WO2021190261A1 - 子网簇划分方法、网络设备及存储介质 - Google Patents

子网簇划分方法、网络设备及存储介质 Download PDF

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WO2021190261A1
WO2021190261A1 PCT/CN2021/078793 CN2021078793W WO2021190261A1 WO 2021190261 A1 WO2021190261 A1 WO 2021190261A1 CN 2021078793 W CN2021078793 W CN 2021078793W WO 2021190261 A1 WO2021190261 A1 WO 2021190261A1
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subnet
distance
cells
cluster
division
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PCT/CN2021/078793
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English (en)
French (fr)
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杜春梅
吕沙沙
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • This application relates to the field of communications, for example, to a method for dividing a subnet cluster, a network device, and a storage medium.
  • wireless terminal equipment With the development and growth of wireless networks, wireless terminal equipment is gradually increasing. People's work and life are increasingly dependent on the network, and the requirements for network quality are getting higher and higher. The maintenance and optimization faced by wireless equipment vendors The challenge of the network is getting bigger and bigger. Intelligent operation and maintenance and network self-optimization can reduce personnel input and optimize network quality. Automatic optimization of wireless parameters is an important topic in network self-optimization. Due to the complexity of network deployment, the adjustment of wireless parameters of a cell will affect the surrounding cells. The adjustment of such parameters requires the adjustment of closely-connected cells. The optimal effect is the collaborative optimization of the community. However, because the wireless network is too large, it is almost impossible to coordinate and optimize all the cells at the same time.
  • the wireless network needs to be divided into multiple subnet clusters, so that the subnet cluster can be used as a unit for cell collaborative optimization during the network self-optimization process.
  • the method is that the network operation and maintenance personnel divide the subnet clusters according to the geographic locations of multiple cells in the wireless network, and the cells with close geographic distances will be divided into the same subnet cluster, but this manual division method not only requires higher Labor cost and time cost, and the accuracy of the division result is not high, which easily affects the effect of subsequent network self-optimization.
  • the subnet cluster division method, network equipment, and storage medium provided in the embodiments of the present application solve the problem that the subnet cluster division scheme in the related art requires a lot of human resources and the division accuracy is not high.
  • An embodiment of the present application provides a method for dividing subnet clusters, which includes: for each of a plurality of cells in a target area, respectively determining the overlap coverage of other cells in the target area in each cell, where the target area is The area to be divided into subnet clusters; the degree of association between every two cells in the target area is determined according to the overlap coverage; the division sub-process is executed, and the division sub-process includes: dividing the cells with the correlation degree greater than or equal to the associated fuse threshold into In the same subnet cluster.
  • the embodiments of the present application also provide a network device.
  • the network device includes a processor, a memory, and a communication bus; the communication bus is set to realize the connection and communication between the processor and the memory; the processor is set to execute one or more Procedures to realize the above-mentioned subnet cluster division method.
  • An embodiment of the present application further provides a storage medium, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above subnet cluster division method.
  • FIG. 1 is a flowchart of a method for dividing a subnet cluster provided in Embodiment 1 of this application;
  • FIG. 2 is a schematic diagram of an association degree network shown in Embodiment 1 of this application;
  • Fig. 3 is a schematic diagram of a network device fusing the correlation degree network in Fig. 2 according to the correlation fuse threshold;
  • Fig. 4 is a schematic diagram of the division result of subnet cluster division for the correlation network in Fig. 2;
  • FIG. 5 is another flowchart of the method for dividing a subnet cluster provided in Embodiment 1 of this application;
  • Figure 6 is a flow chart of the network equipment receiving the manual intervention of the network management personnel on the division result
  • FIG. 7 is a flow chart for evaluating the division result of the most recent division sub-process by the network device in the second embodiment of the application.
  • FIG. 8 is a flowchart of a method for dividing a subnet cluster provided in Embodiment 3 of this application;
  • FIG. 9 is a schematic diagram of an association degree network shown in Embodiment 3 of this application.
  • Fig. 10 is a schematic diagram of a division result of subnet cluster division for the correlation network in Fig. 9;
  • FIG. 11 is a schematic diagram of another division result of subnet cluster division for the correlation network in FIG. 9;
  • Fig. 12a is a schematic diagram of the first division result obtained by network administrators after manual intervention on the division result in Fig. 11;
  • Fig. 12b is a schematic diagram of the second division result obtained by the network administrator after manual intervention on the division result in Fig. 11;
  • Fig. 12c is a schematic diagram of the third division result obtained by network administrators after manual intervention on the division result in Fig. 11;
  • Fig. 12d is a schematic diagram of the fourth division result obtained by the network administrator after manual intervention on the division result in Fig. 11;
  • FIG. 13 is a schematic diagram of a hardware structure of a network device provided in Embodiment 4 of this application.
  • this embodiment provides a subnet cluster division method, which is applied to network equipment. It is a network device in a base station or a network device in a network management system. Please refer to the flowchart of the subnet cluster division method shown in Figure 1 below:
  • the network device For each of the multiple cells in the target area, the network device separately determines the overlap coverage of other cells in the target area in each cell.
  • the target area refers to the area where the subnet cluster is currently to be divided. It can refer to the area corresponding to the entire network or the area corresponding to a subnet. In other examples of this embodiment , The target area can also be an area covered by a base station.
  • the network device may first determine the overlap coverage of other cells in the cell for each cell in the target cell.
  • the overlap coverage of cell A in cell B refers to the ratio of the signal coverage area of cell A in cell B to the total signal coverage area of cell B itself.
  • the overlap coverage of cell B in cell A refers to the cell The ratio of the signal coverage area of B in cell A to the total signal coverage of cell A itself.
  • overlapping coverage of cell A in the cell B is cell A and cell B means a signal coverage area overlap area S overlap ratio of the total B cell coverage area B, S, B cell coverage overlap in the cell A degree, refers to cell a and cell B overlap area coverage ratio of the total area S overlapping signal coverage area of cell a is S a.
  • the network equipment needs to determine the overlapping coverage of cells B, C, D, and E in cell A; B.
  • the network equipment needs to determine the overlap coverage of cells A, C, D, and E in cell B...
  • the network equipment needs to determine the overlap coverage of cells A, B, C, and D in cell E. .
  • the network device For each cell in the target area, the network device separately determines the overlap coverage value of other cells in the cell.
  • the cell currently selected by the network device is referred to herein as the “target cell”.
  • the network device is not required to determine the overlapping coverage of all other cells in the target cell for the target cell before reselecting a new target cell. That is, this embodiment does not limit the order in which the network device determines the overlap coverage of one cell in another cell. Therefore, in this case, "the network device separately determines the overlap coverage of the cell in other cells in the target area for each cell in the target area" is different from the description in S102, but the essence is the same.
  • S104 The network device determines the degree of association between every two cells in the target area according to the overlap coverage.
  • the network equipment For each cell in the target area, the network equipment separately determines the overlap coverage of other cells in the target area, or in other words, the network equipment determines the coverage of each cell in the target area in other cells. After overlapping coverage, the network device may determine the degree of association between every two cells in the target area according to the determined multiple overlapping coverages. In an example of this embodiment, for any two cells whose association degree is to be determined, the network device will obtain the overlap coverage of the two cells in the other cell, and then calculate the overlap between the two cells in the other cell. The sum of the coverage is used as the degree of association between the two cells. In some other examples of this embodiment, the degree of association between the two cells may be the average value of the overlap coverage of the two cells in the other cell.
  • the network equipment selects the overlap coverage Ratio_B nbr A srv of cell A in cell B from the predetermined overlap coverage and cell B in Overlap coverage Ratio_A nbr B srv in cell A, and then calculate the correlation between cell A and cell B according to the formula:
  • Ratio_AB (Ratio_B nbr A srv +Ratio_A nbr B srv )/2;
  • Ratio_AB is the degree of association between cell A and cell B.
  • the network device divides the cells whose correlation degree is greater than or equal to the correlation fuse threshold into the same subnet cluster.
  • the network device After the network device determines the degree of association between every two cells in the target area, it can determine whether one cell can be divided into the same subnet cluster with another cell according to the associated fuse threshold.
  • the associated fuse threshold is set by the network administrator according to the distribution characteristics of the cells in the target area, such as cell density. In other examples, the associated fuse threshold may also be preset in the network device by the developer of the network device according to experience values.
  • the network equipment divides the cells with a degree of correlation greater than or equal to the associated fuse threshold into the same subnet cluster. For those cells with a degree of correlation less than the associated fuse threshold, the association between the two will be "fuse", so Will belong to different subnet clusters. For example, assuming that the associated fuse threshold is 0.3, the network equipment is determined by calculation, and the correlation between the five cells of A, B, C, D, and E is shown in Table 1:
  • the network equipment can “fuse” cells A and C, cells A and D, cells A and E, and cell B through the “fuse” test of the associated fuse threshold.
  • the associations with C, cells B and E, cells C and D, and cells D and E are shown in Figure 3, so that cells A, B, and D can be divided into subnet cluster a, and cells C and E Divided into subnet cluster b, as shown in Figure 4.
  • the network device can not only automatically realize the division of the subnet cluster, but also avoids the dependence on labor in the subnet cluster division process, and saves labor costs.
  • the network equipment divides the subnet cluster, it determines the degree of association between multiple cells based on the overlap coverage of each two cells in the target area with each other, and then determines the degree of association according to the determined degree of association
  • the subnet cluster division method provided in the embodiments of the present application, it is possible to Divide the cells that affect each other's wireless parameter adjustments into the same subnet cluster, so as to provide an accurate basis and reliable foundation for subsequent network self-optimization, which is conducive to improving the effect of network self-optimization and enhancing the quality of wireless communication.
  • the network device after obtaining the subnet cluster division result based on the subnet cluster division process shown in FIG. 1, the network device also evaluates the division result to determine whether the subnet cluster division result meets the evaluation requirements If it does not meet the evaluation requirements, it means that the current associated fuse threshold setting used to divide the subnet clusters is not reasonable enough and needs to be adjusted. After adjusting the associated fuse threshold, the network device can continue to re-divide the subnet clusters according to the process of S106 in FIG. 1. For ease of introduction, the process of S106 in FIG. 1 will be referred to as the "division sub-process" in the following, and another flowchart of the subnet cluster division method is shown in FIG. 5:
  • the network device For each of the multiple cells in the target area, the network device separately determines the overlap coverage of other cells in the target area in each cell.
  • S504 The network device determines the degree of association between every two cells in the target area according to the overlap coverage.
  • S506 The network device divides the cells whose correlation degree is greater than or equal to the correlation fuse threshold into the same subnet cluster.
  • the associated fuse threshold on which the network device divides the subnet cluster may be preset by the network administrator or the developer.
  • the associated fuse threshold value on which the network device is based is the value obtained after adjusting the associated fuse threshold used in the previous division sub-process.
  • S508 The network device evaluates whether the division result of the most recent division sub-process meets the evaluation requirements based on the range of multiple subnet clusters.
  • the network device evaluates the division result of the most recent division sub-process based on the range of multiple subnet clusters. In this way, among the division results that are judged to meet the evaluation requirements after this evaluation, there are many in the same subnet cluster. Not only does each cell meet the requirements in terms of overlapping coverage, but multiple cells also meet the evaluation requirements in terms of geographic distance.
  • S510 is executed to re-divide the target area into subnet clusters after adjusting the associated fuse threshold.
  • S510 The network device adjusts the associated fuse threshold.
  • the network device After the network device adjusts the associated fuse threshold, the network device will continue to perform S506, which loops until the division result of the most recent division sub-process of the network device evaluation meets the evaluation requirements.
  • the division result can also be output to the network administrator, allowing the network administrator to manually intervene the division result as needed, for example, Figure 6 shows the flow after the division result in Figure 5 is determined to meet the evaluation requirements:
  • S604 The network device receives an adjustment instruction for the division result.
  • S606 The network device adjusts the division result according to the adjustment instruction.
  • the network administrator can split a subnet cluster into one or more subnet clusters, or merge two or more subnet clusters into one, or , It can divide the cells in one subnet cluster into another subnet cluster.
  • the subnet cluster division method provided in this embodiment performs subnet cluster division based on the overlapping coverage of multiple cells in the target area, so that the division result is more in line with the actual situation of mutual influence of wireless parameters between multiple cells, and at the same time
  • the division result can also be evaluated based on the range of the subnet cluster, and when the division result is determined to not meet the evaluation requirements, the associated fuse threshold is adjusted adaptively, and the new associated fuse threshold is re-adjusted.
  • the subnet clusters are divided until the subnet clusters that meet the assessment requirements are finally divided.
  • This embodiment mainly uses an example to describe the process in which the network device evaluates the division result of the most recent division sub-process based on the range of multiple subnet clusters, please refer to FIG. 7.
  • the network device For each subnet cluster in the multiple subnet clusters obtained by dividing, the network device separately determines the distance between every two cells in the multiple cells in each subnet cluster, and according to every two cells in each subnet cluster The distance of determines the representative distance of each subnet cluster.
  • the network device evaluates the division result based on the distance between the cells in the divided subnet cluster. For any one of the multiple subnet clusters, the network device can query to obtain the latitude and longitude of multiple cells, and then determine the distance between every two cells in the multiple cells. After determining the distance between every two of the multiple cells in any subnet cluster, the network device can select a representative distance for the subnet cluster based on these distances.
  • the network device may directly select the maximum value of the multiple distances corresponding to a subnet cluster as the representative distance of the subnet cluster. For example, assuming that the maximum distance between two cells in subnet cluster a is the distance d AD between cells A and D, then the representative distance da corresponding to subnet cluster a is d AD . Since there are only two cells in the subnet cluster b, namely, cells C and E, the representative distance d b of the subnet cluster b is the distance d cE between the cells C and E.
  • the network device may calculate the average value of multiple distances in a subnet cluster as the representative distance of the subnet cluster. For example, suppose that one subnet cluster c includes four cells of cells A, C, E, and F, and another subnet cluster d includes cells B, G, H, M, and N, the representative distance of subnet cluster c:
  • the network device determines the representative distance for a subnet cluster by taking the average value, then the network device should also use the average value when determining the representative distance of other subnet clusters; if the network device When determining the representative distance for a subnet cluster, it is performed by taking the maximum value. Then, when determining the representative distance of other subnet clusters, it should also be performed by taking the maximum value.
  • the network device determines the evaluation distance used to evaluate the division result according to the representative distances of the multiple subnet clusters.
  • the network device After the network device determines the representative distances of the multiple subnet clusters, it can determine an evaluation distance based on the representative distances, and the evaluation distance is used to evaluate the quality of the result of this division. Similar to the method of determining representative distances for multiple subnet clusters, the network device can directly select the maximum value of the multiple representative distances as the evaluation distance of the result of this division. Or in some other examples of this embodiment, the network device may also use the average value of multiple representative distances as the evaluation distance of the result of this division.
  • the manner in which the network device determines the evaluation distance is not affected by the manner in which it determines the representative distance for the subnet cluster.
  • the manner in which the network device determines the evaluation distance is not required to be consistent with the manner in which the representative distance is determined, so In some examples, it is possible that the network device uses the average value when determining the representative distance, but uses the maximum value when determining the estimated distance.
  • S706 The network device compares the estimated distance with a preset standard distance interval, and judges whether the estimated distance is outside the standard distance interval.
  • the network device After determining the evaluation distance for the latest new division result, the network device compares the evaluation distance with a preset standard distance interval, and judges whether the evaluation distance is within the standard distance interval.
  • the standard distance interval can be set by the network administrator according to the characteristics of the target area. For example, if the density of the cells in the target area is high, the upper and lower limits of the standard distance interval will be set to be smaller overall. If the density of the cells in the target area For example, if the target area is an open field, the upper and lower limits of the standard distance range will be set a little larger overall.
  • the network administrator may not need to specify the upper and lower limits of the standard distance interval, but input a standard distance to the network device, and then the network device determines the upper limit of the standard distance interval according to a preset determination method.
  • the lower limit For example, in an example of this embodiment, the upper limit of the standard distance interval is 10% above the standard distance, and the lower limit is 10% below the standard distance. Then when the network equipment obtains the standard set by the network administrator After the distance, the corresponding upper and lower limits can be calculated.
  • S708 The network device determines that the division result meets the evaluation requirement.
  • the network device determines that the evaluation distance is within the standard distance interval (including the case where the evaluation distance is equal to the upper or lower limit of the standard distance interval), the network device can determine that the division result obtained in the most recent division sub-process meets the evaluation requirements.
  • S710 The network device determines that the division result does not meet the evaluation requirement.
  • the network device determines that the evaluation distance is not within the standard distance range, the network device can determine that the division result obtained in the most recent division sub-process does not meet the evaluation requirements.
  • the network device Because when it is determined that the division result does not meet the evaluation requirements, the network device also needs to adjust the associated fuse threshold. Therefore, in this embodiment, when the network device determines that the division result corresponding to the most recent division sub-process does not meet the evaluation requirements, It is necessary to understand whether this non-compliance with the evaluation requirements is caused by the associated fusing threshold being too large or too small: If the evaluation distance is greater than the upper limit of the standard distance interval, it means that the current divided subnet cluster is too large. The cells that should not be divided into the same subnet cluster are grouped together. Therefore, it is necessary to "fuse" the association between these cells. The current association fuse threshold is not enough to "fuse" the association between these cells.
  • the same adjustment range (adjustment step length) is used to adjust the associated fusing threshold.
  • this fixed adjustment range may be preset by the network administrator.
  • the network device when the network device adjusts the associated fuse threshold, it will flexibly set the adjustment range according to the difference between the evaluation distance and the standard distance interval. For example, the network device will flexibly set the adjustment range according to the evaluation distance and the standard distance interval.
  • the minimum absolute difference value determines the adjustment range of the associated fusing threshold. If the evaluation distance is greater than the upper limit of the standard distance interval, the smallest absolute difference between the evaluation distance and the standard distance interval is the absolute difference between it and the upper limit of the standard distance interval, and the largest absolute difference between the evaluation distance and the standard distance interval is the absolute difference between it and the standard distance interval.
  • the estimated distance and the standard distance interval The maximum absolute difference is the absolute difference between it and the upper limit of the standard distance interval. It can be seen that the "minimum absolute difference" can represent the smallest gap between the latest division result and the evaluation requirements. The smaller the gap, the more cautious network equipment should be when adjusting the associated fuse threshold and choose a smaller adjustment. ; The larger the gap, the network equipment can choose a larger adjustment range when adjusting the associated fuse threshold, so that the evaluation distance of the division result can be entered into the standard distance range as soon as possible. Therefore, in some examples of this embodiment, the adjustment range of the associated fuse threshold by the network device is in a positive correlation with the magnitude of the "minimum absolute difference".
  • the network device may determine the representative distance that can represent each subnet cluster according to the distance between every two cells in each subnet cluster obtained by division, and then according to multiple The representative distance determines the evaluation distance that best reflects the result of the most recent division.
  • the evaluation distance is used to judge whether the division result of the most recent division sub-process meets the evaluation requirements. In this way, it is ensured that the final divided subnet clusters are not only in the The overlap coverage meets the requirements, and the range of multiple subnet clusters is reasonable, which avoids the high cost of network self-optimization caused by the excessively large subnet clusters, or the too small subnet clusters that make it impossible to coordinately optimize the cells that should be coordinated.
  • the problem of poor self-optimization effect takes into account the cost and effect of network self-optimization.
  • the adjustment range can be flexibly set according to the difference between the current evaluation distance and the standard distance interval, so as to ensure that the current evaluation distance is compared with the standard distance.
  • the minimum absolute difference of the distance interval is large, the evaluation distance can be adjusted to converge within the standard distance interval as soon as possible, reducing the number of process iterations and improving the efficiency of division.
  • the network device generates an association degree network corresponding to the target area according to the overlapping coverage between multiple cells in the target area.
  • Ratio_AB represents the degree of association between cell A and cell B
  • Ratio_B nbr A srv represents the coverage of cell A in cell B
  • Ratio_A nbr B srv represents the coverage of cell B in cell A:
  • Ratio_AB (Ratio_B nbr A srv +Ratio_A nbr B srv )/2;
  • Table 2 shows the degree of association among multiple cells in the target area:
  • the network equipment generates a weighted connection graph-type relevance network for the target area according to the relevance between the cells, as shown in Figure 9.
  • the network device fuses the association degree network into multiple subnet clusters according to the association fuse threshold.
  • Subnet cluster 1 AVGKLM ⁇
  • Subnet cluster 2 B ⁇
  • subnet cluster 3 CDEHIJN ⁇
  • S806 The network device calculates the distance of every two cells in each subnet cluster, and uses the largest distance among the distances of every two cells as the representative distance of the subnet cluster.
  • the network equipment uses the latitude and longitude of the cell to calculate the pairwise distance between every two cells in the subnet cluster, and uses the distance between the two furthest cells in the subnet cluster as the representative distance of the subnet cluster.
  • the cell distances are as shown in Table 3:
  • S808 The network device finds the largest representative distance from the representative distances of all subnet clusters as the evaluation distance.
  • S810 The network device judges whether the evaluation distance is within the standard distance interval.
  • the standard distance preset by the network administrator is 2000 m, and a fluctuation of 10% above and below the standard distance is used as the standard distance interval. Therefore, the upper limit of the standard distance interval is 2200m, the lower line is 1800m, and the standard distance interval is [1800,2200].
  • the network device adjusts the associated fusing threshold according to the relationship between the evaluation distance and the standard distance interval.
  • the network device uses a fixed adjustment step (adjustment range) of 10% to adjust the associated fuse threshold.
  • the evaluation distance> the upper limit of the standard distance interval it means that the divided single subnet cluster is too large, then the associated fuse threshold is adjusted upward by one step, and the new associated fuse threshold after adjustment is 0.22; when the evaluation distance ⁇ the upper and lower limits of the standard distance interval, It means that the divided single subnet cluster is too small, then the associated fuse threshold is adjusted downward by one step, and the new associated fuse threshold after adjustment is 0.18.
  • the network device executes S804 according to the adjusted associated fuse threshold.
  • the network equipment will increase the associated fuse threshold, and the adjusted associated fuse threshold is 0.22. After re-fusing the associated network according to 0.22, the classification results are as follows :
  • the division result can be output, and the process can be ended or manual intervention can be received after the output.
  • the currently automatically divided subnet clusters are: subnet cluster 1 ⁇ AFK ⁇ , subnet cluster 2 ⁇ B ⁇ , subnet cluster 3 ⁇ G ⁇ , subnet cluster 4 ⁇ CH ⁇ , subnet cluster 5 ⁇ DIJN ⁇ , Net cluster 6 ⁇ LM ⁇ , subnet cluster 7 ⁇ O ⁇ , and subnet cluster 8 ⁇ E ⁇ .
  • the network administrator can re-split or merge the cells in the subnet cluster to adjust, for example, merge the subnet clusters, as shown in Figure 12a, the subnet cluster division result obtained after manual intervention is: subnet cluster 1 ⁇ AFK ⁇ , subnet cluster 2 ⁇ BG ⁇ , subnet cluster 3 ⁇ CH ⁇ , subnet cluster 4 ⁇ DEIJN ⁇ , subnet cluster 5 ⁇ LM ⁇ , and subnet cluster 6 ⁇ O ⁇ .
  • the network administrator can also split the subnet cluster into: subnet cluster 1 ⁇ AFK ⁇ , subnet cluster 2 ⁇ B ⁇ , subnet cluster 3 ⁇ G ⁇ , and subnet cluster 4 ⁇ CH ⁇ ,Subnet cluster 5 ⁇ DIJ ⁇ , Subnet cluster 6 ⁇ E ⁇ , Subnet cluster 7 ⁇ LM ⁇ , Subnet cluster 8 ⁇ N ⁇ , Subnet cluster 9 ⁇ O ⁇ , see Figure 12b for the new division result.
  • the network administrator can split and merge the subnet clusters at the same time.
  • the result of subnet cluster division is: subnet cluster 1 ⁇ AFK ⁇ , subnet cluster 2 ⁇ BG ⁇ , subnet cluster Network cluster 3 ⁇ CH ⁇ , subnet cluster 4 ⁇ DEI ⁇ , subnet cluster 5 ⁇ J ⁇ , subnet cluster 6 ⁇ LM ⁇ , and subnet cluster 7 ⁇ NO ⁇ .
  • the new division result is shown in Figure 12c.
  • the network administrator can adjust the cells in one subnet cluster to another subnet cluster, and the subnet cluster division result obtained after manual intervention is: subnet cluster 1 ⁇ AF ⁇ , subnet cluster 2 ⁇ BG ⁇ , subnet cluster 3 ⁇ CH ⁇ , subnet cluster 4 ⁇ DEI ⁇ , subnet cluster 5 ⁇ J ⁇ , subnet cluster 6 ⁇ LMK ⁇ , subnet cluster 7 ⁇ NO ⁇ , as shown in Figure 12d .
  • the wireless communication system is a huge network, it is difficult for the network self-optimization function to consider the optimization effect of the entire network, and the network needs to be divided into subnet clusters.
  • the method of dividing subnet clusters based on overlapping coverage of the cell provided in this embodiment can solve the time-consuming, laborious, and inaccurate problem of manually dividing subnet clusters.
  • automatic network equipment Perform subnet cluster division, but network administrators can control the effect of subnet cluster division by setting evaluation criteria such as standard distance intervals to meet the needs of different scenarios.
  • the division of subnet clusters can be automatically completed after the network administrator completes the task setting, so as to achieve the purpose of improving the operation and maintenance efficiency and reducing the operation and maintenance investment.
  • This embodiment provides a storage medium that can store one or more computer programs that can be read, compiled, and executed by one or more processors.
  • the storage medium can store The subnet cluster division program can be used by one or more processors to execute the process of implementing any one of the subnet cluster division methods introduced in the foregoing embodiments.
  • the network device 13 includes a processor 131, a memory 132, and a communication bus 133 for connecting the processor 131 and the memory 132.
  • the memory 132 may be the aforementioned storage device.
  • the processor 131 may read the subnet cluster division program, compile and execute the process for implementing the subnet cluster division method introduced in the foregoing embodiment:
  • the processor 131 For each of the multiple cells in the target area, the processor 131 respectively determines the overlap coverage of other cells in the target area in each cell, and then determines the difference between each two cells in the target area according to the overlap coverage. The degree of relevance between. Subsequently, the processor 131 executes a division sub-process, and the division sub-process includes: the processor 131 divides the cells whose correlation degree is greater than or equal to the correlation fuse threshold into the same subnet cluster.
  • the processor 131 when the processor 131 determines the degree of association between every two cells in the target area according to the overlap coverage, for any two cells for which the degree of association is to be determined, the processor can obtain the relationship between each other. The overlap coverage in the cell is calculated, and then the average value of the overlap coverage of the any two cells in each other's cell is calculated as the correlation degree between the any two cells.
  • the processor 131 after the processor 131 executes the division sub-process, it also evaluates the division result of the most recent division sub-process based on the range of multiple subnet clusters; if the evaluation result is that the division result does not meet the evaluation requirements, Then the processor 131 adjusts the associated fuse threshold, and re-executes the division sub-process.
  • the processor 131 when the processor 131 evaluates the division result of the most recent division sub-process based on the range of the multiple subnet clusters, it may determine that every two subnet clusters in each of the multiple subnet clusters obtained by the division are evaluated. The distance between the cells, and the representative distance of each sub-network cluster is determined according to the distance between every two cells in each sub-network cluster, and then the evaluation distance for evaluating the division result is determined according to the representative distance of the multiple sub-network clusters. The processor 131 compares the evaluation distance with a preset standard distance interval; if the evaluation distance is outside the standard distance interval, the processor 131 determines that the division result does not meet the evaluation requirement.
  • the processor 131 when the processor 131 determines the representative distance of each subnet cluster according to the distance between every two cells in each subnet cluster, it can select the distance between every two cells in each subnet cluster. The largest distance in is used as the representative distance of each subnet cluster. In some other examples of this embodiment, the processor 131 may also determine the average value of the distance between every two cells in each subnet cluster as the representative distance of each subnet cluster.
  • the processor 131 when the processor 131 determines the evaluation distance for evaluating the division result according to the representative distances of the multiple subnet clusters, it may select the largest distance among the representative distances of the multiple subnet clusters as the division result. Evaluate the distance. In some other examples of this embodiment, the processor 131 may also determine the average value of the representative distances of multiple subnet clusters as the evaluation distance of the division result.
  • the processor 131 when the processor 131 adjusts the associated fusing threshold, if the evaluation distance is higher than the upper limit of the standard distance interval, the processor 131 increases the associated fusing threshold; if the evaluation distance is less than the lower limit of the standard distance interval, the processor 131 Lower the associated fusing threshold.
  • the processor 131 when the processor 131 adjusts the associated fusing threshold, it determines the adjustment range of the associated fusing threshold according to the minimum absolute difference between the evaluation distance and the standard distance interval, and the adjustment range is positive to the minimum absolute difference. relationship.
  • the processor 131 After the processor 131 evaluates the division result of the most recent division sub-process based on the range of multiple subnet clusters, if the evaluation result is that the division result meets the evaluation requirements, the processor 131 displays the division result and receives The adjustment instruction of the division result, and then the division result is adjusted according to the adjustment instruction.
  • the network device provided in the embodiment can not only automatically realize the division of subnet clusters, but also avoids dependence on labor in the process of subnet cluster division, and saves labor costs.
  • the network equipment divides the subnet cluster, it determines the degree of association between multiple cells based on the overlap coverage of each two cells in the target area with each other, and then determines the degree of association according to the determined degree of association
  • the subnet cluster division method provided in the embodiments of the present application, it is possible to Divide the cells that affect each other's wireless parameter adjustments into the same subnet cluster, so as to provide an accurate basis and reliable foundation for subsequent network self-optimization, which is conducive to improving the effect of network self-optimization and enhancing the quality of wireless communication.
  • Network equipment can use the size of the evaluation distance to judge whether the division result of the most recent division sub-process meets the evaluation requirements. In this way, it is ensured that the multiple subnet clusters finally divided not only meet the requirements in terms of overlapping coverage, but also multiple subnet clusters.
  • the range is reasonable, which avoids the problem of too large subnet clusters leading to high cost of network self-optimization, or too small subnet clusters to make it impossible to coordinately optimize cells that should be coordinated, and the network self-optimization effect is poor, taking into account the network self-optimization The cost and effect.
  • Computer-readable media may include computer storage media (or non-transitory media) and communication media (or transitory media).
  • computer storage medium includes volatile and non-volatile data implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Sexual, removable and non-removable media.
  • Computer storage media include but are not limited to Random Access Memory (RAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory, EEPROM) , Flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), Digital Video Disk (DVD) or other optical storage, magnetic cartridges, magnetic tapes, magnetic disk storage or other magnetic A storage device, or any other medium that can be used to store desired information and can be accessed by a computer.
  • communication media usually contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as carrier waves or other transmission mechanisms, and may include any information delivery media. . Therefore, this application is not limited to any specific combination of hardware and software.

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Abstract

本申请实施例提供一种子网簇划分方法、网络设备及存储介质。所述方法包括：对于目标区域中的多个小区中的每个小区，分别确定所述目标区域中其他小区在所述每个小区中的重叠覆盖度，所述目标区域为待进行子网簇划分的区域；根据所述重叠覆盖度确定所述目标区域中每两个小区之间的关联度；执行划分子流程，所述划分子流程包括：将所述关联度大于或等于关联熔断门限的小区划分到同一子网簇中。

Description

子网簇划分方法、网络设备及存储介质 技术领域
本申请涉及通信领域，例如涉及一种子网簇划分方法、网络设备及存储介质。
背景技术
随着无线网络的发展与壮大，无线终端设备逐步的增多，人们的工作与生活对网络的依赖越来越大，对网络质量的要求也越来越高，无线设备商所面临的维护和优化网络的挑战也越来越大。智能运维与网络自优化能够减少人员投入，并使网络质量达到最优。无线参数自动优化就是网络自优化中重要的一个课题，而由于网络部署的复杂性，一个小区无线参数的调整会对周围小区造成影响，这类参数的调整就需要联系紧密的小区一起调整才能达到最优的效果，这就是小区协同优化。但是由于无线网络太大，同时对所有小区一起协同优化几乎是不可能的事情。所以需要将无线网络划分为多个子网簇，以便在网络自优化过程中能够以子网簇为单位进行小区协同优化。做法是由网络运维人员按照无线网络中多个小区的地理位置来进行子网簇划分，地理位置距离近的小区将被划分到同一子网簇，但这种人工划分方式不仅要求较高的人力成本与时间成本，而且划分结果准确率也不高，容易影响后续网络自优化的效果。
发明内容
本申请实施例提供的子网簇划分方法、网络设备及存储介质，解决了相关技术中子网簇划分方案需要耗费大量人力资源，且划分准确率不高的问题。
本申请实施例提供一种子网簇划分方法，包括：对于目标区域中的多个小区中的每个小区，分别确定目标区域中其他小区在所述每个小区中的重叠覆盖度，目标区域为待进行子网簇划分的区域；根据重叠覆盖度确定目标区域中每两个小区之间的关联度；执行划分子流程，划分子流程包括：将关联度大于或等于关联熔断门限的小区划分到同一子网簇中。
本申请实施例还提供一种网络设备，网络设备包括处理器、存储器及通信总线；通信总线设置为实现处理器和存储器之间的连接通信；处理器设置为执行存储器中存储的一个或者多个程序，以实现上述子网簇划分方法。
本申请实施例还提供一种存储介质，存储介质存储有一个或者多个程序，一个或者多个程序可被一个或者多个处理器执行，以实现上述子网簇划分方法。
附图说明
图1为本申请实施例一中提供的子网簇划分方法的一种流程图；
图2为本申请实施例一中示出的一个关联度网络的示意图；
图3为网络设备根据关联熔断门限熔断图2中关联度网络的一种示意图；
图4为针对图2中关联度网络进行子网簇划分的划分结果示意图；
图5为本申请实施例一中提供的子网簇划分方法的另一种流程图；
图6为网络设备接收网管人员对划分结果人工干预的一种流程图；
图7为本申请实施例二中网络设备评估最近一次划分子流程划分结果的一种流程图；
图8为本申请实施例三中提供的子网簇划分方法的一种流程图；
图9为本申请实施例三中示出的一个关联度网络的示意图；
图10为针对图9中关联度网络进行子网簇划分的一种划分结果示意图；
图11为针对图9中关联度网络进行子网簇划分的另一种划分结果示意图；
图12a为网管人员针对图11中划分结果进行人工干预后得到第一种划分结果示意图；
图12b为网管人员针对图11中划分结果进行人工干预后得到第二种划分结果示意图；
图12c为网管人员针对图11中划分结果进行人工干预后得到第三种划分结果示意图；
图12d为网管人员针对图11中划分结果进行人工干预后得到第四种划分结果示意图；
图13为本申请实施例四中提供的网络设备的一种硬件结构示意图。
具体实施方式
下面通过实施方式结合附图对本申请实施例进行说明。应当理解，此处所描述的实施例仅仅用以解释本申请，并不用于限定本申请。
实施例一：
为了解决相关子网簇划分方案对人力成本要求高，划分结果不准确，导致网络自优化效果不佳的问题，本实施例提供一种子网簇划分方法，应用于网络设备上，该网络设备可以是基站中的网络设备，也可以是网管系统中的网络设 备，下面请参见图1示出的子网簇划分方法的流程图：
S102：对于目标区域中的多个小区中的每个小区，网络设备分别确定目标区域中其他小区在所述每个小区中的重叠覆盖度。
在本实施例中，目标区域是指当前待进行子网簇划分的区域，其可以是指全网对应的区域，也可以是一子网所对应的区域，在本实施例的另外一些示例当中，目标区域还可以是一基站所覆盖的区域。
为了对目标区域中的多个小区进行子网簇划分，在本实施例中，网络设备可以先针对目标小区中的每一个小区，确定其他小区在该小区的重叠覆盖度。小区A在小区B中的重叠覆盖度，是指小区A在小区B中信号覆盖区域的面积与小区B本身的总信号覆盖面积的比值，小区B在小区A中的重叠覆盖度，就是指小区B在小区A中信号覆盖区域的面积与小区A本身的总信号覆盖面积的比值。简单来说，小区A在小区B中的重叠覆盖度是指小区A与小区B信号覆盖重叠区域面积S 重叠与小区B的总信号覆盖面积S B的比值，小区B在小区A中的重叠覆盖度，是指小区A与小区B信号覆盖重叠区域面积S 重叠与小区A的总信号覆盖面积S A的比值。
例如，假定当前目标区域中有A、B、C、D、E五个小区，则针对小区A，网络设备需要分别确定小区B、C、D、E在小区A中的重叠覆盖度；针对小区B，网络设备需要分别确定小区A、C、D、E在小区B中的重叠覆盖度……针对小区E，网络设备需要分别确定小区A、B、C、D在小区E中的重叠覆盖度。
网络设备针对目标区域中的每个小区，分别确定其他小区在该小区中的重叠覆盖度值，为了便于介绍，这里将网络设备当前选择的小区称为“目标小区”。在本实施例中，并不要求网络设备一定要先针对该目标小区确定出其他所有小区在该目标小区中的重叠覆盖度之后，才能再重新选择一个新的目标小区。也即，本实施例中并不限定网络设备确定一个小区在另一个小区中重叠覆盖度的顺序。所以在这种情况下，“网络设备针对目标区域中的每个小区，分别确定该小区在目标区域中其他小区中的重叠覆盖度”与S102中的描述方式虽然不同，但实质是一样的。
S104：网络设备根据重叠覆盖度确定目标区域中每两个小区之间的关联度。
网络设备针对目标区域中的每个小区，在分别确定出目标区域中其他小区在该小区内的重叠覆盖度之后，或者说，网络设备在分别确定出目标区域中每个小区在其他小区中的重叠覆盖度之后，网络设备可以根据确定出的多个重叠覆盖度确定目标区域中每两个小区之间的关联度。在本实施例的一种示例当中，对于任意两个待确定关联度的小区，网络设备会获取这两个小区在对方小区中 的重叠覆盖度，然后，计算两个小区彼此在对方小区中重叠覆盖度的和值作为两个小区之间的关联度。在本实施例的另外一些示例当中，两个小区之间的关联度可以是这两个小区在对方小区中重叠覆盖度的均值。例如，假定计算关联度的两个小区分别是小区A与小区B，则网络设备从预先确定出的重叠覆盖度中选择出小区A在小区B中的重叠覆盖度Ratio_B nbrA srv和小区B在小区A中的重叠覆盖度Ratio_A nbrB srv，然后根据公式计算小区A与小区B的关联度：
Ratio_AB＝(Ratio_B nbrA srv+Ratio_A nbrB srv)/2；
Ratio_AB是小区A与小区B的关联度。
S106：网络设备将关联度大于或等于关联熔断门限的小区划分到同一子网簇中。
一个小区与另一个小区间关联度的值越大，则表征这两个小区在无线参数调整方面的关联越紧密，因此越应当被划分到同一子网簇中以便后续进行协同优化。两个小区间关联度的值越小，则说明调整这两个小区中的一个的无线参数对另一个的影响很小，二者可以不必进行协同优化，也就不必被划分到同一子网簇中。
网络设备确定出目标区域中每两个小区间的关联度之后，可以根据关联熔断门限来确定一个小区是否能与另一个小区划分到同一子网簇中。在本实施例的一些示例当中，关联熔断门限是由网管人员根据目标区域内小区的分布特点，例如小区密度等设置。在另一些示例当中，关联熔断门限也可以是由网络设备的开发人员根据经验值预先设置在网络设备当中的。本实施例中网络设备将彼此关联度大于或等于关联熔断门限的小区划分在同一子网簇中，对于那些关联度小于关联熔断门限的小区，二者之间的关联会被“熔断”，因此会分属于不同的子网簇。例如，假定关联熔断阈值为0.3，网络设备经过计算确定，A、B、C、D、E五个小区彼此之间的关联度如表1所示：
表1
Ratio_AB Ratio_AC Ratio_AD Ratio_AE Ratio_BC
0.33 0.2 0.09 0.29 0.25
Ratio_BD Ratio_BE Ratio_CD Ratio_CE Ratio_DE
0.4 0.11 0.05 0.37 0.21
所以，这五个小区形成的关联度网络如图2所示，网络设备通过关联熔断门限的“熔断”检验，可以“熔断”小区A与C、小区A与D、小区A与E、 小区B与C、小区B与E、小区C与D、小区D与E之间的关联，如图3所示，从而可以将小区A、B以及D划分到子网簇a当中，将小区C与E划分到子网簇b中，如图4。
在本申请实施例提供的子网簇划分方法中，网络设备不仅能够自动实现子网簇划分，避免子网簇划分过程对人工的依赖，节约人力成本。同时，因为网络设备在进行子网簇划分的时候，是基于目标区域中每两个小区彼此在对方小区中的重叠覆盖度来确定多个小区之间的关联度，然后根据确定出的关联度来将关联度较大的小区划分到一个子网簇中，而重叠覆盖度能够准确体现小区与小区在无线参数上的关联程度，所以，通过本申请实施例提供的子网簇划分方法，能够将彼此无线参数调整会对对方带来影响的小区划分在同一子网簇，从而为后续网络自优化提供准确的依据与可靠的基础，有利于提升网络自优化的效果，增强无线通信质量。
在本实施例的一些示例当中，在基于图1示出的子网簇划分流程得到子网簇划分结果之后，网络设备还会对划分结果进行评估，以确定子网簇划分结果是否符合评估要求，如果不符合评估要求，则说明当前用于划分子网簇的关联熔断门限设置的不够合理，需要进行调整。调整关联熔断门限之后，网络设备可以继续按照图1中S106的流程重新进行子网簇划分。为了便于介绍，后续将图1中S106的流程称为“划分子流程”，在图5中示出了子网簇划分方法的另一种流程图：
S502：对于目标区域中的多个小区中的每个小区，网络设备分别确定目标区域中其他小区在所述每个小区中的重叠覆盖度。
S504：网络设备根据重叠覆盖度确定目标区域中每两个小区之间的关联度。
S506：网络设备将关联度大于或等于关联熔断门限的小区划分到同一子网簇中。
对于S502-S504中划分子流程的细节，可以参见前面的介绍，这里不再赘述。
在初次执行划分子流程的过程中，网络设备进行子网簇划分时所依据的关联熔断门限可以是由网管人员或者是开发人员预先设置的。在后续执行划分子流程的过程中，网络设备所依据的关联熔断门限值则是对前一划分子流程中所用关联熔断门限进行调整之后得到的值。
S508：网络设备基于多个子网簇的范围评估最近一次划分子流程的划分结果是否符合评估要求。
在本实施例中，网络设备会基于多个子网簇的范围来评估最近一次划分子 流程的划分结果，这样，经此评估并被判定为符合评估要求的划分结果中，同一子网簇中多个小区不仅在重叠覆盖度上满足要求，而且多个小区在地理距离方面也符合评估要求。
如果判断结果为最近一次划分子流程的划分结果符合评估要求，则结束流程，即可以以最近一次划分子流程的划分结果作为最终的划分结果，如果判断结果为最近一次划分子流程的划分结果不符合评估要求，则执行S510，调整关联熔断门限之后重新对目标区域进行子网簇的划分。
S510：网络设备调整关联熔断门限。
网络设备调整关联熔断门限之后，网络设备将继续执行S506，以此循环，直至网络设备评估最近一次划分子流程的划分结果符合评估要求为止。
在本实施的一些示例当中，在网络设备通过划分子流程划分出符合评估要求的子网簇之后，还可以将划分结果输出给网管人员，让网管人员根据需要对划分结果进行人工干预，例如，在图6中示出了图5中确定划分结果符合评估要求后的流程：
S602：网络设备显示划分结果。
S604：网络设备接收针对划分结果的调整指令。
S606：网络设备根据调整指令对划分结果进行调整。
在网络设备显示出最终的划分结果之后，网管人员可以将一个子网簇拆分成一个甚至更多的子网簇，或者是将两个、甚至更多的子网簇合并为一个，又或者，其可以将一个子网簇中的小区划分到另外一个子网簇中。
允许网管人员对划分结果进行人工干预，主要是为了适应一些区域的区域特点，避免一些有特殊情况的区域也只能按照统一的方式进行子网簇划分，从而导致划分结果不适应该区域网络自优化实际需求的问题。
本实施例提供的子网簇划分方法，一方面基于目标区域中多个小区的重叠覆盖度来进行子网簇划分，从而让划分结果更符合多个小区间无线参数相互影响的实际情况，同时，该子网簇划分方法中，还可以基于子网簇的范围对划分结果进行评估，并在确定划分结果不符合评估要求的时候自适应调整关联熔断门限，并重新按照新的关联熔断门限进行子网簇划分，直至最终划分出符合评估要求的子网簇为止。这样网管人员仅需要按照需求设置出合适的评估要求，就能够让网络设备按照评估要求为自己“定制”出合适的子网簇，显著提升了子网簇划分结果的准确性与网络自优化的效果。
实施例二：
本实施例主要结合示例对网络设备基于多个子网簇的范围评估最近一次划分子流程的划分结果的过程进行说明，请参见图7。
S702：对于划分得到的多个子网簇中的每个子网簇，网络设备分别确定所述每个子网簇中多个小区中每两个小区的距离，并根据每个子网簇中每两个小区的距离确定所述每个子网簇的代表距离。
在本实施例中，网络设备基于所划分得到的子网簇中小区与小区的距离来评估划分结果。对于多个子网簇中的任意一个，网络设备可以查询获取其中多个小区的经纬度，然后确定多个小区中每两个小区间的距离。确定出任意一个子网簇中多个小区中每两个小区间的距离之后，网络设备可以根据这些距离为该子网簇选择一个代表距离。
在本实施例的一些示例，网络设备可以直接选择一个子网簇所对应的多个距离中的最大值作为该子网簇的代表距离。例如，假定子网簇a中两个小区间的最大的距离是小区A与D之间的距离d AD，那么子网簇a对应的代表距离d a就是d AD。子网簇b中因为仅有两个小区，即小区C和E，那么该子网簇b的代表距离d b就是小区C和E之间的距离d cE。
在本实施例的另外一些示例当中，网络设备可以计算一个子网簇中多个距离的均值作为该子网簇的代表距离。例如，假定一个子网簇c中包括小区A、C、E、F四个小区，而另一个子网簇d中小区B、G、H、M、N，则子网簇c的代表距离：
Figure PCTCN2021078793-appb-000001
则子网簇d的代表距离：
Figure PCTCN2021078793-appb-000002
如果网络设备在为一个子网簇确定代表距离的时候是按照取均值的方式进行的，那么该网络设备在确定其他子网簇的代表距离时，也应当以取均值的方式计算；如果网络设备在为一个子网簇确定代表距离的时候是通过取最大值的方式进行的，那么其在确定其他子网簇的代表距离时，也应当以取最大值的方式进行。
S704：网络设备根据多个子网簇的代表距离确定用于评估划分结果的评估距离。
网络设备在确定出多个子网簇的代表距离之后，可以根据这些代表距离确定出一个评估距离，该评估距离用以评估本次划分结果的好坏。和为多个子网簇确定代表距离的方式类似，网络设备可以直接选择多个代表距离中的最大值 作为本次划分结果的评估距离。或者在本实施例的另外一些示例当中，网络设备也可以将多个代表距离的均值作为本次划分结果的评估距离。
网络设备确定评估距离的方式并不受其为子网簇确定代表距离的方式的影响，换言之在本实施例中，并不要求网络设备确定评估距离的方式与确定代表距离的方式保持一致，所以，在一些示例中，可能网络设备在确定代表距离的时候采用的是取均值的方式，而在确定评估距离的时候却采用的是取最大值的方式。
S706：网络设备将评估距离与预设的标准距离区间进行比较，判断评估距离是否在标准距离区间之外。
确定出针对最近新划分结果的评估距离之后，网络设备将该评估距离与预设的标准距离区间进行比较，判断该评估距离是否在标准距离区间之内。标准距离区间可以由网管人员根据目标区域的特点设置，例如，如果目标区域中小区的密度大，则标准距离区间上、下限的值整体就会被设置偏小一些，如果目标区域中小区的密度小，例如目标区域是空旷的野外，则标准距离区间上、下限的值整体就会被设置偏大一些。在本实施例的一些示例当中，网管人员可以不用自己指定标准距离区间的上、下限，而是向网络设备输入一个标准距离，然后由网络设备自己根据预先设置确定方式确定出标准距离区间的上、下限，例如，在本实施例的一种示例当中，标准距离区间的上限是标准距离上偏10％，而下限则是标准距离下偏10％，那么当网络设备获取到网管人员设置的标准距离之后，就可以计算出对应的上、下限了。
S708：网络设备判定划分结果符合评估要求。
如果网络设备确定评估距离在标准距离区间内(包含评估距离等于标准距离区间上限或下限的情况)，则网络设备可以判定最近一次划分子流程得到的划分结果符合评估要求。
S710：网络设备判定划分结果不符合评估要求。
如果网络设备确定评估距离不在标准距离区间内，则网络设备可以判定最近一次划分子流程得到的划分结果不符合评估要求。
因为当判定划分结果不符合评估要求的时候，网络设备还需要对关联熔断门限进行调整，所以，在本实施例中，网络设备在确定最近一次划分子流程对应的划分结果不符合评估要求时，需要了解造成这种不符合评估要求的情况出现的原因到底是由于关联熔断门限偏大还是偏小：如果评估距离大于标准距离区间的上限，则说明当前所划分的子网簇的范围过大，将本不应当划分到同一子网簇中的小区划分到了一起，因此，需要“熔断”这些小区之间的关联，当 前的关联熔断门限不足以“熔断”这些小区之间的关联，所以需要在当前关联熔断门限的基础上增大关联熔断门限，从而使得这些小区间的关联度小于关联熔断门限；如果评估距离小于标准距离区间的下限，则说明当前所划分的子网簇的范围太小，需要对目标区域中的小区做进一步的聚集，当前有一些小区间的关联本不应当被“熔断”却被“熔断”了，所以需要在当前关联熔断门限的基础上减小关联熔断门限，从而使得这些小区间的关联度大于或等于关联熔断门限，从而被划分到同一子网簇中。
在本实施例的一些示例当中，无论针对关联熔断门限的调整方向是上调还是下调，无论当前评估距离与标准距离区间的差距有多大，都采用同样的调整幅度(调整步长)调整关联熔断门限。在一些示例当中，这种固定的调整幅度可以是由网管人员预先设置的。
在本实施例的另外一些示例当中，网络设备在对关联熔断门限进行调整的时候，会根据评估距离与标准距离区间的差距大小来灵活设置调整幅度，例如，网络设备根据评估距离与标准距离区间的最小绝对差值确定对关联熔断门限的调整幅度。如果评估距离大于标准距离区间的上限，则评估距离与标准距离区间最小的绝对差值是其与标准距离区间上限间的绝对差值，评估距离与标准距离区间最大的绝对差值是其与标准距离区间下限间的绝对差值；如果评估距离小于标准距离区间的下限，则评估距离与标准距离区间最小的绝对差值是其与标准距离区间下限间的绝对差值，评估距离与标准距离区间最大的绝对差值是其与标准距离区间上限间的绝对差值。可见，“最小绝对差值”能够表征最近一次划分结果与评估要求间的最小差距，这个差距越小，则网络设备在调整关联熔断门限的时候也应当更谨慎，选择以较小的幅度进行调整；差距越大，则网络设备在调整关联熔断门限的时候就可以选择更大的调整幅度，以便能够尽快让划分结果的评估距离进入到标准距离区间中。所以，在本实施例的一些示例中，网络设备对关联熔断门限的调整幅度同“最小绝对差值”的大小成正相关关系。
本申请实施例提供的子网簇划分方法中，网络设备可以根据划分得到的每个子网簇中每两个小区的距离确定出能够代表所述每个子网簇的代表距离，然后再根据多个代表距离确定出最能够体现最近一次划分结果的评估距离，利用评估距离的大小来评判最近一次划分子流程的划分结果是否符合评估要求，通过这种方式确保最终划分出的多个子网簇不仅在重叠覆盖度方面符合要求，而且多个子网簇的范围合理，避免了子网簇过大导致网络自优化代价高，或者是子网簇过小导致无法对本应协同优化的小区进行协同优化、网络自优化效果差的问题，兼顾了网络自优化的代价与效果。
根据本实施例提供的子网簇划分方法，网络设备在对关联熔断门限进行调整的时候，可以根据当前评估距离与标准距离区间的差距大小来灵活设置调整幅度，从而保证在当前评估距离与标准距离区间的最小绝对差值较大时，能够通过调整让评估距离尽快收敛于标准距离区间内，减少流程迭代次数，提升划分效率。
实施例三：
为了使本领域技术人员能够清楚前述实施例中子网簇划分方法的优点与细节，本实施例将结合示例继续对该子网簇划分方案进行说明，请参见图8示出的流程图：
S802：网络设备根据目标区域中多个小区间的重叠覆盖度生成目标区域对应的关联度网络。
网络设备查询目标区域中小区间的重叠覆盖度，然后计算小区间的关联程度。在本实施例中，Ratio_AB代表小区A与小区B之间的关联度，Ratio_B nbrA srv代表小区A在小区B的覆盖度，Ratio_A nbrB srv代表小区B在小区A的覆盖度：
Ratio_AB＝(Ratio_B nbrA srv+Ratio_A nbrB srv)/2；
表2示出了目标区域中多个小区间的关联度：
表2
Ratio_AB Ratio_AG Ratio_AF Ratio_BG Ratio_BC Ratio_BH Ratio_CG
0.1 0.2 0.3 0.1 0.15 0.06 0.09
Ratio_CH Ratio_CD Ratio_CI Ratio_DE Ratio_DI Ratio_DJ Ratio_EI
0.3 0.2 0.15 0.1 0.18 0.3 0.3
Ratio_EJ Ratio_FG Ratio_FK Ratio_FL Ratio_GH Ratio_GL Ratio_GM
0.2 0.02 0.3 0.15 0.1 0.2 0.06
Ratio_HI Ratio_HM Ratio_HN Ratio_IM Ratio_IN Ratio_IJ Ratio_JN
0.2 0.1 0.2 0.09 0.15 0.3 0.23
Ratio_JO Ratio_KL Ratio_LM Ratio_MN Ratio_NO    
0.16 0.09 0.3 0.1 0.08    
网络设备根据小区间的关联度针对目标区域生成加权连通图型的关联度网 络，如图9所示。
S804：网络设备根据关联熔断门限将关联度网络熔断成多个子网簇。
例如，假定初始的关联熔断门限被设置为0.2，网络设备按此关联熔断门限对关联度网络进行分割后形成4个子网簇为，如图10：子网簇1{AFGKLM}，子网簇2{B}，子网簇3{CDEHIJN}，子网簇4{O}。
S806：网络设备计算每个子网簇内每两个小区的距离，将每两个小区的距离中最大的距离作为该子网簇的代表距离。
例如网络设备使用小区的经纬度计算出子网簇内每两个小区间的两两距离，将子网簇内最远的两小区的距离作为该子网簇的代表距离，假定子网簇1中的小区距离分别为如表3所示：
表3
Distance_AF Distance_AG Distance_AK Distance_AL Distance_AM
2000 3000 1500 800 1200
Distance_FG Distance_FK Distance_FL Distance_FM Distance_GK
1400 1300 1900 2100 900
Distance_GL Distance_GM Distance_KL Distance_KM Distance_LM
1000 2100 900 1800 1500
所以，子网簇1的代表距离
d1＝Max{Distance_AF,Distance_AG,Distance_AK,Distance_AL,Distance_AM,Distance_FG,Distance_FK,Distance_FL,Distance_FM,Distance_GK,Distance_GL,Distance_GM,Distance_KL,Distance_KM,Distance_LM}＝3000m。
通过类似的方式，可以确定子网簇2的代表距离d2＝0，子网簇3的代表距离d3＝1800m，子网簇4的代表距离d4＝0。
S808：网络设备从所有子网簇的代表距离中找到最大的代表距离作为评估距离。
评估距离＝Max{d1,d2,d3,d4}＝3000m。
S810：网络设备判断评估距离是否在标准距离区间内。
在本实施例中，假定网管人员预先设置的标准距离为2000m，且采用标准距离上下10％的浮动作为标准距离区间。因此，标准距离区间上限为2200m， 下线为1800m，标准距离区间为[1800,2200]。
S812：网络设备根据评估距离与标准距离区间的关系调整关联熔断门限。
在本实施例中，网络设备采用固定的调整步长(调整幅度)10％来调整关联熔断门限。
当评估距离>标准距离区间上限，说明划分的单个子网簇过大，那么将关联熔断门限向上调整一个步长，调整后新的关联熔断门限为0.22；当评估距离<标准距离区间上下限，说明划分的单个子网簇过小，那么将关联熔断门限向下调整一个步长，调整后新的关联熔断门限为0.18。
调整关联熔断门限之后，网络设备按照调整后的关联熔断门限执行S804。
在上述示例当中，因为评估距离3000m大于标准距离区间上限，所以，网络设备会对关联熔断门限进行上调，调整后的关联熔断门限为0.22，按照0.22对关联度网络进行重新熔断后，划分结果如下：
子网簇1{AFK},子网簇2{B},子网簇3{G},子网簇4{CH},子网簇5{DIJN},子网簇6{LM},子网簇7{O},子网簇8{E}。
假定重新划分后评估距离为2000m，评估距离在标准距离区间[1800,2200]内，则因此结束划分，最终划分完成的子网簇如图11所示：
子网簇1{AFK},子网簇2{B},子网簇3{G},子网簇4{CH},子网簇5{DIJN},子网簇6{LM},子网簇7{O},子网簇8{E}。
S814：输出划分结果并接收人工对划分结果的干预。
当评估距离处于标准距离区间内，说明划分的单个子网簇大小合适，那么可以将划分结果输出，结束流程或者在输出之后接收人工干预。
假定网管人员要求对子网簇划分结果进行人工干预：
当前自动划分的子网簇为：子网簇1{AFK},子网簇2{B},子网簇3{G},子网簇4{CH},子网簇5{DIJN},子网簇6{LM},子网簇7{O},子网簇8{E}。
在一种示例中，网管人员可以重新进行拆分或合并子网簇中的小区进行调整，例如合并子网簇，如图12a，人工干预之后得到的子网簇划分结果为：子网簇1{AFK},子网簇2{BG},子网簇3{CH},子网簇4{DEIJN},子网簇5{LM},子网簇6{O}。
在另一种示例中，网管人员也可以将子网簇拆分为：子网簇1{AFK},子网簇2{B},子网簇3{G},子网簇4{CH},子网簇5{DIJ},子网簇6{E}，子网簇7{LM},子网簇8{N},子网簇9{O}，新的划分结果请参见图12b。
在第三种示例当中，网管人员可以将子网簇同时进行拆分与合并，人工干预之后得到的子网簇划分结果为：子网簇1{AFK},子网簇2{BG},子网簇3{CH},子网簇4{DEI}，子网簇5{J},子网簇6{LM},子网簇7{NO}，新的划分结果请参见图12c。
在第四种示例当中，网管人员可以将一子网簇中的小区调整到另一个子网簇中，人工干预之后得到的子网簇划分结果为：子网簇1{AF},子网簇2{BG},子网簇3{CH},子网簇4{DEI}，子网簇5{J},子网簇6{LMK},子网簇7{NO}，如图12d所示。
虽然无线通信系统是一个庞大的网络，网络自优化功能很难做到整网考虑优化效果，需要把网络划分成一个一个的子网簇。但本实施例提供的这种小区基于重叠覆盖度划分子网簇的方法，可以解决人工划分子网簇的费时费力，且不准确的问题，通过智能化划分与人工控制相结合：网络设备自动执行子网簇划分，但网管人员可以通过设置标准距离区间等评估标准来控制子网簇划分的效果，满足不同场景的需求。采用本实施例中的方案，可以在网管人员设置完任务之后，全自动完成子网簇的划分，达到提升运维效率，减少运维投入的目的。
实施例四：
本实施例提供一种存储介质，该存储介质中可以存储有一个或多个可供一个或多个处理器读取、编译并执行的计算机程序，在本实施例中，该存储介质可以存储有子网簇划分程序，子网簇划分程序可供一个或多个处理器执行实现前述实施例介绍的任意一种子网簇划分方法的流程。
本实施例中还提供一种网络设备，如图13所示：网络设备13包括处理器131、存储器132以及用于连接处理器131与存储器132的通信总线133，其中存储器132可以为前述存储有子网簇划分程序的存储介质。处理器131可以读取子网簇划分程序，进行编译并执行实现前述实施例中介绍的子网簇划分方法的流程：
对于目标区域中的多个小区中的每个小区，处理器131分别确定目标区域中其他小区在所述每个小区中的重叠覆盖度，然后根据重叠覆盖度确定目标区域中每两个小区之间的关联度。随后，处理器131执行划分子流程，划分子流程包括：处理器131将关联度大于或等于关联熔断门限的小区划分到同一子网簇中。
在本实施例的一种示例当中，处理器131根据重叠覆盖度确定目标区域中每两个小区之间的关联度时，对于待确定关联度的任意两个小区，处理器可以 获取彼此在对方小区中的重叠覆盖度，然后计算所述任意两个小区彼此在对方小区中重叠覆盖度的均值作为所述任意两个小区之间的关联度。
在本实施例的一些示例当中，处理器131执行划分子流程之后，还会基于多个子网簇的范围对最近一次划分子流程的划分结果进行评估；若评估结果为划分结果不符合评估要求，则处理器131调整关联熔断门限，并重新执行划分子流程。
在一实施例中，处理器131基于多个子网簇的范围对最近一次划分子流程的划分结果进行评估时，可以分别确定对于划分得到的多个子网簇中的每个子网簇中每两个小区的距离，并根据每个子网簇中每两个小区的距离确定所述每个子网簇的代表距离，然后根据多个子网簇的代表距离确定用于评估划分结果的评估距离。处理器131将评估距离与预设的标准距离区间进行比较；若评估距离在标准距离区间之外，则处理器131判定划分结果不符合评估要求。
在本实施例的一些示例当中，处理器131根据每个子网簇中每两个小区的距离确定所述每个子网簇的代表距离时，其可以选择每个子网簇中每两个小区的距离中最大的一个距离作为所述每个子网簇的代表距离。在本实施例的另外一些示例当中，处理器131也可以确定所述每个子网簇中每两个小区的距离的均值作为所述每个子网簇的代表距离。
在本实施例的一些示例当中，处理器131根据多个子网簇的代表距离确定用于评估划分结果的评估距离时，其可以选择多个子网簇的代表距离中最大的一个距离作为划分结果的评估距离。在本实施例的另外一些示例当中，处理器131也可以确定多个子网簇的代表距离的均值作为划分结果的评估距离。
在一实施例中，处理器131调整关联熔断门限时，如果评估距离高于标准距离区间的上限，则处理器131增大关联熔断门限，如果评估距离小于标准距离区间的下限，则处理器131降低关联熔断门限。
在本实施例的一些示例当中，处理器131调整关联熔断门限时会根据评估距离与标准距离区间的最小绝对差值确定对关联熔断门限的调整幅度，且调整幅度与最小绝对差值的大小成正相关关系。
在一实施例中，处理器131基于多个子网簇的范围对最近一次划分子流程的划分结果进行评估之后，若评估结果为划分结果符合评估要求，则处理器131显示划分结果，并接收针对划分结果的调整指令，然后根据调整指令对划分结果进行调整。
在实施例提供的网络设备不仅能够自动实现子网簇划分，避免子网簇划分过程对人工的依赖，节约人力成本。同时，因为网络设备在进行子网簇划分的 时候，是基于目标区域中每两个小区彼此在对方小区中的重叠覆盖度来确定多个小区之间的关联度，然后根据确定出的关联度来将关联度较大的小区划分到一个子网簇中，而重叠覆盖度能够准确体现小区与小区在无线参数上的关联程度，所以，通过本申请实施例提供的子网簇划分方法，能够将彼此无线参数调整会对对方带来影响的小区划分在同一子网簇，从而为后续网络自优化提供准确的依据与可靠的基础，有利于提升网络自优化的效果，增强无线通信质量。
网络设备可以利用评估距离的大小来评判最近一次划分子流程的划分结果是否符合评估要求，通过这种方式确保最终划分出的多个子网簇不仅在重叠覆盖度方面符合要求，而且多个子网簇的范围合理，避免了子网簇过大导致网络自优化代价高，或者是子网簇过小导致无法对本应协同优化的小区进行协同优化、网络自优化效果差的问题，兼顾了网络自优化的代价与效果。
本领域的技术人员应该明白，上文中所公开方法中的全部或一些步骤、系统、装置中的功能模块/单元可以被实施为软件(可以用计算装置可执行的程序代码来实现)、固件、硬件及其适当的组合。在硬件实施方式中，在以上描述中提及的功能模块/单元之间的划分不一定对应于物理组件的划分；例如，一个物理组件可以具有多个功能，或者一个功能或步骤可以由多个物理组件合作执行。一些物理组件或所有物理组件可以被实施为由处理器，如中央处理器、数字信号处理器或微处理器执行的软件，或者被实施为硬件，或者被实施为集成电路，如专用集成电路。这样的软件可以分布在计算机可读介质上，由计算装置来执行，并且在一些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的，术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于随机存取存储器(Random Access Memory，RAM),只读存储器(Read-Only Memory，ROM),电可擦可编程只读存储器(Electrically Erasable Programmable Read-Only Memory，EEPROM)、闪存或其他存储器技术、只读光盘驱动器(Compact Disc Read-Only Memory，CD-ROM)，数字多功能盘(Digital Video Disk，DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。此外，本领域普通技术人员公知的是，通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据，并且可包括任何信息递送介质。所以，本申请不限制于任何特定的硬件和软件结合。

Claims (11)

  1. 一种子网簇划分方法，包括：
    对于目标区域中的多个小区中的每个小区，分别确定所述目标区域中其他小区在所述每个小区中的重叠覆盖度，所述目标区域为待进行子网簇划分的区域；
    根据所述重叠覆盖度确定所述目标区域中每两个小区之间的关联度；
    执行划分子流程，所述划分子流程包括：将所述关联度大于或等于关联熔断门限的小区划分到同一子网簇中。
  2. 如权利要求1所述的子网簇划分方法，其中，所述根据所述重叠覆盖度确定所述目标区域中每两个小区之间的关联度包括：
    对于待确定关联度的任意两个小区，获取彼此在对方小区中的重叠覆盖度；
    计算所述任意两个小区彼此在对方小区中重叠覆盖度的均值作为所述任意两个小区之间的关联度。
  3. 如权利要求1或2所述的子网簇划分方法，所述执行划分子流程之后，还包括：
    基于多个子网簇的范围对最近一次划分子流程的划分结果进行评估；
    在评估结果为所述划分结果不符合评估要求的情况下，调整所述关联熔断门限，并重新执行所述划分子流程。
  4. 如权利要求3所述的子网簇划分方法，其中，所述基于多个子网簇的范围对最近一次划分子流程的划分结果进行评估包括：
    确定划分得到的多个子网簇的每个子网簇中每两个小区的距离，并根据所述每个子网簇中每两个小区的距离确定所述每个子网簇的代表距离；
    根据所述多个子网簇的代表距离确定用于评估所述划分结果的评估距离；
    将所述评估距离与预设的标准距离区间进行比较；
    在所述评估距离在所述预设的标准距离区间之外的情况下，判定所述划分结果不符合评估要求。
  5. 如权利要求4所述的子网簇划分方法，其中，所述根据所述每个子网簇中每两个小区的距离确定所述每个子网簇的代表距离包括：
    选择所述每个子网簇中每两个小区的距离中最大的一个距离作为所述每个子网簇的代表距离；
    或，
    确定所述每个子网簇中每两个小区的距离的均值作为所述每个子网簇的代表距离。
  6. 如权利要求4所述的子网簇划分方法，其中，所述根据所述多个子网簇的代表距离确定用于评估所述划分结果的评估距离包括：
    选择所述多个子网簇的代表距离中最大的一个距离作为所述划分结果的评估距离；
    或，
    确定所述多个子网簇的代表距离的均值作为所述划分结果的评估距离。
  7. 如权利要求4所述的子网簇划分方法，其中，所述调整所述关联熔断门限包括：
    在所述评估距离高于所述预设的标准距离区间的上限的情况下，增大所述关联熔断门限；
    在所述评估距离小于所述预设的标准距离区间的下限的情况下，降低所述关联熔断门限。
  8. 如权利要求4所述的子网簇划分方法，其中，所述调整所述关联熔断门限包括：
    根据所述评估距离与所述预设的标准距离区间的最小绝对差值确定对所述关联熔断门限的调整幅度，且所述调整幅度与所述最小绝对差值的大小成正相关关系。
  9. 如权利要求3所述的子网簇划分方法，所述基于多个子网簇的范围对最近一次划分子流程的划分结果进行评估之后，还包括：
    在所述评估结果为所述划分结果符合评估要求的情况下，显示所述划分结果；
    接收针对所述划分结果的调整指令；
    根据所述调整指令对所述划分结果进行调整。
  10. 一种网络设备，包括处理器、存储器及通信总线；
    所述通信总线设置为实现处理器和存储器之间的连接通信；
    所述处理器设置为执行存储器中存储的至少一个程序，以实现如权利要求1至9中任一项所述的子网簇划分方法。
  11. 一种存储介质，存储有至少一个程序，所述至少一个程序可被至少一个处理器执行，以实现如权利要求1至9中任一项所述的子网簇划分方法。
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