Indoor distribution system fault identification method, device and equipment and storage medium

Abstract

The application discloses a method for identifying faults of an indoor distribution system, and aims at solving the problem that the existing fault monitoring method for the indoor distribution system cannot monitor the hidden faults of a feeder system in the indoor distribution system. The method comprises the following steps: obtaining operation data of an indoor distribution system to be analyzed; determining evaluation scores corresponding to each evaluation dimension of the indoor distribution system to be analyzed according to the operation data, wherein the evaluation dimensions comprise a voice service dimension, a data service dimension, a coverage scene dimension and a coverage fallback dimension; determining an evaluation total score of the indoor distribution system to be analyzed according to the evaluation scores; and determining a fault area of the indoor distribution system to be analyzed according to the evaluation total score of the indoor distribution system to be analyzed and the evaluation scores corresponding to each evaluation dimension.

Description

Technical Field

[0001] This application relates to the field of wireless network communication technology, and in particular to a method, apparatus, device, and storage medium for fault identification in indoor distributed systems. Background Technology

[0002] Since the advent of the 5G era, users have increasingly higher requirements for network quality. Generally, more than 80% of mobile data services occur indoors. Therefore, the performance of indoor coverage will directly affect the user experience and revenue of operators.

[0003] In existing solutions, operators achieve indoor signal coverage through indoor distribution systems. Indoor distribution systems are a successful solution for improving the mobile communication environment inside buildings, targeting indoor user groups. They utilize relevant technologies to evenly distribute the signals of mobile communication base stations in every corner of the room, thereby ensuring ideal signal coverage in indoor areas.

[0004] Currently, 95% of existing 4G and 5G indoor distribution systems are traditional indoor distribution systems, which are extension systems composed of a large number of passive devices to achieve deep indoor coverage.

[0005] When the passive components of a traditional indoor distribution system fail, it can lead to weak or no coverage in certain areas, resulting in a poor user data service experience and even, in severe cases, dropped calls, seriously affecting the user experience.

[0006] To avoid the aforementioned problems, existing solutions monitor and troubleshoot faults in indoor distributed systems (IDS) cells (i.e., the cells covered by the IDS, hereinafter referred to as IDS cells for ease of description) through methods such as alarm monitoring triggering, regular indoor IDS cell KPI degradation triggering, and user complaint triggering. Among these, the alarm-triggered monitoring method mainly targets active main devices such as indoor baseband units (BBUs) and remote radio units (RRUs). By monitoring the alarm logs reported by active devices, the entire indoor IDS system can be monitored. However, since traditional indoor IDS systems involve many passive devices, the existing alarm-triggered monitoring method has certain limitations, and latent fault alarms cannot be monitored.

[0007] Furthermore, conventional methods for triggering KPI degradation in indoor distributed systems mainly target indoor-outdoor collaborative optimization, enabling large-granularity and medium-granularity monitoring, but they cannot promptly detect hidden problems in small-granularity passive distributed systems. On the other hand, user complaint triggering methods have significant lag and a substantial impact on customer perception, and there has been a lack of effective monitoring methods for hidden faults in antenna feeder systems within indoor distributed system communities.

[0008] Therefore, in order to ensure user experience and improve the network quality of indoor distribution systems, how to monitor the faults of indoor distribution systems has become an urgent problem to be solved. Summary of the Invention

[0009] This application provides a method for identifying faults in an indoor distributed system, which addresses the problem that existing methods for monitoring faults in indoor distributed systems cannot monitor latent faults in the antenna feeder system.

[0010] This application also provides an indoor distribution system fault identification device to solve the problem that existing indoor distribution system fault monitoring methods cannot monitor latent faults in the antenna feeder system of the indoor distribution system.

[0011] This application also provides an indoor distribution system fault identification device to solve the problem that existing indoor distribution system fault monitoring methods cannot monitor hidden faults in the antenna feeder system of the indoor distribution system.

[0012] This application also provides a computer-readable storage medium to address the problem that existing methods for monitoring faults in indoor distribution systems cannot monitor latent faults in the antenna feeder system of an indoor distribution system.

[0013] The embodiments of this application adopt the following technical solutions:

[0014] A method for monitoring faults in an indoor distributed system includes: acquiring operational data of the indoor distributed system to be analyzed; determining evaluation scores for each evaluation dimension of the indoor distributed system to be analyzed based on the operational data, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension; determining the total evaluation score of the indoor distributed system to be analyzed based on the evaluation scores; and determining the fault area of ​​the indoor distributed system to be analyzed based on the total evaluation score of the indoor distributed system to be analyzed and the evaluation scores corresponding to each evaluation dimension.

[0015] An indoor distributed system fault monitoring device includes: a data acquisition unit for acquiring operational data of the indoor distributed system to be analyzed; an evaluation unit for determining evaluation scores corresponding to each evaluation dimension of the indoor distributed system to be analyzed based on the operational data, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension; a scoring calculation unit for determining the total evaluation score of the indoor distributed system to be analyzed based on the evaluation scores; and a fault identification unit for determining the fault area of ​​the indoor distributed system to be analyzed based on the total evaluation score of the indoor distributed system to be analyzed and the evaluation scores corresponding to each evaluation dimension.

[0016] An indoor distributed system fault monitoring device, comprising:

[0017] The system includes a processor and a memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the following operations: acquire operational data of the indoor distribution system to be analyzed; determine, based on the operational data, an evaluation score corresponding to each evaluation dimension of the indoor distribution system to be analyzed, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension; determine, based on the evaluation scores, a total evaluation score of the indoor distribution system to be analyzed; and determine the fault areas of the indoor distribution system to be analyzed based on the total evaluation score of the indoor distribution system to be analyzed and the evaluation scores corresponding to each evaluation dimension.

[0018] A computer-readable storage medium stores one or more programs that, when executed by an electronic device including multiple applications, cause the electronic device to perform the following operations: acquire operational data of an indoor distributed system to be analyzed; determine, based on the operational data, an evaluation score corresponding to each evaluation dimension of the indoor distributed system to be analyzed, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension; determine, based on the evaluation scores, a total evaluation score of the indoor distributed system to be analyzed; and determine the fault area of ​​the indoor distributed system to be analyzed based on the total evaluation score of the indoor distributed system to be analyzed and the evaluation scores corresponding to each evaluation dimension.

[0019] A computer program product includes a computer program that, when executed by a processor, performs the following: acquiring operational data of an indoor distributed system to be analyzed; determining, based on the operational data, evaluation scores corresponding to each evaluation dimension of the indoor distributed system to be analyzed, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension; determining, based on the evaluation scores, a total evaluation score of the indoor distributed system to be analyzed; and determining the fault areas of the indoor distributed system to be analyzed based on the total evaluation score of the indoor distributed system to be analyzed and the evaluation scores corresponding to each evaluation dimension.

[0020] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0021] The indoor distributed system fault identification method provided in this application provides the following: For an indoor distributed cell to be analyzed, operational data of the indoor distributed system to be analyzed can be obtained. Based on the operational data, the evaluation scores corresponding to each evaluation dimension of the indoor distributed system to be analyzed are determined. These evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension. Based on the evaluation scores, the total evaluation score of the indoor distributed system to be analyzed is determined. Based on the total evaluation score of the indoor distributed system to be analyzed and the evaluation scores corresponding to each evaluation dimension, the fault area of ​​the indoor distributed system to be analyzed is determined. Using the method provided in this application, a comprehensive quantitative evaluation method for 4 / 5G distributed systems is constructed by jointly using SEQ voice / data anomaly documents, coverage fallback information, and coverage scenario value information. SEQ anomaly reports can measure user-level service continuity, and coverage fallback information can measure network coverage continuity. Combined with scenario value priority, user perception indicators and network indicators can be truly integrated. Secondly, to address the issue of multiple weight dimensions in the quantification system and the lack of complete decoupling between different dimensions, a hierarchical weight design method is defined to more objectively derive the weight factor of each dimension, improving the accuracy of overall quantification calculation. Finally, a high-reliability indoor distribution fault identification method is defined. By combining the scores of three quantification dimensions and using the Venn diagram method, features are aggregated to mine problem cells with high probability of indoor distribution faults. Using the method provided in this application, network quality monitoring in indoor distribution scenarios can be proactively carried out. It has a strong proactive problem discovery capability and can discover hidden faults in passive indoor distribution devices without relying on on-site testing or customer complaints, greatly improving the efficiency of fault identification for indoor distribution systems. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0023] Figure 1 This application provides a schematic flowchart of a fault identification method for an indoor distribution system.

[0024] Figure 2 A specific schematic diagram of a Venn diagram provided in an embodiment of this application;

[0025] Figure 3 A schematic diagram of the specific structure of an indoor distribution system fault identification device provided in this application embodiment;

[0026] Figure 4 This is a schematic diagram of the specific structure of an indoor distribution system fault identification device provided in an embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0029] This application provides a fault identification method for indoor distribution systems, which solves the problem that existing fault monitoring methods for indoor distribution systems cannot monitor latent faults in the antenna feeder system of indoor distribution systems.

[0030] The indoor distributed system fault identification method provided in this embodiment of the invention can be applied to the server side, that is, the execution subject of the method is the server; in addition, the indoor distributed system fault identification method provided in this embodiment of the invention can also be executed by intelligent devices with computing capabilities, such as laptops, desktop computers, computers, etc. Of course, any device with computing capabilities can execute the method provided in this embodiment of the invention; or, the execution subject of the method can also be an application (APP) or the system itself running on these servers.

[0031] For ease of description, the following description uses a fault identification system as the execution subject of this method as an example to introduce its implementation. It should be understood that using a fault identification system as the execution subject is merely an illustrative example and should not be construed as a limitation of the method.

[0032] The schematic diagram of the specific implementation process of the indoor distribution system fault identification method provided in this application is shown below. Figure 1 As shown, the main steps include the following:

[0033] Step 11: Obtain the operating data of the indoor distributed system to be analyzed;

[0034] In this embodiment of the application, the fault identification system can collect the operating system data of the indoor distribution system to be analyzed through the Service Experience Quality (SEQ) management system.

[0035] The SEQ system's metrics are divided into two main categories of user-perceived data: voice and data. Voice services primarily rely on signaling plane probes for service analysis and data collection; while data services require a combination of signaling plane and user plane probe data for analysis and data collection.

[0036] To improve the accuracy of identifying hidden faults in indoor distribution systems, this application proposes a method for comprehensive fault identification based on multiple evaluation dimensions. In one embodiment, the evaluation dimensions used by the fault identification system may include, but are not limited to, the following four:

[0037] 1. Voice service dimension;

[0038] 2. Data business dimension;

[0039] 3. Coverage of various scenarios;

[0040] 4. Coverage of fallback dimensions.

[0041] To achieve fault identification based on the aforementioned multiple evaluation dimensions, in this embodiment of the application, the fault identification system first needs to obtain the operational data corresponding to the aforementioned evaluation dimensions. In one implementation, the fault identification system can specifically obtain operational data for different evaluation dimensions using the following method:

[0042] I. Obtain operational data corresponding to the voice service dimension:

[0043] The fault identification system can obtain the outgoing call records of the indoor distributed system to be analyzed within a preset time period, and obtain the real-time transmission timeout records by filtering the outgoing call records, and use the real-time transmission timeout records as the corresponding operation data of the voice service dimension.

[0044] Specifically, the fault identification system can extract SEQ call records from the past 7 days through the SEQ system, filter these call records, and extract the records with a finish_reason value of RTP Timeout (real-time transmission timeout, generally caused by weak air interface coverage or interference) as the corresponding operation data for the voice service dimension.

[0045] II. Obtaining operational data corresponding to data business dimensions:

[0046] The fault identification system can acquire interface documents (including 4G interface documents and / or 5G interface documents) of the indoor distributed system to be analyzed within a preset time period. By filtering the interface documents, a second interface document is obtained to characterize the failure of data service, and this second interface document is used as the operation data corresponding to the data service dimension.

[0047] Specifically, the fault identification system can extract the S1 Interface Mobility Management Entity (S1-MME) documents (corresponding to 4G data services) or N1 / N2 interface documents (corresponding to 5G data services) from the S1 interface within the last 7 days through the SEQ system. The N1 and N2 interfaces are used to support the terminal (UE) to access and mobility management in the Next Generation Radio Access Network (NG-RAN) and 5G core network. They are the physical interface N2 between the terminal (UE) and the AMF, and the logical interface N1 between the terminal (UE) and the AMF.

[0048] III. Obtaining the runtime data corresponding to the coverage fallback dimension:

[0049] The fault identification system can obtain the number of times the indoor distributed system under analysis has fallen back due to coverage reasons within a preset time period. This indicator is used to represent the number of times the end user experiences a fallback from high to low network standard due to weak network coverage.

[0050] IV. Obtaining runtime data corresponding to the coverage scenario dimensions:

[0051] In this embodiment, the fault identification system can classify the coverage scenarios according to the importance level of the business scenario, and generate a coverage scenario score for each scenario, thereby generating a coverage scenario score table. Subsequently, the fault identification system can use the coverage scenario score table to query the scenario score of the indoor distribution system to be analyzed and use it as the running data corresponding to the coverage scenario dimension.

[0052] Step 12: Based on the operational data obtained by performing Step 11, determine the evaluation scores corresponding to each evaluation dimension of the indoor distribution system to be analyzed.

[0053] In this embodiment of the application, the fault identification system can calculate the evaluation score corresponding to each evaluation dimension according to the following method:

[0054] a. Calculate the voice anomaly assessment score corresponding to the voice service dimension:

[0055] Sub-step a1: Determine the number of real-time transmission timeout documents;

[0056] The fault identification system determines the number of real-time transmission timeout documents in the SQE system that were filtered out by executing step 11, and denots them as X.

[0057] Sub-step a2: Determine the voice anomaly evaluation score corresponding to the voice service dimension based on the number of real-time transmission timeout documents, the preset voice anomaly baseline value, and the voice anomaly challenge value.

[0058] In this embodiment of the application, the fault assessment system can determine the voice abnormality values ​​(VoiceAbnScores) according to the following formulas [1] to [3]:

[0059] VoiceAbnScores = 0; X ∈ [0, baseline value) [1]

[0060] VoiceAbnScores=100*(0.6+0.4×(X-B1)) / (M1-B1); X∈[benchmark value, challenge value)[2]

[0061] VoiceAbnScores = 100; X ∈ [challenge value, positive infinity) [3]

[0062] Wherein, B1 represents the baseline value for voice abnormality = BasedVoiceAbnormalRelThd;

[0063] M1 represents the Voice Abnormality Challenge Value = MaxVoiceAbnomalRelThd.

[0064] If the weak coverage probability factor of the indoor distribution system corresponding to the speech anomaly is set to weight1, then the speech anomaly evaluation score K1 can be calculated according to the following formula [4]:

[0065] K1=Weight1*VoiceAbnScores[4]

[0066] b. Calculate the data anomaly assessment score corresponding to the data business dimension:

[0067] Sub-step b1: Determine the number of second interface documents obtained by executing step 11 to characterize data service failure, denoted as Y;

[0068] Sub-step b2: Determine the data anomaly assessment score corresponding to the data business dimension based on the number of documents in the second interface, the preset data anomaly baseline value, and the data anomaly challenge value.

[0069] In this embodiment of the application, the fault assessment system can determine the data outliers (DataAbnScores) according to the following formulas [5] to [7]:

[0070] DataAbnScores = 0; Y ∈ [0, baseline value) [5]

[0071] DataAbnScores=100*(0.6+0.4×(X-B2)) / (M2-B2); Y∈[benchmark value, challenge value)[6]

[0072] DataAbnScores = 100; Y ∈ [challenge value, positive infinity) [7]

[0073] Wherein, B2 represents the baseline value for data anomalies = BasedDataAbnormalRelThd;

[0074] M2 represents the data anomaly challenge value = MaxDataAbnormalRelThd.

[0075] If the probability factor of weak coverage of the indoor distribution system corresponding to the data anomaly is set as weight2, then the data anomaly assessment score K2 can be calculated according to the following formula [8]:

[0076] K2 = Weight2 * DataAbnScores[8]

[0077] c. Calculate the coverage fallback anomaly assessment score corresponding to the data business dimension:

[0078] Sub-step c1: Determine the number of network fallbacks obtained by executing step 11, denoted as Z;

[0079] Sub-step c2: Determine the coverage fallback anomaly evaluation score corresponding to the coverage fallback dimension based on the number of network fallbacks, the preset network fallback anomaly baseline value, and the network fallback anomaly challenge value.

[0080] In this embodiment of the application, the fault assessment system can determine the data outliers (FallBackAbnScores) according to the following formulas [9] to

[11] :

[0081] FallBackAbnScores = 0; Z ∈ [0, baseline) [9]

[0082] FallBackAbnScores=100*(0.6+0.4×(X-B3)) / (M3-B3); Z∈[benchmark value, challenge value)

[10]

[0083] FallBackAbnScores = 100; Z ∈ [challenge value, positive infinity)

[11]

[0084] Wherein, B3 represents the baseline value for data anomalies = BasedFallBackAbnormalRelThd;

[0085] M3 represents the data anomaly challenge value = MaxFallBackAbnormalRelThd.

[0086] If the probability factor of weak coverage of indoor distributed antenna system corresponding to network fallback anomaly is set to weight3, then the coverage fallback anomaly evaluation score K3 can be calculated according to the following formula

[12] :

[0087] K3=Weight3*FallBackAbnScores

[12]

[0088] d. Calculate the scene evaluation score corresponding to the covered scene dimension:

[0089] Sub-step d1: The fault assessment system queries the SceneScores corresponding to the scene covered by the indoor distribution system to be analyzed, based on the pre-set coverage scene score table.

[0090] In sub-step d2, the weak coverage probability factor of the indoor distribution system corresponding to the scene evaluation score is set to weight4. Then, the coverage fallback anomaly evaluation score K4 can be calculated according to the following formula

[13] :

[0091] K4 = Weight4 * SceneScores

[13]

[0092] In this embodiment, the baseline value and challenge value can be obtained based on empirical data values ​​within a preset time period (e.g., one week). For example, in one implementation, the baseline value and challenge value can be set as shown in Table 1 below:

[0093] Table 1

[0094]

[0095] Step 13: Determine the total evaluation score of the indoor distribution system to be analyzed based on the evaluation scores corresponding to each evaluation dimension obtained by performing Step 12.

[0096] By executing steps 11 and 12 above, the fault identification system can identify fault points in the indoor distribution system through the evaluation scores corresponding to four evaluation dimensions: voice dimension, data dimension, coverage scene dimension, and coverage fallback dimension, thereby improving the accuracy of indoor distribution system problem identification. However, due to the significant differences between the different dimensions, in order to improve the accuracy of fault identification, in this embodiment of the application, the fault identification system can comprehensively quantify and evaluate the total evaluation score of the indoor distribution system under analysis from both vertical and horizontal perspectives:

[0097] I. Vertical Quantitative Assessment – ​​This involves comprehensively weighting the assessment scores from the multiple assessment dimensions mentioned above to quantify the severity of anomalies in the analytical distribution system. In one implementation, the specific vertical quantitative assessment method is as follows:

[0098] 1-1. Determine the multi-level weights corresponding to each evaluation dimension based on the business type;

[0099] Each weighting calculation method has its applicable scope. Sometimes, it is necessary to use multiple methods to measure the weight of the same data to obtain a comprehensive weight with higher performance and a better reflection of the true characteristics of the data. In the embodiments of this application, the weights at the same level can be set according to expert experience, but the weights at different levels need to be set in layers. The comprehensive weight is obtained through a secondary calculation, which is the first-level weight * the second-level weight.

[0100] In this embodiment of the application, the comprehensive weight of each indicator can be calculated as shown in Table 2 below:

[0101] Table 2

[0102]

[0103] 1-2, Root, multi-level weights, weighted summation of the evaluation scores to obtain the vertical quantitative score corresponding to the indoor distribution system to be analyzed.

[0104] II. Horizontal Quantitative Assessment – ​​also known as Abnormal Feature Repetition Assessment. Since the three dimensions of voice / data / fallback are based on statistical information obtained from the current network, they are subject to fluctuations. In order to avoid the deviation of results caused by abnormal fluctuations in a single dimension, it is necessary to assess the degree of abnormality from different dimensions, and then further assess the degree of repetition. The more dimensions that are satisfied, the higher the probability of accurate results.

[0105] In one implementation method, the specific level quantification assessment method is as follows:

[0106] Fault assessment systems can, for example, Figure 2 The division is made based on the Venn diagram shown. Figure 2 The Venn diagram shown determines {set2}U{set3}U{set4}U{set6}. Since each set has a large quantity and fluctuates greatly, the above method of finding sets is converted into a repetition threshold decision.

[0107] Step 14: Based on the total evaluation score of the indoor distribution system to be analyzed obtained by performing Step 13 and the evaluation scores corresponding to each evaluation dimension, determine the fault area of ​​the indoor distribution system to be analyzed.

[0108] In one implementation, if "total evaluation score > maximum score in a single dimension" it can be considered that at least two abnormal dimensions are satisfied.

[0109] In one implementation, the maximum score for a single dimension can be calculated using the following method:

[0110] Maximum score for a single dimension = max{voice anomaly weight, data anomaly weight, coverage fallback weight} * maximum score for a single dimension (100) + scene weight * highest score for a scene (100).

[0111] The evaluation dimension with the highest score in a single dimension that is higher than the total evaluation score is an area with a high probability of hidden faults.

[0112] The indoor distributed system fault identification method provided in this application provides the following: For an indoor distributed cell to be analyzed, operational data of the indoor distributed system to be analyzed can be obtained. Based on the operational data, the evaluation scores corresponding to each evaluation dimension of the indoor distributed system to be analyzed are determined. These evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension. Based on the evaluation scores, the total evaluation score of the indoor distributed system to be analyzed is determined. Based on the total evaluation score of the indoor distributed system to be analyzed and the evaluation scores corresponding to each evaluation dimension, the fault area of ​​the indoor distributed system to be analyzed is determined. Using the method provided in this application, a comprehensive quantitative evaluation method for 4 / 5G distributed systems is constructed by jointly using SEQ voice / data anomaly documents, coverage fallback information, and coverage scenario value information. SEQ anomaly reports can measure user-level service continuity, and coverage fallback information can measure network coverage continuity. Combined with scenario value priority, user perception indicators and network indicators can be truly integrated. Secondly, to address the issue of multiple weight dimensions in the quantification system and the lack of complete decoupling between different dimensions, a hierarchical weight design method is defined to more objectively derive the weight factor of each dimension, improving the accuracy of overall quantification calculation. Finally, a high-reliability indoor distribution fault identification method is defined. By combining the scores of three quantification dimensions and using the Venn diagram method, features are aggregated to mine problem cells with high probability of indoor distribution faults. Using the method provided in this application, network quality monitoring in indoor distribution scenarios can be proactively carried out. It has a strong proactive problem discovery capability and can discover hidden faults in passive indoor distribution devices without relying on on-site testing or customer complaints, greatly improving the efficiency of fault identification for indoor distribution systems.

[0113] In one embodiment, this application also provides an indoor distributed system fault identification device to address the problem that existing indoor distributed system fault monitoring methods cannot monitor latent faults in the antenna feeder system of an indoor distributed system. A schematic diagram of the specific structure of this indoor distributed system fault identification device is shown below. Figure 3 As shown, it includes: a data acquisition unit 31, an evaluation unit 32, a scoring calculation unit 33, and a fault identification unit 34.

[0114] Among them, the data acquisition unit 31 is used to acquire the operating data of the indoor distribution system to be analyzed;

[0115] Evaluation unit 32 is used to determine the evaluation scores corresponding to each evaluation dimension of the indoor distribution system to be analyzed based on the operation data, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension and coverage fallback dimension.

[0116] The scoring calculation unit 33 is used to determine the total evaluation score of the indoor distribution system to be analyzed based on the evaluation score;

[0117] The fault identification unit 34 is used to determine the fault area of ​​the indoor distribution system to be analyzed based on the total evaluation score of the indoor distribution system to be analyzed and the evaluation scores corresponding to each evaluation dimension.

[0118] In one implementation, the evaluation unit 32 is specifically used to: acquire the outgoing call records of the indoor distribution system to be analyzed within a preset time period; filter the outgoing call records to obtain real-time transmission timeout records; determine the number of real-time transmission timeout records; and determine the voice anomaly evaluation score corresponding to the voice service dimension based on the number of real-time transmission timeout records, a preset voice anomaly benchmark value, and a voice anomaly challenge value.

[0119] In one implementation, the evaluation unit 32 is specifically configured to: acquire interface documents of the indoor distribution system to be analyzed within a preset time period, the interface documents including 4G interface documents and / or 5G interface documents; filter the interface documents to obtain second interface documents used to characterize data service failure; determine the number of second interface documents; and determine the data anomaly evaluation score corresponding to the data service dimension based on the number of second interface documents, a preset data anomaly benchmark value, and a data anomaly challenge value.

[0120] In one implementation, the evaluation unit 32 is specifically used to: obtain the number of network fallbacks of the indoor distribution system to be analyzed within a preset time period, wherein the number of fallbacks represents the number of times the terminal user experiences a fallback from a high to a low network standard due to weak network coverage; and determine the coverage fallback anomaly evaluation score corresponding to the coverage fallback dimension based on the number of network fallbacks, a preset network fallback anomaly benchmark value, and a network fallback anomaly challenge value.

[0121] In one implementation, the evaluation unit 32 is specifically used to: determine the importance level corresponding to the coverage scene; and determine the scene evaluation score corresponding to the coverage scene dimension based on the importance level.

[0122] In one implementation, the scoring calculation unit 33 is specifically used for: determining the multi-level weights corresponding to each evaluation dimension according to the business type; performing a weighted summation of the evaluation scores according to the multi-level weights to obtain a vertical quantitative score corresponding to the indoor distribution system to be analyzed; determining outliers corresponding to each evaluation dimension according to the evaluation scores corresponding to each evaluation dimension; performing repeatability quantification on the outliers according to a Venn diagram to obtain a horizontal quantitative score corresponding to the indoor distribution system to be analyzed; and determining the total evaluation score of the indoor distribution system to be analyzed based on the vertical quantitative score and the horizontal quantitative score.

[0123] Using the indoor distributed system fault identification device provided in this application embodiment, for the indoor distributed cell to be analyzed, the operating data of the indoor distributed system to be analyzed can be obtained. Based on the operating data, the evaluation scores corresponding to each evaluation dimension of the indoor distributed system to be analyzed can be determined. The evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension. Based on the evaluation scores, the total evaluation score of the indoor distributed system to be analyzed can be determined. Based on the total evaluation score of the indoor distributed system to be analyzed and the evaluation scores corresponding to each evaluation dimension, the fault area of ​​the indoor distributed system to be analyzed can be determined. Using the method provided in this application embodiment, a comprehensive quantitative evaluation method for 4 / 5G distributed systems can be constructed by jointly using SEQ voice / data anomaly documents, coverage fallback information, and coverage scenario value information. SEQ anomaly reports can measure user-level service continuity, and coverage fallback information can measure network coverage continuity. Combined with scenario value priority, user perception indicators and network indicators can be truly integrated. Secondly, to address the issue of multiple weight dimensions in the quantification system and the lack of complete decoupling between different dimensions, a hierarchical weight design method is defined to more objectively derive the weight factor of each dimension, improving the accuracy of overall quantification calculation. Finally, a high-reliability indoor distribution fault identification method is defined. By combining the scores of three quantification dimensions and using the Venn diagram method, features are aggregated to mine problem cells with high probability of indoor distribution faults. Using the method provided in this application, network quality monitoring in indoor distribution scenarios can be proactively carried out. It has a strong proactive problem discovery capability and can discover hidden faults in passive indoor distribution devices without relying on on-site testing or customer complaints, greatly improving the efficiency of fault identification for indoor distribution systems.

[0124] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 4At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0125] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0126] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0127] The processor reads the corresponding computer program from non-volatile memory into memory and then runs it, forming a network fault root cause analysis device at the logical level. The processor executes the program stored in memory and specifically performs the following operations: acquiring operational data of the indoor distributed system to be analyzed; determining the evaluation scores corresponding to each evaluation dimension of the indoor distributed system to be analyzed based on the operational data, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension; determining the total evaluation score of the indoor distributed system to be analyzed based on the evaluation scores; and determining the fault area of ​​the indoor distributed system to be analyzed based on the total evaluation score of the indoor distributed system to be analyzed and the evaluation scores corresponding to each evaluation dimension.

[0128] The above is as stated in this application. Figure 4The method executed by the indoor distributed system fault monitoring electronic device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0129] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0130] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by a portable electronic device including multiple applications, enable the portable electronic device to perform... Figure 1 The method of the illustrated embodiment is specifically used to perform the following operations:

[0131] Obtain operational data of the indoor distributed system to be analyzed; based on the operational data, determine the evaluation scores corresponding to each evaluation dimension of the indoor distributed system to be analyzed, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension; based on the evaluation scores, determine the total evaluation score of the indoor distributed system to be analyzed; based on the total evaluation score of the indoor distributed system to be analyzed and the evaluation scores corresponding to each evaluation dimension, determine the fault areas of the indoor distributed system to be analyzed.

[0132] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 program instructions. These computer program 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0134] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium 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.

[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment 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.

[0136] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0137] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0138] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0139] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0140] 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 embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0141] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A method for fault identification in an indoor distributed system, characterized in that, include: Obtain operational data from the indoor distributed system to be analyzed; Based on the operational data, the evaluation scores corresponding to each evaluation dimension of the indoor distribution system to be analyzed are determined, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension. Based on the evaluation scores, determine the total evaluation score of the indoor distribution system to be analyzed; Based on the total evaluation score of the indoor distribution system to be analyzed and the evaluation scores corresponding to each evaluation dimension, the fault areas of the indoor distribution system to be analyzed are determined. The determination of the total evaluation score of the indoor distribution system to be analyzed based on the evaluation score specifically includes: Based on the business type, determine the multi-level weights corresponding to each evaluation dimension, and perform a weighted summation of the evaluation scores according to the multi-level weights to obtain the vertical quantitative score corresponding to the indoor distribution system to be analyzed. Based on the evaluation scores corresponding to each evaluation dimension, outliers corresponding to each evaluation dimension are determined respectively. The outliers are then subjected to repeatability quantification using Venn diagrams to obtain the level quantification score corresponding to the indoor distribution system to be analyzed. The total evaluation score of the indoor distribution system to be analyzed is determined based on the vertical quantitative score and the horizontal quantitative score.

2. The method according to claim 1, characterized in that, When the evaluation dimension is the voice service dimension, determining the evaluation score corresponding to each evaluation dimension of the indoor distribution system to be analyzed based on the operational data specifically includes: Obtain the outgoing call records of the indoor distribution system to be analyzed within a preset time period; The calling documents are filtered to obtain real-time transmission timeout documents; Determine the number of real-time transmission timeout documents; Based on the number of real-time transmission timeout documents, the preset voice anomaly baseline value, and the voice anomaly challenge value, the voice anomaly evaluation score corresponding to the voice service dimension is determined.

3. The method according to claim 1, characterized in that, When the evaluation dimension is a data service dimension, determining the evaluation score corresponding to each evaluation dimension of the indoor distribution system to be analyzed based on the operational data specifically includes: Obtain the interface documents of the indoor distributed system to be analyzed within a preset time period, including 4G interface documents and / or 5G interface documents; The interface documents are filtered to obtain a second interface document used to characterize data service failure; Determine the number of documents for the second interface; Based on the number of documents in the second interface, the preset data anomaly baseline value, and the data anomaly challenge value, the data anomaly assessment score corresponding to the data business dimension is determined.

4. The method according to claim 1, characterized in that, When the evaluation dimension is the coverage fallback dimension, the step of determining the evaluation score corresponding to each evaluation dimension of the indoor distribution system to be analyzed based on the operational data specifically includes: The number of network fallbacks of the indoor distribution system to be analyzed within a preset time period is obtained, wherein the number of fallbacks is used to represent the number of times the end user experiences a fallback from a high to a low network standard due to weak network coverage; Based on the number of network fallbacks, the preset network fallback anomaly baseline value, and the network fallback anomaly challenge value, the coverage fallback anomaly evaluation score corresponding to the coverage fallback dimension is determined.

5. The method according to claim 1, characterized in that, When the evaluation dimension is the coverage scenario dimension, determining the evaluation score corresponding to each evaluation dimension of the indoor distribution system to be analyzed based on the operational data specifically includes: Determine the importance level corresponding to the coverage scenarios; Based on the importance classification, the scene evaluation score corresponding to the coverage scene dimension is determined.

6. A fault identification device for an indoor distributed system, characterized in that, include: The data acquisition unit is used to acquire the operational data of the indoor distributed system to be analyzed. An evaluation unit is used to determine the evaluation scores corresponding to each evaluation dimension of the indoor distribution system to be analyzed based on the operational data. The evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension. The scoring calculation unit is used to determine the total evaluation score of the indoor distribution system to be analyzed based on the evaluation scores. The fault identification unit is used to determine the fault area of ​​the indoor distribution system to be analyzed based on the total evaluation score of the indoor distribution system to be analyzed and the evaluation scores corresponding to each evaluation dimension. In the scoring calculation unit, the total evaluation score of the indoor distribution system to be analyzed is determined based on the evaluation score, specifically including: Based on the business type, determine the multi-level weights corresponding to each evaluation dimension, and perform a weighted summation of the evaluation scores according to the multi-level weights to obtain the vertical quantitative score corresponding to the indoor distribution system to be analyzed. Based on the evaluation scores corresponding to each evaluation dimension, outliers corresponding to each evaluation dimension are determined respectively. The outliers are then subjected to repeatability quantification using Venn diagrams to obtain the level quantification score corresponding to the indoor distribution system to be analyzed. The total evaluation score of the indoor distribution system to be analyzed is determined based on the vertical quantitative score and the horizontal quantitative score.

7. An indoor distributed system fault identification device, comprising: processor; A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the following operations: Obtain the feature data of the network to be analyzed; Obtain operational data from the indoor distributed system to be analyzed; Based on the operational data, the evaluation scores corresponding to each evaluation dimension of the indoor distribution system to be analyzed are determined, wherein the evaluation dimensions include voice service dimension, data service dimension, coverage scenario dimension, and coverage fallback dimension. Based on the evaluation scores, determine the total evaluation score of the indoor distribution system to be analyzed; Based on the total evaluation score of the indoor distribution system to be analyzed and the evaluation scores corresponding to each evaluation dimension, the fault areas of the indoor distribution system to be analyzed are determined. The determination of the total evaluation score of the indoor distribution system to be analyzed based on the evaluation score specifically includes: Based on the business type, determine the multi-level weights corresponding to each evaluation dimension, and perform a weighted summation of the evaluation scores according to the multi-level weights to obtain the vertical quantitative score corresponding to the indoor distribution system to be analyzed. Based on the evaluation scores corresponding to each evaluation dimension, outliers corresponding to each evaluation dimension are determined respectively. The outliers are then subjected to repeatability quantification using Venn diagrams to obtain the level quantification score corresponding to the indoor distribution system to be analyzed. The total evaluation score of the indoor distribution system to be analyzed is determined based on the vertical quantitative score and the horizontal quantitative score.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the indoor distribution system fault identification method as claimed in any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the indoor distribution system fault identification method as described in any one of claims 1-5.

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