Network coverage abnormity monitoring method, electronic equipment and program product
By subdividing the network coverage to microgrids and using T-test method, the problem of inaccurate monitoring of network coverage abnormalities in the prior art is solved, and a more stable and accurate monitoring effect is achieved.
Patent Information
- Application Number
- CN202510390667.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2025-07-18
AI Technical Summary
The prior art cannot accurately monitor abnormal fluctuations in network coverage capabilities in the area, especially in the case of multi-base station collaboration and sparse terminal data, resulting in unstable and inaccurate monitoring results.
By minimizing the MDT data of the road measurement, the network coverage is divided into the target grid, and further subdividing it into micro grids, level fluctuations are calculated using the T-test method to output the network coverage abnormal monitoring results.
It improves the stability and accuracy of network coverage monitoring, reduces the influence of extreme position points, and avoids the indiscriminate treatment deviations for different coverage areas.
Smart Images
Figure CN120343570A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the technical field of wireless networks, and particularly relates to a method for monitoring abnormal network coverage, an electronic device, and a program product. Background Art
[0002] With the rapid development of wireless data services, the scale of 4G and 5G networks has grown rapidly. Network coverage has become the primary condition for ensuring service quality. Therefore, it is crucial for operators to monitor abnormal fluctuations in network coverage capabilities in a timely and accurate manner. Currently, there are two main ways to monitor abnormal fluctuations in network coverage capabilities. One is the monitoring method based on the base station measurement report (MR), and the other is the grid statistics method.
[0003] The monitoring method based on the base station MR obtains the average coverage level by collecting the MR of the base station, and then compares the differences between two periods to obtain the fluctuations in the base station coverage. The main problem with this method is that when multiple base stations in a certain area cooperate to provide coverage, the situation of a single base station cannot fully reflect the overall coverage level in the area.
[0004] The grid-based statistics method generally uses user measurement information with longitude and latitude markings to statistically analyze the coverage changes in the grid area. The main problem with this method at present is that the data is restricted by factors such as mobile phone terminal protocol support and user privacy protection permissions, and the data with longitude and latitude markings is very sparse. Calculating the confidence level of the average level of the area according to the conventional statistical means is very low, and it is easily affected by abnormal points.
[0005] In summary, the current monitoring methods cannot accurately monitor the abnormal fluctuations in network coverage capabilities in the area. Summary of the Invention
[0006] Embodiments of this application provide a method for monitoring abnormal network coverage, an electronic device, and a program product, which can solve the problem of being unable to accurately monitor the abnormal fluctuations in network coverage capabilities in the area.
[0007] In a first aspect, embodiments of this application provide a method for monitoring abnormal network coverage. The method includes: determining a first micro-grid and a second micro-grid of a target grid at different time periods according to the obtained first minimized drive test (MDT) data, where the target grid is obtained by dividing the network coverage range, and the first micro-grid and the second micro-grid are obtained by dividing the target grid; calculating the level fluctuation of the target grid according to the first level of the first micro-grid, the second level of the second micro-grid, and the T-test method; and outputting the monitoring result of the abnormal network coverage of the target grid according to the level fluctuation of the target grid.
[0008] Second aspect, an embodiment of the present application provides a network coverage anomaly monitoring device, which includes: a determination module, configured to determine a first micro-grid and a second micro-grid of a target grid in different time periods according to the acquired first minimized drive test (MDT) data, where the target grid is obtained by dividing the network coverage area, and the first micro-grid and the second micro-grid are obtained by dividing the target grid; a calculation module, configured to calculate the level fluctuation of the target grid according to the first level of the first micro-grid, the second level of the second micro-grid, and the T-test method; and an output module, configured to output the network coverage anomaly monitoring result of the target grid according to the level fluctuation of the target grid.
[0009] Third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.
[0010] Fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.
[0011] Fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is enabled to execute the steps of the method described in the first aspect.
[0012] In the embodiment of the present application, by determining the first micro-grid and the second micro-grid of the target grid in different time periods according to the acquired first minimized drive test (MDT) data, where the target grid is obtained by dividing the network coverage area, and the first micro-grid and the second micro-grid are obtained by dividing the target grid; calculating the level fluctuation of the target grid according to the first level of the first micro-grid, the second level of the second micro-grid, and the T-test method; and outputting the anomaly monitoring result of the target network coverage area corresponding to the target grid according to the level fluctuation of the target grid, the stability and accuracy of network coverage monitoring can be improved. Description of the Drawings
[0013] Figure 1 is a schematic flowchart of a network coverage anomaly monitoring method provided by an embodiment of the present application; Figure 2 is a schematic diagram of a network coverage anomaly monitoring architecture provided by an embodiment of the present application; Figure 3 is a schematic diagram of MDT data provided by an embodiment of the present application; Figure 4 is a schematic diagram of grid division provided by an embodiment of the present application; Figure 5 is a schematic diagram of a level matrix provided by an embodiment of the present application; Figure 6 is a schematic diagram of a common matrix provided by an embodiment of the present application; Figure 7 is a schematic diagram of another level matrix provided by an embodiment of the present application; Figure 8 is a schematic diagram of the structure of a network coverage anomaly monitoring device provided by an embodiment of the present application; Figure 9 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners
[0014] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
[0015] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order different from those illustrated or described herein, and the objects distinguished by "first", "second", etc. generally belong to the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally represents an "or" relationship between the associated objects before and after.
[0016] Next, a network coverage anomaly monitoring method, an electronic device, and a program product provided by an embodiment of the present application will be described in detail with reference to the accompanying drawings, through specific embodiments and their application scenarios.
[0017] Figure 1 shows a network coverage anomaly monitoring method provided by an embodiment of the present application. This method can be executed by an electronic device, and the electronic device can include: a server and / or a terminal device. In other words, this method can be executed by software or hardware installed in the electronic device. The method includes the following steps: Step 102: Determine a first micro-grid and a second micro-grid of a target grid in different time periods according to the obtained first minimized drive test (MDT) data.
[0018] Figure 2 Shows a schematic diagram of an architecture provided by an embodiment of the present application. As Figure 2 shown, the network coverage anomaly monitoring architecture provided by the embodiment of the present application mainly includes: a Minimization Drive Test (MDT) collector, an MDT filter, a grid projector, a model calculator, an early warning device, and a Geographic Information System (GIS) display device. Among them, the main function of the MDT collector is to obtain the MDT data file from the network management system. The MDT filter mainly completes the cleaning work of abnormal data, including deleting duplicate data, unreasonable data, data with too large deviation, etc. Grid projector: mainly completes the geographical projection of legal MDT data (sampling points) and stores them in the corresponding micro-grids. The model calculator mainly completes the quantity statistics of the MDT data in the micro-grid according to the MDT data contained in the grid, which can also be considered as the quantity statistics of the MDT data sampling points. At the same time, it is also necessary to calculate the signal level and construct a test model. The early warning device is mainly used to determine the monitoring result according to the model test calculation result and store it in the database. GIS display device: Geographically displays and renders the overall monitoring result.
[0019] In the embodiment of the present application, the network coverage area can be divided into grids first, and then the grids can be further divided into micro-grids. As an example, the network coverage area can be divided into multiple grids. For example, the network coverage area can be divided into grids of 200m * 200m as the unit of network coverage anomaly monitoring. To solve the influence of the aggregation of abnormal data points on the overall monitoring result, the embodiment of the present application can further divide the grid into multiple micro-grids. For example, a grid can be divided into multiple micro-grids of 20m * 20m. As Figure 3 shown, such a grid can be divided into 10 * 10 micro-grids.
[0020] In the embodiment of the present application, the MDT data records the user terminal measurement information, including but not limited to user terminal identification, serving cell signal strength, user longitude, user latitude and other information. The data fields of the MDT data involved in the embodiment of the present application are as Figure 4 shown. Due to restrictions such as mobile phone terminal protocol support and user privacy protection permissions, data with longitude and latitude annotations is very sparse. Currently, the measurement reports of MDT data with longitude and latitude annotations only account for about 3% of all measurement reports. Therefore, to obtain a relatively stable statistical result, the embodiment of the present application collects a sufficient number of measurement reports, and at the same time, considering the difference in business models between weekends and weekdays, at least 1 week of MDT data needs to be collected.
[0021] In one implementation manner, determining a first micro-grid and a second micro-grid of a target grid in different time periods according to the obtained first minimized drive test (MDT) data includes: dividing the target grid into a plurality of third micro-grids; projecting the first MDT data onto the plurality of third micro-grids; and screening out the first micro-grid and the second micro-grid from the third micro-grids according to the quantity of the first MDT data in the third micro-grids in the different time periods.
[0022] In the embodiments of the present application, the MDT data obtained within the network coverage range can be projected into a grid. For a target grid within the network coverage range, the target grid can be divided into a plurality of third micro-grids, and then the first MDT data corresponding to the target grid is projected onto the plurality of third micro-grids. The target grid can be any grid within the network coverage range. Then, the quantity of the first MDT data in the third micro-grids in different time periods can be counted, and the first micro-grid and the second micro-grid are screened out from the third micro-grids.
[0023] In the embodiments of the present application, the grid corresponding to the MDT data can be calculated according to the longitude and latitude of the user terminal in the MDT data. In one embodiment, represents a grid, where , , represents the maximum horizontal number after grid division, represents the maximum vertical number after grid division. ,where , represents the micro-grid belonging to of. , , , respectively represent the maximum longitude, minimum longitude, maximum latitude, and minimum latitude of the vertices of the micro-grid. Let be any MDT data. If then . In this way, the MDT data of each sampling point can be projected onto the corresponding micro-grid, that is, the first MDT data corresponding to the target grid can be projected onto the third micro-grid of the target grid. At the same time, the quantity and level of the MDT data can be added to the micro-grid, and finally the total quantity of the MDT data in the micro-grid and the average level are calculated, and then the quantity of the first MDT data in the third micro-grid of the target grid can be obtained.
[0024] In one implementation, the different time periods include a first time period and a second time period. Screening out the first micro-grid and the second micro-grid from the third micro-grid according to the number of the first MDT data of the third micro-grid in the different time periods includes: determining the third micro-grid with the number of the first MDT data greater than a preset threshold in the first time period as the first micro-grid; determining the third micro-grid with the number of the first MDT data greater than the preset threshold in the second time period as the second micro-grid.
[0025] In the embodiments of the present application, all grids can be searched in units of grids, for a target grid Suppose two time periods to be compared are respectively a first time period and a second time period . For the first time period , determine the third micro-grid in the target grid with the number of the first MDT data greater than the preset threshold in the first time period as the above-mentioned first micro-grid. The preset threshold can be, for example, 30, and the preset threshold can be set according to the accuracy requirement. For the second time period , determine the third micro-grid in the target grid with the number of the first MDT data greater than the preset threshold in the second time period as the above-mentioned second micro-grid. In this way, the first micro-grid and the second micro-grid for comparison can be obtained from the target grid. In this way, the target grid is split into micro-grids, and the confidence level of abnormal coverage range monitoring can be improved by grid dimension elevation.
[0026] Step 104: Calculate the level fluctuation of the target grid according to the first level of the first micro-grid, the second level of the second micro-grid and the T-test method.
[0027] In the embodiments of the present application, since the level is added to the micro-grid when dividing the micro-grid, after obtaining the first micro-grid and the second micro-grid, the first level of each first micro-grid can be obtained, where the first level is the average level of each first micro-grid. Similarly, the second level of each second micro-grid can be obtained, where the second level is the average level of each second micro-grid.
[0028] In the embodiments of the present application, the level fluctuation of the target grid can be calculated through the T - test method according to the first levels of the first micro - grids and the second levels of the second micro - grids, and then the abnormal fluctuation of the coverage range corresponding to the target grid can be determined according to the level fluctuation of the target grid. The T - test method is mainly used for normal distributions with small sample sizes and unknown population standard deviations. The T - test uses the T - distribution theory to infer the probability of the occurrence of differences, so as to compare whether the differences between two averages are significant. Using the T - test method to monitor regional coverage anomalies, rather than relying on traditional absolute threshold values to determine coverage degradation, reduces the impact caused by a single extreme position point, and at the same time avoids the deviation of the current method of treating different coverage areas without discrimination.
[0029] Step 106: Output the network coverage anomaly monitoring result of the target grid according to the level fluctuation of the target grid.
[0030] In the embodiments of the present application, after obtaining the level fluctuation of the target grid, analyzing the level fluctuation can determine and output the network coverage anomaly monitoring result corresponding to the target grid.
[0031] The network coverage anomaly monitoring method provided by the embodiments of the present application determines the first micro - grids and the second micro - grids of the target grid at different time periods according to the obtained first minimized drive test (MDT) data. The target grid is obtained by dividing the network coverage range, and the first micro - grids and the second micro - grids are obtained by dividing the target grid; calculates the level fluctuation of the target grid according to the first level of the first micro - grid, the second level of the second micro - grid and the T - test method; and outputs the anomaly monitoring result of the target network coverage range corresponding to the target grid. The grid dimension - elevation and the T - test method can solve the problems that data such as MDT are restricted by mobile - phone terminal protocol support, user privacy protection permissions, etc., and the data with longitude and latitude annotations is very sparse. It can solve the problem that the confidence level of the calculated regional average level is very low according to conventional statistical means and is easily affected by abnormal points. At the same time, using the T - test method to monitor regional coverage anomalies, rather than relying on traditional absolute threshold values to determine coverage degradation, reduces the impact caused by a single extreme position point, and at the same time avoids the deviation of the current method of treating different coverage areas without discrimination, improving the monitoring stability and accuracy of the abnormal fluctuation of the network coverage range.
[0032] In one implementation, before determining the first micro-grid and the second micro-grid of the target grid in different time periods according to the obtained first minimized drive test (MDT) data, the method further includes: obtaining second MDT data of the target grid from within the network coverage; performing duplicate data removal processing on the second MDT data according to the time stamp and user terminal identifier of the second MDT data, and performing filtering processing on the second MDT data according to the longitude information and latitude information of the second MDT data to obtain the first MDT data.
[0033] In an embodiment of the present application, a large amount of second MDT data can be first obtained within the network coverage, and then by analyzing the second MDT data, it is found that there are some duplicate data in the second MDT data, which will affect the weight of the statistical results. Therefore, duplicate data removal processing is performed on the second MDT data according to the user dimension, that is, duplicate data removal is performed according to the time stamp and user terminal identifier in the second MDT data to ensure the uniqueness of the data in the two-dimensional space of (TimeStamp, MME UE S1AP ID). Then, for the second MDT data, due to the influence of the terminal and the GPS signal strength, there is a certain degree of uncertainty, and further filtering is required. In an embodiment of the present application, the second MDT data can be filtered according to the longitude information and latitude information of the second MDT data, and the second MDT data in which both the UncertaintySemiMajor and UncertaintySemiMinor fields are less than 7 or empty can be selected as the first MDT data to enter the next statistical stage. In this way, performing duplicate data removal processing and filtering processing on the second MDT data can ensure the uniqueness and stability of the data, while considering the filtering of duplicate measurement data of the same terminal, and at the same time, from the influence of the major axis uncertainty and the minor axis uncertainty, the stability and accuracy of network coverage anomaly monitoring are improved.
[0034] In one implementation, calculating the level fluctuation of the target grid according to the first level of the first micro-grid, the second level of the second micro-grid, and the T-test method includes: forming a first level matrix of the target grid according to the first level; forming a second level matrix of the target grid according to the second level; performing an intersection operation on the first level matrix and the second level matrix to obtain a first common matrix of the first level matrix and a second common matrix of the second level matrix; calculating the level fluctuation of the target grid according to the first common matrix, the second common matrix, and the T-test method.
[0035] In an embodiment of the present application, a first level matrix can be formed according to the first level of the first micro-grid . Similarly, a characteristic second level matrix is generated for the second level data of the second micro-grid Since the number of MDT data (data sampling points) for each micro-grid within different time periods may not all meet the requirements, the first level matrix , and the second level matrix may not have the same contour, as shown in Figure 5 .
[0036] In the embodiments of the present application, an intersection operation can be performed on the first level matrix and the second level matrix, that is, the common part corresponding to the two matrices is taken out. As shown in Figure 6 , that is, the first common matrix of the first level matrix and the second common matrix of the second level matrix are obtained. As an embodiment, a grid can be composed of 100 micro-grids. In order to improve the representativeness of the grid coverage characteristics, the proportion of the common part of the first level matrix and the second level matrix should reach more than 50%, that is, the number of effective overlapping micro-grids should reach more than 50. Then, according to the first common matrix, the second common matrix, and the T-test method, the level fluctuation of the target grid can be calculated. In this way, through the T-test method, it can be detected whether the levels of the first common matrix and the second common matrix have changed significantly, and then the level fluctuation of the target grid can be calculated, ensuring the stability and accuracy of the abnormal monitoring of the network coverage range.
[0037] In one implementation manner, calculating the level fluctuation of the target grid according to the first common matrix, the second common matrix, and the T-test method includes: converting the first common matrix into a first one-dimensional vector; converting the second common matrix into a second one-dimensional vector; calculating the level fluctuation of the target grid according to the first one-dimensional vector, the second one-dimensional vector, and the T-test method.
[0038] Specifically, according to the first common matrix and the second common matrix obtained by the intersection operation, the first common matrix can be converted into a first one-dimensional vector , and the second common matrix can be converted into a second one-dimensional vector , and its position still corresponds one-to-one with the common matrix. Here, the T-test method is used to detect whether there is a significant change in the average level of the two one-dimensional vectors, that is, to calculate the level fluctuation of the target grid. According to the T-test formula: , where represents , the average value of the level differences at the corresponding positions in represents , the number of elements of , represents , the standard deviation of the level differences at the corresponding positions in
[0039] In the embodiments of the present application, the T - test method is used to monitor abnormal area coverage, rather than relying on the traditional absolute threshold value to determine coverage degradation. This reduces the impact caused by a single extreme position point, and also avoids the deviation of the current method in treating different coverage areas without discrimination, improving the monitoring stability and accuracy of abnormal fluctuations in the network coverage range.
[0040] In one implementation manner, outputting the network coverage anomaly monitoring result of the target grid according to the level fluctuation of the target grid includes: determining a target value corresponding to the level fluctuation from a preset distribution table; when the target value is less than a preset threshold value, outputting the network coverage anomaly monitoring result that the target grid has an abnormal fluctuation; when the target value is greater than or equal to the preset threshold value, outputting the network coverage anomaly monitoring result that the target grid has no abnormal fluctuation.
[0041] In the embodiments of the present application, it can be assumed that there is no significant difference in the level of the network coverage range of this area, and it is assumed that there is a significant difference in the level of the network coverage range of this area, that is, there is an abnormal fluctuation in this area. After calculating the level fluctuation of the target grid through the T - test method, the target value corresponding to the level fluctuation can be found according to the preset distribution table . If the target value is less than the preset threshold value, it means that the difference between the two groups of levels of the first level matrix and the second level matrix is not caused by random sampling, but by systematic reasons, thus negating the hypothesis, the hypothesis holds, that is, there is an abnormal fluctuation in the network coverage of the target grid area. If the target value is greater than or equal to the preset threshold value, it means that the difference between the two groups of levels of the first level matrix and the second level matrix is random, and it cannot be determined that there is an abnormal fluctuation in the network coverage of the target grid area. For example Figure 7 as shown, is 0.02. If the preset threshold value is 0.05, it indicates that there is an abnormal fluctuation in the network coverage of this area. In the embodiments of the present application, after overall evaluation of the network coverage range area, the grids with abnormal fluctuations in coverage within the network coverage range can be rendered to achieve the geographical display and rendering of the test results. In the embodiments of the present application, the T - test method, the calculated target value, and the preset threshold value are used to monitor abnormal area coverage, rather than relying on the traditional absolute threshold value to determine coverage degradation. This reduces the impact caused by a single extreme position point, and also avoids the deviation of the current method in treating different coverage areas without discrimination, improving the monitoring stability and accuracy of abnormal fluctuations in the network coverage range.
[0042] In one implementation, before determining the target value corresponding to the level fluctuation from the preset distribution table, it further includes: calculating the preset threshold according to the number of the second MDT data of the target grid, the maximum number and the minimum number of the third MDT data in all grids within the network coverage range.
[0043] In the embodiments of the present application, the preset threshold can be selected in two ways. One is to use 0.05, which is commonly used in statistics, as the preset threshold, and a more stringent requirement can be set to 0.01. Narrowing the preset threshold represents an increase in the confidence level. In actual operation and maintenance work, it can be sorted from small to large according to the calculated values in each region, so as to determine the on-site investigation priority. The smaller the value, the higher the priority.
[0044] The other is to calculate and determine the preset threshold according to the importance of the region. It is considered that the grids with high traffic volume should have higher importance, and more attention should be paid to the coverage fluctuation of the grids. Therefore, a larger threshold should be defined to determine abnormality, while a smaller threshold can be used for the grids with lower traffic volume. According to this idea, using the number of the second MDT data obtained from the initial sampling of each grid as the weight, calculate the preset threshold as a value between 0.01 and 0.05. denotes grid, the number of sampling points is , the maximum number of sampling points in all grids, the minimum number of sampling points in all grids. The specific calculation method is as follows: .
[0045] In this way, the T-test method, the calculated target value and the preset threshold are used to monitor the abnormal area coverage, rather than relying on the traditional absolute threshold to determine the coverage degradation. This reduces the influence caused by a single extreme position point, and at the same time avoids the deviation of the current method in treating different coverage areas without discrimination, improving the monitoring stability and accuracy of the network coverage range fluctuation abnormality.
[0046] It should be noted that for the network coverage abnormality monitoring method provided in the embodiments of the present application, the execution subject can be a network coverage abnormality monitoring device, or a control module in the network coverage abnormality monitoring device for executing the network coverage abnormality monitoring method. In the embodiments of the present application, taking the network coverage abnormality monitoring device as an example to execute the network coverage abnormality monitoring method, the network coverage abnormality monitoring device provided in the embodiments of the present application is described.
[0047] Figure 8 is a schematic structural diagram of the network coverage abnormality monitoring device according to the embodiments of the present application. As Figure 8As shown, the network coverage anomaly monitoring device 800 includes: a determination module 810, a calculation module 820, and an output module 830.
[0048] The determination module 810 is configured to determine a first micro-grid and a second micro-grid of a target grid in different time periods according to the acquired first minimized drive test (MDT) data, where the target grid is obtained by dividing the network coverage range, and the first micro-grid and the second micro-grid are obtained by dividing the target grid; the calculation module 820 is configured to calculate the level fluctuation of the target grid according to the first level of the first micro-grid, the second level of the second micro-grid, and the T-test method; the output module 830 is configured to output the anomaly monitoring result of the target grid according to the level fluctuation of the target grid.
[0049] In one implementation, the determination module 810 is configured to divide the target grid into multiple third micro-grids; project the first MDT data onto the multiple third micro-grids; and screen out the first micro-grid and the second micro-grid from the third micro-grids according to the quantity of the first MDT data in the different time periods in the third micro-grids.
[0050] In one implementation, the different time periods include a first time period and a second time period, and the determination module 810 is configured to determine the third micro-grids with the quantity of the first MDT data greater than a preset threshold in the first time period as the first micro-grid; and determine the third micro-grids with the quantity of the first MDT data greater than the preset threshold in the second time period as the second micro-grid.
[0051] In one implementation, the determination module 810 is further configured to obtain second MDT data of the target grid from within the network coverage range; perform duplicate data removal processing on the second MDT data according to the time stamp and user terminal identifier of the second MDT data, and perform filtering processing on the second MDT data according to the longitude information and latitude information of the second MDT data to obtain the first MDT data.
[0052] In one implementation, the calculation module 820 is configured to form a first level matrix of the target grid according to the first level; form a second level matrix of the target grid according to the second level; perform an intersection operation on the first level matrix and the second level matrix to obtain a first common matrix of the first level matrix and a second common matrix of the second level matrix; and calculate the level fluctuation of the target grid according to the first common matrix, the second common matrix, and the T-test method.
[0053] In one implementation, the computing module 820 is configured to convert the first common matrix into a first one-dimensional vector; convert the second common matrix into a second one-dimensional vector; and calculate the level fluctuation of the target grid according to the first one-dimensional vector, the second one-dimensional vector, and the T-test method.
[0054] In one implementation, the output module 830 is configured to determine a target value corresponding to the level fluctuation from a preset distribution table; output a network coverage anomaly monitoring result indicating that the target grid has an abnormal fluctuation when the target value is less than a preset threshold; and output a network coverage anomaly monitoring result indicating that the target grid does not have an abnormal fluctuation when the target value is greater than or equal to the preset threshold.
[0055] In one implementation, the computing module 820 is further configured to calculate the preset threshold according to the number of the second MDT data of the target grid, the maximum number and the minimum number of the third MDT data among all grids within the network coverage.
[0056] The network coverage anomaly monitoring device in the embodiments of the present application may be a device, or a component, an integrated circuit, or a chip in a terminal. The device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a palmtop computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device may be a server, a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc. The embodiments of the present application do not make specific limitations.
[0057] The network coverage anomaly monitoring device in the embodiments of the present application may be a device with an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems. The embodiments of the present application do not make specific limitations.
[0058] The network coverage anomaly monitoring device provided in the embodiments of the present application can implement Figures 1 to 7 each process implemented by the method embodiments. To avoid repetition, details are not described herein again.
[0059] Such as Figure 9As shown in the figure, another embodiment of the present application provides an electronic device 900, including a processor 901 and a memory 902. A program or instruction that can run on the processor 901 is stored on the memory 902. When the program or instruction is executed by the processor 901, it realizes: determining a first micro-grid and a second micro-grid of a target grid in different time periods according to the acquired first minimized drive test (MDT) data, where the target grid is obtained by dividing the network coverage area, and the first micro-grid and the second micro-grid are obtained by dividing the target grid; calculating the level fluctuation of the target grid according to the first level of the first micro-grid, the second level of the second micro-grid, and the T-test method; and outputting the abnormal monitoring result of the target grid according to the level fluctuation of the target grid.
[0060] In one implementation, the target grid is divided into a plurality of third micro-grids; the first MDT data is projected onto the plurality of third micro-grids; and the first micro-grid and the second micro-grid are selected from the third micro-grids according to the number of the first MDT data in the different time periods in the third micro-grids.
[0061] In one implementation, the different time periods include a first time period and a second time period. The third micro-grid in which the number of the first MDT data in the first time period is greater than a preset threshold is determined as the first micro-grid; and the third micro-grid in which the number of the first MDT data in the second time period is greater than the preset threshold is determined as the second micro-grid.
[0062] In one implementation, before determining the first micro-grid and the second micro-grid of the target grid in different time periods according to the acquired first minimized drive test (MDT) data, second MDT data of the target grid is acquired from the network coverage area; the second MDT data is de-duplicated according to the time stamp and user terminal identifier of the second MDT data, and the second MDT data is filtered according to the longitude information and latitude information of the second MDT data to obtain the first MDT data.
[0063] In one implementation, a first level matrix of the target grid is formed according to the first level; a second level matrix of the target grid is formed according to the second level; an intersection operation is performed on the first level matrix and the second level matrix to obtain a first common matrix of the first level matrix and a second common matrix of the second level matrix; and the level fluctuation of the target grid is calculated according to the first common matrix, the second common matrix, and the T-test method.
[0064] In one implementation, convert the first common matrix into a first one-dimensional vector; convert the second common matrix into a second one-dimensional vector; calculate the level fluctuation of the target grid according to the first one-dimensional vector, the second one-dimensional vector, and the T-test method.
[0065] In one implementation, determine the target value corresponding to the level fluctuation from a preset distribution table; in the case where the target value is less than a preset threshold, output a network coverage anomaly monitoring result that the target grid has an abnormal fluctuation; in the case where the target value is greater than or equal to the preset threshold, output a network coverage anomaly monitoring result that the target grid does not have an abnormal fluctuation.
[0066] In one implementation, before determining the target value corresponding to the level fluctuation from the preset distribution table, calculate the preset threshold according to the number of the second MDT data of the target grid, the maximum number and the minimum number of the third MDT data among all grids within the network coverage.
[0067] For the specific implementation steps, reference can be made to the steps in the embodiments of the above network coverage anomaly monitoring method, and the same technical effects can be achieved. To avoid repetition, details are not described here.
[0068] It should be noted that the electronic device in the embodiments of the present application includes: a server, a terminal, or other devices other than the terminal.
[0069] The above structure of the electronic device does not limit the electronic device. The electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. For example, the input unit may include a Graphics Processing Unit (GPU) and a microphone, and the display unit may be configured with a display panel in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit includes at least one of a touch panel and other input devices. The touch panel is also called a touch screen. Other input devices may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, which are not described in detail here.
[0070] The memory can be used to store software programs and various data. The memory may mainly include a first storage area for storing programs or instructions and a second storage area for storing data. Among them, the first storage area can store an operating system, application programs or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory can include volatile memory or non-volatile memory, or the memory can include both volatile and non-volatile memory. Among them, the non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synchlink DRAM (SLDRAM), and a Direct Rambus RAM (DRRAM).
[0071] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor. Among them, the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above modem processor may not be integrated into the processor either.
[0072] The embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by the processor, it implements each process of the above embodiment of the network coverage anomaly monitoring method and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.
[0073] Among them, the processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as ROM, RAM, magnetic disk, or optical disc, etc.
[0074] The embodiments of the present application also provide a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute each process of the embodiments of the above network coverage anomaly monitoring method, and the same technical effects can be achieved. To avoid repetition, details are not described herein again.
[0075] It should be noted that in this document, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device including a series of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "including a..." does not exclude the presence of additional identical elements in the process, method, article or device including such element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be executed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
[0076] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present application.
[0077] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.
Claims
1. A method for monitoring abnormal network coverage, characterized in that, Including: Determine a first micro-grid and a second micro-grid of a target grid in different time periods according to the obtained first minimized drive test (MDT) data, where the target grid is obtained by dividing the network coverage area, and the first micro-grid and the second micro-grid are obtained by dividing the target grid; Calculate the level fluctuation of the target grid according to the first level of the first micro-grid, the second level of the second micro-grid, and the T-test method; Output the network coverage anomaly monitoring result of the target grid according to the level fluctuation of the target grid.
2. The method according to claim 1, wherein The step of determining a first micro-grid and a second micro-grid of a target grid in different time periods according to the obtained first minimized drive test (MDT) data includes: Divide the target grid into multiple third micro-grids; Project the first MDT data onto multiple third micro-grids; Select the first micro-grid and the second micro-grid from the third micro-grids according to the number of the first MDT data in the third micro-grids in the different time periods.
3. The method according to claim 2, wherein The different time periods include a first time period and a second time period. The step of selecting the first micro-grid and the second micro-grid from the third micro-grids according to the number of the first MDT data in the third micro-grids in the different time periods includes: Determine the third micro-grids with the number of the first MDT data greater than a preset threshold in the first time period as the first micro-grid; Determine the third micro-grids with the number of the first MDT data greater than the preset threshold in the second time period as the second micro-grid.
4. The method according to claim 1, characterized in that, Before determining a first micro-grid and a second micro-grid of a target grid in different time periods according to the obtained first minimized drive test (MDT) data, it further includes: Obtain the second MDT data of the target grid from within the network coverage area; Deduplicate the second MDT data according to the timestamp and user terminal identifier of the second MDT data, and filter the second MDT data according to the longitude information and latitude information of the second MDT data to obtain the first MDT data.
5. The method according to claim 1, wherein The step of calculating the level fluctuation of the target grid according to the first level of the first micro-grid, the second level of the second micro-grid, and the T-test method includes: Form a first level matrix of the target grid according to the first level; Form a second level matrix of the target grid according to the second level; Perform an intersection operation on the first level matrix and the second level matrix to obtain a first common matrix of the first level matrix and a second common matrix of the second level matrix; Calculate the level fluctuation of the target grid according to the first common matrix, the second common matrix, and the T-test method.
6. The method according to claim 5, wherein The step of calculating the level fluctuation of the target grid according to the first common matrix, the second common matrix, and the T-test method includes: Convert the first common matrix into a first one-dimensional vector; Convert the second common matrix into a second one-dimensional vector; Calculate the level fluctuation of the target grid according to the first one-dimensional vector, the second one-dimensional vector, and the T-test method.
7. The method according to claim 1, wherein Output the network coverage anomaly monitoring result of the target grid according to the level fluctuation of the target grid, including: Determine the target value corresponding to the level fluctuation from a preset distribution table; When the target value is less than a preset threshold, output the network coverage anomaly monitoring result that the target grid has an abnormal fluctuation; When the target value is greater than or equal to the preset threshold, output the network coverage anomaly monitoring result that the target grid has no abnormal fluctuation.
8. The method according to claim 7, wherein Before determining the target value corresponding to the level fluctuation from the preset distribution table, it further includes: Calculate the preset threshold according to the number of the second MDT data of the target grid, the maximum number and the minimum number of the third MDT data in all grids within the network coverage.
9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, it implements the steps of the network coverage anomaly monitoring method according to any one of claims 1-8.
10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the steps of the network coverage anomaly monitoring method according to any one of claims 1-8.