CN114449541B - Abnormal perception cell positioning method, device, equipment and readable medium - Google Patents
Abnormal perception cell positioning method, device, equipment and readable medium Download PDFInfo
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Abstract
The embodiment of the invention relates to the technical field of network optimization and discloses an anomaly-aware cell positioning method, which comprises the following steps: comparing the average downlink rate of each cell of the whole network with a preset first threshold rate in a historical time interval, and determining the cell with the average downlink rate smaller than the first threshold rate as the cell to be positioned; constructing a perception state coordinate system, and determining a perception trend line in the perception state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be positioned; determining a current network CQI average value on a target frequency band, and determining a perception state inflection point according to the current network CQI average value and a perception trend line; and comparing the horizontal and vertical coordinate values of each cell to be positioned in the historical time interval with the horizontal and vertical coordinate values of the perception inflection point, and determining the perception abnormality type of each cell to be positioned according to the comparison result. Through the mode, the embodiment of the invention realizes the accuracy of the abnormal notification cell positioning.
Description
Technical Field
The embodiment of the invention relates to the technical field of wireless network optimization, in particular to an anomaly-aware cell positioning method, device and equipment and a readable medium.
Background
Along with the increasing competition of the mobile communication market, the optimization of the user experience perception becomes the focus of each operator, and how to define the root cause of the perception problem becomes the bottleneck for restricting the improvement of the perception experience.
The existing positioning means of the abnormal sensing cells are mainly characterized in that the specific performance indexes in the KPI data of each cell are independently analyzed, and the aim of optimization and adjustment is also to improve individual indexes.
The problem in doing so is that the reasons of poor user perception experience are often related to multiple indexes, such as poor network quality or insufficient current network capacity, and under the condition that some single indexes are not up to standard, the user can still obtain better perception experience because the user is in a central coverage area and the like, so that the means for carrying out the positioning of the perception abnormal cell aiming at the specific index in the prior art is simple, rough and fine, and the accuracy and the efficiency of the positioning of the cause of abnormal perception are low.
Disclosure of Invention
In view of the above problems, the embodiment of the present invention provides an anomaly-aware cell positioning method, which is used to solve the problem in the prior art that the accuracy of positioning the cause of the perceived anomaly of the anomaly-aware cell is not high, so that the network optimization rate is low.
According to an aspect of an embodiment of the present invention, there is provided an anomaly-aware cell positioning method, including:
comparing the average downlink rate of each cell of the whole network in each preset period in a historical time interval with a preset first threshold rate, and determining a cell with the average downlink rate smaller than the first threshold rate as a cell to be positioned;
constructing a perception state coordinate system, and determining a perception trend line in the perception state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be positioned in each preset period, wherein the abscissa of the perception state coordinate system is CQI, the ordinate of the perception state coordinate system is PRB, points in the perception state coordinate system represent the average downlink rate of each cell of the whole network, and the perception trend line represents a curve formed by the closest approach of each cell of the whole network to the first threshold rate under the PRB utilization rate and CQI;
determining a current network CQI average value on a target frequency band, and determining a perception state inflection point according to the current network CQI average value and the sensing trend line;
and comparing the horizontal and vertical coordinate values of each cell to be positioned in the historical time interval with the horizontal and vertical coordinate values of the perception inflection point, and determining the perception abnormality type of each cell to be positioned according to the comparison result.
In an optional embodiment, the determining the current network CQI average value on the target frequency band, determining a perceived status inflection point according to the current network CQI average value and the perceived status boundary, further includes:
taking a straight line parallel to the axis of abscissa as a current network average CQI horizontal line by passing the current network CQI average value;
and acquiring an intersection point of the current network average CQI horizontal line and the perception state dividing line as the perception state inflection point on a target frequency band.
In an optional embodiment, the comparing the abscissa value of each cell to be located in the historical time interval with the abscissa value of the perceived inflection point, and determining the perceived anomaly type of each cell to be located according to the comparison result, further includes:
determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the perception inflection point and the ordinate value is larger than the ordinate value of the perception inflection point, as a capacity problem;
determining that the type of the sensing abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the sensing inflection point and the ordinate value is smaller than the ordinate value of the sensing inflection point, is a mixing problem;
Determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception inflection point and the ordinate value is smaller than the ordinate value of the perception inflection point, as a quality problem;
and determining the perception abnormality type of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception inflection point and the ordinate value is larger than the ordinate value of the perception inflection point, as a perception structure without problems.
In an optional embodiment, the comparing the abscissa value of each cell to be located in the historical time interval with the abscissa value of the perceived inflection point, and after determining the perceived anomaly type of each cell to be located according to the comparison result, further includes:
and under the condition that the sensing abnormality types corresponding to the preset time periods in the cell to be positioned are different, determining the sensing abnormality type with the maximum acquired frequency and larger than a preset frequency threshold as the sensing abnormality type of the cell to be positioned.
In an alternative embodiment, before comparing the average downlink rate of each cell of the whole network with a preset first threshold rate, the method further includes:
Respectively determining a plurality of test rate set values corresponding to a plurality of target service types, and acquiring user perception data when the downlink rate is respectively the test rate set values as target perception test data;
determining target test indexes corresponding to the target service types;
determining a user perception state corresponding to each test rate set value under the target test index according to the target perception test data;
and determining the first threshold rate according to the user perception state corresponding to each test rate set value under the target test index.
In an optional embodiment, after determining the user perception state corresponding to each of the test rate set values under the target test index according to the target perception test data, the method further includes:
determining a second door speed limit rate according to the user perception state corresponding to each test speed set value under the target test index;
and determining all cells of the whole network as a good-perception cell, a general-perception cell and a poor-perception cell according to the first threshold rate and the second threshold rate, and determining the poor-perception cell as the cell to be positioned.
In an optional embodiment, before comparing the average downlink rate of each cell of the whole network in each preset period with the preset first threshold rate, the method further includes:
acquiring all-network KPI data of a plurality of preset time periods, and preprocessing the all-network KPI data;
and determining the average downlink rate of each cell of the whole network in each preset period according to the preprocessed KPI data of the whole network.
According to another aspect of the embodiments of the present invention, there is provided an abnormality sensing cell positioning apparatus, the apparatus including:
the abnormal cell determining module is used for comparing the average downlink rate of each preset time period in each cell of the whole network in a historical time interval with a preset first threshold rate respectively, and determining a cell with the average downlink rate smaller than the first threshold rate as a cell to be positioned;
the trend line determining module is used for constructing a sensing state coordinate system, and determining a sensing trend line in the sensing state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be positioned in each preset period, wherein the abscissa of the sensing state coordinate system is CQI, the ordinate of the sensing state coordinate system is PRB, points in the sensing state coordinate system represent the average downlink rate of each cell of the whole network, and the sensing trend line represents a curve formed by the closest approach of each cell of the whole network to the first threshold rate under the PRB utilization rate and CQI;
The inflection point determining module is used for determining a current network CQI average value on a target frequency band and determining a perception state inflection point according to the current network CQI average value and the perception trend line;
and the abnormality type determining module is used for comparing the horizontal coordinate value and the vertical coordinate value of each cell to be positioned in the historical time interval with the horizontal coordinate value and the vertical coordinate value of the perception inflection point, and determining the perception abnormality type of each cell to be positioned according to the comparison result.
According to another aspect of an embodiment of the present invention, there is provided an abnormality sensing cell positioning apparatus including: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform the operations of the anomaly aware cell location method according to any one of the preceding embodiments.
According to another aspect of an embodiment of the present invention, there is provided a computer readable storage medium having stored therein at least one executable instruction that, when run on an abnormality aware cell positioning device/apparatus, causes the abnormality aware cell positioning device/apparatus to perform the operations of the abnormality aware cell positioning method according to any one of the preceding embodiments.
Comparing the average downlink rate of each preset time period of each cell of the whole network in a historical time interval with a preset first threshold rate, and determining the cell with the average downlink rate smaller than the first threshold rate as a cell to be positioned; constructing a perception state coordinate system, and determining a perception trend line in the perception state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be positioned in each preset period, wherein the abscissa of the perception state coordinate system is CQI, the ordinate of the perception state coordinate system is PRB, points in the perception state coordinate system represent the average downlink rate of each cell of the whole network, and the perception trend line represents a curve formed by the closest approach of each cell of the whole network to the first threshold rate under the PRB utilization rate and CQI; determining a current network CQI average value on a target frequency band, and determining a perception state inflection point according to the current network CQI average value and the perception trend line; and comparing the horizontal and vertical coordinate values of each cell to be positioned in the historical time interval with the horizontal and vertical coordinate values of the perception inflection point, and determining the perception abnormality type of each cell to be positioned according to the comparison result.
In the invention, the sensing inflection point is determined through the sensing threshold rate, the PRB utilization rate and CQI index corresponding to the sensing inflection point are determined for comparison, and finally the sensing anomaly type of each cell is determined, thereby improving the accuracy of sensing anomaly cell positioning.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and may be implemented according to the content of the specification, so that the technical means of the embodiments of the present invention can be more clearly understood, and the following specific implementation of the present invention will be more clearly understood.
Drawings
The drawings are only for purposes of illustrating embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a schematic flow chart of an anomaly-aware cell positioning method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an abnormality sensing cell apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an abnormality sensing cell device 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 present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
Fig. 1 shows a flow chart of an embodiment of the anomaly aware cell location method of the present invention, which is performed by a computer processing device. Specific computer processing devices may include notebook computers, cell phones, and the like. As shown in fig. 1, the method comprises the steps of:
step 110: and respectively comparing the average downlink rate of each cell of the whole network in each preset period in the historical time interval with a preset first threshold rate, and determining the cell with the average downlink rate smaller than the first threshold rate as the cell to be positioned.
The historical time interval may be 24 hours in the past or a certain period of time such as one week, and each preset period may refer to 8 to 22 points per day of the past week. The first threshold rate is that through current limiting, the perception condition of the user under each service type is obtained under different rates, such as the number of blocking times and average buffering duration in a high-definition mode when video is played, whether game experience is smooth, path search time delay and success rate of navigation application and the like, so that the average rate of the user when perception is poor is obtained. The process of determining the first threshold rate includes at least the following steps 1101-1104.
Step 1101: and respectively determining a plurality of test rate set values corresponding to the plurality of target service types, and acquiring user perception data when the downlink rate is respectively the test rate set values as target perception test data.
The target traffic types may include common traffic such as: IM, video, live video, short video, web browsing, gaming, navigation, payment, application download, VOIP, etc.
The test rate set point may be representative of a number of different intervals, such as 128Kbps, 384Kbps, 512Kbps, 1Mbps, 1.5Mbps, 2Mbps, 3Mbps, 4Mbps, 5Mbps, 7.2Mbps, and testing without limiting the speed.
Step 1102: and determining target test indexes corresponding to each target service type.
Different target test indexes exist for different target service types and are used for reflecting specific aspects of the sensing condition of the user. Such as the number of times of blocking and buffer duration in a preset unit duration in different play modes (such as a high definition mode and a standard definition mode) under video service, and the connection success rate, the connection time delay and the like of video call may be possible in IM software.
Step 1103: and determining the user perception state corresponding to each test rate set value under the target test index according to the target perception test data.
For example, a user may experience a game at 128Kbps as not playable, a game at 384Kbps as churning, a game at 512Kbps as playable but occasional churning, and a fluency at 2 Mbps.
Step 1104: and determining a first threshold rate according to the user perception state corresponding to each test rate set value under the target test index.
In combination with the above example, the first threshold rate may be determined to be 3Mbps by integrating the average perceived status of a plurality of users under each service type. It is to be readily understood that the first threshold rate herein may be an average rate when the user perceives the difference, and further, the user perception state may be determined according to the user perception state corresponding to each test rate set value under the target test index, that is, a rate at which the perception may meet, but is not ideal, that is, in an alternative embodiment, after step 1104, the following steps 1111-1112 are further included:
step 1111: and determining a second threshold rate according to the user perception state corresponding to each test rate set value under the target test index, wherein the second threshold rate is larger than the first threshold rate.
It is readily appreciated that the user perceived status indicators below the second threshold rate are better in character than the first threshold rate.
For example, the second threshold rate may be 5Mbps, and in the case of the rate of 3Mbps, the user may be 4 times and 30 seconds under the two user perceived status indexes of the number of camping in a unit time and the buffer duration in a unit time when using the service type of watching live broadcast, and may be 0 times and 5 seconds in the case of the rate of 5 Mbps.
Step 1112: and determining all cells of the whole network as a good-perception cell, a general-perception cell and a poor-perception cell according to the first threshold rate and the second threshold rate, and determining the poor-perception cell as a cell to be positioned.
Specifically, a cell with an average sensing rate smaller than a first threshold rate is determined as a sensing difference cell, a cell with an average sensing rate smaller than a second threshold rate and larger than the first threshold rate is determined as a sensing general cell, and a cell with an average sensing rate larger than the second threshold rate is determined as a sensing difference cell.
Step 120: a sensing state coordinate system is constructed, and a sensing trend line is determined in the sensing state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be positioned in each preset period, wherein the abscissa of the sensing state coordinate system is CQI, the ordinate of the sensing state coordinate system is PRB, points in the sensing state coordinate system represent the average downlink rate of each cell of the whole network, and the sensing trend line represents a curve formed by the closest first threshold rate of each cell of the whole network under the PRB utilization rate and CQI.
First, each point in the sensing state coordinate system may represent an average rate of one cell in the whole network for a preset period, such as an average rate of 8 to 9 of a past day of the cell B, that is, a certain cell or a plurality of different cells that may correspond to different periods in a coordinate point corresponding to the same abscissa.
In addition, the perceived trend line is the boundary line that the average rate of each cell of the whole network under the PRB utilization rate and CQI is closest to the first threshold rate.
That is, it is theorized that the combination of the various PRB utilization and the abscissa pair of CQI will cause the rate of one cell to reach a rate at the first threshold rate, so that the coordinate points corresponding to the various abscissas form a perceived trend line.
Step 130: and determining the current network CQI average value on the target frequency band, and determining a perception state inflection point according to the current network CQI average value and the perception trend line.
I.e. acquisition of CQI near the first threshold rate at the average CQI level of the current network.
The specific step 130 of determining a perceived inflection point further includes the following steps 1301-1302:
step 1301: the average value of the CQI of the current network is taken as a straight line parallel to the axis of abscissa as the average CQI level line of the current network.
Step 1302: and acquiring an intersection point of the average CQI horizontal line of the current network and the perception state boundary line as a perception state inflection point on the target frequency band.
The perceived inflection point is the point at which the perceived state is significantly changed, so that the CQI and the PRB utilization corresponding to the point can be used as the judging threshold of the CQI and the PRB utilization.
Step 140: and comparing the horizontal and vertical coordinate values of each cell to be positioned in the historical time interval with the horizontal and vertical coordinate values of the perception inflection point, and determining the perception abnormality type of each cell to be positioned according to the comparison result.
The meaning of the parameters on the abscissa is explained, and the CQI on the ordinate, i.e. the channel quality indicator (Channel Quality Indication), is mainly used to measure the quality of the downlink channel of the cell.
The higher the PRB utilization of the abscissa, the more saturated the allocated resources on each channel are, and no more free PRB resources can be called.
Therefore, when the current downlink rate is not good, the PRB utilization rate corresponding to the cell may be poor in slow rate perception due to congestion caused by insufficient network capacity, and when the corresponding CQI is small, the PRB utilization rate corresponding to the cell may be poor in slow rate perception due to poor network quality.
The specific step 140 further includes the following steps 1401-1404:
step 1401: and determining the type of the sensing abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the sensing inflection point and the ordinate value is larger than the ordinate value of the sensing inflection point, as a capacity problem.
Specifically, a larger CQI indicates no problem in channel quality, but a larger PRB utilization indicates that the current network is heavily loaded, so that the reason for the poor network rate may be that the network capacity cannot meet the actual requirement, so that downlink congestion occurs, and the perceived anomaly type is determined as a capacity problem.
Step 1402: and determining the type of the sensing abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the sensing inflection point and the ordinate value is smaller than the ordinate value of the sensing inflection point, as a mixing problem.
The larger PRB utilization ratio indicates that the current network load is heavier, and the smaller CQI indicates that the quality is poor, so that under the conditions of poor downlink rate and poor user perception, the quality and capacity have problems to be optimized, and the perception abnormality type under the conditions is determined as a mixing problem.
Step 1403: and determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception inflection point and the ordinate value is smaller than the ordinate value of the perception inflection point, as a quality problem.
The larger PRB utilization ratio and smaller PRB utilization ratio indicate good network load, and the smaller CQI indicates poor channel quality, so that poor downlink rate caused by poor channel quality can cause poor user perception, and the perception anomaly type is determined to be a quality problem.
Step 1404: and determining the type of the sensing abnormality of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the sensing inflection point and the ordinate value is larger than the ordinate value of the sensing inflection point, as a sensing structure.
A smaller PRB utilization indicates a good network load, a larger CQI indicates a better channel quality, and in this case, the poor downlink rate may be caused by other reasons unrelated to the sensing structure, so that the sensing anomaly type is determined to be a sensing structure without a problem.
Therefore, in an alternative embodiment, after determining the type of the sensing abnormality of each cell to be located according to the comparison result with the abscissa of the sensing inflection point, frequency screening may be further performed, that is:
under the condition that the sensing abnormality types corresponding to the preset time periods in the cells to be positioned are different, the sensing abnormality type with the largest acquired frequency and larger than the preset frequency threshold value is determined to be the sensing abnormality type of the cells to be positioned.
For example, there may be 4 times of sensing abnormality types as sensing structures without problems, 8 times of sensing abnormality types as capacity problems, and 2 times as mixing problems in 14 preset time periods with each hour as a statistical unit in 8-22 busy hours of the past day of a certain cell a. The perceived anomaly type of cell a is thus determined as a capacity problem.
In an alternative embodiment, in order to make the result of abnormal positioning clearer and more intuitive and improve the efficiency of network optimization according to the positioning result, different corresponding forms of presentation can be performed for different types of abnormal cells.
For example, on a map device, an abnormal cell of a capacity problem type is marked red, an abnormal cell of a mixed problem type is marked yellow, an abnormal cell of a quality problem type is marked blue, etc., a cell of a perception structure non-type is marked green, etc.
Furthermore, the related information of the abnormal cells under each type can be sent to related network optimization personnel for processing.
As specific, location information of an abnormal cell of the quality problem type, cell identification information, transmission to a network optimization person, and the like.
It should be noted that, for each frequency band, the method provided in the embodiment of the present invention may be used to determine the anomaly type of each perceived anomaly cell. The difference is that KPI data of each cell on different frequency bands is acquired for processing.
In an alternative embodiment, before comparing the average downlink rate of each cell of the whole network in each preset period with the preset first threshold rate, steps 151-152 are further included:
step 151: and acquiring the whole-network KPI data of a plurality of preset time periods, and preprocessing the whole-network KPI data.
The specific full-network KPI data may include uplink rate data of each cell of the full network in each preset period.
In the actual KPI data acquisition process, there may be data missing or data abnormality (too large or too small) due to the limitation of the acquisition means, and thus the acquired KPI data needs to be preprocessed, including: and cleaning the KPI data of the whole network, removing abnormal data with uplink rate greater than a certain upper limit and less than a certain lower limit, and rate null data corresponding to the cell which is not acquired at a certain time point.
Step 152: and determining the average downlink rate of each cell of the whole network in each preset period according to the preprocessed KPI data of the whole network.
Fig. 2 shows a schematic structural diagram of an embodiment of the abnormality sensing cell positioning device of the present invention. As shown in fig. 2, the apparatus 300 includes: abnormal cell determination module 310, trend line determination module 320, inflection point determination module 330, and abnormal type determination module 340.
The abnormal cell determining module 310 is configured to compare an average downlink rate of each preset period of each cell of the whole network in a historical time interval with a preset first threshold rate, and determine a cell with the average downlink rate smaller than the first threshold rate as a cell to be located;
a trend line determining module 320, configured to construct a sensing state coordinate system, and determine a sensing trend line in the sensing state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be located in each preset period, where an abscissa of the sensing state coordinate system is CQI, and an ordinate of the sensing state coordinate system is PRB, a point in the sensing state coordinate system represents an average downlink rate of each cell in the whole network, and the sensing trend line represents a curve formed by each cell in the whole network closest to the first threshold rate under the PRB utilization rate and CQI;
the inflection point determining module 330 is configured to determine a current network CQI average value on a target frequency band, and determine a perceived status inflection point according to the current network CQI average value and the perceived trend line;
The anomaly type determining module 340 is configured to compare the abscissa value and the ordinate value of each cell to be located in the historical time interval with the abscissa value of the perceived inflection point, and determine the perceived anomaly type of each cell to be located according to the comparison result.
In an alternative approach, the inflection point determination module 330 is further configured to: taking a straight line parallel to the axis of abscissa as a current network average CQI horizontal line after the current network CQI average value;
and acquiring an intersection point of the current network average CQI horizontal line and the perception state dividing line as the perception state inflection point on a target frequency band.
In an alternative manner, the anomaly type determination module 340 is further configured to:
determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the perception inflection point and the ordinate value is larger than the ordinate value of the perception inflection point, as a capacity problem;
determining that the type of the sensing abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the sensing inflection point and the ordinate value is smaller than the ordinate value of the sensing inflection point, is a mixing problem;
determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception inflection point and the ordinate value is smaller than the ordinate value of the perception inflection point, as a quality problem;
And determining the perception abnormality type of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception inflection point and the ordinate value is larger than the ordinate value of the perception inflection point, as a perception structure without problems.
In an alternative manner, the anomaly type determination module 340 is further configured to:
and under the condition that the sensing abnormality types corresponding to the preset time periods in the cell to be positioned are different, determining the sensing abnormality type with the maximum acquired frequency and larger than a preset frequency threshold as the sensing abnormality type of the cell to be positioned.
In an alternative manner, the abnormal cell determination module 340 is further configured to:
respectively determining a plurality of test rate set values corresponding to a plurality of target service types, and acquiring user perception data when the downlink rate is respectively the test rate set values as target perception test data;
determining target test indexes corresponding to the target service types;
determining a user perception state corresponding to each test rate set value under the target test index according to the target perception test data;
and determining the first threshold rate according to the user perception state corresponding to each test rate set value under the target test index.
In an alternative manner, the abnormal cell determination module 340 is further configured to: determining a second threshold speed according to the user perception state corresponding to each test speed set value under the target test index;
and determining all cells of the whole network as a good-perception cell, a general-perception cell and a poor-perception cell according to the first threshold rate and the second threshold rate, and determining the poor-perception cell as the cell to be positioned.
In an alternative manner, the abnormal cell determination module 340 is further configured to:
acquiring all-network KPI data of a plurality of preset time periods, and preprocessing the all-network KPI data;
and determining the average downlink rate of each cell of the whole network in each preset period according to the preprocessed KPI data of the whole network.
The implementation process of the device for locating an abnormal sensing cell provided by the embodiment of the present invention is the same as that of the method for locating an abnormal sensing cell in the foregoing embodiment, and will not be repeated.
The abnormal sensing cell positioning device provided by the embodiment of the invention determines the sensing inflection point through the sensing threshold rate, compares the PRB utilization rate corresponding to the sensing inflection point with the CQI index, and finally determines the sensing abnormal type of each cell, thereby improving the accuracy of sensing abnormal cell positioning.
Fig. 3 shows a schematic structural diagram of an embodiment of the abnormality sensing cell positioning device of the present invention, and the embodiment of the present invention does not limit the specific implementation of the abnormality sensing cell positioning device.
As shown in fig. 3, the abnormality aware cell location apparatus may include: a processor 402, a communication interface (Communications Interface) 404, a memory 406, and a communication bus 408.
Wherein: processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. A communication interface 404 for communicating with network elements of other devices, such as clients or other servers. The processor 402 is configured to execute the program 410, and may specifically perform the relevant steps in the embodiment of the method for positioning an anomaly sensing cell described above.
In particular, program 410 may include program code including computer-executable instructions.
The processor 402 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors comprised by the abnormality aware cell location apparatus may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
Memory 406 for storing programs 410. Memory 406 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
Program 410 may be specifically invoked by processor 402 to cause an anomaly aware cell location device to:
comparing the average downlink rate of each cell of the whole network in each preset period in a historical time interval with a preset first threshold rate, and determining a cell with the average downlink rate smaller than the first threshold rate as a cell to be positioned;
constructing a perception state coordinate system, and determining a perception trend line in the perception state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be positioned in each preset period, wherein the abscissa of the perception state coordinate system is CQI, the ordinate of the perception state coordinate system is PRB, points in the perception state coordinate system represent the average downlink rate of each cell of the whole network, and the perception trend line represents a curve formed by the closest approach of each cell of the whole network to the first threshold rate under the PRB utilization rate and CQI;
determining a current network CQI average value on a target frequency band, and determining a perception state inflection point according to the current network CQI average value and the sensing trend line;
And comparing the horizontal and vertical coordinate values of each cell to be positioned in the historical time interval with the horizontal and vertical coordinate values of the perception inflection point, and determining the perception abnormality type of each cell to be positioned according to the comparison result.
In an alternative, program 410 is invoked by processor 402 to cause an anomaly aware cell location device to:
taking a straight line parallel to the axis of abscissa as a current network average CQI horizontal line by passing the current network CQI average value;
and acquiring an intersection point of the current network average CQI horizontal line and the perception state dividing line as the perception state inflection point on a target frequency band.
In an alternative, program 410 is invoked by processor 402 to cause an anomaly aware cell location device to:
determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the perception inflection point and the ordinate value is larger than the ordinate value of the perception inflection point, as a capacity problem;
determining that the type of the sensing abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the sensing inflection point and the ordinate value is smaller than the ordinate value of the sensing inflection point, is a mixing problem;
Determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception inflection point and the ordinate value is smaller than the ordinate value of the perception inflection point, as a quality problem;
and determining the perception abnormality type of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception inflection point and the ordinate value is larger than the ordinate value of the perception inflection point, as a perception structure without problems.
In an alternative, program 410 is invoked by processor 402 to cause an anomaly aware cell location device to:
and under the condition that the sensing abnormality types corresponding to the preset time periods in the cell to be positioned are different, determining the sensing abnormality type with the maximum acquired frequency and larger than a preset frequency threshold as the sensing abnormality type of the cell to be positioned.
In an alternative, program 410 is invoked by processor 402 to cause an anomaly aware cell location device to:
respectively determining a plurality of test rate set values corresponding to a plurality of target service types, and acquiring user perception data when the downlink rate is respectively the test rate set values as target perception test data;
Determining target test indexes corresponding to the target service types;
determining a user perception state corresponding to each test rate set value under the target test index according to the target perception test data;
and determining the first threshold rate according to the user perception state corresponding to each test rate set value under the target test index.
In an alternative, program 410 is invoked by processor 402 to cause an anomaly aware cell location device to:
determining a second door speed limit rate according to the user perception state corresponding to each test speed set value under the target test index;
and determining all cells of the whole network as a good-perception cell, a general-perception cell and a poor-perception cell according to the first threshold rate and the second threshold rate, and determining the poor-perception cell as the cell to be positioned.
In an alternative, program 410 is invoked by processor 402 to cause an anomaly aware cell location device to:
acquiring all-network KPI data of a plurality of preset time periods, and preprocessing the all-network KPI data;
and determining the average downlink rate of each cell of the whole network in each preset period according to the preprocessed KPI data of the whole network.
The implementation process of the abnormality sensing cell positioning device provided by the embodiment of the present invention is the same as that of the abnormality sensing cell positioning method in the foregoing embodiment, and will not be repeated.
The abnormal sensing cell positioning equipment provided by the embodiment of the invention determines the sensing inflection point through the sensing threshold rate, compares the PRB utilization rate corresponding to the sensing inflection point with the CQI index, and finally determines the sensing abnormal type of each cell, thereby improving the accuracy of sensing abnormal cell positioning. The embodiment of the invention provides a computer readable storage medium, which stores at least one executable instruction, and when the executable instruction runs on an abnormality sensing cell positioning device/apparatus, the abnormality sensing cell positioning device/apparatus executes the abnormality sensing cell positioning method in any of the above method embodiments.
The executable instructions may be specifically for causing an anomaly aware cell locating apparatus/device to:
comparing the average downlink rate of each cell of the whole network in each preset period in a historical time interval with a preset first threshold rate, and determining a cell with the average downlink rate smaller than the first threshold rate as a cell to be positioned;
Constructing a perception state coordinate system, and determining a perception trend line in the perception state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be positioned in each preset period, wherein the abscissa of the perception state coordinate system is CQI, the ordinate of the perception state coordinate system is PRB, points in the perception state coordinate system represent the average downlink rate of each cell of the whole network, and the perception trend line represents a curve formed by the closest approach of each cell of the whole network to the first threshold rate under the PRB utilization rate and CQI;
determining a current network CQI average value on a target frequency band, and determining a perception state inflection point according to the current network CQI average value and the sensing trend line;
and comparing the horizontal and vertical coordinate values of each cell to be positioned in the historical time interval with the horizontal and vertical coordinate values of the perception inflection point, and determining the perception abnormality type of each cell to be positioned according to the comparison result.
In an alternative, the executable instructions may be specifically configured to cause an anomaly-aware cell locating device/apparatus to:
taking a straight line parallel to the axis of abscissa as a current network average CQI horizontal line by passing the current network CQI average value;
And acquiring an intersection point of the current network average CQI horizontal line and the perception state dividing line as the perception state inflection point on a target frequency band.
In an alternative, the executable instructions may be specifically configured to cause an anomaly-aware cell locating device/apparatus to:
determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the perception inflection point and the ordinate value is larger than the ordinate value of the perception inflection point, as a capacity problem;
determining that the type of the sensing abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the sensing inflection point and the ordinate value is smaller than the ordinate value of the sensing inflection point, is a mixing problem;
determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception inflection point and the ordinate value is smaller than the ordinate value of the perception inflection point, as a quality problem;
and determining the perception abnormality type of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception inflection point and the ordinate value is larger than the ordinate value of the perception inflection point, as a perception structure without problems.
In an alternative, the executable instructions may be specifically configured to cause an anomaly-aware cell locating device/apparatus to:
and under the condition that the sensing abnormality types corresponding to the preset time periods in the cell to be positioned are different, determining the sensing abnormality type with the maximum acquired frequency and larger than a preset frequency threshold as the sensing abnormality type of the cell to be positioned.
In an alternative, the executable instructions may be specifically configured to cause an anomaly-aware cell locating device/apparatus to:
respectively determining a plurality of test rate set values corresponding to a plurality of target service types, and acquiring user perception data when the downlink rate is respectively the test rate set values as target perception test data;
determining target test indexes corresponding to the target service types;
determining a user perception state corresponding to each test rate set value under the target test index according to the target perception test data;
and determining the first threshold rate according to the user perception state corresponding to each test rate set value under the target test index.
In an alternative, the executable instructions may be specifically configured to cause an anomaly-aware cell locating device/apparatus to: determining a second door speed limit rate according to the user perception state corresponding to each test speed set value under the target test index;
And determining all cells of the whole network as a good-perception cell, a general-perception cell and a poor-perception cell according to the first threshold rate and the second threshold rate, and determining the poor-perception cell as the cell to be positioned.
In an alternative, the executable instructions may be specifically configured to cause an anomaly-aware cell locating device/apparatus to:
acquiring all-network KPI data of a plurality of preset time periods, and preprocessing the all-network KPI data;
and determining the average downlink rate of each cell of the whole network in each preset period according to the preprocessed KPI data of the whole network.
The implementation process of the computer readable storage medium provided in the embodiment of the present invention is the same as the implementation process in the method for locating an abnormality sensing cell in the foregoing embodiment, and will not be repeated.
The computer readable storage medium provided by the embodiment of the invention determines the sensing turning point through the sensing threshold rate, compares the PRB utilization rate corresponding to the sensing turning point with the CQI index, and finally determines the sensing abnormality type of each cell, thereby improving the accuracy of positioning the sensing abnormality cells.
The embodiment of the invention provides an abnormal sensing cell positioning device which is used for executing the abnormal sensing cell positioning method.
The embodiment of the invention provides a computer program which can be called by a processor to enable abnormal sensing cell positioning equipment to execute the abnormal sensing cell positioning method in any method embodiment.
An embodiment of the present invention provides a computer program product, including a computer program stored on a computer readable storage medium, the computer program including program instructions which, when executed on a computer, cause the computer to perform the anomaly aware cell location method in any of the method embodiments described above. 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 a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention as described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood 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 above 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 disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of an embodiment may be combined into one module or unit or component and they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units 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.
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 use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically described otherwise.
Claims (9)
1. An anomaly aware cell location method, the method comprising:
comparing the average downlink rate of each cell of the whole network in each preset period in a historical time interval with a preset first threshold rate, and determining a cell with the average downlink rate smaller than the first threshold rate as a cell to be positioned; before comparing the average downlink rate of each cell of the whole network with a preset first threshold rate, the method further comprises:
Respectively determining a plurality of test rate set values corresponding to a plurality of target service types, and acquiring user perception data when the downlink rate is respectively the test rate set values as target perception test data;
determining target test indexes corresponding to the target service types;
determining a user perception state corresponding to each test rate set value under the target test index according to the target perception test data;
determining the first threshold rate according to the user perception state corresponding to each test rate set value under the target test index;
constructing a perception state coordinate system, and determining a perception trend line in the perception state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be positioned in each preset period, wherein the abscissa of the perception state coordinate system is CQI, the ordinate of the perception state coordinate system is PRB, points in the perception state coordinate system represent the average downlink rate of each cell of the whole network, and the perception trend line represents a curve formed by the closest approach of each cell of the whole network to the first threshold rate under the PRB utilization rate and CQI;
determining a current network CQI average value on a target frequency band, and determining a perception state inflection point according to the current network CQI average value and the perception trend line;
And comparing the horizontal and vertical coordinate values of each cell to be positioned in the historical time interval with the horizontal and vertical coordinate values of the inflection point of the perception state, and determining the perception abnormality type of each cell to be positioned according to the comparison result.
2. The method of claim 1, wherein said determining a current network CQI average over a target frequency band, determining a perceived status inflection point from said current network CQI average and said perceived trend line, further comprises:
a straight line parallel to the axis of abscissa is made by the current network CQI average value to be used as a current network average CQI horizontal line;
and acquiring an intersection point of the current network average CQI horizontal line and the perception trend line as the perception state inflection point on a target frequency band.
3. The method of claim 1, wherein comparing the abscissa value of each cell to be located in the historical time interval with the abscissa value of the perceived status inflection point, and determining the perceived anomaly type of each cell to be located according to the comparison result, further comprises:
determining the type of the sensing abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the sensing state inflection point and the ordinate value is larger than the ordinate value of the sensing state inflection point, as a capacity problem;
Determining that the type of the perception abnormality of the cell to be positioned, of which the abscissa value is larger than the abscissa value of the perception state inflection point and the ordinate value is smaller than the ordinate value of the perception state inflection point, is a mixing problem;
determining the type of the perception abnormality of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the perception state inflection point and the ordinate value is smaller than the ordinate value of the perception state inflection point, as a quality problem;
and determining that the type of the sensing abnormality of the cell to be positioned, of which the abscissa value is smaller than the abscissa value of the sensing state inflection point and the ordinate value is larger than the ordinate value of the sensing state inflection point, is a sensing structure.
4. The method according to claim 1, wherein comparing the abscissa value of each cell to be located in the history time interval with the ordinate value of the perceived inflection point, and determining the perceived anomaly type of each cell to be located according to the comparison result, further comprises:
and under the condition that the sensing abnormality types corresponding to the preset time periods in the cell to be positioned are different, determining the sensing abnormality type with the maximum acquired frequency and larger than a preset frequency threshold as the sensing abnormality type of the cell to be positioned.
5. The method of claim 1, further comprising, after determining the user perceived status for each of the test rate set points for the target test indicator based on the target perceived test data:
determining a second door speed limit rate according to the user perception state corresponding to each test speed set value under the target test index;
and determining all cells of the whole network as a good-perception cell, a general-perception cell and a poor-perception cell according to the first threshold rate and the second threshold rate, and determining the poor-perception cell as the cell to be positioned.
6. The method of claim 1, wherein before comparing the average downlink rate of each cell of the whole network for each preset period with the preset first threshold rate, respectively, further comprises:
acquiring all-network KPI data of a plurality of preset time periods, and preprocessing the all-network KPI data;
and determining the average downlink rate of each cell of the whole network in each preset period according to the preprocessed KPI data of the whole network.
7. An anomaly aware cell location apparatus, the apparatus comprising:
the abnormal cell determining module is used for comparing the average downlink rate of each preset time period of each cell of the whole network in a historical time interval with a preset first threshold rate respectively, and determining a cell with the average downlink rate smaller than the first threshold rate as a cell to be positioned; before comparing the average downlink rate of each cell of the whole network with a preset first threshold rate, the method further comprises:
Respectively determining a plurality of test rate set values corresponding to a plurality of target service types, and acquiring user perception data when the downlink rate is respectively the test rate set values as target perception test data;
determining target test indexes corresponding to the target service types;
determining a user perception state corresponding to each test rate set value under the target test index according to the target perception test data;
determining the first threshold rate according to the user perception state corresponding to each test rate set value under the target test index;
the trend line determining module is used for constructing a sensing state coordinate system, and determining a sensing trend line in the sensing state coordinate system according to the PRB utilization rate, CQI and corresponding average downlink rate of each cell to be positioned in each preset period, wherein the abscissa of the sensing state coordinate system is CQI, the ordinate of the sensing state coordinate system is PRB, points in the sensing state coordinate system represent the average downlink rate of each cell of the whole network, and the sensing trend line represents a curve formed by the closest approach of each cell of the whole network to the first threshold rate under the PRB utilization rate and CQI;
the inflection point determining module is used for determining a current network CQI average value on a target frequency band and determining a perception state inflection point according to the current network CQI average value and the perception trend line;
And the abnormal type determining module is used for comparing the horizontal coordinate value and the vertical coordinate value of each cell to be positioned in the historical time interval with the horizontal coordinate value of the inflection point of the perception state, and determining the perception abnormal type of each cell to be positioned according to the comparison result.
8. An anomaly aware cell location device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform the operations of the anomaly aware cell location method of any one of claims 1 to 6.
9. A computer readable storage medium, characterized in that at least one executable instruction is stored in the storage medium, which executable instruction, when run on an abnormality aware cell location apparatus/device, causes the abnormality aware cell location apparatus/device to perform the operations of the abnormality aware cell location method according to any of claims 1-6.
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