CN112449369A - Method, device and equipment for identifying problem cell and computer storage medium - Google Patents

Method, device and equipment for identifying problem cell and computer storage medium Download PDF

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CN112449369A
CN112449369A CN201910804800.1A CN201910804800A CN112449369A CN 112449369 A CN112449369 A CN 112449369A CN 201910804800 A CN201910804800 A CN 201910804800A CN 112449369 A CN112449369 A CN 112449369A
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cell
index
same sector
capacity
cluster
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CN112449369B (en
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张灿淋
胡国峰
朱峰
姚志华
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China Mobile Communications Group Co Ltd
China Mobile Group Zhejiang Co Ltd
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China Mobile Communications Group Co Ltd
China Mobile Group Zhejiang Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports

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Abstract

The embodiment of the invention relates to the technical field of communication, and discloses a method, a device, equipment and a computer storage medium for identifying problem cells, wherein the method comprises the following steps: acquiring hardware information of RRU equipment; determining the same sector cell cluster according to the hardware information; acquiring state information of each cell in the same sector cell cluster; determining the state index of each cell according to the state information; determining the matching degree of the cell cluster with the same sector according to the state index of each cell; and identifying the problem cells in the same sector according to the matching degree. Through the mode, the embodiment of the invention can improve the timeliness and the accuracy of identifying the problem cells in the same sector.

Description

Method, device and equipment for identifying problem cell and computer storage medium
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a method, a device, equipment and a computer storage medium for identifying a problem cell.
Background
With the promotion of speed increasing and cost reducing of operators, the traffic of wireless networks is rapidly increased. An RRU (Radio Remote Unit) device usually has multiple frequency points added to one channel, where one channel of the RRU device usually corresponds to one sector and each frequency point corresponds to one cell. Therefore, by adding multiple frequency points to one channel of the RRU device, multiple cells can cover the same sector, that is, multiple communication cells overlap the same coverage area, thereby alleviating the conditions of intensive service and overload traffic in the coverage area. However, after frequent network optimization adjustment, parameter mismatch between cells in the same sector may be caused, which easily causes user perception to decrease.
In the process of implementing the embodiment of the present invention, the inventors found that: at present, the method for identifying the cells with the same sector, the parameters of which are not matched, mainly comprises the steps of collecting the telephone traffic index information of each cell and screening out the cells with abnormal indexes. And then, checking the hardware equipment information of the abnormal cells one by one to find out other frequency point cells in the same sector corresponding to the abnormal cells. However, important links such as telephone traffic statistics, abnormal point analysis, cell hardware information verification and the like are all completed manually, and a large number of manual operations avoid errors which are not introduced, so that the identification is inaccurate. Secondly, the current method only identifies the cells with abnormal traffic statistic indexes, but cannot identify the cells in the same sector with the indexes which are normal but still have larger differences. These cells tend to degrade the indicators due to the rise in traffic, so the hysteresis of the current approach is significant.
Disclosure of Invention
In view of the above, embodiments of the present invention provide an apparatus, device, and computer storage medium for identifying problem cells, which overcome or at least partially solve the above problems.
According to an aspect of the embodiments of the present invention, there is provided a method for identifying a problem cell, the method including: acquiring hardware information of RRU equipment; determining the same sector cell cluster according to the hardware information; acquiring state information of each cell in the same sector cell cluster; determining the state index of each cell according to the state information; determining the matching degree of the cell cluster with the same sector according to the state index of each cell; and identifying the problem cells in the same sector according to the matching degree.
In an optional manner, the determining the state index of each cell according to the state information specifically includes: calculating a coverage index, a capacity index and a quality index of each cell according to the state information; determining a coverage class mean value, a capacity class mean value and a quality class mean value of the cell cluster of the same sector according to the coverage class index, the capacity class index and the quality class index of each cell; multiplying the coverage index of each cell by a preset coverage weight value and dividing the coverage index by a coverage mean value to obtain a coverage index of each cell; multiplying the capacity index of each cell by a preset capacity weighted value and dividing the capacity index by a capacity mean value to obtain a capacity index of each cell; multiplying the quality index of each cell by a preset quality weight value and dividing the quality index by a quality mean value to obtain a quality index of each cell; and summing the coverage index, the capacity index and the quality index of each cell to obtain the state index of each cell.
In an optional manner, the calculating, according to the state information, a coverage index, a capacity index, and a quality index of each cell specifically includes:
calculating the downlink MR coverage rate and the uplink signal-to-noise ratio of each cell according to the cell MR data in the state information; wherein, the downlink MR coverage rate is the coverage index, and the uplink signal-to-noise ratio is the quality index; calculating the uplink PRB utilization rate of the cell busy period, the downlink PRB utilization rate of the cell busy period and the number of synchronous state users of the cell busy period of each cell according to the PRB utilization rate and the number of synchronous state users of the cell in the state information; and the uplink PRB utilization rate of the cell busy hour segment, the downlink PRB utilization rate of the cell busy hour segment and the synchronous state user number of the cell busy hour segment are all the capacity indexes.
In an optional manner, the capacity class index of each cell is obtained by multiplying the capacity class index of each cell by a preset capacity weight value and dividing by the capacity class mean value, and specifically includes: dividing the uplink PRB utilization rate of the cell in the busy period of each cell by the average value of the uplink PRB utilization rates in the capacity class average value and multiplying the average value by a first preset weight to obtain the uplink PRB utilization rate index of each cell; dividing the downlink PRB utilization rate of the cell busy period of each cell by the average value of the downlink PRB utilization rates in the capacity class average value and multiplying the average value by a second preset weight value to obtain the downlink PRB utilization rate index of each cell; dividing the number of synchronous state users in the busy period of each cell by the average number of synchronous state users in the capacity class average value and multiplying the average number by a third preset weight to obtain the index of the number of synchronous state users in each cell; and summing the uplink PRB utilization index, the downlink PRB utilization index and the synchronous state user number index of each cell and multiplying the sum by the preset capacity weighted value to obtain the capacity class index of each cell.
In an optional manner, the matching degree of the cell cluster in the same sector is determined according to the state index of each cell, specifically; calculating the standard deviation of the state index of each cell in the cell cluster of the same sector; determining the inverse of the standard deviation as the degree of match.
In an optional manner, the identifying the problem cell in the same sector according to the matching degree specifically includes: when the matching degree is smaller than a first preset threshold value, determining that the cell cluster in the same sector does not reach the standard; when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than a second preset number of days, determining that each community in the community cluster in the same sector is a problem community in the same sector, and sending warning information; wherein the second preset number of days is less than the first preset number of days.
In an optional manner, after the number of days that the co-sector cell cluster does not meet the standard in the first preset number of days is greater than a second preset number of days, determining that each cell in the co-sector cell cluster is a problem cell in the same sector, and sending warning information, the method further includes: when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than a third preset number of days, determining each community in the community cluster in the same sector as a serious community with the problem in the same sector, and sending serious alarm information; and the third preset number of days is greater than the second preset number of days and less than the first preset number of days.
According to another aspect of the embodiments of the present invention, there is provided an apparatus for identifying a problem cell, including: the first acquisition module is used for acquiring hardware information of the RRU equipment; a first determining module, configured to determine a cell cluster in the same sector according to the hardware information; a second obtaining module, configured to obtain state information of each cell in the cell cluster of the same sector; a second determining module, configured to determine a state index of each cell according to the state information; a third determining module, configured to determine a matching degree of the cell cluster in the same sector according to the state index of each cell; and the identification module is used for identifying the problem cells in the same sector according to the matching degree.
According to another aspect of the embodiments of the present invention, there is provided an apparatus for checking to identify a problem cell, including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus; the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the method for identifying the problem cell.
According to yet another aspect of the embodiments of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing the processor to perform a corresponding operation of identifying a problem cell as described above.
The embodiment of the invention determines the cell cluster with the same sector, namely the set of all frequency point cells belonging to the same channel of the same RRU equipment through the acquired hardware information of the RRU equipment. And then, determining the state index of each cell, namely parameters representing the conditions of coverage, capacity, quality and the like of wireless signals of each cell in the cell cluster with the same sector, according to the acquired state information of each cell in the cell cluster with the same sector. Finally, the matching degree of the cell cluster in the same sector can be obtained according to the state index of each cell, which represents the difference situation of the state index of each cell in the cell cluster in the same sector, i.e. the difference situation of the parameters of each cell can be reflected. Therefore, whether the parameters of the cells in the cell cluster with the same sector are matched or not can be determined according to the matching degree, and whether the cells in the cell cluster with the same sector are problem cells with the same sector or not can be determined. Compared with the prior art, the embodiment of the invention does not need manual operation, and avoids errors caused by human factors. Meanwhile, the embodiment of the invention directly identifies the cells of the same sector with unmatched parameters, but indirectly determines the cells of the same sector with unmatched parameters according to the abnormal condition of the traffic statistic index. Therefore, the method and the device can identify the cells with the same sector problem and the potential possibility of index degradation, and are higher in accuracy and timeliness.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a method for identifying a problem cell according to an embodiment of the present invention;
FIG. 2 is a flow diagram illustrating the substeps of determining a status index in an embodiment of the present invention;
FIG. 3 is a flow diagram illustrating sub-steps of calculating a coverage class indicator, a capacity class indicator, and a quality class indicator in an embodiment of the present invention;
FIG. 4 is a flow diagram illustrating sub-steps in calculating a capacity class index according to an embodiment of the present invention;
FIG. 5 is a flow diagram illustrating sub-steps in an embodiment of the present invention for identifying a co-sectored problem cell;
fig. 6 is a schematic structural diagram of an apparatus for identifying a problem cell according to an embodiment of the present invention;
fig. 7 shows a schematic structural diagram of an apparatus for identifying a problem cell according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
With the promotion of speed increasing and cost reducing of operators, the traffic cost is continuously reduced, and the traffic of the wireless network is rapidly increased. To address this high load challenge, many RRU devices add multiple frequency points to one channel to provide higher capacity guarantee, even if multiple cells overlap to cover the same sector, thereby increasing the capacity of the sector. However, the complicated networking topology and frequent network optimization adjustment often result in parameter mismatching between different cells of the same device. The parameters of the cell include a PCI (physical cell ID), a cell power, and the like. The configuration of these parameters may affect the state information such as coverage, capacity, and signal quality of the cell. For example, the PCI configuration may affect the degree to which a cell is interfered, i.e., affect the signal quality of the cell, while the power of the cell may affect the coverage and capacity size of the cell. If the parameter configurations of the cells in the same sector are not matched, the difference of the state information of the cells in the same sector is too large, and when the traffic of the sector is too large, the user terminal is easily caused to have poor perception in the use process of the sector, such as interruption during voice dialing or video playing jamming, and the like, and simultaneously, the problem of too high call drop rate of the user is also caused.
The existing method for identifying the cells with the same sector and unmatched parameters mainly identifies the abnormal cells through the abnormal conditions of the traffic statistic indexes, and then checks the hardware equipment information of the abnormal cells one by one to find out the corresponding cells with other frequency points in the same sector. The telephone traffic statistical indexes mainly comprise telephone traffic, congestion rate, call drop rate, switching success rate and the like, and can effectively reflect the conditions of user perception, call drop rate and the like, so that problem cells in the same sector can be indirectly identified through whether the indexes are abnormal or not. However, important links such as traffic statistics, abnormal point analysis, cell hardware information verification and the like in the method are all completed manually, and a large amount of manual operations avoid errors which are not introduced, so that the identification is inaccurate. Secondly, because the cells in the same sector with unmatched parameters have abnormal conditions on the traffic statistic indexes only when the traffic is large, the current mode can only identify the problem cells in the same sector which cause abnormal traffic statistic indexes. However, these cell indicators have been degraded, and it is difficult for the subsequent re-compensation optimization measures to change the reality of the decrease in user satisfaction. Meanwhile, the current strategy does not analyze the cells with large difference among the same sectors although the indexes are normal, and the indexes of the cells are often degraded due to the rise of the traffic. Therefore, the embodiment of the invention provides a method for identifying problem cells, which has higher accuracy and timeliness when identifying problem cells in the same sector.
The following describes embodiments of the present invention with reference to the drawings.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for identifying a problem cell according to an embodiment of the present invention, where the method includes the following steps:
step S110: and acquiring hardware information of the RRU equipment.
In this step, the RRU device is a radio frequency unit in a base station, and is configured to transmit a signal. In the distributed Base station architecture, a Base station includes RRU equipment and BBU (baseband processing Unit) equipment, which are connected by an optical fiber. Wherein, RRU devices usually have single or multiple channels. For example, when solving the coverage problem of high floors, one aisle may be configured for each floor. In addition, each channel corresponds to a sector, i.e., a coverage area. The hardware information includes sector number information corresponding to each cell, which reflects a channel of the RRU device corresponding to each cell. These hardware information are typically stored in the OMC (Operation and Maintenance Center), which can be called directly from the OMC.
Step S120: and determining the cell cluster in the same sector according to the hardware information.
At present, in order to increase the capacity of a certain coverage area to cope with higher user volume and service volume, multiple frequency points may be set on the same channel of an RRU device, so that multiple cells overlap and cover the coverage area. Therefore, the same channel of the RRU device usually corresponds to multiple cells, which are cells in the same sector. The cells belonging to the same sector number are searched in the hardware information, so that the cells of the same sector can be determined, and the set of the cells of the same sector is the cell cluster of the same sector. For example, if the sector numbers of the cells a1 and a2 are a and the sector numbers of the cells B1 and B2 are B in the hardware information, the set of the cells a1 and a2 is a co-sector cell cluster, and the set of the cells B1 and B2 is another co-sector cell cluster.
Step S130: and acquiring the state information of each cell in the cell cluster with the same sector.
In this step, the state information includes cell MR (Measurement Report) data, cell PRB (Physical Resource Block) utilization rate data, the number of synchronous state users, and the like. These data are updated periodically, which may be a day. These data are uploaded to the base station and can be recalled directly from the base station when in use.
The cell MR data includes information such as a cell ID, a TA (Timing Advance) value, a Reference Signal Receiving Power (RSRP) value, and a Signal to Interference plus Noise Ratio (SINR) value. When a user uses the flow to surf the internet or carries out a call, the user terminal reports the report value specified by the 3GPP once every 5 seconds, and each report value specified by the 3GPP corresponds to MR data. And each MR data of a cell can be considered as one MR sample point of the cell.
The cell PRB utilization rate is the number of practically used PRBs/the total number of system PRBs and is divided into an uplink PRB utilization rate and a downlink PRB utilization rate. The uplink PRB utilization rate refers to the proportion of the number of used PRBs in the total number of PRBs in the system when a user of the cell uploads data to the base station, and the downlink PRB utilization rate refers to the proportion of the number of used PRBs in the total number of PRBs in the system when the base station sends data to the user of the cell. The number of the synchronous users refers to the number of users in a cell for information interaction with a base station. When calculating the utilization rate of PRBs in a cell and the number of users in a synchronous state, one cycle may be divided into a plurality of periods and the utilization rate of PRBs and the number of users in a synchronous state in each period may be calculated. For example, each period may be 1 hour, when the period of data update is one day, the period may be divided into 24 periods, each period may be calculated to obtain a PRB utilization rate and a number of synchronous users, and the calculated 24 PRB utilization rates and the number of synchronous users in one period are uploaded to the base station.
Step S140: and determining the state index of each cell according to the state information.
In this step, the status index is an index for comprehensively evaluating the signal coverage, signal quality and capacity conditions of one cell. As described in the above step, the cell MR data in the status information includes an RSRP value and an SINR value. The RSRP value of each MR data of a cell may reflect the signal coverage of the cell, and the SINR value of each MR data of a cell may reflect the signal interference of the cell, that is, the signal quality of the cell. And the PRB utilization rate data and the synchronous state user number in the cell in the state information can reflect the capacity condition of the cell. Therefore, the state index of the cell can be determined by the state information.
As shown in fig. 2, which shows a flow chart of sub-steps of determining a status index according to an embodiment of the present invention, step S140 specifically includes:
step S141: and calculating the coverage index, the capacity index and the quality index of each cell according to the state information.
As described in step S140, the cell signal characteristics reflected by each state information are different, and the coverage class index, the capacity class index, and the quality class index of each cell are calculated based on the state information, so as to reflect the signal coverage, the signal quality, and the capacity of each cell. The signal coverage, signal quality and capacity of a cell affect users differently, for example, the signal coverage and signal quality of a cell affect the call drop rate of users, and the capacity of a cell affects operations such as streaming media download or voice dialing of users. Therefore, the coverage index, the capacity index and the quality index of each cell need to be calculated according to the state information, so that when the method is applied to different scenes, different weight values are set for the indexes, and the subsequently calculated matching degree is more accurate.
Step S142: and determining the coverage class mean value, the capacity class mean value and the quality class mean value of the cell cluster in the same sector according to the coverage class index, the capacity class index and the quality class index of each cell.
Step S143: and multiplying the coverage index of each cell by a preset coverage weight value and dividing the coverage index by a coverage mean value to obtain the coverage index of each cell.
Step S144: and multiplying the capacity index of each cell by a preset capacity weighted value and dividing the result by a capacity mean value to obtain the capacity index of each cell.
Step S145: and multiplying the quality index of each cell by a preset quality weight value and dividing the quality index by a quality mean value to obtain the quality index of each cell.
The coverage class index, the capacity class index and the quality class index can be calculated by the following formulas:
Figure BDA0002183321390000081
wherein x isiIndicating coverage class indicator for ith cell in same sector cell clusterCapacity-type index or quality-type index;
Figure BDA0002183321390000082
representing a coverage class mean, a capacity class mean or a quality class mean of a cell cluster of the same sector; δ represents a preset coverage weight value, a preset capacity weight value, or a preset quality weight value.
It can be understood that: the calculation mode of the coverage class index, the capacity class index and the quality class index is not limited to the above-described mode, and the coverage class mean value, the capacity class mean value or the quality class mean value in the formula can be replaced by the sum of the coverage class indexes, the sum of the capacity class indexes and the sum of the quality class indexes of all the cells in the cell cluster of the same sector respectively, or replaced by other parameters capable of reflecting the whole condition of the cell cluster of the same sector.
Step S146: and summing the coverage index, the capacity index and the quality index of each cell to obtain the state index of each cell.
The state index of the cell is the sum of the coverage index, the capacity index and the quality index of the cell, so the calculation formula is as follows:
Figure BDA0002183321390000091
wherein the content of the first and second substances,
Figure BDA0002183321390000092
indicating the state index, F, of the ith cell in the same sector cell clusteriIndicating a coverage class indicator for the ith cell in the same sector cell cluster,
Figure BDA0002183321390000093
and the coverage class index of the ith cell in the same sector cell cluster is represented. RiIndicating a capacity class indicator for the ith cell in the same sector cell cluster,
Figure BDA0002183321390000094
indicating the first in a co-sector cell clusterCapacity class index of i cells. ZiIndicating a quality class indicator for the ith cell in the same sector cell cluster,
Figure BDA0002183321390000095
and the quality class index of the ith cell in the same sector cell cluster is shown. Delta1、δ2And delta3Respectively representing a preset coverage weight value, a preset capacity weight value and a preset quality weight value.
It should be noted that: the preset coverage weight value, the preset capacity weight value and the preset quality weight value can be set according to the scene of the cell cluster in the same sector. For example, if the area covered by the cell cluster with the same sector is a suburban area, the preset coverage weight value needs to be set to be larger, because the suburban area focuses more on the coverage of the signal. If the area covered by the cell cluster in the same sector is a government unit, the preset quality weight value needs to be set to be larger, because the signal quality can influence the call drop rate of a user, the call of the government unit is usually more important, and the signal quality needs to be paid more attention.
Step S150: and determining the matching degree of the cell cluster with the same sector according to the state index of each cell.
Step S160: and identifying the problem cells in the same sector according to the matching degree.
The matching degree represents the difference degree of the state indexes of the cells in the cell cluster of the same sector. Specifically, the calculation method of the matching degree may be: calculating the standard deviation of the state index of each cell in the cell cluster of the same sector; determining the inverse of the standard deviation as the degree of match.
The standard deviation can effectively represent the jitter condition of the state index of each cell relative to the average value of the state indexes of all cells in the cell cluster with the same sector. The larger the standard deviation is, the more mismatched the parameters of the cells in the same sector cell cluster are. Since the matching degree is the reciprocal of the standard deviation, the higher the matching degree with the sector cell cluster is, the better the matching of the parameters of the cells in the sector cell cluster is. It can thus be identified whether the cells in the co-sectored cell cluster are co-sectored problem cells.
It can be understood that: the calculation method of the matching degree is not limited to the above-described method, and may be other characteristic numbers representing the dispersion degree of the state indexes of the cells in the cell cluster of the same sector, which is not described herein again.
The embodiment of the invention determines the cell cluster with the same sector, namely the set of all frequency point cells belonging to the same channel of the same RRU equipment through the acquired hardware information of the RRU equipment. And then, determining the state index of each cell, namely parameters representing the conditions of coverage, capacity, quality and the like of wireless signals of each cell in the cell cluster with the same sector, according to the acquired state information of each cell in the cell cluster with the same sector. Finally, the matching degree of the cell cluster in the same sector can be obtained according to the state index of each cell, which represents the difference situation of the state index of each cell in the cell cluster in the same sector, i.e. the difference situation of the parameters of each cell can be reflected. Therefore, whether the parameters of the cells in the cell cluster with the same sector are matched or not can be determined according to the matching degree, and whether the cells in the cell cluster with the same sector are problem cells with the same sector or not can be determined. Compared with the prior art, the embodiment of the invention does not need manual operation, and avoids errors caused by human factors. Meanwhile, the embodiment of the invention directly identifies the cells of the same sector with unmatched parameters, but indirectly determines the cells of the same sector with unmatched parameters according to the abnormal condition of the traffic statistic index. Therefore, the method and the device can identify the cells with the same sector problem and the potential possibility of index degradation, and are higher in accuracy and timeliness.
The step S141 may have various implementation manners, and in some embodiments, as shown in fig. 3, it shows a flow chart of sub-steps of calculating the coverage class index, the capacity class index and the quality class index in the embodiment of the present invention, where the step S141 specifically is:
step S1411: calculating the downlink MR coverage rate and the uplink signal-to-noise ratio of each cell according to the cell MR data in the state information; wherein, the downlink MR coverage rate is the coverage index, and the uplink signal-to-noise ratio is the quality index.
Step S1412: calculating the uplink PRB utilization rate of the cell busy period, the downlink PRB utilization rate of the cell busy period and the number of synchronous state users of the cell busy period of each cell according to the PRB utilization rate and the number of synchronous state users of the cell in the state information; and the uplink PRB utilization rate of the cell busy hour segment, the downlink PRB utilization rate of the cell busy hour segment and the synchronous state user number of the cell busy hour segment are all the capacity indexes.
The downlink MR coverage of the cell may be a ratio of the number of the MR sampling points of the cell, in which the RSRP value is smaller than a preset value, to all the MR sampling points of the cell. For example, if the total number of MR sampling points of the cell is n, and the number of RSRP values smaller than a preset value is m, the downlink MR coverage of the cell is m/n. The RSRP value of the MR sampling point of the cell may indicate the signal quality of the user accessing the cell in the actual internet surfing or conversation process, and a smaller RSRP value indicates a weaker signal when the user accesses the cell, that is, indicates that the user is located in the edge area covered by the cell signal. Therefore, the higher the downlink MR coverage of the cell is, the more users located in the edge area covered by the cell are indicated, and further, the signal radiation of the cell is wider, so that many users far away from the coverage center position can access the cell.
The uplink snr of the cell may be a ratio of the number of the MR data of the cell in which the SINR value is smaller than another preset value to all the MR data of the cell. The SINR value is a parameter reflecting the situation that the user in the cell is interfered, and the smaller the SINR value is, the more serious the interference is. Therefore, the uplink snr of the cell indicates the proportion of the cell where the user access signal is severely interfered, and the higher the uplink snr is, the more severely interfered the signal of the cell is, i.e. the worse the signal quality is.
The number of synchronous state users in the busy period of the cell, the uplink PRB utilization rate in the busy period of the cell and the downlink PRB utilization rate in the busy period of the cell refer to the number of synchronous state users with the largest value in each period, the uplink PRB utilization rate and the downlink PRB utilization rate. For example, if the period in one update cycle is 3, the number of synchronized users in the cell in the 3 periods is a, 2a and 3a, respectively, and at this time, the number of synchronized users in the busy period of the cell is 3 a. The uplink PRB utilization rate of the cell in the 3 time periods is respectively 0 percent, 40 percent and 80 percent, and the uplink PRB utilization rate of the cell in the busy time period is 80 percent. Similarly, if the downlink PRB utilization rates of the cell in these 3 time periods are 0, 40% and 80%, respectively, then the downlink PRB utilization rate of the cell in the busy time period is also 80%. The uplink PRB utilization rate and the downlink PRB utilization rate represent how many PRB resources are occupied when a user executes a service, and therefore, the larger the traffic is, that is, the more users execute services, the larger the uplink PRB utilization rate and the downlink PRB utilization rate are. And the number of the synchronous users directly reflects the number of the users interacting with the cell. Therefore, the number of the users in the synchronous state in the busy period of the cell, the utilization rate of the uplink PRB in the busy period of the cell and the utilization rate of the downlink PRB in the busy period of the cell can reflect the number condition of the access users of the cell, namely the capacity condition of the cell.
It can be understood that: the coverage index, capacity index and quality index are not limited to the above-described downlink MR coverage of the cell, uplink PRB utilization of the busy segment of the cell, downlink PRB utilization of the busy segment of the cell, number of synchronized users of the busy segment of the cell and uplink snr of the cell, but may also be their reciprocals or other indexes capable of reflecting signal coverage, signal quality and capacity conditions of the cell. In addition, the downlink MR coverage of the cell, the uplink PRB utilization of the busy period of the cell, the downlink PRB utilization of the busy period of the cell, the number of synchronized users of the busy period of the cell, and the uplink signal-to-noise ratio of the cell can also be calculated directly on the base station side, that is, the base station calculates and stores the downlink MR coverage, the uplink PRB utilization of the busy period, the downlink PRB utilization of the busy period, the number of synchronized users of the busy period, and the uplink signal-to-noise ratio of all cells contained in the base station in advance, and these data can be called directly from the base station when needed.
In this embodiment, since the capacity class index includes the uplink PRB utilization rate of the cell busy period, the downlink PRB utilization rate of the cell busy period, and the number of synchronous state users of the cell busy period, as shown in fig. 4, it shows a flow chart of sub-steps of calculating the capacity class index in the embodiment of the present invention, and step S144 specifically includes:
step S1441: and dividing the uplink PRB utilization rate of the cell in the busy period of each cell by the average value of the uplink PRB utilization rates in the capacity class average value and multiplying the average value by a first preset weight to obtain the uplink PRB utilization rate index of each cell.
Step S1442: and dividing the downlink PRB utilization rate of the cell in the busy period of each cell by the average value of the downlink PRB utilization rates in the capacity class average value and multiplying the average value by a second preset weight value to obtain the downlink PRB utilization rate index of each cell.
Step S1443: and dividing the number of synchronous state users in the busy period of each cell by the average number of synchronous state users in the capacity class average and multiplying the average number by a third preset weight to obtain the index of the number of synchronous state users in each cell.
Step S1444: and summing the uplink PRB utilization index, the downlink PRB utilization index and the synchronous state user number index of each cell and multiplying the sum by the preset capacity weighted value to obtain the capacity class index of each cell.
In summary, the capacity class index of each cell may adopt the following calculation formula:
Figure BDA0002183321390000131
wherein, PGo up i、PLower iAnd UiRespectively representing the uplink PRB utilization rate of the busy period of the cell, the downlink PRB utilization rate of the busy period of the cell and the number of synchronous state users of the busy period of the cell;
Figure BDA0002183321390000132
and
Figure BDA0002183321390000133
respectively representing an uplink PRB utilization rate average value, a downlink PRB utilization rate average value and a synchronous state user number average value; epsilon1、ε2And ε3Respectively representing a first preset weight, a second preset weight and a third preset weight.
It can be understood that: the first preset weight, the second preset weight and the third preset weight may be set according to a scene where the cell cluster of the same sector is located, and details are not repeated here.
Referring to fig. 5, which shows a flowchart of sub-steps of identifying problem cells in the same sector in an embodiment of the present invention, step S160 may also be implemented in various ways, where step S160 specifically includes:
and step S161, when the matching degree is smaller than a first preset threshold value, determining that the cell cluster in the same sector does not reach the standard.
In this step, the first preset threshold is a preset threshold, and when the matching degree is smaller than the first preset threshold, it indicates that the difference between the state indexes of the cells in the cell cluster of the same sector is large, so that it may indicate that the possibility that the parameter configurations of the cells in the same sector belonging to the cell cluster of the same sector are not matched is large, that is, it indicates that the cell cluster of the same sector does not reach the standard.
Step S162, when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than the second preset number of days, determining that each community in the community cluster in the same sector is a problem community in the same sector, and sending warning information; wherein the second preset number of days is less than the first preset number of days.
As described above in step S130, since the state information of each cell in the cell cluster of the same sector is periodically updated, the matching degree obtained from the state information is also periodically updated. In this embodiment, the updating period is 1 day, so that the same cell cluster in the same sector can obtain a matching degree every day, that is, it can be determined whether the cell cluster in the same sector meets the standard every day. The mismatch of the parameter configurations of the cells in the same sector can cause that the clusters in the cells in the same sector do not reach the standard for many consecutive days, so that if the cluster in the cell in the same sector does not reach the standard, the condition that the cluster in the cell in the same sector does not reach the standard is only a sporadic condition, and the condition that the cluster in the cell in the same sector does not reach the standard is probably not caused by the mismatch of the parameter configurations of the cells in the same sector. Therefore, the embodiment of the present invention also needs to detect the frequency of the substandard occurrence of the same-sector cell cluster. For example, in this step, the first preset number of days may be one week, and the second preset number of days may be 3 days. And when the same-sector cell cluster does not reach the standard within 3 days of the week, the frequency that the same-sector cell cluster does not reach the standard is more frequent, namely, each cell in the same-sector cell cluster is determined to be the same-sector problem cell. Meanwhile, after the cells in the same sector are checked again, in order to avoid traffic statistic index degradation caused by the cells in the same sector, the embodiment of the invention also sends alarm information to the OMC, so that the cells in the same sector with problems can be optimized as soon as possible.
In addition, in this step, if the number of days that the same-sector cell cluster does not meet the standard in the first preset number of days does not exceed the second preset number of days, it is indicated that the non-meeting of the same-sector cell cluster is not caused by mismatching of parameter configurations of the same-sector cell, and each cell in the same-sector cell cluster can be regarded as a non-same-sector problem cell.
Further, with continuing reference to fig. 5, step S160 further includes:
step S163: when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than a third preset number of days, determining each community in the community cluster in the same sector as a serious community with the problem in the same sector, and sending serious alarm information; and the third preset number of days is greater than the second preset number of days and less than the first preset number of days.
In this step, the same-sector cell clusters with the number of substandard days greater than the second preset number of days are further divided, for example, if the first preset number of days is one week and the second preset number of days is 3 days, the third preset number of days may be 5 days. By the method, the same-sector cell cluster with more substandard days is determined to be the cell with serious same-sector problem, namely, the parameter configuration mismatching degree of the same-sector cell belonging to the same-sector cell cluster is large, the same-sector cell needs to be processed as soon as possible, otherwise, the subsequent traffic statistic index is easy to degrade. Therefore, when the cell with the serious problem in the same sector is detected, the embodiment of the invention can send the serious alarm information to the OMC, so that the cell with the serious problem in the same sector can be processed as soon as possible.
It can be understood that: in the embodiment of the present invention, the update cycle of the state information of the cell is not limited to one day, but may also be one hour, one week, one month, or the like, and accordingly, the first preset number of days, the second preset number of days, and the third preset number of days may also be the first preset number of weeks, the second preset number of weeks, the third preset number of weeks, or the like.
The embodiment of the invention determines the cell cluster with the same sector, namely the set of all frequency point cells belonging to the same channel of the same RRU equipment through the acquired hardware information of the RRU equipment. And then, determining the state index of each cell, namely parameters representing the conditions of coverage, capacity, quality and the like of wireless signals of each cell in the cell cluster with the same sector, according to the acquired state information of each cell in the cell cluster with the same sector. Finally, the matching degree of the cell cluster in the same sector can be obtained according to the state index of each cell, which represents the difference situation of the state index of each cell in the cell cluster in the same sector, i.e. the difference situation of the parameters of each cell can be reflected. Therefore, whether the parameters of the cells in the cell cluster with the same sector are matched or not can be determined according to the matching degree, and whether the cells in the cell cluster with the same sector are problem cells with the same sector or not can be determined. Compared with the prior art, the embodiment of the invention does not need manual operation, and avoids errors caused by human factors. Meanwhile, the embodiment of the invention directly identifies the cells of the same sector with unmatched parameters, but indirectly determines the cells of the same sector with unmatched parameters according to the abnormal condition of the traffic statistic index. Therefore, the method and the device can identify the cells with the same sector problem and the potential possibility of index degradation, and are higher in accuracy and timeliness. In addition, the embodiment of the invention also determines the frequency degree of the substandard community clusters of the same sector according to the matching degrees calculated by the community clusters of the same sector in different periods, respectively carries out the substandard community of the problem of the different sector, the community of the problem of the same sector and the community of the problem of the serious same sector in the community clusters of the same sector according to the frequency degree, and sends corresponding alarm information, so that the community of the same sector with more serious parameter mismatching can be preferentially processed.
Fig. 6 is a schematic structural diagram illustrating an apparatus for identifying a problem cell according to an embodiment of the present invention. As shown in fig. 6, the apparatus 100 includes a first obtaining module 10, a first determining module 20, a second obtaining module 30, a second determining module 40, a third determining module 50, and an identifying module 60.
A first obtaining module 10, configured to obtain hardware information of an RRU device; a first determining module 20, configured to determine a cell cluster in the same sector according to the hardware information; a second obtaining module 30, configured to obtain state information of each cell in the cell cluster of the same sector; a second determining module 40, configured to determine a state index of each cell according to the state information; a third determining module 50, configured to determine a matching degree of the cell cluster in the same sector according to the state index of each cell; and an identifying module 60, configured to identify the problem cell in the same sector according to the matching degree.
In an optional manner, the second determining module 40 specifically includes: calculating a coverage index, a capacity index and a quality index of each cell according to the state information; determining a coverage class mean value, a capacity class mean value and a quality class mean value of the cell cluster of the same sector according to the coverage class index, the capacity class index and the quality class index of each cell; multiplying the coverage index of each cell by a preset coverage weight value and dividing the coverage index by a coverage mean value to obtain a coverage index of each cell; multiplying the capacity index of each cell by a preset capacity weighted value and dividing the capacity index by a capacity mean value to obtain a capacity index of each cell; multiplying the quality index of each cell by a preset quality weight value and dividing the quality index by a quality mean value to obtain a quality index of each cell; and summing the coverage index, the capacity index and the quality index of each cell to obtain the state index of each cell.
In an optional manner, the calculating, according to the state information, a coverage index, a capacity index, and a quality index of each cell specifically includes: calculating the downlink MR coverage rate and the uplink signal-to-noise ratio of each cell according to the cell MR data in the state information; wherein, the downlink MR coverage rate is the coverage index, and the uplink signal-to-noise ratio is the quality index; calculating the uplink PRB utilization rate of the cell busy period, the downlink PRB utilization rate of the cell busy period and the number of synchronous state users of the cell busy period of each cell according to the PRB utilization rate and the number of synchronous state users of the cell in the state information; and the uplink PRB utilization rate of the cell busy hour segment, the downlink PRB utilization rate of the cell busy hour segment and the synchronous state user number of the cell busy hour segment are all the capacity indexes.
In an optional manner, the capacity class index of each cell is obtained by multiplying the capacity class index of each cell by a preset capacity weight value and dividing by the capacity class mean value, and specifically includes: dividing the uplink PRB utilization rate of the cell in the busy period of each cell by the average value of the uplink PRB utilization rates in the capacity class average value and multiplying the average value by a first preset weight to obtain the uplink PRB utilization rate index of each cell; dividing the downlink PRB utilization rate of the cell busy period of each cell by the average value of the downlink PRB utilization rates in the capacity class average value and multiplying the average value by a second preset weight value to obtain the downlink PRB utilization rate index of each cell; dividing the number of synchronous state users in the busy period of each cell by the average number of synchronous state users in the capacity class average value and multiplying the average number by a third preset weight to obtain the index of the number of synchronous state users in each cell; and summing the uplink PRB utilization index, the downlink PRB utilization index and the synchronous state user number index of each cell and multiplying the sum by the preset capacity weighted value to obtain the capacity class index of each cell.
In an optional manner, the third determining module 50 is specifically: calculating the standard deviation of the state index of each cell in the cell cluster of the same sector; determining the inverse of the standard deviation as the degree of match.
In an optional manner, the identifying module 60 specifically determines that the co-sector cell cluster does not reach the standard when the matching degree is smaller than a first preset threshold; when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than a second preset number of days, determining that each community in the community cluster in the same sector is a problem community in the same sector, and sending warning information; wherein the second preset number of days is less than the first preset number of days.
In an optional manner, the identification module 60 further includes: when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than a third preset number of days, determining each community in the community cluster in the same sector as a serious community with the problem in the same sector, and sending serious alarm information; and the third preset number of days is greater than the second preset number of days and less than the first preset number of days.
In the embodiment of the present invention, hardware information of an RRU device is acquired by a first acquisition module 10, and a cell cluster in the same sector, that is, a set of cells of all frequency points belonging to the same channel of the same RRU device, is determined by a first determination module 20. Then, the second obtaining module 30 obtains the state information of each cell in the cell cluster with the same sector, and the second determining module 40 determines the state index of each cell, that is, the parameters indicating the conditions of coverage, capacity, quality, and the like of the wireless signal of each cell in the cell cluster with the same sector. Finally, the third determining module 50 can obtain the matching degree of the cell cluster with the sector, which represents the difference of the state indexes of the cells in the cell cluster with the sector, i.e. the difference of the parameters of the cells can be reflected. Thus, the identifying module 60 can determine whether the parameters of the cells in the co-sectored cell cluster match, so as to determine whether the cells in the co-sectored cell cluster are the co-sectored problem cells. Compared with the prior art, the embodiment of the invention does not need manual operation, and avoids errors caused by human factors. Meanwhile, the embodiment of the invention directly identifies the cells of the same sector with unmatched parameters, but indirectly determines the cells of the same sector with unmatched parameters according to the abnormal condition of the traffic statistic index. Therefore, the method and the device can identify the cells with the same sector problem and the potential possibility of index degradation, and are higher in accuracy and timeliness.
An embodiment of the present invention provides a non-volatile computer storage medium, where the computer storage medium stores at least one executable instruction, and the computer executable instruction may execute the method for identifying a problem cell in any of the above method embodiments.
Fig. 7 is a schematic structural diagram of a device for identifying a problem cell according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the device for identifying a problem cell.
As shown in fig. 7, the apparatus for identifying a problem cell may include: a processor (processor)202, a communication Interface (Communications Interface)204, a memory (memory)206, and a communication bus 208.
Wherein: the processor 202, communication interface 204, and memory 206 communicate with each other via a communication bus 208. A communication interface 204 for communicating with network elements of other devices, such as clients or other servers. The processor 202 is configured to execute the program 210, and may specifically execute the relevant steps in the above-described method embodiment for identifying the problem cell.
In particular, the program 210 may include program code that includes computer operating instructions.
The processor 202 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention. The device for identifying the problem cell comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 206 for storing a program 210. Memory 206 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 210 may specifically be used to cause the processor 202 to perform the following operations:
acquiring hardware information of RRU equipment;
determining the same sector cell cluster according to the hardware information;
acquiring state information of each cell in the same sector cell cluster;
determining the state index of each cell according to the state information;
determining the matching degree of the cell cluster with the same sector according to the state index of each cell;
and identifying the problem cells in the same sector according to the matching degree.
In an alternative manner, the program 210 may be further specifically configured to cause the processor 202 to perform the following operations:
calculating a coverage index, a capacity index and a quality index of each cell according to the state information;
determining a coverage class mean value, a capacity class mean value and a quality class mean value of the cell cluster of the same sector according to the coverage class index, the capacity class index and the quality class index of each cell;
multiplying the coverage index of each cell by a preset coverage weight value and dividing the coverage index by a coverage mean value to obtain a coverage index of each cell;
multiplying the capacity index of each cell by a preset capacity weighted value and dividing the capacity index by a capacity mean value to obtain a capacity index of each cell;
multiplying the quality index of each cell by a preset quality weight value and dividing the quality index by a quality mean value to obtain a quality index of each cell;
and summing the coverage index, the capacity index and the quality index of each cell to obtain the state index of each cell.
In an alternative manner, the program 210 may be further specifically configured to cause the processor 202 to perform the following operations:
calculating the downlink MR coverage rate and the uplink signal-to-noise ratio of each cell according to the cell MR data in the state information; wherein, the downlink MR coverage rate is the coverage index, and the uplink signal-to-noise ratio is the quality index;
calculating the uplink PRB utilization rate of the cell busy period, the downlink PRB utilization rate of the cell busy period and the number of synchronous state users of the cell busy period of each cell according to the PRB utilization rate and the number of synchronous state users of the cell in the state information; and the uplink PRB utilization rate of the cell busy hour segment, the downlink PRB utilization rate of the cell busy hour segment and the synchronous state user number of the cell busy hour segment are all the capacity indexes.
In an alternative manner, the program 210 may be further specifically configured to cause the processor 202 to perform the following operations:
dividing the uplink PRB utilization rate of the cell in the busy period of each cell by the average value of the uplink PRB utilization rates in the capacity class average value and multiplying the average value by a first preset weight to obtain the uplink PRB utilization rate index of each cell;
dividing the downlink PRB utilization rate of the cell busy period of each cell by the average value of the downlink PRB utilization rates in the capacity class average value and multiplying the average value by a second preset weight value to obtain the downlink PRB utilization rate index of each cell;
dividing the number of synchronous state users in the busy period of each cell by the average number of synchronous state users in the capacity class average value and multiplying the average number by a third preset weight to obtain the index of the number of synchronous state users in each cell;
and summing the uplink PRB utilization index, the downlink PRB utilization index and the synchronous state user number index of each cell and multiplying the sum by the preset capacity weighted value to obtain the capacity class index of each cell.
In an alternative manner, the program 210 may be further specifically configured to cause the processor 202 to perform the following operations:
calculating the standard deviation of the state index of each cell in the cell cluster of the same sector;
determining the inverse of the standard deviation as the degree of match.
In an alternative manner, the program 210 may be further specifically configured to cause the processor 202 to perform the following operations:
when the matching degree is smaller than a first preset threshold value, determining that the cell cluster in the same sector does not reach the standard;
when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than a second preset number of days, determining that each community in the community cluster in the same sector is a problem community in the same sector, and sending warning information; wherein the second preset number of days is less than the first preset number of days.
In an alternative manner, the program 210 may be further specifically configured to cause the processor 202 to perform the following operations:
when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than a third preset number of days, determining each community in the community cluster in the same sector as a serious community with the problem in the same sector, and sending serious alarm information; and the third preset number of days is greater than the second preset number of days and less than the first preset number of days.
The embodiment of the invention is as follows.
An embodiment of the present invention provides an executable program, where the executable program may execute the method for identifying a problem cell in any of the above method embodiments.
The embodiment of the invention determines the cell cluster with the same sector, namely the set of all frequency point cells belonging to the same channel of the same RRU equipment through the acquired hardware information of the RRU equipment. And then, determining the state index of each cell, namely parameters representing the conditions of coverage, capacity, quality and the like of wireless signals of each cell in the cell cluster with the same sector, according to the acquired state information of each cell in the cell cluster with the same sector. Finally, the matching degree of the cell cluster in the same sector can be obtained according to the state index of each cell, which represents the difference situation of the state index of each cell in the cell cluster in the same sector, i.e. the difference situation of the parameters of each cell can be reflected. Therefore, whether the parameters of the cells in the cell cluster with the same sector are matched or not can be determined according to the matching degree, and whether the cells in the cell cluster with the same sector are problem cells with the same sector or not can be determined. Compared with the prior art, the embodiment of the invention does not need manual operation, and avoids errors caused by human factors. Meanwhile, the embodiment of the invention directly identifies the cells of the same sector with unmatched parameters, but indirectly determines the cells of the same sector with unmatched parameters according to the abnormal condition of the traffic statistic index. Therefore, the method and the device can identify the cells with the same sector problem and the potential possibility of index degradation, and are higher in accuracy and timeliness.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.

Claims (10)

1. A method for identifying a problem cell, comprising:
acquiring hardware information of RRU equipment;
determining the same sector cell cluster according to the hardware information;
acquiring state information of each cell in the same sector cell cluster;
determining the state index of each cell according to the state information;
determining the matching degree of the cell cluster with the same sector according to the state index of each cell;
and identifying the problem cells in the same sector according to the matching degree.
2. The method according to claim 1, wherein the determining the state index of each cell according to the state information specifically comprises:
calculating a coverage index, a capacity index and a quality index of each cell according to the state information;
determining a coverage class mean value, a capacity class mean value and a quality class mean value of the cell cluster of the same sector according to the coverage class index, the capacity class index and the quality class index of each cell;
multiplying the coverage index of each cell by a preset coverage weight value and dividing the coverage index by a coverage mean value to obtain a coverage index of each cell;
multiplying the capacity index of each cell by a preset capacity weighted value and dividing the capacity index by a capacity mean value to obtain a capacity index of each cell;
multiplying the quality index of each cell by a preset quality weight value and dividing the quality index by a quality mean value to obtain a quality index of each cell;
and summing the coverage index, the capacity index and the quality index of each cell to obtain the state index of each cell.
3. The method according to claim 2, wherein the calculating, according to the state information, a coverage index, a capacity index, and a quality index of each cell specifically includes:
calculating the downlink MR coverage rate and the uplink signal-to-noise ratio of each cell according to the cell MR data in the state information; wherein, the downlink MR coverage rate is the coverage index, and the uplink signal-to-noise ratio is the quality index;
calculating the uplink PRB utilization rate of the cell busy period, the downlink PRB utilization rate of the cell busy period and the number of synchronous state users of the cell busy period of each cell according to the PRB utilization rate and the number of synchronous state users of the cell in the state information; and the uplink PRB utilization rate of the cell busy hour segment, the downlink PRB utilization rate of the cell busy hour segment and the synchronous state user number of the cell busy hour segment are all the capacity indexes.
4. The method according to claim 3, wherein the capacity class index of each cell is obtained by multiplying the capacity class index of each cell by a preset capacity weight value and dividing by a capacity class mean value, and specifically comprises:
dividing the uplink PRB utilization rate of the cell in the busy period of each cell by the average value of the uplink PRB utilization rates in the capacity class average value and multiplying the average value by a first preset weight to obtain the uplink PRB utilization rate index of each cell;
dividing the downlink PRB utilization rate of the cell busy period of each cell by the average value of the downlink PRB utilization rates in the capacity class average value and multiplying the average value by a second preset weight value to obtain the downlink PRB utilization rate index of each cell;
dividing the number of synchronous state users in the busy period of each cell by the average number of synchronous state users in the capacity class average value and multiplying the average number by a third preset weight to obtain the index of the number of synchronous state users in each cell;
and summing the uplink PRB utilization index, the downlink PRB utilization index and the synchronous state user number index of each cell and multiplying the sum by the preset capacity weighted value to obtain the capacity class index of each cell.
5. The method according to claim 1, wherein the determining the matching degree of the co-sector cell cluster is performed according to the state index of each cell, specifically;
calculating the standard deviation of the state index of each cell in the cell cluster of the same sector;
determining the inverse of the standard deviation as the degree of match.
6. The method according to claim 1, wherein said identifying a problem cell in a same sector according to said matching degree comprises:
when the matching degree is smaller than a first preset threshold value, determining that the cell cluster in the same sector does not reach the standard;
when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than a second preset number of days, determining that each community in the community cluster in the same sector is a problem community in the same sector, and sending warning information; wherein the second preset number of days is less than the first preset number of days.
7. The method of claim 6, wherein after determining that each cell in the cluster of cells in the same sector is a problem cell in the same sector and sending warning information when the number of days in the cluster of cells in the same sector that does not meet the criteria for the first predetermined number of days is greater than a second predetermined number of days, the method further comprises:
when the number of days which do not reach the standard in the first preset number of days of the community cluster in the same sector is larger than a third preset number of days, determining each community in the community cluster in the same sector as a serious community with the problem in the same sector, and sending serious alarm information; and the third preset number of days is greater than the second preset number of days and less than the first preset number of days.
8. An apparatus for identifying a problem cell, comprising:
the first acquisition module is used for acquiring hardware information of the RRU equipment;
a first determining module, configured to determine a cell cluster in the same sector according to the hardware information;
a second obtaining module, configured to obtain state information of each cell in the cell cluster of the same sector;
a second determining module, configured to determine a state index of each cell according to the state information;
a third determining module, configured to determine a matching degree of the cell cluster in the same sector according to the state index of each cell;
and the identification module is used for identifying the problem cells in the same sector according to the matching degree.
9. An apparatus for identifying a problem cell, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform the method of identifying problem cells according to any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform a method for identifying a problem cell according to any one of claims 1-7.
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