A data analysis method, device, apparatus and storage medium

Abstract

Embodiments of the present application provide a data analysis method, device, apparatus and storage medium, the method comprising: determining a first measurement report (MR) file for data analysis, wherein a table header format of each file in the first MR file is the same; determining a target object for data analysis; performing cluster statistics on the first MR file based on the target object to obtain a statistical result, wherein the statistical result includes one or more worst cells satisfying a first set condition, and the first set condition includes one or more measurement characteristic values. Therefore, the embodiments of the present application realize MR data analysis and query with a granularity of a base station or a TOP cell, improve the data analysis granularity and efficiency, and especially, the table header format of each file in the first MR file is the same, which reduces data misoperation and improves the reliability of data analysis.

Description

Technical Field

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

[0002] Since the commercialization of Long Term Evolution (LTE) networks and Standalone (SA) networks, the demand for self-organized network (SON) network self-optimization has been increasing, and network optimization big data platforms and artificial intelligence (AI) algorithms have been increasingly widely used in the communications industry.

[0003] However, when using existing network optimization big data platforms to analyze measurement report (MR) data, the efficiency is low and there is a large error rate. Summary of the Invention

[0004] This application provides a data analysis method, device, apparatus, and storage medium to address the problems of low efficiency and high error rate in the analysis of MR data using existing network optimization big data platforms. It enables MR data analysis and querying down to the granularity of base stations or top cells, improving the granularity and efficiency of data analysis. In particular, the header format of each file in the first MR file is the same, which reduces data misoperation and improves the reliability of data analysis.

[0005] In a first aspect, embodiments of this application provide a data analysis method, including:

[0006] A first measurement report (MR) file is determined for data analysis, wherein each file in the first MR file has the same header format;

[0007] Identify the target objects for data analysis;

[0008] Clustering statistics are performed on the first MR file based on the target object to obtain statistical results. The statistical results include one or more worst cells that meet a first set condition, and the first set condition includes one or more measurement feature values.

[0009] Optionally, according to a data analysis method of one embodiment of this application, determining the first MR file for data analysis includes:

[0010] Use the defined document object model to read multiple second MR files;

[0011] The plurality of second MR files are format-converted to obtain the first MR file; wherein, the format conversion includes matching the headers of the plurality of second MR files and filling the difference field with null values.

[0012] Optionally, according to a data analysis method of one embodiment of this application, the plurality of second MR files include Measurement Report Samples (MRO) and / or Event Measurement Reports (MRE).

[0013] Optionally, according to a data analysis method of one embodiment of this application, determining the target object for data analysis includes:

[0014] Receive object information for determining the target object, the object information including one or more of base station identifier, physical cell identifier (PCI), and analysis time period;

[0015] The target object is determined based on the object information.

[0016] Optionally, the data analysis method according to one embodiment of this application further includes:

[0017] If the statistical results include the first cell, then the neighboring cells of the first cell are determined according to the neighboring cell missing configuration detection method. The first cell is a cell that lacks neighboring cell relationships.

[0018] Optionally, according to a data analysis method of one embodiment of this application, determining the neighboring cells of the first cell according to the neighboring cell missing configuration detection method includes:

[0019] Identify each target cell that meets the second set conditions, the second set conditions including the set cell frequency and the set cell PCI;

[0020] Calculate the distance between the first cell and each of the target cells;

[0021] The target cell that is closest to the first cell is determined as the neighboring cell of the first cell.

[0022] Optionally, the data analysis method according to one embodiment of this application further includes:

[0023] For the second cell in the statistical results, determine the minimum inter-cell spacing of the second cell, where the second cell is any cell in the statistical results.

[0024] Optionally, according to a data analysis method of one embodiment of this application, determining the minimum inter-cell spacing of the second cell includes:

[0025] Based on the orientation of the second cell, determine each adjacent cell that has a confrontation relationship with the second cell, wherein the confrontation relationship is that the orientation of the second cell and the orientation of the adjacent cell have a convergence point;

[0026] Calculate the distance between the second cell and each of the neighboring cells;

[0027] The nearest neighbor cell to the second cell is determined as the nearest neighbor cell of the second cell, and the distance between the nearest neighbor cell and the second cell is the minimum inter-cell spacing.

[0028] Secondly, embodiments of this application also provide a network device, including a memory, a transceiver, and a processor:

[0029] A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs in the memory and implementing the steps of the data analysis method described in the first aspect above.

[0030] Thirdly, embodiments of this application provide a data analysis apparatus, including:

[0031] The first determining unit is used to determine a first measurement report MR file for data analysis, wherein the header format of each file in the first MR file is the same.

[0032] The second determining unit is used to determine the target object for data analysis;

[0033] The clustering statistics unit is used to perform clustering statistics on the first MR file based on the target object to obtain statistical results. The statistical results include one or more worst cells that meet a first set condition. The first set condition includes one or more measurement feature values.

[0034] Fourthly, embodiments of this application provide a processor-readable storage medium storing a computer program for causing the processor to perform the steps of the data analysis method described in the first aspect above.

[0035] The data analysis method, device, apparatus, and storage medium provided in this application embodiment determine a first MR file for data analysis, identify the target object for data analysis, and perform cluster statistics on the first MR file based on the target object to obtain statistical results. The statistical results include one or more worst cells that meet a first set condition, thereby realizing MR data analysis and querying at the granularity of base stations or top cells, improving the granularity and efficiency of data analysis. In particular, the header format of each file in the first MR file is the same, which reduces data misoperation and improves the reliability of data analysis. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the data analysis method provided in an embodiment of this application;

[0038] Figure 2 This is one of the scenario diagrams illustrating the data analysis method provided in the embodiments of this application;

[0039] Figure 3 This is a second scenario illustration of the data analysis method provided in the embodiments of this application;

[0040] Figure 4 This is the third scenario illustration of the data analysis method provided in the embodiments of this application;

[0041] Figure 5 This is a schematic diagram of the structure of the data analysis device provided in the embodiments of this application;

[0042] Figure 6 This is a schematic diagram of the network device provided in the embodiments of this application. Detailed Implementation

[0043] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0044] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0046] Since the commercialization of LTE and SA networks, the demand for SON network self-optimization has been increasing, and network optimization big data platforms and AI algorithms have been increasingly widely used in the communications industry.

[0047] However, when using a network optimization big data platform to analyze MR data, the output latency is relatively long (e.g., approximately 1 to 2 days), it has high requirements for network configuration (e.g., analysis is based on raw data such as SON function, Automatic Neighbor Relation (ANR) neighbor self-optimization, and Angle of Arrival (AOA), and also has a relatively high error rate. Furthermore, the network optimization big data platform only supports regional-level MR data analysis and querying, and cannot be refined to the granularity of base stations or top cells. Top cells typically refer to the worst-performing cells that have a particularly significant impact on overall network metrics.

[0048] This application provides a data analysis method, device, apparatus, and storage medium. By determining a first MR file for data analysis, identifying the target object for data analysis, and performing cluster statistics on the first MR file based on the target object, statistical results are obtained. The statistical results include one or more worst cells that meet a first set condition, thereby realizing MR data analysis and querying at the granularity of base stations or top cells, improving the granularity and efficiency of data analysis. In particular, the header format of each file in the first MR file is the same, which reduces data misoperation and improves the reliability of data analysis.

[0049] The method and apparatus are based on the same concept of the application. Since the methods and apparatus solve problems in similar ways, the implementation of the apparatus and methods can refer to each other, and the repeated parts will not be described again.

[0050] The technical solutions provided in this application can be applied to various systems, especially 5G systems. For example, applicable systems may include Global System for Mobile Communication (GSM), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (GPRS), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), Long Term Evolution Advanced (LTE-A), Universal Mobile Telecommunication System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), and 5G New Radio (NR). All of these systems include terminal equipment and network equipment. The systems may also include a core network component, such as Evolved Packet System (EPS) and 5G system (5GS).

[0051] The terminal devices involved in the embodiments of this application can be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. The names of the terminal devices may differ in different systems; for example, in a 5G system, a terminal device can be called User Equipment (UE). Wireless terminal devices can communicate with one or more core networks (CNs) via a Radio Access Network (RAN). Wireless terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones) and computers with mobile terminal devices, for example, portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the RAN. Examples include Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistants (PDAs). Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station, remote station, access point, remote terminal, access terminal, user terminal, user agent, or user device, but this application does not limit the terminology.

[0052] The network device involved in this application embodiment can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, a base station may also be called an access point, or a device in an access network that communicates with a wireless terminal device through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network equipment involved in the embodiments of this application can be a base transceiver station (BTS) in a Global System for Mobile communications (GSM) or Code Division Multiple Access (CDMA), a NodeB in a Wide-band Code Division Multiple Access (WCDMA) system, an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a next generation system, a Home evolved Node B (HeNB), a relay node, a femto, a pico, etc., and is not limited in the embodiments of this application. In some network structures, the network equipment may include centralized unit (CU) nodes and distributed unit (DU) nodes, and the centralized unit and distributed unit may be geographically separated.

[0053] Figure 1 This is a flowchart illustrating the data analysis method provided in an embodiment of this application. This data analysis method can be used in network devices, such as core network devices in an SA (Standalone Application). Figure 1 As shown, this data analysis method may include the following steps:

[0054] Step 101: Determine the first MR file for data analysis. The header format of each file in the first MR file is the same.

[0055] Specifically, the Document Object Model (DOM) of the Python platform can be used to determine the first MapReduce (MR) file. Each file in this first MR file has implemented multi-header format alignment and header slicing. Python is a computer programming language with a supporting class library and platform (i.e., the Python platform).

[0056] For example: The first MR file is:

[0057] <header> H1 H2 H3 H4 H5 H6< / header>

[0058] <v> 1 2 3 4 5 6< / v>

[0059] Custom field slicing can be implemented using tag query rules. Custom field slicing can include, but is not limited to, the following two scenarios:

[0060] Scenario 1: If the default header format is H1, H2, H3, then only the reported measurement values ​​of the corresponding three fields 1, 2, and 3 will be retrieved;

[0061] Scenario 2: If the default is full data (i.e., H1, H2, H3, H4, H5, H6), then the six fields 1, 2, 3, 4, 5, and 6 will be retrieved and the reported measurement values ​​will be obtained.

[0062] Step 102: Determine the target objects for data analysis.

[0063] Specifically, a preset interface can be used to receive the object name input by the user. The object name can include the base station number (gNBID) or the physical cell identifier (PCI). If the object name is the base station number, the final result obtained after data analysis is the base station-level aggregation result. If the object name is the PCI, the final result obtained after data analysis is the cell-level aggregation result.

[0064] Step 103: Perform clustering statistics on the first MR file based on the target object to obtain statistical results. The statistical results include one or more worst cells that meet the first set conditions. The first set conditions include one or more measurement feature values.

[0065] Specifically, when performing clustering statistics on the first MR file, Pandas clustering statistics can be used. This allows for data filtering and pivoting based on various dimensions such as object level and time granularity. Using Pandas' built-in aggregation methods, statistical results are generated in seconds. That is, hourly clustering statistics of objects can be achieved using a preset interface and Pandas queries. Pandas is a Python data analysis package created to solve data analysis tasks. Pandas incorporates a large number of libraries and some standard data models, and also provides a large number of functions and methods for processing data, which can improve the efficiency of data analysis.

[0066] In this context, one or more worst-performing cells in the statistical results can refer to the top cells for one or more measured characteristic values. These top cells typically refer to the worst-performing cells that have a particularly significant impact on the overall network performance.

[0067] For example, if the number of cells reported by MR is 25 and the total number of Reference Signal Receiving Quality (RSRQ) sampling points is 465, the top N worst cells can be output and sorted according to the RSRQ measurement feature value through Pandas clustering statistics.

[0068] As can be seen from the above embodiments, by determining the first MR file for data analysis, the target object for data analysis is determined, and the first MR file is clustered and statistically analyzed based on the target object to obtain statistical results. The statistical results include one or more worst cells that meet the first set conditions, thereby realizing MR data analysis and querying at the granularity of base stations or top cells, improving the granularity and efficiency of data analysis. In particular, the header format of each file in the first MR file is the same, which reduces data misoperation and improves the reliability of data analysis.

[0069] Optionally, determining the first MR file for data analysis includes:

[0070] Use the defined document object model to read multiple second MR files;

[0071] The plurality of second MR files are format-converted to obtain the first MR file; wherein, the format conversion includes matching the headers of the plurality of second MR files and filling the difference field with null values.

[0072] Specifically, defining the Document Object Model (DOM) can refer to XML.DOM, which defines the standard methods for accessing and manipulating XML documents. XML, or Extensible Markup Language, is a simple data storage language. Its simplicity makes it easy to read and write data in any application, which quickly made it the only common language for data exchange.

[0073] The second MR file can be the physical layer and radio link control (RLC) layer of the UE and the Radio Access Network (RAN), as well as the measurement report calculated during the radio resource management process.

[0074] The following example (i.e., multiple second MR files including XML file 1 and XML file 2 below) illustrates the process of determining the first MR file used for data analysis:

[0075] XML file 1:

[0076] <header> H1 H3 H5< / header>

[0077] <v> 1 3 5< / v>

[0078] XML file 2:

[0079] <header> H2 H4 H6< / header>

[0080] <v> 2 4 6< / v>

[0081] The merged and stored data (comma-separated file format) of XML file 1 and XML file 2 is as follows:

[0082] H1, H3, H5

[0083] 1,3,5

[0084] H2, H4, H6

[0085] 2,4,6

[0086] The above-mentioned document summary storage results are at risk of data misoperation, such as misidentifying H2, H4, and H6 as attribute feature values ​​of H1, H3, and H5.

[0087] To avoid the risk of data misoperation, this application requires format conversion of XML file 1 and XML file 2 (i.e.):

[0088] XML file 1 format conversion:

[0089] <header> H1 H3 H5 H2 H4 H6< / header>

[0090] <v> 1 3 5NIL NIL NIL< / v>

[0091] XML file 2 format conversion:

[0092] <header> H2 H4 H6 H1 H3 H5< / header>

[0093] <v> 2 4 6NIL NIL NIL< / v>

[0094] The merged and stored XML file 1 and XML file 2 after format conversion (comma-separated file format) are as follows: (i.e., the first MR file):

[0095] H1, H2, H3, H4, H5, H6

[0096] 1,NIL,3,NIL,5,NIL

[0097] NIL,2,NIL,4,NIL,6

[0098] As can be seen from the above embodiments, by converting the format, the header format of each file in the first MR file can be the same. In this way, XML.DOM can automatically match and retrieve data block tags, and associate multiple slicing methods and compatibility schemes, so as to realize the editability and alignment of data storage headers, which can improve data extraction efficiency and reduce data misoperation.

[0099] Optionally, the plurality of second MR files include a Measurement Report Original (MRO) and / or a Measurement Report Event (MRE). Here, MRO represents a measurement report original data file, excluding event-triggered sample data; MRE represents event-triggered measurement report sample data.

[0100] As can be seen from the above embodiments, MRO is the original measurement report file reported by the UE, and MRE is the terminal measurement report based on event triggering. This application can preprocess and extract massive amounts of original network data to complete correlation analysis such as MR coverage, user distribution perspective, and overlapping coverage perspective.

[0101] Optionally, determining the target object for data analysis includes:

[0102] Receive object information for determining the target object, the object information including one or more of base station identifier, physical cell identifier (PCI), and analysis time period;

[0103] The target object is determined based on the object information.

[0104] Specifically, object information can refer to information entered by the user through a preset interface. For example... Figure 2As shown, the input includes object name, time period, and aggregation statistics. The object name and time period inputs can be user-provided input interfaces. When determining the target object for data analysis, all records matching the user-inputted cell name and time period can be filtered, and all records matching the time period attribute can be filtered. Then, object-based classification statistics are implemented, and the top N worst-performing cells are output according to the reported measurement feature values.

[0105] As can be seen from the above embodiments, by obtaining object information to determine the target objects for data analysis, hourly clustering statistics of objects can be achieved, thus improving the efficiency of data analysis.

[0106] Optionally, the data analysis method may also include:

[0107] If the statistical results include the first cell, then the neighboring cells of the first cell are determined according to the neighboring cell missing configuration detection method. The first cell is a cell that lacks neighboring cell relationships.

[0108] Specifically, if the statistical results include cells that lack neighbor cell relationships, it is necessary to identify their neighbor cells to establish a neighbor cell relationship table. Subsequently, the terminal can be notified to perform cell handover based on the neighbor cell relationship table.

[0109] When determining neighboring cells, if the neighboring cell information in the MR reported event is incomplete and does not carry the neighboring cell base station identifier (gNBID) information, the neighboring cell can be determined by the neighboring cell missing configuration detection method, and the neighboring cell relationship can be added.

[0110] As can be seen from the above embodiments, the addition of neighbor cell relationships can be completed according to the neighbor cell missing configuration detection method, thereby solving the problem of missing neighbor cells and improving the planning and auditing functions of network operation and maintenance parameters.

[0111] Optionally, determining the neighboring cells of the first cell according to the neighboring cell missing configuration detection method includes:

[0112] Identify each target cell that meets the second set conditions, the second set conditions including the set cell frequency and the set cell PCI;

[0113] Calculate the distance between the first cell and each of the target cells;

[0114] The target cell that is closest to the first cell is determined as the neighboring cell of the first cell.

[0115] Specifically, setting the cell frequency can be related to network configuration. If only one frequency is used, the set cell frequency can be the same as the frequency of the first cell. For example, if the frequency of the first cell is 504990, the set cell frequency is also 504990. If it is a different system (e.g., NR to LTE neighbor cell reporting event), the set cell frequency can be different from the frequency of the first cell. For example, if the frequency of the first cell is 504990, the set cell frequency can be 38400, 1300, 40936, etc.

[0116] The cell PCI can be one or more pre-defined PCIs. For example, the cell PCI can be set to 108.

[0117] The following example illustrates the process of determining neighboring cells:

[0118] Associate the network configuration cell information table and filter the list of all cells that meet the above-mentioned set cell frequency and set cell PCI;

[0119] Calculate the distance between the first cell and each cell in the cell list, sort them in ascending order of distance, and retain the information of the nearest neighbor cell;

[0120] According to the NR cell station height, such as 2km for effective coverage in urban areas and 5km for effective coverage in rural and suburban areas, invalid statistical results are removed;

[0121] Based on the number of event requests in MR statistics, high-frequency events with more than N requests are selected and the neighbor cell relationship addition is completed first.

[0122] For example: Figure 3 As shown, S (Serving) represents the serving cell (i.e., the first cell), and N (Neighbor) represents the neighboring cells, with the numbers representing their sequence numbers. The serving cell S0 is matched against the neighboring cell relationships of cells with a PCI of 108. According to the network configuration cell information table, two matching cells, N1 and N3, are found. The distance between S0 and N1 is calculated to be 100 meters, and the distance between S0 and N3 is 150 meters, with the same wireless environment (this is a constraint). Based on the principle of closest distance, S0 and N1 can be determined as a cell pair lacking a neighboring relationship.

[0123] As can be seen from the above embodiments, neighboring cells can be determined from each target cell that meets the second set conditions, thereby improving the reliability of data analysis.

[0124] Optionally, the data analysis method may also include:

[0125] For the second cell in the statistical results, determine the minimum inter-cell spacing of the second cell, where the second cell is any cell in the statistical results.

[0126] Specifically, for any cell in the statistical results, it is necessary to determine the minimum inter-cell distance (i.e., the distance between the cell and its nearest neighboring cell). This can be used to determine whether the signal coverage of the cell is reasonable. If unreasonable coverage occurs, it can be further resolved by adjusting the antenna.

[0127] This application does not limit how to determine whether the signal coverage of a cell is reasonable based on the minimum inter-cell spacing. For example, the minimum inter-cell spacing of the cell can be compared with the average Timing Advance (TA). If the minimum inter-cell spacing is less than the average TA, it indicates that the signal coverage of the cell is unreasonable, resulting in a signal overshoot. If the minimum inter-cell spacing is greater than or equal to the average TA, it indicates that the signal coverage of the cell is reasonable. For example, if the minimum inter-cell spacing is 200 meters and the average TA is 300 meters, it indicates that the signal coverage of the cell is unreasonable, resulting in a signal overshoot. If the minimum inter-cell spacing is 200 meters and the average TA is 150 meters, it indicates that the signal coverage of the cell is reasonable.

[0128] Among them, TA can be the time advance specified in the agreement, which can be converted into signal propagation distance through the conversion relationship specified in the agreement. Cluster analysis is used to statistically analyze the number and proportion of samples in the TA interval, the mean of TA, the information of the nearest directly covered cell, and to comprehensively judge whether the signal coverage exceeds the area.

[0129] As can be seen from the above embodiments, for the second cell in the statistical results, the minimum inter-cell spacing of the second cell can be determined. In this way, the signal coverage of the second cell can be determined based on the minimum inter-cell spacing, thereby solving the signal overlay problem and improving the overlapping coverage perspective function.

[0130] Optionally, determining the minimum inter-cell spacing of the second cell includes:

[0131] Based on the orientation of the second cell, determine each adjacent cell that has a confrontation relationship with the second cell, wherein the confrontation relationship is that the orientation of the second cell and the orientation of the adjacent cell have a convergence point;

[0132] Calculate the distance between the second cell and each of the neighboring cells;

[0133] The nearest neighbor cell to the second cell is determined as the nearest neighbor cell of the second cell, and the distance between the nearest neighbor cell and the second cell is the minimum inter-cell spacing.

[0134] Specifically, the distance between the two cells is calculated using the latitude and longitude information of the second cell and the adjacent cell.

[0135] By comparing the orientation angles of the second cell and its neighboring cell, it is determined whether there is an overlapping area of ​​signal coverage between the two cells, and the two cells are classified into different attributes, including head-on, parallel, and back-to-back relationships. Head-on relationship refers to the two cells having a convergence point in their orientation; parallel relationship refers to the two cells having the same orientation; and back-to-back relationship refers to the two cells not having a convergence point in their orientation.

[0136] The filter retains the information of the closest neighboring cell, which includes the nearest neighboring cell of the second cell and the distance between the nearest neighboring cell and the second cell, i.e., the minimum inter-station spacing of the second cell.

[0137] For example: Figure 4 As shown, S represents the serving base station; S0, S1, and S2 represent the 0th, 1st, and 2nd cells of the serving base station; N10, N11, and N12 represent the 0th, 1st, and 2nd cells of neighboring base station 1; N20, N21, and N22 represent the 0th, 1st, and 2nd cells of neighboring base station 2; N30, N31, and N32 represent the 0th, 1st, and 2nd cells of neighboring base station 3; and N40, N41, and N42 represent the 0th, 1st, and 2nd cells of neighboring base station 4. Figure 4 As we know, S1 is closest to base station 2. S1 is also in contact with N22, N12, N30, and N42. By comparing the distance and orientation of S1 with the four adjacent base stations, it can be confirmed that N22 is the closest neighboring cell of S1. Finally, the distance between the two closest coverage cells is confirmed.

[0138] As can be seen from the above embodiments, the nearest neighboring cell can be determined from all neighboring cells that have a fighting relationship with the second cell. The distance between the nearest neighboring cell and the second cell is the minimum inter-cell spacing of the second cell, thereby improving the accuracy and efficiency of determining the minimum inter-cell spacing.

[0139] Figure 5 This is a schematic diagram of the structure of a data analysis device provided in an embodiment of this application. This data analysis device is used in network equipment; as shown... Figure 5 As shown, the data analysis device includes:

[0140] The first determining unit 51 is used to determine a first measurement report MR file for data analysis, wherein the header format of each file in the first MR file is the same.

[0141] The second determining unit 52 is used to determine the target object for data analysis;

[0142] Clustering statistics unit 53 is used to perform clustering statistics on the first MR file based on the target object to obtain statistical results. The statistical results include one or more worst cells that meet a first set condition. The first set condition includes one or more measurement feature values.

[0143] Furthermore, based on the above-described apparatus, the first determining unit 51 may include:

[0144] The read subunit is used to read multiple second MR files using a defined document object model.

[0145] The format conversion subunit is used to convert the format of the plurality of second MR files to obtain the first MR file; wherein, the format conversion includes matching the headers of the plurality of second MR files and filling the difference field with null values.

[0146] Furthermore, based on the aforementioned apparatus, the plurality of second MR files include Measurement Report Samples (MRO) and / or Event Measurement Reports (MRE).

[0147] Furthermore, based on the above-described apparatus, the second determining unit 52 may include:

[0148] A receiving subunit is configured to receive object information for determining the target object, the object information including one or more of base station identifier, physical cell identifier (PCI), and analysis time period;

[0149] The first determining subunit is used to determine the target object based on the object information.

[0150] Furthermore, based on the aforementioned device, the data analysis device may also include:

[0151] The third determining unit is used to determine the neighboring cells of the first cell according to the neighboring cell missing configuration detection method if the statistical results include the first cell, wherein the first cell is a cell that lacks neighboring cell relationships.

[0152] Furthermore, based on the aforementioned device, the third determining unit may include:

[0153] The second determining subunit is used to determine each target cell that meets the second set conditions, the second set conditions including the set cell frequency point and the set cell PCI;

[0154] The first calculation subunit is used to calculate the distance between the first cell and each of the target cells;

[0155] The third determining subunit is used to determine the target cell with the smallest distance from the first cell as the neighboring cell of the first cell.

[0156] Furthermore, based on the aforementioned device, the data analysis device may also include:

[0157] The fourth determining unit is used to determine the minimum inter-cell spacing of the second cell in the statistical results, wherein the second cell is any cell in the statistical results.

[0158] Furthermore, based on the aforementioned device, the fourth determining unit may include:

[0159] The fourth determining subunit is used to determine each adjacent cell that has a confrontation relationship with the second cell based on the orientation of the second cell, wherein the confrontation relationship is that the orientation of the second cell and the orientation of the adjacent cell have a convergence point;

[0160] The second calculation subunit is used to calculate the distance between the second cell and each of the neighboring cells;

[0161] The fifth determining subunit is used to determine the nearest neighbor cell of the second cell as the nearest neighbor cell of the second cell, and the distance between the nearest neighbor cell and the second cell is the minimum inter-cell spacing.

[0162] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0163] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0164] It should be noted that the apparatus provided in this embodiment of the invention can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0165] Figure 6 This is a schematic diagram of the network device provided in the embodiments of this application; as shown Figure 6 As shown, the network device includes a memory 620, a transceiver 600, and a processor 610, wherein:

[0166] The memory 620 is used to store computer programs; the transceiver 600 is used to send and receive data under the control of the processor 610; the processor 610 is used to read the computer program in the memory 620 and perform the following operations:

[0167] A first measurement report (MR) file is determined for data analysis, wherein each file in the first MR file has the same header format;

[0168] Identify the target objects for data analysis;

[0169] Clustering statistics are performed on the first MR file based on the target object to obtain statistical results. The statistical results include one or more worst cells that meet a first set condition, and the first set condition includes one or more measurement feature values.

[0170] Specifically, transceiver 600 is used to receive and send data under the control of processor 610.

[0171] Among them, Figure 6 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 610) and memory (memory 620). The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 600 can be multiple elements, including transmitters and receivers, providing units for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor 610 is responsible for managing the bus architecture and general processing, and the memory 620 can store data used by the processor 610 during operation.

[0172] The processor 610 can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0173] Optionally, according to a network device of one embodiment of this application, determining the first MR file for data analysis includes:

[0174] Use the defined document object model to read multiple second MR files;

[0175] The plurality of second MR files are format-converted to obtain the first MR file; wherein, the format conversion includes matching the headers of the plurality of second MR files and filling the difference field with null values.

[0176] Optionally, in a network device according to one embodiment of this application, the plurality of second MR files include a Measurement Report Sample (MRO) and / or an Event Measurement Report (MRE).

[0177] Optionally, according to one embodiment of the network device of this application, determining the target object for data analysis includes:

[0178] Receive object information for determining the target object, the object information including one or more of base station identifier, physical cell identifier (PCI), and analysis time period;

[0179] The target object is determined based on the object information.

[0180] Optionally, in a network device according to an embodiment of this application, the processor is further configured to:

[0181] If the statistical results include the first cell, then the neighboring cells of the first cell are determined according to the neighboring cell missing configuration detection method. The first cell is a cell that lacks neighboring cell relationships.

[0182] Optionally, according to one embodiment of the network device of this application, the step of determining the neighboring cells of the first cell according to the neighboring cell missing configuration detection method includes:

[0183] Identify each target cell that meets the second set conditions, the second set conditions including the set cell frequency and the set cell PCI;

[0184] Calculate the distance between the first cell and each of the target cells;

[0185] The target cell that is closest to the first cell is determined as the neighboring cell of the first cell.

[0186] Optionally, in a network device according to an embodiment of this application, the processor is further configured to:

[0187] For the second cell in the statistical results, determine the minimum inter-cell spacing of the second cell, where the second cell is any cell in the statistical results.

[0188] Optionally, according to a network device of one embodiment of this application, determining the minimum inter-cell spacing of the second cell includes:

[0189] Based on the orientation of the second cell, determine each adjacent cell that has a confrontation relationship with the second cell, wherein the confrontation relationship is that the orientation of the second cell and the orientation of the adjacent cell have a convergence point;

[0190] Calculate the distance between the second cell and each of the neighboring cells;

[0191] The nearest neighbor cell to the second cell is determined as the nearest neighbor cell of the second cell, and the distance between the nearest neighbor cell and the second cell is the minimum inter-cell spacing.

[0192] It should be noted that the network device provided in this application embodiment can implement all the method steps implemented by the method embodiment with the network device as the execution subject, and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0193] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing the processor to execute the methods provided in the above embodiments, including:

[0194] A first measurement report (MR) file is determined for data analysis, wherein each file in the first MR file has the same header format;

[0195] Identify the target objects for data analysis;

[0196] Clustering statistics are performed on the first MR file based on the target object to obtain statistical results. The statistical results include one or more worst cells that meet a first set condition, and the first set condition includes one or more measurement feature values.

[0197] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0198] On the other hand, this application provides a computer program product, which includes instructions that, when the computer program product is run on a computer, cause the computer to perform the steps of the above method. For details, please refer to the content of the above method embodiments, which will not be repeated here.

[0199] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0200] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0201] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0202] These processors can execute instructions that can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0203] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A data analysis method, characterized by, The method comprises the following steps: determining a first measurement report (MR) file for data analysis, the header format of each file in the first MR file being the same; determining a target object for data analysis; performing cluster statistics on the first MR file based on the target object to obtain a statistical result, the statistical result including one or more worst cells satisfying a first set condition, the first set condition including one or more measurement characteristic values, wherein the cluster statistics use Pandas cluster statistics to realize data filtering and perspective based on object level and time granularity.

2. The data analysis method of claim 1, wherein, The determination of the first MR file for data analysis comprises: reading a plurality of second MR files using a set document object model; performing format conversion on the plurality of second MR files to obtain the first MR file; wherein the format conversion includes matching the headers of the plurality of second MR files, and using null values to fill in the difference set fields.

3. The data analysis method of claim 2, wherein, The plurality of second MR files include measurement report samples (MROs) and / or event measurement reports (MREs).

4. The data analysis method of claim 1, wherein, The determination of the target object for data analysis comprises: receiving object information for determining the target object, the object information including one or more of a base station identifier, a physical cell identifier (PCI), and an analysis time period; determining the target object according to the object information.

5. The data analysis method of claim 1, wherein, Further comprising: if the statistical result includes a first cell, determining a neighbor cell of the first cell in a neighbor missing configuration detection mode, the first cell being a cell lacking a neighbor relationship.

6. The data analysis method of claim 5, wherein, The determination of the neighbor cell of the first cell in the neighbor missing configuration detection mode comprises: determining each target cell satisfying a second set condition, the second set condition including a set cell frequency point and a set cell PCI; calculating the distance between the first cell and the each target cell; determining the target cell with the smallest distance from the first cell as the neighbor cell of the first cell.

7. The data analysis method of claim 1, wherein, Further comprising: for a second cell in the statistical result, determining the minimum inter-site distance of the second cell, the second cell being any cell in the statistical result.

8. The data analysis method of claim 7, wherein, The determination of the minimum inter-site distance of the second cell comprises: determining each neighboring cell having a pairing relationship with the second cell according to the orientation of the second cell, the pairing relationship being that the orientation of the second cell and the orientation of the neighboring cell have a convergence point; calculating the distance between the second cell and the each neighboring cell; determining the neighboring cell with the smallest distance from the second cell as the nearest neighboring cell of the second cell, the distance between the nearest neighboring cell and the second cell being the minimum inter-site distance.

9. A network device, comprising: The method comprises a memory, a transceiver, and a processor: the memory is used to store a computer program; the transceiver is used to transceive data under the control of the processor; the processor is used to read the computer program in the memory and perform the following operations: determining a first measurement report (MR) file for data analysis, the header format of each file in the first MR file being the same; determining a target object for data analysis; The first MR file is clustered and counted based on the target object, and a counting result is obtained, the counting result including one or more worst cells satisfying a first set condition, the first set condition including one or more measurement characteristic values, wherein the clustering and counting uses Pandas clustering and counting, and data filtering and perspective based on object level and time granularity are realized.

10. The network device of claim 9, wherein, The first MR file used for data analysis is determined, including: reading a plurality of second MR files using a set document object model; format conversion is performed on the plurality of second MR files to obtain the first MR file; wherein the format conversion includes matching table headers of the plurality of second MR files, and difference set fields are filled with null values.

11. The network device of claim 10, wherein, The plurality of second MR files include measurement report samples MRO and / or event measurement reports MRE.

12. The network device of claim 9, wherein, The target object used for data analysis is determined, including: receiving object information used for determining the target object, the object information including one or more of a base station identifier, a physical cell identifier PCI, and an analysis time period; determining the target object according to the object information.

13. The network device of claim 9, wherein, The processor is further configured to: if the counting result includes a first cell, determining a neighbor cell of the first cell in a neighbor cell missing configuration detection mode, the first cell being a cell lacking a neighbor cell relationship.

14. The network device of claim 13, wherein, The determination of the neighbor cell of the first cell in the neighbor cell missing configuration detection mode includes: determining each target cell satisfying a second set condition, the second set condition including a set cell frequency point and a set cell PCI; calculating distances between the first cell and the each target cell; determining a target cell with a minimum distance from the first cell as a neighbor cell of the first cell.

15. The network device of claim 9, wherein, The processor is further configured to: for a second cell in the counting result, determining a minimum inter-site distance of the second cell, the second cell being any cell in the counting result.

16. The network device of claim 15, wherein, The determination of the minimum inter-site distance of the second cell includes: determining each adjacent cell having a pairing relationship with the second cell according to an orientation of the second cell, the pairing relationship being that there is a convergence point between the orientation of the second cell and an orientation of the adjacent cell; calculating distances between the second cell and the each adjacent cell; determining an adjacent cell with a minimum distance from the second cell as a nearest adjacent cell of the second cell, the distance between the nearest adjacent cell and the second cell being the minimum inter-site distance.

17. A data analysis device, characterized by including: a first determination unit configured to determine a first measurement report MR file used for data analysis, table header formats of each file in the first MR file being the same; a second determination unit configured to determine a target object used for data analysis; a clustering and counting unit configured to cluster and count the first MR file based on the target object, and obtain a counting result, the counting result including one or more worst cells satisfying a first set condition, the first set condition including one or more measurement characteristic values, wherein the clustering and counting uses Pandas clustering and counting, and data filtering and perspective based on object level and time granularity are realized.

18. A processor-readable storage medium, comprising: The processor readable storage medium stores a computer program for causing the processor to execute the method of any one of claims 1 to 8.

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