CN111988813B - Method, device and computer equipment for determining weak coverage cell in mobile communication network - Google Patents
Method, device and computer equipment for determining weak coverage cell in mobile communication network Download PDFInfo
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Abstract
The embodiment of the application provides a method, a device and computer equipment for determining a weak coverage cell in a mobile communication network, wherein the method comprises the steps of obtaining a network coverage prediction value of each grid area in a second period after a first period according to a trained network coverage prediction model; then calculating the relative error of the network coverage rate predicted value and the network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative error; acquiring a first data file of the associated cell in the first period and a second data file in the second period; and finally, positioning a weak coverage cell according to a comparison result of fluctuation parameters in the first data file and the second data file, and determining the weak coverage cell as a cell to be optimized, thereby greatly improving the judgment accuracy. Further, the weak coverage cell may also be determined according to the comparison of the fluctuation parameters in the neighboring cells.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a method, an apparatus, and a computer device for determining a weak coverage cell in a mobile communication network.
Background
The current evaluation of long term evolution (Long Term Evolution; hereinafter referred to as LTE) network coverage capability is mainly measured from the whole network, area or base station level by coverage index. The evaluation method is to extract coverage rate of a specific period, and compare the coverage rate with a preset uniform qualification threshold to judge the quality of coverage performance. But has the following problems:
(1) Problem identification phase: each base station has respective coverage fluctuation characteristics including geographic environment difference, user behavior fluctuation, seasonal time period influence and the like, and the coverage ratio absolute threshold is utilized to judge the coverage abnormality of the area and the base station without considering the factors, so that the judgment error is larger.
(2) Problem analysis stage: the conventional method considers the influence of the serving cell and the adjacent cell on the coverage rate at the same time, but fails to effectively measure the importance of the influence of the adjacent cell. In other words, the current network is an overlapping coverage network to a large extent, and the loss and performance degradation of some neighboring cells do not have a substantial effect on coverage of a local area. Therefore, the conventional method cannot identify and evaluate the criticality of the neighboring cells.
Disclosure of Invention
The embodiment of the application provides a method, a device and computer equipment for determining a weak coverage cell in a mobile communication network, wherein differentiated data in each grid area is extracted through a machine learning technology, a corresponding model is built according to the differentiated data, and then prediction is carried out according to the model, and the weak coverage cell is accurately positioned.
In a first aspect, an embodiment of the present application provides a method for determining a weak coverage cell in a mobile communication network, including:
acquiring a network coverage rate predicted value of each grid area in a second period after a first period according to a trained network coverage rate predicted model, wherein the grid areas are grid areas which are divided in advance according to geographic areas covered by a network;
calculating the relative error of the network coverage rate predicted value and the network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative error, wherein a cell corresponding to a network coverage signal of the key grid area is an associated cell of the key grid area;
acquiring a first data file of the associated cell during the first period and a second data file during the second period, and,
And positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file.
In one possible implementation manner, before obtaining the network coverage prediction value of each grid area in the second period after the first period according to the trained network coverage prediction model, the method further includes:
acquiring a network coverage rate historical value and a historical time corresponding to the network coverage rate historical value;
performing exponential smoothing on the network coverage rate historical value and the historical time, and obtaining a smoothing factor;
and obtaining a trained network coverage rate prediction model according to the smoothing factor.
In one possible implementation manner, the calculating the relative error between the network coverage prediction value and the actual network coverage value corresponding to the network coverage prediction value, and the screening the key grid area according to the relative error includes:
and when the relative error is greater than or equal to a preset discrete threshold value corresponding to the grid region, determining the grid region as the key grid region.
Wherein in one possible implementation, the locating the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file includes:
Acquiring a first fluctuation parameter in the first data file and a second fluctuation parameter in the second data file, wherein the first fluctuation parameter comprises a first time advance and a first signal arrival angle, and the second fluctuation parameter comprises a second time advance and a second signal arrival angle;
when the second time advance is greater than the first time advance or the second signal arrival angle is smaller than the first signal arrival angle, determining that the corresponding associated cell is the weak coverage cell; the method comprises the steps of,
and positioning the weak coverage cell according to the second time advance and the second signal arrival angle.
In one possible implementation manner, after calculating the relative error between the network coverage prediction value and the actual network coverage value corresponding to the network coverage prediction value and screening the key grid area according to the relative error, the method further includes:
acquiring a third data file of a neighboring cell of the associated cell in the first period and a fourth data file in the second period, and,
and positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the third data file and the fourth data file.
Wherein in a possible implementation manner, the positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the third data file and the fourth data file includes:
acquiring a third fluctuation parameter in the third data file and a fourth fluctuation parameter in the fourth data file, wherein the third fluctuation parameter comprises a third time advance and a third signal arrival angle, and the fourth fluctuation parameter comprises a fourth time advance and a fourth signal arrival angle;
when the fourth time advance is greater than the third time advance or the fourth signal arrival angle is smaller than the third signal arrival angle, determining that the corresponding associated cell is the weak coverage cell; the method comprises the steps of,
and positioning the weak coverage cell according to the fourth time advance and the fourth signal arrival angle.
Wherein in one possible implementation, the predictive discrete threshold may be calculated by the following formula:
preset discrete threshold = a+2*S
Wherein A represents the average value of each corresponding relative error in a specified period of the grid region, S represents the standard deviation of each corresponding relative error in the specified period of the grid region, and the relative error is the ratio of the absolute error of the network coverage predicted value to the actual network coverage value to the network coverage predicted value.
In a second aspect, an embodiment of the present application further provides a device for determining a weak coverage cell in a mobile communications network, including:
the first acquisition module is used for acquiring a network coverage rate predicted value of each grid area in a second period after a first period according to the trained network coverage rate predicted model, wherein the grid areas are grid areas which are divided in advance according to geographic areas covered by the network;
the calculation module is connected with the first acquisition module and is used for calculating the relative error between the network coverage rate predicted value and the network coverage rate actual value corresponding to the network coverage rate predicted value;
the screening module is connected with the computing module and is used for screening out a key grid area according to the relative error, and a cell corresponding to a network coverage signal of the key grid area is an associated cell of the key grid area;
the second acquisition module is connected with the screening module and is used for acquiring a first data file of the associated cell in the first period and a second data file of the associated cell in the second period;
and the positioning module is connected with the second acquisition module and is used for positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file.
In a third aspect, embodiments of the present application further provide a computer device, including:
at least one processor; and
at least one memory communicatively coupled to the processor;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method for determining a weak coverage cell in a mobile communications network described above.
In a fourth aspect, embodiments of the present application further provide a non-transitory computer readable storage medium storing computer instructions that cause the computer to perform the method for determining a weak coverage cell in a mobile communication network described above.
In the above technical solution, aiming at the technical problem that coverage rate judgment is inaccurate due to respective coverage fluctuation characteristics of each base station in the related technical solution, the present application obtains a network coverage rate predicted value of each grid area in a second period after a first period according to a trained network coverage rate predicted model; then calculating the relative error of the network coverage rate predicted value and the network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative error; acquiring a first data file of the associated cell in the first period and a second data file in the second period; and finally, positioning a weak coverage cell according to a comparison result of fluctuation parameters in the first data file and the second data file, and determining the weak coverage cell as a cell to be optimized, thereby greatly improving the judgment accuracy. Further, the weak coverage cell may also be determined according to the comparison of the fluctuation parameters in the neighboring cells.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the prior art descriptions, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a flowchart of an embodiment of a method for determining a weak coverage cell in a mobile communication network according to the present application;
fig. 2 is a flowchart of another embodiment of a method for determining a weak coverage cell in a mobile communication network according to the present application;
fig. 3 is a flowchart of still another embodiment of a method for determining a weak coverage cell in a mobile communication network according to the present application;
fig. 4 is a flowchart of still another embodiment of a method for determining a weak coverage cell in a mobile communication network according to the present application;
fig. 5 is a schematic diagram of a connection structure of a determining device of a weak coverage cell in the mobile communication network of the present application;
FIG. 6 is a schematic diagram of an embodiment of a computer device of the present application.
Detailed Description
For a better understanding of the technical solutions of the present application, embodiments of the present application are described in detail below with reference to the accompanying drawings.
It should be understood that the described embodiments are merely some, but not all, of the embodiments of the present application. All other embodiments, based on the embodiments herein, which would be apparent to one of ordinary skill in the art without making any inventive effort, are intended to be within the scope of the present application.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
Fig. 1 is a flowchart of an embodiment of a method for determining a weak coverage cell in a mobile communication network according to the present application, where, as shown in fig. 1, the method includes:
step 101: and acquiring a network coverage rate predicted value of each grid area in a second period after the first period according to the trained network coverage rate predicted model, wherein the grid areas are grid areas which are divided in advance according to the geographic areas covered by the network.
The first period is the last historical time of inputting the network coverage prediction model, the second period is the time corresponding to the network coverage prediction value obtained by the network coverage prediction model, and the network coverage prediction model is obtained by training, and the method can comprise the following steps:
(1) Acquiring a network coverage rate historical value and a historical time corresponding to the network coverage rate historical value;
(2) Performing exponential smoothing on the network coverage rate historical value and the historical time, and obtaining a smoothing factor;
(3) And obtaining a trained network coverage rate prediction model according to the smoothing factor.
In a specific implementation, the network coverage prediction model may be a holter-temperature (HOLT-window) model as the network coverage prediction model in the present application. In practical applications, since the network coverage and the historical time involved in the application will be affected by certain natural conditions, the network coverage and the historical time have seasonal characteristics in a period of one year or less due to regular variation caused by time sequence changes, and the three-time exponential smoothing method can predict a time sequence with seasonality. Therefore, in the step (2), the historical value and the historical time of the network coverage rate can be subjected to three exponential smoothing processes. The HOLT-WINTER model adopted by the application is a model which is very important and very flowing in the machine learning at present, and therefore has good performance.
Step 102: and calculating the relative error of the network coverage rate predicted value and the network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative error, wherein a cell corresponding to a network coverage signal of the key grid area is an associated cell of the key grid area.
In one embodiment, the actual network coverage value may be obtained by calculating according to formula (1):
wherein Cov represents an actual value of network coverage of the grid region, k represents the number of cells corresponding to the network coverage signal of the grid region,
indicating the number of levels in the kth cell that fall in the ith coverage level.
For example, when the coverage level is 7, the number of levels between-110 dbm and-105 dbm can be considered as the number of levels falling in the 7 th coverage level. The value range of the level value corresponding to the coverage level can be automatically set according to the actual requirement, which is not limited in the application.
In one embodiment, the screening the key grid region according to the relative error includes:
and when the relative error is greater than or equal to a preset discrete threshold value corresponding to the grid region, determining the grid region as the key grid region.
Preset discrete threshold = a +2*S formula (2)
Wherein A represents the average value of each corresponding relative error in a specified period of the grid region, S represents the standard deviation of each corresponding relative error in the specified period of the grid region, and the relative error is the ratio of the absolute error of the network coverage predicted value to the actual network coverage value to the network coverage predicted value.
In practical application, the geographical environment and user behavior of each grid region are not fixed, so that the method based on probability density is adopted to set the preset discrete threshold value in each grid region. Specifically, firstly, the present application calculates the relative error between the network coverage rate predicted value and the network coverage rate actual value corresponding to the network coverage rate predicted value in each grid region, then calculates two key statistics of the average value of the relative error and the standard deviation of the relative error, and finally obtains a preset discrete threshold value corresponding to each grid region by referring to formula (2). Thus, according to the normal distribution statistical characteristics, when the relative error occurs in a region outside of the two times of the standard deviation on both sides of the mean, the probability is 4.55%, and the event is regarded as a small probability event. That is, when the relative error is greater than or equal to a preset discrete threshold value corresponding to the grid region, the grid region is determined to be the critical grid region.
Step 103: and acquiring a first data file of the associated cell in the first period and a second data file of the associated cell in the second period.
Step 104: and positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file.
Specifically, referring to fig. 2, the step 104 may include:
step 201: a first fluctuation parameter in the first data file and a second fluctuation parameter in the second data file are obtained. The first fluctuation parameter comprises a first time advance and a first signal arrival angle, and the second fluctuation parameter comprises a second time advance and a second signal arrival angle;
step 202: judging whether the second time advance is greater than the first time advance, if so, entering a step 203, otherwise, ending the whole process;
step 203: determining the corresponding associated cell as a weak coverage cell;
step 204: and positioning the weak coverage cell according to the second time advance and the second signal arrival angle.
The comparison of the time advance in step 202 may also be obtained by comparing the arrival angles of the signals. Specifically, when the second signal arrival angle is smaller than the first signal arrival angle, the corresponding associated cell may also be determined to be a weak coverage cell.
Specifically, the first data file and the second data file refer to measurement report sample data files MROs in the first period/the second period, respectively, which are long term evolution (Long Term Evolution, LTE) measurement report sample data files MROs. The step 204 specifically includes:
Firstly, determining a distance range from a base station to the weak coverage cell according to a second time advance, and determining a range of a horizontal distance from the base station to the weak coverage cell according to the distance range and the height of an antenna of the base station from the ground;
secondly, converting longitude and latitude of the base station into a rectangular coordinate value of a Gaussian plane according to a Gaussian projection forward calculation formula;
then, according to the plane right angle coordinate value of the base station, the range of the horizontal distance and the second signal arrival angle, determining the plane coordinate range of the weak coverage cell projection;
specifically, the second signal arrival angle is used to define an estimated angle of a cell relative to a measurement reference direction, where the measurement reference direction is a north-right direction of the base station.
Preferably, referring to fig. 3 to 4, after step 102 of the present application, the method further includes:
step 303: acquiring a third data file of a neighboring cell of the associated cell in the first period and a fourth data file of the neighboring cell of the associated cell in the second period;
step 304: and positioning a weak coverage cell according to the comparison result of the fluctuation parameters in the third data file and the fourth data file, and determining the weak coverage cell as a cell to be optimized.
Specifically, referring to fig. 4, the step 304 may include:
step 401: and acquiring a third fluctuation parameter in the third data file and a fourth fluctuation parameter in the fourth data file. The third fluctuation parameter comprises a third time advance and a third signal arrival angle, and the fourth fluctuation parameter comprises a fourth time advance and a fourth signal arrival angle;
step 402: judging whether the fourth time advance is greater than the third time advance, if so, entering step 403, otherwise, ending the whole process;
step 403: determining the corresponding associated cell as a weak coverage cell;
step 404: and positioning the weak coverage cell according to the fourth time advance and the fourth signal arrival angle.
The comparison of the time advance in step 402 may also be obtained by comparing the arrival angles of the signals. Specifically, when the fourth signal arrival angle is smaller than the third signal arrival angle, the corresponding associated cell may also be determined to be a weak coverage cell.
Specifically, the third data file and the fourth data file refer to measurement report sample data files MROs in the first period/the second period, respectively, which are long term evolution (Long Term Evolution, LTE) measurement report sample data files MROs. The step 504 specifically includes:
Firstly, determining a distance range from a base station to the weak coverage cell according to a fourth time advance, and determining a range of a horizontal distance from the base station to the weak coverage cell according to the distance range and the height of an antenna of the base station from the ground;
secondly, converting longitude and latitude of the base station into a rectangular coordinate value of a Gaussian plane according to a Gaussian projection forward calculation formula;
then, according to the plane right angle coordinate value of the base station, the range of the horizontal distance and the fourth signal arrival angle, determining the plane coordinate range of the weak coverage cell projection;
specifically, the fourth signal arrival angle is used to define an estimated angle of a cell relative to a measurement reference direction, where the measurement reference direction is the north direction of the base station.
Fig. 5 is a schematic connection structure of a determining device for a weak coverage cell in a mobile communication network according to the present application, where, as shown in fig. 5, the device includes:
a first obtaining module 501, configured to obtain, according to a trained network coverage prediction model, a network coverage prediction value of each grid area in a second period after a first period, where the grid area is a rasterized area that is divided in advance according to a geographic area covered by a network;
The calculating module 502 is connected to the first obtaining module 501, and is configured to calculate a relative error between the network coverage predicted value and an actual network coverage value corresponding to the network coverage predicted value;
the screening module 503 is connected to the calculating module 502, and is configured to screen out a key grid area according to the relative error, where a cell corresponding to a network coverage signal of the key grid area is an associated cell of the key grid area;
a second obtaining module 504, connected to the screening module 503, configured to obtain a first data file of the associated cell in the first period and a second data file of the associated cell in the second period;
and the positioning module 505 is connected with the second obtaining module 504 and is used for positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file.
The network coverage prediction model is obtained through training and can comprise:
(1) Acquiring a network coverage rate historical value and a historical time corresponding to the network coverage rate historical value;
(2) Performing exponential smoothing on the network coverage rate historical value and the historical time, and obtaining a smoothing factor;
(3) And obtaining a trained network coverage rate prediction model according to the smoothing factor.
In a specific implementation, the network coverage prediction model may be a holter-temperature (HOLT-window) model as the network coverage prediction model in the present application. In practical applications, since the network coverage and the historical time involved in the application will be affected by certain natural conditions, the network coverage and the historical time have seasonal characteristics in a period of one year or less due to regular variation caused by time sequence changes, and the three-time exponential smoothing method can predict a time sequence with seasonality. Therefore, in the step (2), the historical value and the historical time of the network coverage rate can be subjected to three exponential smoothing processes. The HOLT-WINTER model adopted by the application is a model which is very important and very flowing in the machine learning at present, and therefore has good performance.
In one embodiment, the actual network coverage value may be obtained by calculating according to formula (1):
wherein Cov represents an actual value of network coverage of the grid region, k represents the number of cells corresponding to the network coverage signal of the grid region, 
Indicating the number of levels in the kth cell that fall in the ith coverage level.
For example, when the coverage level is 7, the number of levels between-110 dbm and-105 dbm can be considered as the number of levels falling in the 7 th coverage level. The value range of the level value corresponding to the coverage level can be automatically set according to the actual requirement, which is not limited in the application.
In one embodiment, the screening the key grid region according to the relative error includes:
and when the relative error is greater than or equal to a preset discrete threshold value corresponding to the grid region, determining the grid region as the key grid region.
Preset discrete threshold = a +2*S formula (2)
Wherein A represents the average value of each corresponding relative error in a specified period of the grid region, S represents the standard deviation of each corresponding relative error in the specified period of the grid region, and the relative error is the ratio of the absolute error of the network coverage predicted value to the actual network coverage value to the network coverage predicted value.
In practical application, the geographical environment and user behavior of each grid region are not fixed, so that the method based on probability density is adopted to set the preset discrete threshold value in each grid region. Specifically, firstly, the present application calculates the relative error between the network coverage rate predicted value and the network coverage rate actual value corresponding to the network coverage rate predicted value in each grid region, then calculates two key statistics of the average value of the relative error and the standard deviation of the relative error, and finally obtains a preset discrete threshold value corresponding to each grid region by referring to formula (2). Thus, according to the normal distribution statistical characteristics, when the relative error occurs in a region outside of the two times of the standard deviation on both sides of the mean, the probability is 4.55%, and the event is regarded as a small probability event. That is, when the relative error is greater than or equal to a preset discrete threshold value corresponding to the grid region, the grid region is determined to be the critical grid region.
Specifically, the positioning module 505 is specifically configured to obtain a first fluctuation parameter in the first data file and a second fluctuation parameter in the second data file. The first fluctuation parameter comprises a first time advance and a first signal arrival angle, and the second fluctuation parameter comprises a second time advance and a second signal arrival angle; then judging whether the second time advance is greater than the first time advance, if so, determining that the corresponding associated cell is a weak coverage cell, and positioning the weak coverage cell according to the second time advance and a second signal arrival angle; otherwise, ending the whole process.
The comparison of the time advance can also be obtained by comparing the arrival angles of the signals. Specifically, when the second signal arrival angle is smaller than the first signal arrival angle, the corresponding associated cell may also be determined to be a weak coverage cell.
Specifically, the first data file and the second data file refer to measurement report MR in a first period/a second period, respectively, which is long term evolution (Long Term Evolution, LTE) measurement report MR. The positioning process specifically comprises the following steps:
firstly, determining a distance range from a base station to the weak coverage cell according to a second time advance, and determining a range of a horizontal distance from the base station to the weak coverage cell according to the distance range and the height of an antenna of the base station from the ground;
Secondly, converting longitude and latitude of the base station into a rectangular coordinate value of a Gaussian plane according to a Gaussian projection forward calculation formula;
then, according to the plane right angle coordinate value of the base station, the range of the horizontal distance and the second signal arrival angle, determining the plane coordinate range of the weak coverage cell projection;
specifically, the second signal arrival angle is used to define an estimated angle of a cell relative to a measurement reference direction, where the measurement reference direction is a north-right direction of the base station.
FIG. 6 is a schematic diagram of one embodiment of a computer device of the present application, which may include at least one processor; and at least one memory communicatively coupled to the processor; the memory stores program instructions executable by the processor, and the processor invokes the program instructions to be capable of executing the method for determining the weak coverage cell in the mobile communication network, so that the method for determining the weak coverage cell in the mobile communication network provided by the embodiment of the application can be implemented.
The computer device may be a server, for example: the cloud server, or the above-mentioned computer device, may also be a computer device, for example: the embodiment of the present invention is not limited to a specific form of a smart device such as a smart phone, a smart watch, a personal computer (Personal Computer; hereinafter referred to as a PC), a notebook computer, or a tablet computer.
Fig. 6 illustrates a block diagram of an exemplary computer device 52 suitable for use in implementing embodiments of the present application. The computer device 52 shown in fig. 6 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 6, the computer device 52 is in the form of a general purpose computing device. Components of computer device 52 may include, but are not limited to: one or more processors or processing units 56, a system memory 78, a bus 58 that connects the various system components, including the system memory 78 and the processing units 56.
Computer device 52 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 52 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 78 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory; hereinafter: RAM) 70 and/or cache memory 72. The computer device 52 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system 74 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard disk drive"). Although not shown in fig. 6, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a compact disk read only memory (Compact Disc Read Only Memory; hereinafter CD-ROM), digital versatile read only optical disk (Digital Video Disc Read Only Memory; hereinafter DVD-ROM), or other optical media) may be provided. In such cases, each drive may be coupled to bus 58 through one or more data media interfaces. Memory 78 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the present application.
A program/utility 80 having a set (at least one) of program modules 82 may be stored, for example, in the memory 78, such program modules 82 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 82 generally perform the functions and/or methods in the embodiments described herein.
The computer device 52 can also communicate with one or more external devices 54 (e.g., keyboard, pointing device, display 64, etc.), one or more devices that enable a user to interact with the computer device 52, and/or any device (e.g., network card, modem, etc.) that enables the computer device 52 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 62. Also, the computer device 52 may communicate with one or more networks such as a local area network (Local Area Network; hereinafter: LAN), a wide area network (Wide Area Network; hereinafter: WAN) and/or a public network such as the Internet via the network adapter 60. As shown in fig. 6, the network adapter 60 communicates with other modules of the computer device 52 via the bus 58. It should be appreciated that although not shown in fig. 6, other hardware and/or software modules may be used in connection with computer device 52, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 56 executes various functional applications and data processing by running programs stored in the system memory 78, for example, to implement the method for determining a weak coverage cell in a mobile communication network provided in the embodiment of the present application.
The embodiment of the application also provides a non-transitory computer readable storage medium, which stores computer instructions that cause the computer to execute the method for determining the weak coverage cell in the mobile communication network.
The non-transitory computer readable storage media described above may employ any combination of one or more computer readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory; EPROM) or flash Memory, an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (Local Area Network; hereinafter: LAN) or a wide area network (Wide Area Network; hereinafter: WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection". Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.
The foregoing description of the preferred embodiments of the present invention is not intended to limit the invention to the precise form disclosed, and any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. A method for determining a weak coverage cell in a mobile communication network, the method comprising:
acquiring a network coverage rate predicted value of each grid area in a second period after a first period according to a trained network coverage rate predicted model, wherein the grid areas are grid areas which are divided in advance according to geographic areas covered by a network;
calculating the relative error of the network coverage rate predicted value and the network coverage rate actual value corresponding to the network coverage rate predicted value, and screening out a key grid area according to the relative error, wherein a cell corresponding to a network coverage signal of the key grid area is an associated cell of the key grid area;
acquiring a first data file of the associated cell during the first period and a second data file during the second period, and,
positioning a weak coverage cell according to a comparison result of fluctuation parameters in the first data file and the second data file;
the step of acquiring the network coverage rate predicted value of each grid area in a second period after the first period according to the trained network coverage rate predicted model further comprises:
Acquiring a network coverage rate historical value and a historical time corresponding to the network coverage rate historical value;
performing exponential smoothing on the network coverage rate historical value and the historical time, and obtaining a smoothing factor;
obtaining a trained network coverage prediction model according to the smoothing factor
The actual value of network coverage can be obtained by the following formula:
wherein Cov represents the actual value of the network coverage of the grid region, and k represents the value corresponding to the grid regionThe number of cells to which the network coverage signal corresponds,
indicating the number of levels in the kth cell that fall in the ith coverage level.
2. The method of claim 1, wherein calculating a relative error of the network coverage prediction value and an actual network coverage value corresponding to the network coverage prediction value, and screening out a key grid region based on the relative error comprises:
and when the relative error is greater than or equal to a preset discrete threshold value corresponding to the grid region, determining the grid region as the key grid region.
3. The method of claim 1, wherein locating the weak coverage cell based on the comparison of the fluctuation parameters in the first data file and the second data file comprises:
Acquiring a first fluctuation parameter in the first data file and a second fluctuation parameter in the second data file, wherein the first fluctuation parameter comprises a first time advance and a first signal arrival angle, and the second fluctuation parameter comprises a second time advance and a second signal arrival angle;
when the second time advance is greater than the first time advance or the second signal arrival angle is smaller than the first signal arrival angle, determining that the corresponding associated cell is the weak coverage cell; the method comprises the steps of,
and positioning the weak coverage cell according to the second time advance and the second signal arrival angle.
4. The method of claim 1, wherein after calculating a relative error between the network coverage prediction value and an actual network coverage value corresponding to the network coverage prediction value, and screening the key grid region according to the relative error, further comprising:
acquiring a third data file of a neighboring cell of the associated cell in the first period and a fourth data file in the second period, and,
and positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the third data file and the fourth data file.
5. The method of claim 4, wherein locating the weak coverage cell based on the comparison of the fluctuation parameters in the third data file and the fourth data file comprises:
acquiring a third fluctuation parameter in the third data file and a fourth fluctuation parameter in the fourth data file, wherein the third fluctuation parameter comprises a third time advance and a third signal arrival angle, and the fourth fluctuation parameter comprises a fourth time advance and a fourth signal arrival angle;
when the fourth time advance is greater than the third time advance or the fourth signal arrival angle is smaller than the third signal arrival angle, determining that the corresponding associated cell is the weak coverage cell; the method comprises the steps of,
and positioning the weak coverage cell according to the fourth time advance and the fourth signal arrival angle.
6. The method of claim 2, wherein the predetermined discrete threshold is calculated by the following formula:
preset discrete threshold = a+2*S
Wherein A represents the average value of each corresponding relative error in a specified period of the grid region, S represents the standard deviation of each corresponding relative error in the specified period of the grid region, and the relative error is the ratio of the absolute error of the network coverage predicted value to the actual network coverage value to the network coverage predicted value.
7. An apparatus for determining a weak coverage cell in a mobile communication network, the apparatus comprising:
the first acquisition module is used for acquiring a network coverage rate predicted value of each grid area in a second period after a first period according to the trained network coverage rate predicted model, wherein the grid areas are grid areas which are divided in advance according to geographic areas covered by the network;
the calculation module is connected with the first acquisition module and is used for calculating the relative error between the network coverage rate predicted value and the network coverage rate actual value corresponding to the network coverage rate predicted value;
the screening module is connected with the computing module and is used for screening out a key grid area according to the relative error, and a cell corresponding to a network coverage signal of the key grid area is an associated cell of the key grid area;
the second acquisition module is connected with the screening module and is used for acquiring a first data file of the associated cell in the first period and a second data file of the associated cell in the second period;
the positioning module is connected with the second acquisition module and is used for positioning the weak coverage cell according to the comparison result of the fluctuation parameters in the first data file and the second data file;
The network coverage rate prediction model is obtained through the following training mode:
acquiring a network coverage rate historical value and a historical time corresponding to the network coverage rate historical value, performing exponential smoothing on the network coverage rate historical value and the historical time, obtaining a smoothing factor, and obtaining a trained network coverage rate prediction model according to the smoothing factor;
the actual value of network coverage can be obtained by the following formula:
wherein Cov represents the actual value of the network coverage of the grid region, k represents the number of cells corresponding to the network coverage signal of the grid region,
indicating the number of levels in the kth cell that fall in the ith coverage level.
8. A computer device, comprising:
at least one processor; and
at least one memory communicatively coupled to the processor;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-6.
9. A non-transitory computer readable storage medium storing computer instructions that cause the computer to perform the method of any one of claims 1 to 6.
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