Network performance analysis method, apparatus, device, and medium

Abstract

The present disclosure provides a network performance analysis method, device, equipment and medium, wherein the network performance analysis method comprises: obtaining a plurality of to-be-processed minimization of drive test (MDT) data of a target terminal; performing processing on the plurality of to-be-processed MDT data in at least one dimension to obtain at least one target MDT data, wherein the dimension comprises any one of the following: a time advance (TA) dimension, an angle of arrival (AOA) dimension, a power headroom report (PHR) dimension, a height information dimension of the target terminal, and a latitude and longitude dimension of the target terminal; and analyzing a network performance index based on the at least one target MDT data, thereby solving the technical problem that the collected MDT data in the prior art is not accurate enough, thereby affecting the network performance analysis effect.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a network performance analysis method, apparatus, device, and medium. Background Technology

[0002] Minimization Drive Tests (MDT) data is widely used in data analysis, network optimization, user evaluation, and wireless network performance prediction. However, MDT data may be constrained by factors such as wireless network control resources, communication protocols, and data collection cycles, resulting in inaccurate MDT data and thus affecting the effectiveness of network performance analysis. Summary of the Invention

[0003] This disclosure aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, this disclosure proposes a network performance analysis method, apparatus, electronic device, non-transitory computer-readable storage medium storing computer instructions, and computer program product to improve the accuracy of MDT data and enhance the effectiveness of network performance analysis.

[0005] The first aspect of this disclosure proposes a network performance analysis method, comprising: acquiring multiple minimum drive test (MDT) data to be processed for a target terminal; processing the multiple MDT data to be processed in at least one dimension to obtain at least one target MDT data, wherein the dimension includes any one of the following: time advance (TA) dimension, angle of arrival (AOA) dimension, power headroom report (PHR) dimension, altitude information of the target terminal dimension, and latitude and longitude of the target terminal dimension; and analyzing network performance indicators based on the at least one target MDT data.

[0006] A second aspect of this disclosure provides a network performance analysis apparatus, comprising: an acquisition module for acquiring multiple Minimum Drive Test (MDT) data to be processed from a target terminal; a processing module for processing the multiple MDT data to be processed in at least one dimension to obtain at least one target MDT data, wherein the dimension includes any one of the following: Time Advance (TA) dimension, Angle of Arrival (AOA) dimension, Power Headroom Report (PHR) dimension, Altitude Information of the target terminal dimension, and Latitude and Longitude of the target terminal dimension; and an analysis module for analyzing network performance indicators based on the at least one target MDT data.

[0007] A third aspect of this disclosure provides an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method described above.

[0008] A fourth aspect of this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method described above.

[0009] A fifth aspect of this disclosure provides a computer program product including a computer program that, when executed by a processor, implements the method described above.

[0010] The network performance analysis method, apparatus, electronic device, non-transitory computer-readable storage medium storing computer instructions, and computer program product disclosed herein acquire multiple minimized drive test (MDT) data points from a target terminal and process these MDT data points in at least one dimension to obtain at least one target MDT data point. The dimension includes any one of the following: timing advance (TA), angle of arrival (AOA), power headroom report (PHR), altitude information of the target terminal, and latitude and longitude of the target terminal. Based on this at least one target MDT data point, network performance indicators are analyzed. This aims to improve the accuracy of MDT data and enhance the effectiveness of network performance analysis.

[0011] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0012] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0013] Figure 1 A flowchart illustrating a network performance analysis method provided in an embodiment of this disclosure;

[0014] Figure 2 A flowchart illustrating a network performance analysis method provided in an embodiment of this disclosure;

[0015] Figure 3 This is a schematic diagram of the TA critical value in an embodiment of this disclosure;

[0016] Figure 4 This is a schematic diagram of AOA in an embodiment of this disclosure;

[0017] Figure 5 This is a schematic diagram illustrating the non-singularity of the AOA vector in an embodiment of this disclosure;

[0018] Figure 6 This is a schematic diagram of the MDT data cleaning algorithm flow in an embodiment of this disclosure;

[0019] Figure 7This is a schematic diagram of the structure of a network performance analysis device provided in an embodiment of the present disclosure;

[0020] Figure 8 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0021] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0022] Figure 1 This is a flowchart illustrating a network performance analysis method provided in an embodiment of the present disclosure.

[0023] This embodiment illustrates the example of a network performance analysis method configured in a network performance analysis device. In this embodiment, the network performance analysis method can be configured in a network performance analysis device, which can be located in a server or an electronic device, without limitation.

[0024] This embodiment uses the example of a network performance analysis method configured in an electronic device. The electronic device includes hardware devices with various operating systems, such as smartphones, tablets, personal digital assistants, and e-readers.

[0025] It should be noted that the execution entity of the embodiments disclosed herein may be, in hardware, a central processing unit (CPU) in a server or electronic device, and in software, a related background service in a server or electronic device, without limitation.

[0026] like Figure 1 As shown, this network performance analysis method includes:

[0027] S101: Obtain multiple minimum road test MDT data to be processed from the target terminal.

[0028] The terminal from which MDT data is to be collected can be referred to as the target terminal. There can be one or more target terminals, and multiple MDT data points can be collected from the target terminals. Then, network performance analysis is performed based on the collected MDT data.

[0029] In some embodiments, the collected initial MDT data can be processed to obtain multiple MDT data to be processed.

[0030] Optionally, in some embodiments, the process of acquiring multiple unprocessed MDT data from the target terminal may involve acquiring multiple initial MDT data from the target terminal. Each initial MDT data has corresponding first identification information and a timestamp. The first identification information is assigned to the target terminal by the Mobility Management Entity (MME), and the timestamp indicates the time of data collection. Based on the first identification information and timestamp, the multiple initial MDT data are sorted, and the sorted initial MDT data are used as multiple unprocessed MDT data. This makes the multiple unprocessed MDT data more ordered, supports efficient and orderly cleaning of the multiple unprocessed MDT data, and improves the effectiveness of MDT data cleaning.

[0031] The first identification information is used to identify the target terminal. This first identification information may be, for example, the S1 Application Protocol IDentifier (S1AP ID) assigned to the target terminal by the MME entity. The S1AP ID is content within the terminal context and is also an index field in the S1AP message. The timestamp indicates the time when the corresponding initial MDT data was collected.

[0032] In some embodiments, during the data preparation stage, the first identification information S1AP ID and timestamp corresponding to each MDT data in the multiple pre-collected initial MDT data can be parsed. Then, the multiple initial MDT data can be sorted based on the S1AP ID and timestamp, and the sorted multiple initial MDT data can be used as multiple MDT data to be processed.

[0033] S102: Process at least one dimension of multiple MDT data to obtain at least one target MDT data, wherein the dimension includes any one of the following: the dimension of time lead (TA), the dimension of angle of arrival (AOA), the dimension of power margin report (PHR), the dimension of altitude information of the target terminal, and the dimension of latitude and longitude of the target terminal.

[0034] After obtaining multiple MDT data to be processed, the multiple MDT data to be processed can be cleaned in at least one dimension, and the MDT data obtained from the cleaning process can be used as the target MDT data. The cleaning process is used to represent the process of improving the accuracy of MDT data representation.

[0035] In some embodiments, at least one dimension can be at least one of the following: Time Advanced (TA) dimension, Angle-of-Arrival (AOA) dimension, Power Headroom Report (PHR) dimension, target terminal altitude information dimension, and target terminal latitude and longitude dimension. This effectively improves the flexibility of MDT data cleaning and processing, making it suitable for personalized communication scenarios.

[0036] TA, also known as Service Cell Time Advanced (SCTadv), is measured in units such as microseconds (μs).

[0037] In some embodiments, at least one of the above dimensions can be selected to clean and process multiple MDT data to be processed based on the MDT data cleaning instructions; or, each of the above dimensions can be applied directly to the multiple MDT data to be processed for cleaning and processing; or, at least one dimension adapted to the actual communication scenario can be selected to clean and process the multiple MDT data to be processed, without any limitation.

[0038] Among them, the Time Lead (TA) dimension refers to the TA taken when collecting each MDT data point during the cleaning process of multiple MDT data points; the Angle of Arrival (AOA) dimension refers to the AOA taken when collecting each MDT data point during the cleaning process of multiple MDT data points; correspondingly, other dimensions indicate that relevant data, such as Power Headroom Report (PHR), target terminal altitude information, and target terminal latitude and longitude, were taken into account when cleaning multiple MDT data points, and there are no restrictions on these.

[0039] In some embodiments, when cleaning multiple MDT data to be processed, an artificial intelligence model corresponding to each dimension can be obtained, and cleaning processing of the corresponding dimension can be performed based on the corresponding artificial intelligence model. The artificial intelligence model has modeled and analyzed the corresponding dimension, multiple MDT data to be processed, and the cleaning processing rules under the corresponding dimension, thereby ensuring that multiple MDT data to be processed can be cleaned based on the corresponding dimension; or the cleaning rules for each dimension can be obtained, and the multiple MDT data to be processed can be cleaned directly based on the cleaning rules; of course, cleaning processing of multiple MDT data to be processed based on at least one dimension can also be achieved in any other possible way, without limitation.

[0040] S103: Analyze network performance metrics based on at least one target MDT data.

[0041] The MDT data obtained after cleaning and processing can be referred to as the target MDT data. The metrics used to characterize network performance results can be referred to as network performance metrics.

[0042] After processing multiple MDT data sets in at least one dimension to obtain at least one target MDT data set, network performance indicators can be analyzed based on this target MDT data set. For example, network performance analysis modeling can be performed based on the target MDT data set, and network performance indicators can be determined based on the modeling results; alternatively, a comparison can be made between preset performance evaluation rules and the target MDT data set, and network performance indicators can be determined based on the comparison results; of course, any other possible method can be used to analyze network performance indicators based on the target MDT data set, without any limitations.

[0043] In this embodiment, multiple Minimum Drive Test (MDT) data points from the target terminal are acquired, and at least one dimension of these MDT data points is processed to obtain at least one target MDT data point. The dimension includes any one of the following: Time Advance (TA), Angle of Arrival (AOA), Power Headroom Report (PHR), target terminal altitude information, and target terminal latitude and longitude. Based on this target MDT data, network performance indicators are analyzed. This aims to improve the accuracy of the MDT data and enhance the effectiveness of network performance analysis.

[0044] Figure 2 This is a flowchart illustrating a network performance analysis method provided in an embodiment of the present disclosure.

[0045] This embodiment illustrates a schematic diagram of the process of cleaning multiple MDT data to be processed based on the TA dimension. For example... Figure 2 As shown, this network performance analysis method includes:

[0046] S201: Obtain multiple minimum road test (MDT) data to be processed from the target terminal.

[0047] S202: Determine the TA threshold of the serving cell of the target terminal based on multiple unprocessed MDT data.

[0048] Understandably, according to the acquisition rules, if the target terminal remains stationary during two measurement cycles, then during continuous sampling (5120 millisecond intervals), there will be two reported values ​​at the TA critical point. For example, this situation would occur if the target terminal is approximately 78 meters away from the antenna at line-of-sight distance.

[0049] In this embodiment of the disclosure, the above-mentioned characteristics can be used to determine the sampled MDT data to be processed corresponding to the TA threshold. After the data is collected and reported, the TA value corresponding to the MDT data to be processed will be reported as 1 at one time point and as 2 at another time point adjacent to the TA threshold.

[0050] like Figure 3 As shown, Figure 3 This is a schematic diagram of the TA critical value in an embodiment of this disclosure.

[0051] Optionally, in some embodiments, in the process of determining the TA threshold of the serving cell of the target terminal based on multiple MDT data to be processed, a first latitude and longitude corresponding to the first MDT data to be processed and a second latitude and longitude corresponding to the second MDT data to be processed can be determined. The first and second MDT data to be processed are collected in two adjacent measurement periods. If the first and second latitude and longitudes are the same, and the first TA contained in the first MDT data and the second TA contained in the second MDT data are different, then the TA threshold is determined based on the first and second TAs. This allows for accurate and convenient determination of the TA threshold of the serving cell of the target terminal.

[0052] The first MDT data to be processed can be any one of multiple MDT data to be processed. The second MDT data to be processed can be a data point collected at a timestamp adjacent to the timestamp of the first MDT data. The first latitude and longitude are used to indicate the location of the first MDT data, and the second latitude and longitude are used to indicate the location of the second MDT data. If the first latitude and longitude are the same as the second latitude and longitude, and the first TA contained in the first MDT data is different from the second TA contained in the second MDT data, it indicates that the target terminal did not move within two adjacent measurement periods and reported two different TAs. This indicates that the TA threshold value is between the first TA and the second TA. The first TA can be, for example, 1, and the second TA can be, for example, 2.

[0053] S203: Based on the MDT data to be processed corresponding to the TA threshold, process multiple MDT data to obtain at least one target MDT data.

[0054] After determining the TA threshold of the serving cell of the target terminal based on multiple MDT data to be processed, the TA threshold can be used to detect abnormal MDT data in the multiple MDT data to be processed, remove abnormal MDT data from the multiple MDT data to be processed, and use the remaining MDT data to be processed after removing abnormal MDT data as the target MDT data.

[0055] Optionally, in some embodiments, during the process of processing multiple MDT data based on the MDT data corresponding to the TA threshold, abnormal MDT data can be identified from the multiple MDT data based on the TA threshold, and the abnormal MDT data can be deleted from the multiple MDT data. This effectively improves the accuracy of the processed MDT data.

[0056] For example, after determining the TA threshold of the serving cell of the target terminal based on multiple MDT data to be processed, the TA threshold can be used to detect abnormal MDT data in the multiple MDT data to be processed, remove abnormal MDT data from the multiple MDT data to be processed, and use the remaining MDT data to be processed after removing abnormal MDT data as the target MDT data.

[0057] Optionally, in some embodiments, in the process of identifying abnormal MDT data from multiple unprocessed MDT data based on the TA threshold, the reference latitude and longitude of the target terminal corresponding to the TA threshold may be determined, and at least one associated TA value may be obtained based on the first identification information, the Physical Cell Identifier (PCI) of the serving cell, the frequency of the serving cell, and the reference latitude and longitude. The associated TA value belongs to the corresponding unprocessed MDT data, and the first identification information is assigned to the target terminal by the Mobility Management Entity (MME). If the number of at least one associated TA value is greater than a threshold, the unprocessed MDT data to which the associated TA value belongs is considered abnormal MDT data. This effectively improves the accuracy of identifying abnormal MDT data.

[0058] For example, the judgment rule for abnormal MDT data is as follows: When a terminal reports MDT data to be processed, if the terminal's location is at the TA threshold, the terminal may report two different TA values. However, the number of reported TA values ​​will not exceed two. Therefore, the above-mentioned quantity threshold can be set to two. In the process of identifying abnormal MDT data, the S1AP ID (an optional example of the first identification information) assigned to the target terminal by the MME entity + the Physical Cell Identifier (PCI) of the serving cell + the serving cell frequency + the UE longitude + the UE latitude (UE longitude + UE latitude is an optional example of reference longitude and latitude) can be used as the primary key to match the associated TA values. If the number of associated TA values ​​obtained by the association matching is greater than the quantity threshold (2), then the MDT data reporting the associated TA values ​​is determined to be abnormal, and such abnormal MDT data can be removed.

[0059] In this embodiment of the disclosure, after determining the TA threshold of the serving cell of the target terminal based on multiple MDT data to be processed, the multiple MDT data to be processed can be cleaned first based on the dimension of the angle of arrival (AOA), and then abnormal MDT data can be detected and removed from the multiple MDT data obtained after cleaning. There are no restrictions on this.

[0060] S204: Analyze network performance metrics based on at least one target MDT data.

[0061] After processing multiple MDT data points based on the MDT data points corresponding to the TA threshold to obtain at least one target MDT data point, network performance indicators can be analyzed based on at least one target MDT data point.

[0062] In this embodiment, firstly, abnormal MDT data is identified based on multiple MDT data to be processed. Then, abnormal MDT data is removed from the multiple MDT data to be processed, thereby ensuring that at least one target MDT data obtained from the processing has high accuracy. This effectively avoids the impact of abnormal MDT data on network performance analysis, thereby supporting the improvement of network performance analysis results.

[0063] Optionally, in some embodiments, during the process of processing the AOA dimension of multiple MDT data to be processed, if the first AOA contained in the first MDT data to be processed and the second AOA contained in the second MDT data to be processed are different, then the first AOA and the second AOA are used as AOA variables. The first MDT data to be processed and the second MDT data to be processed are collected continuously, and the MDT data to be processed containing AOA variables are deleted from the multiple MDT data to be processed. This preserves the single information data of AOA, making the AOA of the target terminal relative to the antenna a constant value, thus effectively reducing the computational difficulty.

[0064] like Figure 4 As shown, Figure 4 This is a schematic diagram of AOA in an embodiment of this disclosure. AOA can be specifically represented in vector form.

[0065] Understandably, some versions of communication protocols define an estimated angle of the terminal relative to the antenna in a counter-clockwise direction. The reference direction can be, for example, the antenna normal. As shown in Table 1, the reported AOA range is 000–719, which is 720 units divided in 0.5° increments clockwise from the RF antenna normal (0°). This vector can be used to determine the relative orientation of the terminal and the antenna. The corresponding serving cell AOA changes relative to the terminal during movement. However, this change can lead to greater computational complexity in subsequent calculations.

[0066] Therefore, in this embodiment of the disclosure, AOA variables generated by the same serving cell of the same terminal can be cleaned, retaining only the single information data of the AOA. The determination rule includes: if the AOA obtained by continuous sampling of the target terminal (an optional example of the first AOA and the second AOA) changes, then the first AOA and the second AOA are determined to be AOA variables, and the unprocessed MDT data containing non-AOA variables can be retained, while the unprocessed MDT data containing AOA variables can be deleted.

[0067] Table 1

[0068] AOA reported values Distribution of measurement data intervals (unit: degrees) AOA_ANGLE_000 0≤AOA_ANGLE<0.5 … … AOA_ANGLE_009 4.5≤AOA_ANGLE<5.0 … … AOA_ANGLE_710 355.0≤AOA_ANGLE<355.5 … … AOA_ANGLE_719 359.5≤AOA_ANGLE<360

[0069] Optionally, in some embodiments, during the PHR dimension processing of multiple MDT data to be processed, if the PHR contained in the first MDT data to be processed is less than or equal to the PHR threshold, then the first MDT data to be processed may be deleted from the multiple MDT data to be processed. This ensures that each retained target MDT data is collected at a relatively optimal sampling position of the target terminal. A relatively optimal sampling position can be understood as a sampling position with good line of sight and excluding sampling positions where electromagnetic wave obstruction and fading occur.

[0070] For example, the first MDT data to be processed can be any one of multiple MDT data to be processed. The PHR contained in each MDT data to be processed can be determined. If the PHR is less than or equal to the PHR threshold, it is determined that the corresponding MDT data to be processed was not collected at a better sampling location. The PHR threshold can be a threshold value for determining that the corresponding MDT data to be processed was collected at a better sampling location.

[0071] Wherein, PHR represents the difference between the estimated transmit power of the serving cell's Uplink Shared Channel (UL-SCH) and the configured maximum transmit power. As shown in Table 2 below, Table 2 is a schematic diagram of the distribution of the PHR reported value and the corresponding Power Headroom (PH) intervals specified by the communication protocol. The PHR threshold can be 35dB.

[0072] Table 2

[0073] PHR reported value Distribution of measurement data intervals (unit: dB) POWER_HEADROOM_0 -23≤PH<-22 POWER_HEADROOM_1 -22≤PH<-21 POWER_HEADROOM_2 -21≤PH<-20 POWER_HEADROOM_3 -20≤PH<-19 … … POWER_HEADROOM_60 37≤PH<38 POWER_HEADROOM_61 38≤PH<39 POWER_HEADROOM_62 39≤PH<40 POWER_HEADROOM_63 pH ≥ 40

[0074] Optionally, in some embodiments, the dimension includes the latitude and longitude of the target terminal. In the process of processing multiple MDT data sets according to the latitude and longitude of the target terminal, if the first latitude and longitude corresponding to the first MDT data set and the second latitude and longitude corresponding to the second MDT data set are the same, the first or second MDT data set can be deleted from the multiple MDT data sets. This removes MDT data sets collected at duplicate latitude and longitude coordinates.

[0075] The first MDT data to be processed can be any one of multiple MDT data to be processed, and the second MDT data to be processed can also be any one of multiple MDT data to be processed. The first latitude and longitude are used to indicate the location of the first MDT data to be processed, and the second latitude and longitude are used to indicate the location of the second MDT data to be processed.

[0076] For example, latitude and longitude uniqueness determination: Determine the uniqueness of latitude and longitude by using longitude + latitude, find the sampling information with the same latitude and longitude (an optional example of the MDT data to be processed), and remove the MDT data to be processed that are sampled at the same latitude and longitude.

[0077] Optionally, in some embodiments, multiple MDT data to be processed can be processed based on the dimension of the target terminal's height information. This processing method can also be called the UE height cleaning algorithm.

[0078] Optionally, in some embodiments, during the process of processing multiple MDT data to be processed based on the height information of the target terminal, if the first latitude and longitude corresponding to the first MDT data to be processed and the second latitude and longitude corresponding to the second MDT data to be processed are the same, then the first height of the target terminal corresponding to the first MDT data to be processed and the second height of the target terminal corresponding to the second MDT data to be processed are determined. If the first height and the second height do not meet the preset conditions, then the first MDT data to be processed or the second MDT data to be processed is deleted from the multiple MDT data to be processed.

[0079] The preset condition refers to a threshold condition that may exist for the height anomaly of the target terminal when different MDT data collected based on the same latitude and longitude. Optionally, in some embodiments, the preset condition includes: the height difference between terminals corresponding to each latitude and longitude of the same latitude and longitude is less than or equal to a threshold (e.g., 3 meters).

[0080] In other words, if the height difference of the target terminal based on different MDT data to be processed collected at the same latitude and longitude is greater than the height threshold, then it is determined that the height of the target terminal represented by the MDT data to be processed collected at that latitude and longitude is not accurate enough (i.e., the height is abnormal). Then, the first MDT data or the second MDT data to be processed can be deleted from the multiple MDT data to be processed.

[0081] Alternatively, another implementation method for processing multiple MDT data to be processed using AOA dimensionality (this processing method can also be called AOA desingle vector) can be provided, including: if the number of first AOA vectors corresponding to the first MDT data to be processed is one, then the first AOA vector is divided into multiple first AOA sub-vectors, and the first AOA vector in the first MDT data to be processed is replaced according to the multiple first AOA sub-vectors. By increasing the number of AOA vectors, the diversity of the calculation results can be improved, thereby helping to achieve non-singularity.

[0082] like Figure 5 As shown, Figure 5 This is a schematic diagram illustrating the non-singularity of the AOA vector in an embodiment of this disclosure.

[0083] For example, the first MDT data to be processed is any one of multiple MDT data to be processed. The number of first AOA vectors corresponding to the first MDT data to be processed is one, which means that the correspondence between the first MDT data to be processed and the first AOA vector is one-to-one. It can also be understood that the first AOA vector corresponds to one first MDT data to be processed. This kind of AOA vector can also be called a single AOA vector.

[0084] Therefore, in this embodiment of the disclosure, in order to achieve non-singularity of the MDT data to be processed on a single AOA vector, thereby improving the accuracy of the calculation results and avoiding the introduction of calculation errors, the number of AOA vectors can be increased. By increasing the number of AOA vectors, the diversity of the calculation results can be improved, thus helping to achieve non-singularity. For example, an AOA vector can be divided into multiple AOA sub-vectors, and the resulting multiple AOA sub-vectors can replace the original AOA vector. This maintains the independence of each AOA sub-vector while making full use of the information from multiple AOA sub-vectors.

[0085] Optionally, in some embodiments, the overlapping item balancing dimension processing can be performed on multiple MDT data to be processed. The overlapping item balancing dimension processing refers to using AOA+TA as the input point to perform data balancing processing on the latitude and longitude information of the overlapping samples (which may be included in the MDT data to be processed) to reduce errors and retain all latitude and longitude balancing values ​​under the same AOA+TA.

[0086] Optionally, in some embodiments, a first AOA and a first TA can be determined, and based on the first AOA and the first TA, the Physical Cell Identifier (PCI) of the serving cell, and the frequency of the serving cell, associated first latitude and longitude and second latitude and longitude can be obtained. The first latitude and longitude belong to the first MDT data to be processed, and the second latitude and longitude belong to the second MDT data to be processed. The first latitude and longitude are then balanced to obtain a latitude and longitude balanced value. The first latitude and longitude in the first MDT data to be processed are replaced with the latitude and longitude balanced value, and the second latitude and longitude in the second MDT data to be processed are also replaced with the latitude and longitude balanced value. This achieves the processing of the overlapping item dimensional balance.

[0087] In other words, it is possible to connect the first AOA and the first TA, the physical cell identifier (PCI) of the serving cell, and the frequency point of the serving cell, and perform latitude and longitude retrieval based on the connection results to obtain the first latitude and longitude in the first MDT data to be processed and the second latitude and longitude in the second MDT data to be processed. Then, the first latitude and longitude and the second latitude and longitude can be averaged to obtain the average latitude and longitude, and the average latitude and longitude can be used as the latitude and longitude balance value.

[0088] In this embodiment of the disclosure, the MDT data obtained by the above cleaning already has high accuracy. To reduce the amount of subsequent calculations, only the MDT data that meets the conditions can be retained. For example, two different MDT data on the same vector can be retained to facilitate subsequent calculations.

[0089] In this embodiment, multiple MDT data to be processed can be cleaned using TA threshold cleaning, AOA variable cleaning algorithm, TA outlier removal, PHR data cleaning, UE altitude cleaning algorithm, AOA single quantity removal, and overlap term equalization to obtain at least one target MDT data, thereby improving the accuracy of the target MDT data. Specifically, TA threshold cleaning and TA outlier removal can be optional examples of processing methods for the Time Advance (TA) dimension; AOA variable cleaning algorithm and AOA single quantity removal can be optional examples of processing methods for the Angle of Arrival (AOA) dimension; PHR data cleaning can be an optional example of processing methods for the Power Headroom Report (PHR) dimension; UE altitude cleaning algorithm can be an optional example of processing methods for the target terminal's altitude information dimension; and overlap term equalization can be an optional example of processing methods for the target terminal's latitude and longitude dimension. No limitation is imposed on these methods.

[0090] like Figure 6 As shown, Figure 6 This is a schematic diagram of the MDT data cleaning algorithm flow in this embodiment. The various cleaning algorithms described above can be executed in a certain order, such as... Figure 6 As shown, they can also be executed separately, without restriction.

[0091] Figure 7 This is a schematic diagram of the structure of a network performance analysis device provided in an embodiment of the present disclosure.

[0092] like Figure 7 As shown, the network performance analysis device 70 includes:

[0093] The acquisition module 701 is used to acquire multiple minimal road test (MDT) data to be processed from the target terminal.

[0094] The processing module 702 is used to process multiple MDT data to be processed in at least one dimension to obtain at least one target MDT data, wherein the dimension includes any one of the following: the time lead (TA) dimension, the angle of arrival (AOA) dimension, the power margin report (PHR) dimension, the altitude information of the target terminal dimension, and the latitude and longitude of the target terminal dimension.

[0095] Analysis module 703 is used to analyze network performance metrics based on at least one target MDT data.

[0096] It should be noted that the foregoing explanation of the network performance analysis method also applies to the network performance analysis device of this embodiment, and will not be repeated here.

[0097] In this embodiment, multiple Minimum Drive Test (MDT) data points from the target terminal are acquired, and at least one dimension of these MDT data points is processed to obtain at least one target MDT data point. The dimension includes any one of the following: Time Advance (TA), Angle of Arrival (AOA), Power Headroom Report (PHR), target terminal altitude information, and target terminal latitude and longitude. Based on this target MDT data, network performance indicators are analyzed. This aims to improve the accuracy of the MDT data and enhance the effectiveness of network performance analysis.

[0098] Figure 8 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 8 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0099] like Figure 8 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, memory 28, and bus 18 connecting different system components (including memory 28 and processing unit 16).

[0100] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0101] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0102] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 8 Not shown; usually referred to as a "hard drive".

[0103] although Figure 8 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0104] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0105] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with electronic device 12, and / or with any device that enables electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0106] The processing unit 16 executes various functional applications and data processing by running programs stored in the memory 28, such as implementing the network performance analysis method mentioned in the foregoing embodiments.

[0107] To implement the above embodiments, this disclosure also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0108] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0109] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0110] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0111] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0112] This disclosure is intended to provide implementation schemes for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0113] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0114] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0115] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0116] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0117] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0118] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0119] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0120] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A network performance analysis method, characterized in that, Includes the following steps: Acquire multiple unprocessed MDT data from the target terminal; The multiple MDT data to be processed are processed in at least one dimension to obtain at least one target MDT data, wherein the dimension includes any one of the following: the time lead (TA) dimension, the angle of arrival (AOA) dimension, the power headroom report (PHR) dimension, the altitude information of the target terminal dimension, and the latitude and longitude of the target terminal dimension. Analyze network performance metrics based on the at least one target MDT data; The dimension includes the dimension of TA; wherein, processing the plurality of MDT data to be processed in at least one dimension includes: Determine the first latitude and longitude corresponding to the first MDT data to be processed, and determine the second latitude and longitude corresponding to the second MDT data to be processed, wherein the first MDT data to be processed and the second MDT data to be processed are collected in two adjacent measurement cycles; If the first latitude and longitude and the second latitude and longitude are the same, and the first TA contained in the first MDT data to be processed and the second TA contained in the second MDT data to be processed are different, then the TA critical value is determined based on the first TA and the second TA. Determine the reference latitude and longitude of the target terminal corresponding to the TA threshold value; Based on the first identification information, the physical cell identifier (PCI) of the serving cell, the frequency of the serving cell, and the reference latitude and longitude, at least one associated TA value is obtained, wherein the associated TA value belongs to the corresponding MDT data to be processed, and the first identification information is allocated to the target terminal by the mobility management MME entity. If the number of at least one associated TA value is greater than the number threshold, then the MDT data to be processed to which the associated TA value belongs is regarded as MDT data with anomalies. Delete the MDT data containing anomalies from the plurality of pending MDT data.

2. The method according to claim 1, characterized in that, The acquisition of multiple unprocessed MDT data from the target terminal includes: Multiple initial MDT data of the target terminal are acquired, wherein the initial MDT data has corresponding first identification information and timestamp, the first identification information is assigned to the target terminal by the mobility management MME entity, and the timestamp is used to indicate the time of collection of the corresponding initial MDT data; Based on the first identification information and the timestamp, the plurality of initial MDT data are sorted, and the sorted plurality of initial MDT data are respectively used as the plurality of MDT data to be processed.

3. The method according to claim 1, characterized in that, The dimension includes the dimension of AOA; wherein, processing the plurality of MDT data to be processed in at least one dimension includes: If the first AOA contained in the first MDT data to be processed and the second AOA contained in the second MDT data to be processed are different, then the first AOA and the second AOA are used as AOA variables. The first MDT data to be processed and the second MDT data to be processed are collected continuously. Delete the MDT data containing the AOA variable from the plurality of pending MDT data.

4. The method according to claim 1, characterized in that, The dimension includes the PHR dimension; wherein, processing the plurality of MDT data to be processed in at least one dimension includes: If the PHR contained in the first MDT data to be processed is less than or equal to the PHR threshold, then the first MDT data to be processed is deleted from the plurality of MDT data to be processed.

5. The method according to claim 1, characterized in that, The dimension includes the latitude and longitude of the target terminal; wherein, processing the plurality of MDT data to be processed in at least one dimension includes: If the first latitude and longitude corresponding to the first MDT data to be processed and the second latitude and longitude corresponding to the second MDT data to be processed are the same, then delete the first MDT data to be processed or the second MDT data to be processed from the plurality of MDT data to be processed.

6. The method according to claim 1, characterized in that, The dimension includes the dimension of the height information of the target terminal; wherein, processing the plurality of MDT data to be processed in at least one dimension includes: If the first latitude and longitude corresponding to the first MDT data to be processed and the second latitude and longitude corresponding to the second MDT data to be processed are the same, then the first altitude of the target terminal corresponding to the first MDT data to be processed and the second altitude of the target terminal corresponding to the second MDT data to be processed are determined. If the preset conditions are not met between the first height and the second height, then the first MDT data to be processed or the second MDT data to be processed is deleted from the plurality of MDT data to be processed.

7. The method according to claim 6, characterized in that, The preset conditions include: the height difference between terminals corresponding to each latitude and longitude of the same latitude and longitude is less than or equal to a threshold.

8. The method according to claim 1, characterized in that, The dimension includes the dimension of AOA; wherein, processing the plurality of MDT data to be processed in at least one dimension includes: If the number of first AOA vectors corresponding to the first MDT data to be processed is one, then the first AOA vector is divided into sub-vectors to obtain multiple first AOA sub-vectors. The first AOA vector in the first MDT data to be processed is replaced according to the plurality of first AOA sub-vectors.

9. The method according to claim 1, characterized in that, The dimension also includes an overlap term balancing dimension; wherein, processing the multiple MDT data to be processed in at least one dimension includes: Determine the first AOA and the first TA; Based on the first AOA and the first TA, the physical cell identifier (PCI) of the serving cell and the frequency of the serving cell, obtain the associated first latitude and longitude and second latitude and longitude, wherein the first latitude and longitude belong to the first MDT data to be processed and the second latitude and longitude belong to the second MDT data to be processed. The first and second latitude and longitude coordinates are balanced to obtain the latitude and longitude balanced values; Replace the first latitude and longitude in the first MDT data to be processed with the latitude and longitude balanced value, and replace the second latitude and longitude in the second MDT data to be processed with the latitude and longitude balanced value.

10. A network performance analysis device, characterized in that, include: The acquisition module is used to acquire multiple unprocessed MDT data from the target terminal; The processing module is used to process the multiple MDT data to be processed in at least one dimension to obtain at least one target MDT data, wherein the dimension includes any one of the following: the dimension of time lead (TA), the dimension of angle of arrival (AOA), the dimension of power margin report (PHR), the dimension of the altitude information of the target terminal, and the dimension of the latitude and longitude of the target terminal. The analysis module is used to analyze network performance metrics based on the at least one target MDT data; The dimension includes the dimension of TA; wherein, processing the plurality of MDT data to be processed in at least one dimension includes: Determine the first latitude and longitude corresponding to the first MDT data to be processed, and determine the second latitude and longitude corresponding to the second MDT data to be processed, wherein the first MDT data to be processed and the second MDT data to be processed are collected in two adjacent measurement cycles; If the first latitude and longitude and the second latitude and longitude are the same, and the first TA contained in the first MDT data to be processed and the second TA contained in the second MDT data to be processed are different, then the TA critical value is determined based on the first TA and the second TA. Determine the reference latitude and longitude of the target terminal corresponding to the TA threshold value; Based on the first identification information, the physical cell identifier (PCI) of the serving cell, the frequency of the serving cell, and the reference latitude and longitude, at least one associated TA value is obtained, wherein the associated TA value belongs to the corresponding MDT data to be processed, and the first identification information is allocated to the target terminal by the mobility management MME entity. If the number of at least one associated TA value is greater than the number threshold, then the MDT data to be processed to which the associated TA value belongs is regarded as MDT data with anomalies. Delete the MDT data containing anomalies from the plurality of pending MDT data.

11. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-9.

13. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-9.

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