic candidate cells in the currently updated high-traffic candidate cell set, and the third correlation threshold is less than the second correlation threshold. It should be noted that as the number of identified high-traffic areas increases, the correlation threshold determined each time will be less than the previously determined correlation threshold.

[0132] The technical solution in this application embodiment determines a high-traffic candidate cell as a high-traffic area based on the correlation between each high-traffic candidate cell and the seed cell. The target high-traffic candidate cell is then removed from the high-traffic candidate cell set to obtain an updated high-traffic candidate cell set. High-traffic areas are then determined based on this updated set until the total number of high-traffic candidate cells in the updated set is less than or equal to a preset threshold, resulting in multiple high-traffic areas. This method does not require deep learning algorithms to determine multiple high-traffic areas within the target network region. Based on these determined high-traffic areas, the rationality of paging area configuration can be further improved, ensuring that the final paging area configuration meets real-time service development changes, thereby improving network resource utilization during the paging process. Furthermore, this method does not require the use of the latitude and longitude information of the base stations corresponding to each cell, avoiding the impact of incorrect latitude and longitude information input and thus accurately determining each high-traffic area.

[0133] The following describes the process of configuring a paging area for a target network region based on multiple high-traffic areas within the target network region. In one embodiment, the steps in S300 may include: obtaining at least two high-traffic areas from multiple high-traffic areas and merging them, configuring the merged high-traffic area as a first-level paging area; and configuring each of the multiple high-traffic areas as an independent paging area and configuring it as a second-level paging area.

[0134] In this embodiment of the application, at least two high traffic areas can be obtained from all the high traffic areas, and the at least two high traffic areas can be merged. The merged high traffic area is configured as a first-level paging area, wherein the total number of first-level paging areas can be equal to 1.

[0135] Simultaneously, each high-traffic area acquired can be treated as an independent paging area and configured as a second-tier paging area. That is, the total number of paging areas in the second-tier paging area can be equal to the total number of all acquired high-traffic areas.

[0136] In practical applications, after the network sends a paging message to the paging area, the paging area can send a paging message through the cell control channel to locate the terminal.

[0137] Furthermore, in one embodiment, the above method may further include: after completing the paging area configuration for the target network area, configuring a paging area identifier for each paging area according to the level to which each paging area belongs; wherein, the paging area identifiers for paging areas at different levels are different.

[0138] In practical applications, the paging area identifiers of different paging areas within the second-level paging area can be the same or different. It should be noted that configuring the paging area identifier after completing the paging area configuration in the target network area is for the network side to use during the paging process.

[0139] Meanwhile, in one embodiment, the above method may further include: after completing the paging area configuration for the target network area, configuring paging priority for each paging area according to the level to which each paging area belongs; wherein, the paging priority indicates the order in which the network sends terminal paging messages to different paging areas; the paging priority of the first-level paging area is greater than the paging priority of the second-level paging area.

[0140] It should be noted that after the paging area is configured in the target network area, the purpose of configuring the paging area priority is to enable the network side to use it during the paging process.

[0141] The network side can store the terminal's location information to communicate with the terminal based on the terminal's location information. In practical applications, the terminal can move back and forth between different paging areas, so it is necessary to update the terminal's location information stored in the network side. However, in this embodiment, the terminal only reports its current location information to the network side after initiating a service request and completing the service processing, so as to instruct the network side to update its own location information. This can avoid frequent location updates for the terminal, which would increase network resource consumption.

[0142] The technical solution in this application embodiment obtains at least two high-traffic areas from multiple high-traffic areas and merges them. The merged high-traffic area is configured as a first-level paging area, and each of the multiple high-traffic areas is treated as an independent paging area and configured as a second-level paging area. This method can configure paging areas hierarchically based on multiple high-traffic areas within a precisely determined target network area, making the paging area configuration more reasonable. At the same time, based on the reasonable configuration of paging areas, the network side can quickly send paging messages to find the corresponding terminal, so that the terminal can quickly enter the communication state, thereby saving the power consumption required for the terminal to enter the communication state and extending the terminal standby time.

[0143] In one embodiment, this application also provides a paging area configuration method, such as... Figure 8 As shown, the method includes the following procedures:

[0144] S10. Determine the total number of cell connections for all cells based on the number of cell connections in each cell.

[0145] S11. Determine the activity level of each cell's services based on the number of cell connections and the total number of cell connections.

[0146] S11. For any cell, if the cell's service activity level is greater than the preset activity level threshold, then the cell is identified as a candidate cell for traffic.

[0147] S12. The candidate cell with the highest service activity among the identified candidate cells is identified as the seed cell. A high-traffic candidate cell set is constructed based on the other candidate cells in each candidate cell except the seed cell. The seed cell represents the cell with the highest service activity among all cells.

[0148] S13. Obtain the first signal quality measurement data of the seed cell relative to each high traffic candidate cell, and the second signal quality measurement data of each high traffic candidate cell relative to the seed cell.

[0149] S14. Determine the correlation between each high traffic candidate cell and the seed cell based on the first signal quality measurement data and the second signal quality measurement data.

[0150] S15. Based on the correlation between each high traffic candidate cell and the seed cell, the target high traffic candidate cell with a correlation greater than or equal to the first correlation threshold is determined as a high traffic area.

[0151] S16. Remove the target high traffic candidate cell from the high traffic candidate cell set to obtain the updated high traffic candidate cell set.

[0152] S17. Based on the total number of high-traffic candidate cells in the updated high-traffic candidate cell set, adjust the first correlation threshold to obtain the second correlation threshold; the second correlation threshold is less than the first correlation threshold.

[0153] S18. The target high traffic candidate cell whose correlation degree of the updated high traffic candidate cell set is greater than or equal to the second correlation degree threshold is identified as a high traffic area, until the total number of high traffic candidate cells in the updated high traffic candidate cell set is less than or equal to the preset threshold, thus obtaining multiple high traffic areas.

[0154] S19. Obtain at least two high traffic areas from multiple high traffic areas and merge them, then configure the merged high traffic area as a first-level paging area; and configure each high traffic area from the multiple high traffic areas as an independent paging area and as a second-level paging area.

[0155] S20. When paging area configuration is completed for the target network area, paging area identifiers are configured for each paging area according to its level. When paging area configuration is completed for the target network area, paging priority is configured for each paging area according to its level. The paging area identifiers are different for paging areas at different levels. The paging priority indicates the order in which the network sends terminal paging messages to different paging areas. The paging priority of the first-level paging area is higher than that of the second-level paging area.

[0156] The specific execution process of S10 to S20 can be found in the description of the above embodiments. The implementation principle and technical effect are similar, and will not be repeated here.

[0157] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0158] Based on the same inventive concept, this application also provides a paging area configuration device for implementing the paging area configuration method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more paging area configuration device embodiments provided below can be found in the limitations of the paging area configuration method described above, and will not be repeated here.

[0159] In one embodiment, Figure 9 This is a schematic diagram of a paging area configuration device in one embodiment of this application. The paging area configuration device provided in this embodiment can be applied to computer equipment. Figure 9 As shown, the paging area configuration device of this application embodiment may include: a first determining module 11, a second determining module 12, and a configuration module 13, wherein:

[0160] The first determining module 11 is used to determine the seed cell and the high traffic candidate cell set from each cell based on the busy-hour network operation data of each cell in the target network area; the seed cell represents the cell with the highest cell service activity among all cells.

[0161] The second determining module 12 is used to determine multiple high traffic areas in the target network area based on the correlation between each high traffic candidate cell and the seed cell in the high traffic candidate cell set.

[0162] Configuration module 13 is used to configure the paging area of ​​the target network area based on multiple high traffic areas in the target network area.

[0163] The paging area configuration device provided in this application embodiment can be used to execute the technical solutions in the above-described paging area configuration method embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0164] In one embodiment, the first determining module 11 includes: an activity determining unit, a candidate cell determining unit, and a candidate cell set construction unit, wherein:

[0165] The activity level determination unit is used to determine the activity level of each cell's services based on the busy-hour network operation data of each cell.

[0166] The candidate cell determination unit is used to determine a cell as a traffic candidate cell if the cell's service activity level is greater than a preset activity level threshold.

[0167] The candidate cell set construction unit is used to identify the candidate cell with the highest cell service activity among the identified candidate cells as the seed cell, and to construct a high-traffic candidate cell set based on the other candidate cells in each candidate cell besides the seed cell.

[0168] The paging area configuration device provided in this application embodiment can be used to execute the technical solutions in the above-described paging area configuration method embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0169] In one embodiment, busy-hour network operation data includes the number of cell connections; the activity determination unit is specifically used for:

[0170] Determine the total number of cell connections for all cells based on the number of cell connections in each cell.

[0171] The activity level of each community's services is determined based on the number of community connections and the total number of community connections.

[0172] The paging area configuration device provided in this application embodiment can be used to execute the technical solutions in the above-described paging area configuration method embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0173] In one embodiment, the paging area configuration device further includes: a measurement data acquisition module and a correlation determination module, wherein:

[0174] The measurement data acquisition module is used to acquire the first signal quality measurement data of the seed cell relative to each high traffic candidate cell, and the second signal quality measurement data of each high traffic candidate cell relative to the seed cell.

[0175] The correlation determination module is used to determine the correlation between each high-traffic candidate cell and the seed cell based on each first signal quality measurement data and each second signal quality measurement data.

[0176] The paging area configuration device provided in this application embodiment can be used to execute the technical solutions in the above-described paging area configuration method embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0177] In one embodiment, the second determining module 12 includes: a high traffic area determining unit, a candidate cell removal unit, and a loop execution unit, wherein:

[0178] The high traffic area determination unit is used to determine a target high traffic candidate cell as a high traffic area based on the correlation between each high traffic candidate cell and the seed cell. The correlation is greater than or equal to the first correlation threshold.

[0179] The candidate cell removal unit is used to remove the target high-traffic candidate cell from the high-traffic candidate cell set to obtain an updated high-traffic candidate cell set.

[0180] The loop execution unit is used to continue to determine high traffic areas based on the updated high traffic candidate cell set until the total number of high traffic candidate cells in the updated high traffic candidate cell set is less than or equal to a preset threshold, thus obtaining multiple high traffic areas.

[0181] The paging area configuration device provided in this application embodiment can be used to execute the technical solutions in the above-described paging area configuration method embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0182] In one embodiment, the loop execution unit is specifically used for:

[0183] Based on the total number of high-traffic candidate cells in the updated high-traffic candidate cell set, the first correlation threshold is adjusted to obtain the second correlation threshold; the second correlation threshold is less than the first correlation threshold.

[0184] A high-traffic candidate cell whose cluster correlation is greater than or equal to the second correlation threshold is identified as a high-traffic area.

[0185] The paging area configuration device provided in this application embodiment can be used to execute the technical solutions in the above-described paging area configuration method embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0186] In one embodiment, the configuration module 13 is specifically used for:

[0187] At least two high-traffic areas are obtained from multiple high-traffic areas and merged. The merged high-traffic area is configured as a first-level paging area. Each of the multiple high-traffic areas is treated as an independent paging area and configured as a second-level paging area.

[0188] The paging area configuration device provided in this application embodiment can be used to execute the technical solutions in the above-described paging area configuration method embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0189] In one embodiment, the configuration module 13 includes: an identification configuration unit, wherein:

[0190] The identification configuration unit is specifically used to configure a paging area identifier for each paging area according to the level to which each paging area belongs, after the paging area configuration is completed for the target network area; wherein, the paging area identifier is different for paging areas at different levels.

[0191] The paging area configuration device provided in this application embodiment can be used to execute the technical solutions in the above-described paging area configuration method embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0192] In one embodiment, the configuration module 13 includes: a priority configuration unit, wherein:

[0193] The priority configuration unit is specifically used to configure paging priority for each paging area according to the level to which each paging area belongs, after the paging area configuration is completed for the target network area; wherein, the paging priority indicates the order in which the network sends terminal paging messages to different paging areas; the paging priority of the first-level paging area is greater than the paging priority of the second-level paging area.

[0194] The paging area configuration device provided in this application embodiment can be used to execute the technical solutions in the above-described paging area configuration method embodiments of this application. Its implementation principle and technical effect are similar, and will not be repeated here.

[0195] Specific limitations regarding the paging area configuration device can be found in the limitations of the paging area configuration method described above, and will not be repeated here. Each module in the aforementioned paging area configuration device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0196] In one embodiment, a computer device is provided, the internal structure of which can be as follows: Figure 10 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides processing power. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores busy-hour network operation data for each cell within the target network area. The network interface communicates with external endpoints via a network connection. When executed by the processor, the computer program implements a paging area configuration method.

[0197] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0198] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0199] Based on the busy-hour network operation data of each cell in the target network area, seed cells and high-traffic candidate cell sets are determined from each cell; the seed cell represents the cell with the highest service activity among all cells.

[0200] Based on the correlation between each high traffic candidate cell and the seed cell in the high traffic candidate cell set, multiple high traffic areas in the target network area are determined;

[0201] Configure paging areas for the target network area based on multiple high-traffic areas within the target network area.

[0202] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, the computer program performing the following steps when executed by a processor:

[0203] Based on the busy-hour network operation data of each cell in the target network area, seed cells and high-traffic candidate cell sets are determined from each cell; the seed cell represents the cell with the highest service activity among all cells.

[0204] Based on the correlation between each high traffic candidate cell and the seed cell in the high traffic candidate cell set, multiple high traffic areas in the target network area are determined;

[0205] Configure paging areas for the target network area based on multiple high-traffic areas within the target network area.

[0206] In one embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, performs the following steps:

[0207] Based on the busy-hour network operation data of each cell in the target network area, seed cells and high-traffic candidate cell sets are determined from each cell; the seed cell represents the cell with the highest service activity among all cells.

[0208] Based on the correlation between each high traffic candidate cell and the seed cell in the high traffic candidate cell set, multiple high traffic areas in the target network area are determined;

[0209] Configure paging areas for the target network area based on multiple high-traffic areas within the target network area.

[0210] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0211] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0212] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A paging area configuration method, characterized by, The method includes: Based on the busy-hour network operation data of each cell in the target network area, seed cells and a set of high-traffic candidate cells are determined from each of the cells; the seed cell represents the cell with the highest service activity among the cells. Based on the correlation between each high traffic candidate cell in the high traffic candidate cell set and the seed cell, multiple high traffic areas in the target network area are determined; At least two high-traffic areas are obtained from the plurality of high-traffic areas and merged, and the merged high-traffic area is configured as a first-level paging area; and each of the plurality of high-traffic areas is treated as an independent paging area and configured as a second-level paging area.

2. The method of claim 1, wherein, The step of determining seed cells and a high-traffic candidate cell set from each cell based on busy-hour network operation data of each cell within the target network area includes: Based on the busy-hour network operation data of each of the aforementioned cells, the activity level of cell services in each of the aforementioned cells is determined; For any cell, if the cell service activity level of the cell is greater than the preset activity level threshold, then the cell is identified as a call traffic candidate cell. The candidate cell with the highest service activity among the identified candidate cells is designated as the seed cell, and the high traffic candidate cell set is constructed based on the other candidate cells in each candidate cell besides the seed cell.

3. The method of claim 2, wherein, The busy-hour network operation data includes the number of cell connections; determining the cell service activity level of each cell based on the busy-hour network operation data includes: The total number of cell connections for all cells is determined based on the number of cell connections for each cell. The activity level of each cell service is determined based on the number of cell connections and the total number of cell connections in each cell.

4. The method according to any one of claims 1 to 3, characterized in that, Before determining multiple high-traffic areas in the target network area based on the correlation between each high-traffic candidate cell in the high-traffic candidate cell set and the seed cell, the method further includes: Acquire first signal quality measurement data of the seed cell relative to each of the high traffic candidate cells, and second signal quality measurement data of each of the high traffic candidate cells relative to the seed cell; Based on the first signal quality measurement data and the second signal quality measurement data, the correlation between each high traffic candidate cell and the seed cell is determined.

5. The method according to any one of claims 1-3, characterized in that, The step of determining multiple high-traffic areas in the target network area based on the correlation between each high-traffic candidate cell in the high-traffic candidate cell set and the seed cell includes: Based on the correlation between each high traffic candidate cell and the seed cell, the target high traffic candidate cell with a correlation greater than or equal to the first correlation threshold is determined as a high traffic area; The target high-traffic candidate cell is removed from the high-traffic candidate cell set to obtain an updated high-traffic candidate cell set; Based on the updated high traffic candidate cell set, high traffic areas are further determined until the total number of high traffic candidate cells in the updated high traffic candidate cell set is less than or equal to a preset threshold, thus obtaining multiple high traffic areas.

6. The method according to claim 5, characterized in that, The step of further determining high-traffic areas based on the updated high-traffic candidate cell set includes: Based on the total number of high-traffic candidate cells in the updated high-traffic candidate cell set, the first correlation threshold is adjusted to obtain a second correlation threshold; the second correlation threshold is less than the first correlation threshold. The target high traffic candidate cell whose central correlation degree is greater than or equal to the second correlation degree threshold after the update is determined as a high traffic area.

7. The method according to claim 1, characterized in that, The method further includes: When paging area configuration is completed for the target network area, a paging area identifier is configured for each paging area according to the level to which each paging area belongs; wherein, the paging area identifier is different for paging areas at different levels.

8. The method according to claim 1, characterized in that, The method further includes: When paging area configuration is completed for the target network area, paging priority is configured for each paging area according to the level to which each paging area belongs; wherein, the paging priority indicates the order in which the network sends terminal paging messages to different paging areas; the paging priority of the first-level paging area is greater than the paging priority of the second-level paging area.

9. A paging area configuration device, characterized in that, The device includes: The first determining module is used to determine a seed cell and a high-traffic candidate cell set from each cell based on the busy-hour network operation data of each cell in the target network area; the seed cell represents the cell with the highest service activity among all the cells. The second determining module is used to determine multiple high-traffic areas in the target network area based on the correlation between each high-traffic candidate cell in the high-traffic candidate cell set and the seed cell; The configuration module is used to obtain at least two high traffic areas from multiple high traffic areas and merge them, and configure the merged high traffic area as a first-level paging area; and to configure each of the multiple high traffic areas as an independent paging area and configure it as a second-level paging area.

10. A computer device comprising a transceiver, a memory, and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-8.

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