A method, apparatus and medium for processing CellTrace data

Abstract

This application provides a method, apparatus, and medium for processing CellTrace data. The method involves acquiring CellTrace data; converting the CellTrace data to a target format data object; extracting information from the target format data object to obtain valid information data; and obtaining personalized indicator data based on the valid information data. This personalized indicator data is used for online analysis processing in an end-to-end analysis system. By processing CellTrace data, this application enables it to effectively support end-to-end analysis systems, thereby improving mobile network quality and ultimately enhancing the user experience for mobile network users.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method, apparatus and medium for processing CellTrace data. Background Technology

[0002] The operator's end-to-end analysis system incorporates CellTrace as a data source. CellTrace contains all radio signaling data, providing complete statistical call detail records (CDRs) of user-level air interface services. CDR information is categorized according to analysis content, including coverage, UE capabilities, access, hold-up, mobility, interoperability, and service rates. It can extract network quality information such as perceived metrics, network events, parameter configurations, and anomaly cause values. This fills the gap in air interface information within the end-to-end analysis system, addressing the final weakness.

[0003] Since end-to-end analysis systems have certain requirements on data analysis latency, it is necessary to consider the processing time of CellTrace data and the query time of calling CellTrace data.

[0004] However, the original CellTrace is a binary code stream. Developers of end-to-end analysis systems have varying abilities and experience in using CellTrace data. Improper use can cause various anomalies in end-to-end analysis systems, such as missing page data, long query times, and poor user experience. Summary of the Invention

[0005] The technical problem to be solved by this application is to address the above-mentioned shortcomings of the prior art by providing a method, apparatus and medium for processing CellTrace data, so as to solve the problems existing in the prior art.

[0006] Firstly, this application provides a method for processing CellTrace data, wherein...

[0007] The methods include:

[0008] S1. Obtain CellTrace data;

[0009] S2. Convert the CellTrace data to obtain a data object in the target format;

[0010] S3. Extract information from the data object in the target format to obtain valid information data;

[0011] S4. Based on the effective information data, personalized indicator data is obtained, and the personalized indicator data is used for online analysis processing of the end-to-end analysis system.

[0012] In some embodiments, S1 includes:

[0013] CellTrace data is obtained through real-time streaming, and the CellTrace data is in DAT binary format.

[0014] In some embodiments, S2 includes:

[0015] Based on CellTrace data in Dat binary format, a binary code stream serial streaming single-frame decoding application is used to depack the binary code stream to obtain the signaling frames in the data frames.

[0016] Multiple signaling frames are converted to plaintext in parallel to obtain a JSON format signaling frame data object.

[0017] In some embodiments, S3 includes:

[0018] Based on the JSON format signaling frame data object, effective information is extracted according to the business requirements of the end-to-end analysis system to obtain the data columns in the dataset that need to be analyzed, and these columns are used as effective information data.

[0019] In some embodiments, S4 includes:

[0020] Data is merged based on the valid information data in the JSON format, and the merged data object is converted into CSV data format.

[0021] The merged data object in CSV format is pushed to the Hadoop cluster to generate personalized indicator data for the end-to-end analysis system.

[0022] In some embodiments, it also includes:

[0023] The personalized indicator data generated by the Hadoop cluster is synchronized to the Doris database in real time to support online analysis and processing of the end-to-end analysis system.

[0024] In some embodiments, personalized indicator data generated by the Hadoop cluster is synchronized to the Doris database in real time to support online analysis processing of the end-to-end analysis system, including:

[0025] The personalized indicator data generated by the Hadoop cluster is synchronized to the Doris database in real time, so that the end-to-end analysis system can use JDBC to operate the Doris database to obtain personalized indicator data and perform online analysis and processing.

[0026] In some embodiments, the CellTrace data includes at least one of the following:

[0027] Data from the UU, X2, S1MME, and UU_EXTEND interfaces in a 4G communication system;

[0028] UU, Xn, N1N2, and UU_EXTEND interface data in 5G communication systems.

[0029] Secondly, this application provides a CellTrace data processing apparatus, the apparatus comprising:

[0030] The data acquisition module is configured to acquire CellTrace data.

[0031] The format conversion module is configured to convert the CellTrace data to obtain a data object in the target format.

[0032] The information extraction module is configured to extract information based on the data object in the target format to obtain valid information data.

[0033] The indicator acquisition module is configured to obtain personalized indicator data based on the valid information data, and the personalized indicator data is used for online analysis processing of the end-to-end analysis system.

[0034] Thirdly, this application provides a CellTrace data processing apparatus, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the CellTrace data processing method described in the first aspect above.

[0035] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the CellTrace data processing method described in the first aspect.

[0036] The CellTrace data processing method, apparatus, and medium provided in this application specifically involve: acquiring CellTrace data; converting the CellTrace data into a format to obtain a data object in a target format; extracting information from the data object in the target format to obtain valid information data; and obtaining personalized indicator data based on the valid information data. This personalized indicator data is used for online analysis processing in an end-to-end analysis system. By processing CellTrace data, this application enables it to effectively support end-to-end analysis systems, thereby improving mobile network quality and ultimately enhancing the user experience for mobile network users. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] Figure 1 A schematic diagram of a CellTrace data processing system provided in an embodiment of this application;

[0039] Figure 2 A schematic diagram illustrating the CellTrace data processing method provided in an embodiment of this application;

[0040] Figure 3 Another schematic diagram of the CellTrace data processing method provided in the embodiments of this application;

[0041] Figure 4 A schematic diagram illustrating the data streaming process provided in the embodiments of this application;

[0042] Figure 5 A schematic diagram illustrating the data parsing and processing procedure provided in the embodiments of this application;

[0043] Figure 6 A schematic diagram illustrating the data extraction and processing procedure provided in the embodiments of this application;

[0044] Figure 7 A schematic diagram illustrating the data integration processing procedure provided in the embodiments of this application;

[0045] Figure 8 A schematic diagram illustrating the data storage and processing procedure provided in the embodiments of this application;

[0046] Figure 9 A schematic diagram illustrating the heterogeneous database synchronization and end-to-end analysis system access processing procedure provided in the embodiments of this application;

[0047] Figure 10 A schematic diagram of a CellTrace data processing device provided in an embodiment of this application;

[0048] Figure 11 This is a schematic diagram of another CellTrace data processing device provided in an embodiment of this application.

[0049] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0050] To enable those skilled in the art to better understand the technical solution of this application, the embodiments of this application will be further described in detail below with reference to the accompanying drawings.

[0051] It is understood that the specific embodiments and accompanying drawings described herein are merely for explaining this application and are not intended to limit this application.

[0052] It is understood that, without conflict, the various embodiments and features in the embodiments of this application can be combined with each other.

[0053] It is understood that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, while parts unrelated to this application are not shown in the drawings.

[0054] It is understood that each unit or module involved in the embodiments of this application may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple units or modules may be integrated into one entity structure.

[0055] It is understood that the terms "first," "second," etc., used in the embodiments of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0056] It is understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of this application may occur in a different order than those marked in the accompanying drawings.

[0057] It is understood that the flowcharts and block diagrams of this application illustrate the possible architecture, functions, and operations of systems, apparatuses, devices, and methods according to various embodiments of this application. Each block in a flowchart or block diagram may represent a unit, module, program segment, or code, containing executable instructions for implementing the specified function. Furthermore, each block or combination of blocks in the block diagrams and flowcharts may be implemented using a hardware-based system to implement the specified function, or using a combination of hardware and computer instructions.

[0058] It is understood that the units and modules involved in the embodiments of this application can be implemented by software or by hardware. For example, the units and modules can be located in the processor.

[0059] The purpose of this application is to provide a method for processing CellTrace data, thereby solving the aforementioned problem of poor user experience in analysis systems. This application, through processing CellTrace data, enables it to effectively support end-to-end analysis systems, thereby improving mobile network quality and ultimately enhancing the user experience for mobile network users.

[0060] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0061] This application provides a method for processing CellTrace data. The processing flow of this method can be implemented by electronic devices, such as computers, handheld smart terminals, etc. For ease of explanation, the embodiments of this application are described with the computer as the subject of the method execution.

[0062] Figure 1 This is a schematic diagram of the CellTrace data processing system provided in the embodiments of this application. Figure 2 This is a schematic diagram of the CellTrace data processing method provided in an embodiment of this application. Figure 3 Another schematic diagram of the CellTrace data processing method provided in the embodiments of this application is shown below. Figures 1-3 As shown, this application provides a method and system for processing CellTrace data, wherein the system includes a data acquisition layer, a data processing layer, and an application support layer.

[0063] refer to Figures 1-3 The data acquisition layer includes data streaming processing applications, the data processing layer includes applications such as data parsing, data extraction, data integration, and data storage and computation, and the application support layer includes heterogeneous database synchronization and end-to-end analysis system access applications.

[0064] refer to Figures 1-3 The method includes S1-S4, as follows:

[0065] S1. Obtain CellTrace data;

[0066] In some embodiments, the CellTrace data includes at least one of the following: UU, X2, S1MME, and UU_EXTEND interface data in a 4G communication system; and UU, Xn, N1N2, and UU_EXTEND interface data in a 5G communication system.

[0067] In some embodiments, S1 includes: acquiring CellTrace data by real-time streaming, wherein the CellTrace data is in DAT binary format.

[0068] Specifically, Figure 4 This is a schematic diagram of the data streaming processing procedure provided in the embodiments of this application, such as... Figure 4 As shown, CellTrace data can be accessed from the mobile network northbound interface through a data streaming application, including the following steps:

[0069] Step S101: Periodically check the CellTrace data (gz format) that was not acquired in the previous cycle at the northbound interface of the mobile network.

[0070] Step S102: Connect the unacquired data to the CellTrace interface machine cluster, store it in local memory address 1, the file format is gz, and overwrite the original data object in local memory address 1.

[0071] Step S103: Decompress the data at local memory address 1 and store it in local memory address 2. The file format is DAT, which overwrites the original data object at local memory address 2.

[0072] S2. Convert the CellTrace data to obtain a data object in the target format;

[0073] In some embodiments, S2 includes: based on CellTrace data in Dat binary format, using a binary code stream serial streaming single-frame decoding application to depacket the binary code stream to obtain signaling frames in the data frame; and performing parallel plaintext conversion on multiple signaling frames to obtain a signaling frame data object in JSON format.

[0074] Specifically, Figure 5 A schematic diagram of the data parsing and processing procedure provided in the embodiments of this application is shown below. Figure 5 As shown, the data parsing application can parse the data object at local memory address 2 into a single frame of plaintext signaling, including the following steps:

[0075] Step S201: Referring to the relevant technical requirements (e.g., "Technical Requirements for Radio Signaling and Call Record Collection of China Unicom TDD / FDD LTE Digital Cellular Mobile Communication v3.0") regarding interface file format requirements, serially extract the data frames of the DAT binary code stream from local memory address 2, store them in local memory address 3, and overwrite the original data object in local memory address 3.

[0076] Step S202: Referring to the interface file format requirements in the relevant technical requirements (e.g., "Technical Requirements for Radio Signaling and Call Record Collection of China Unicom TDD / FDD LTE Digital Cellular Mobile Communication v3.0"), extract the signaling frames of the data frames in local memory address 3 serially, store them in local memory address 4, and overwrite the original data object in local memory address 4.

[0077] Step S203: Referring to relevant technical requirements (such as the call record (Trace) collection requirements in "China Unicom TDD / FDD LTE Digital Cellular Mobile Communication Wireless Signaling and Call Record Collection Technical Requirements v3.0"), as well as information from "3GPP TS36.331" (LTE Uu), "3GPP TS 36.423" (LTE X2), "3GPP TS 36.413" (LTE S1MME), "3GPP TS38.331" (5G Uu), "3GPP TS 38.423" (5G Xn), and "3GPP TS 38.413" (5G N1N2), the signaling frames in local memory address 4 are parsed in parallel as plaintext information and stored in local memory address 5 in JSON format, overwriting the original data object in local memory address 5.

[0078] S3. Extract information from the data object in the target format to obtain valid information data;

[0079] In some embodiments, S3 includes: based on the signaling frame data object in JSON format, extracting effective information according to the business requirements of the end-to-end analysis system to obtain data columns in the dataset to be analyzed, and using them as effective information data.

[0080] Specifically, Figure 6 This is a schematic diagram of the data extraction and processing process provided in the embodiments of this application, such as... Figure 6 As shown, the effective information in the data object at local memory address 5 is extracted using a data extraction application, including the following steps:

[0081] Step S301: Analyze the business requirements of the end-to-end analysis system, refer to the reference information in step S203, and generate valid CellTrace information. Valid information refers to the counter information for constructing the call-level CellTrace indicator system.

[0082] Step S302: Based on the counter information of the call-level CellTrace indicator system in step S301, intersect it with the counter information of the data object at local memory address 5 to generate valid signaling plaintext information, store it in local memory address 6 in JSON format, and overwrite the original data object at local memory address 6.

[0083] S4. Based on the effective information data, personalized indicator data is obtained, and the personalized indicator data is used for online analysis processing of the end-to-end analysis system.

[0084] Online Analytical Processing (OLAP) is a technology that performs high-speed, complex queries or multidimensional analyses on large amounts of data stored in data warehouses, data lakes, or other data repositories. OLAP is used in Business Intelligence (BI), decision support, and various business forecasting and reporting applications.

[0085] In some embodiments, S4 includes: merging data based on the valid information data in the JSON format, and converting the merged data object into a CSV data format; pushing the merged data object in the CSV data format to the Hadoop cluster to generate personalized indicator data corresponding to the end-to-end analysis system.

[0086] Specifically, Figure 7 A schematic diagram of the data integration processing procedure provided in the embodiments of this application, as shown below. Figure 7 As shown, through data integration applications, JSON data objects at local memory address 6 can be merged and converted into CSV format data of fixed file size. This involves the following steps:

[0087] Step S401: Convert the JSON data format to CSV format.

[0088] Step S402: To better adapt to cluster storage, accumulate approximately 256MB of CSV format data objects.

[0089] Step S403: Save the data object from step S402 to the local machine, and store it at local hard disk address 1 (data lifecycle 7 days).

[0090] also, Figure 8 This is a schematic diagram of the data storage and processing procedure provided in the embodiments of this application, such as... Figure 8 As shown, through data storage and computing applications, CSV data at local hard drive address 1 can be periodically stored into a Hadoop storage and computing cluster, and the required end-to-end application metrics can be calculated. This includes the following steps:

[0091] Step S404: Periodically check the interface machine cluster for CSV data that was not retrieved in the previous cycle. Then, connect the unretrieved data to the Hadoop in-store computing cluster data warehouse ODS layer (data lifecycle 14 days).

[0092] Step S405: The ODS data from step S404 is modeled based on dimensions, detailed wide tables, and reusable relational calculations, and then stored in the DWD layer of the Hadoop in-store computing cluster data warehouse (data lifecycle 30 days).

[0093] Step S406: Perform a light summary of the DWD data from step S405, create a summary wide table, and store it in the DWS layer of the Hadoop in-store computing cluster data warehouse (data lifecycle 90 days).

[0094] Step S407: According to the business requirements of the end-to-end analysis system, the DWS data from step S406 is summarized, a personalized data table is created, and stored in the ADS layer of the Hadoop storage and computing cluster data warehouse (data lifecycle 180 days).

[0095] The personalized data table is the indicator system required by the end-to-end analysis system, which includes 8 categories and a total of 36 indicators, namely coverage, UE capability, access, persistence, mobility, interoperability, service awareness, and abnormal events, as detailed below:

[0096] (1) Coverage classes: RSRP, CQI, TA, RSRQ, TX_POWER, RI;

[0097] (2) UE capabilities: SA support capability, VoLTE / VoNR support capability, BandList, antenna capability, CA capability;

[0098] (3) RRC access success rate, RRC establishment reasons, and Qos Flow establishment success rate for 5QI;

[0099] (4) The number of RRC releases, the reasons for RRC releases, the number of RRC reconstructions, the number of Qos Flow releases per 5QI, and the reasons for Qos Flow releases per 5QI;

[0100] (5) Mobility in-frequency handover success rate, inter-frequency handover success rate, signaling plane handover delay, mobility parameters;

[0101] (6) Number of 5G fallback attempts to 4G, number of 4G fallback attempts to 5G, and inter-system interoperability parameters;

[0102] (7) Service-aware PDCP uplink rate, PDCP downlink rate, RLC uplink rate, RLC downlink rate, PDCP uplink packet loss rate, and PDCP downlink packet loss rate;

[0103] (8) Abnormal events such as dropped calls, access failures, intra-system handover failures and their causes, and inter-RAT interoperability failures and their causes.

[0104] In some embodiments, the method further includes: S5, synchronizing the personalized indicator data generated by the Hadoop cluster to the Doris database in real time to support online analysis processing of the end-to-end analysis system.

[0105] In some embodiments, the personalized indicator data generated by the Hadoop cluster is synchronized to the Doris database in real time to support the online analysis processing of the end-to-end analysis system. This includes: synchronizing the personalized indicator data generated by the Hadoop cluster to the Doris database in real time, so that the end-to-end analysis system can obtain the personalized indicator data and perform online analysis processing by operating the Doris database through JDBC.

[0106] Specifically, Figure 9 This is a schematic diagram illustrating the heterogeneous database synchronization and end-to-end analysis system access processing procedure provided in the embodiments of this application, as shown below. Figure 9 As shown, by synchronizing heterogeneous databases and accessing the application through an end-to-end analysis system, the data from step S407 can be synchronized to the Doris database in real time. The "end-to-end analysis system" operates on the Doris database via JDBC to obtain CellTrace metric data, including the following steps:

[0107] Step S501: Using Stream Load, synchronize the CSV data from step S404 to the ADS layer of the Doris data warehouse in real time, naming it as a signaling prefix followed by the data (data lifecycle 30 days); synchronize the CSV data from step S407 to the ADS layer of the Doris data warehouse in real time, naming it as an indicator prefix followed by the data (data lifecycle 360 ​​days).

[0108] Step S502: Using JDBC connection, the "end-to-end analysis system" calls the CellTrace indicator data from the Doris database. Signaling backtracking applications call the signaling prefix from the ADS layer; indicator applications call the indicator prefix from the ADS layer.

[0109] JDBC (Java Database Connectivity) is a standard interface in the Java language used for connecting to and manipulating databases. It allows Java applications to interact with relational databases and perform operations such as creating, deleting, modifying, and querying data. JDBC database operations are mainly divided into two categories: modification (create, delete, update) and querying (query).

[0110] This application provides a method for processing CellTrace data. By processing CellTrace data, this application enables it to better support end-to-end analysis systems, thereby improving mobile network quality and ultimately enhancing the user experience of mobile network users.

[0111] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0112] Figure 10 A schematic diagram of the CellTrace data processing apparatus provided in the embodiments of this application is shown below. Figure 10 As shown, this application provides a CellTrace data processing apparatus, the apparatus comprising:

[0113] Data acquisition module 11 is configured to acquire CellTrace data;

[0114] The format conversion module 12 is configured to convert the CellTrace data to obtain a data object in the target format.

[0115] Information extraction module 13 is configured to extract information based on the data object in the target format to obtain valid information data;

[0116] The indicator acquisition module 14 is configured to obtain personalized indicator data based on the valid information data, and the personalized indicator data is used for online analysis processing of the end-to-end analysis system.

[0117] Regarding the limitations on the CellTrace data processing device, please refer to the limitations on the CellTrace data processing method in the above embodiments of this application, which will not be repeated here.

[0118] Figure 11 Another schematic diagram of the CellTrace data processing apparatus provided in the embodiments of this application is shown below. Figure 11 As shown, in some embodiments, this application provides a CellTrace data processing apparatus, including a memory 22 and a processor 21. The memory stores a computer program, and the processor is configured to run the computer program to execute the CellTrace data processing methods in the above embodiments of this application.

[0119] The memory is connected to the processor. The memory can be flash memory, read-only memory or other types of memory. The processor can be a central processing unit or a microcontroller.

[0120] In some embodiments, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the CellTrace data processing method in the above embodiments of this application.

[0121] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules, or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory or other memory technologies, CD-ROM (Compact Disc Read-Only Memory), DVD or other optical disc storage, cartridges, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer.

[0122] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A method for processing CellTrace data, characterized in that, The method includes: S1. Obtain CellTrace data, which includes at least one of the following: UU, X2, S1MME, and UU_EXTEND interface data in the 4G communication system; UU, Xn, N1N2, and UU_EXTEND interface data in the 5G communication system. The original CellTrace is a binary code stream. S1 specifically includes: acquiring CellTrace data through real-time streaming, wherein the CellTrace data is in DAT binary format; S2. Convert the CellTrace data to obtain a data object in the target format; S2 specifically includes: CellTrace data based on Dat binary format, using a binary code stream serial streaming single-frame decoding application to depacket the binary code stream to obtain signaling frames in the data frame; and performing parallel plaintext conversion on multiple signaling frames to obtain a JSON format signaling frame data object. S3. Extract information from the data object in the target format to obtain valid information data; S3 specifically includes: based on the signaling frame data object in the JSON format, extracting effective information according to the business requirements of the end-to-end analysis system to obtain data columns in the dataset that need to be analyzed, and using them as effective information data; S4. Based on the effective information data, personalized indicator data is obtained, and the personalized indicator data is used for online analysis processing of the end-to-end analysis system. S4 specifically includes: Data is merged based on the valid information data in the JSON format, and the merged data object is converted into CSV data format. The merged data object in CSV format is pushed to the Hadoop cluster to generate personalized indicator data corresponding to the end-to-end analysis system. The categories of the personalized indicator data include coverage, UE capabilities, access, retention, mobility, interoperability, service awareness, and abnormal events. The method further includes: S5. Synchronize the personalized indicator data generated by the Hadoop cluster to the Doris database in real time to support the online analysis and processing of the end-to-end analysis system, specifically including: The personalized indicator data generated by the Hadoop cluster is synchronized to the Doris database in real time, so that the end-to-end analysis system can use JDBC to operate the Doris database to obtain personalized indicator data and perform online analysis and processing.

2. A CellTrace data processing device, characterized in that, The device includes: The data acquisition module is configured to acquire CellTrace data, which includes at least one of the following: UU, X2, S1MME, and UU_EXTEND interface data in a 4G communication system; and UU, Xn, N1N2, and UU_EXTEND interface data in a 5G communication system. The original CellTrace is a binary bitstream. Acquiring CellTrace data specifically includes: acquiring CellTrace data through real-time streaming, wherein the CellTrace data is in DAT binary format. The format conversion module is configured to convert the CellTrace data to obtain a data object in the target format. Specifically, it includes: based on the Dat binary format CellTrace data, using a binary code stream serial streaming single-frame decoding application to depack the binary code stream to obtain the signaling frames in the data frame; and performing parallel plaintext conversion on multiple signaling frames to obtain a JSON format signaling frame data object. The information extraction module is configured to extract information based on the data object in the target format to obtain valid information data. Specifically, it includes: extracting valid information based on the signaling frame data object in the JSON format according to the business requirements of the end-to-end analysis system to obtain the data columns in the data set to be analyzed, and using them as valid information data. The indicator acquisition module is configured to obtain personalized indicator data based on the valid information data. The personalized indicator data is used for online analysis processing of the end-to-end analysis system. Specifically, it includes: merging data based on the valid information data in JSON format and converting the merged data object into CSV data format; pushing the merged data object in CSV format to the Hadoop cluster to generate personalized indicator data corresponding to the end-to-end analysis system; wherein, the categories of the personalized indicator data include coverage, UE capability, access, persistence, mobility, interoperability, business awareness, and abnormal events. The device is also used to: synchronize the personalized indicator data generated by the Hadoop cluster to the Doris database in real time, so as to support the online analysis processing of the end-to-end analysis system. Specifically, it includes: synchronizing the personalized indicator data generated by the Hadoop cluster to the Doris database in real time, so that the end-to-end analysis system can obtain personalized indicator data and perform online analysis processing by operating the Doris database through JDBC.

3. A CellTrace data processing device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to implement the CellTrace data processing method as described in claim 1.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the CellTrace data processing method as described in claim 1.

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