Network health check method, system and apparatus, and storage medium

Abstract

Embodiments of the present disclosure provide a network health check method, system and apparatus, and a storage medium. The network health check method comprises: extracting original network data from a network layer, and acquiring key data on the basis of the original network data; one or more preset service systems respectively carrying out network service anomaly judgment on the basis of the key data; and determining a network health check result on the basis of a network service anomaly judgment result output by the one or more service systems.

Description

A network health check method, system, device and storage medium

[0001] Cross-reference to Related Applications

[0002] This patent application claims priority to Chinese Patent Application No. 202410474883.3, filed on April 19, 2024, in the National Intellectual Property Office of China, the disclosure of which is incorporated herein in its entirety by reference. TECHNICAL FIELD

[0003] Embodiments of the present disclosure relate to the field of communications, and in particular, to a network health check method, system, device and storage medium. BACKGROUND

[0004] With the development of communication networks, the complexity of network networking is increasing, and the checking work for network operation health is also gradually complex. At present, although some periodic network checking actions exist in part of the communication networks, too much manual intervention is still required, which has certain skill requirements for operation and maintenance personnel, and the checking period is too long, and some communication network problems cannot be discovered in time, which is difficult to guarantee the stability of the operation of the communication network. SUMMARY

[0005] Embodiments of the present disclosure provide a network health check method, system, device and storage medium.

[0006] In a first aspect, embodiments of the present disclosure provide a network health check method, comprising: extracting original network data from a network layer, and obtaining key data based on the original network data; performing network service anomaly judgment by one or more preset business systems based on the key data, respectively; and determining a network health check result based on network service anomaly judgment results output by the one or more business systems.

[0007] In a second aspect, embodiments of the present disclosure provide a network health check system, comprising: a network layer, a data layer and a business layer; the data layer is configured to extract original network data from the network layer, and obtain key data based on the original network data; the business layer is configured to perform network service anomaly judgment by one or more preset business systems based on the key data, respectively; and the business layer is further configured to determine a network health check result based on network service anomaly judgment results output by the one or more business systems.

[0008] In a third aspect, embodiments of the present disclosure provide a network health check device, comprising: one or more processors; and a memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the network health check method described above.

[0009] In a fourth aspect, the embodiments of the present disclosure provide a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the network health check method.

[0010] In a fifth aspect, the embodiments of the present disclosure provide a computer program product, and the computer program product includes a computer program. The computer program is executed by a processor to implement the network health check method. BRIEF DESCRIPTION OF DRAWINGS

[0011] In the drawings of the embodiments of the present disclosure:

[0012] FIG. 1 is a flowchart of a network health check method according to an embodiment of the present disclosure;

[0013] FIG. 2 is a schematic diagram of a network health check method according to an embodiment of the present disclosure;

[0014] FIG. 3 is a schematic diagram of a display interface according to an embodiment of the present disclosure;

[0015] FIG. 4 is a schematic diagram of network health check management according to an embodiment of the present disclosure;

[0016] FIG. 5 is a block diagram of a network health check system according to an embodiment of the present disclosure;

[0017] FIG. 6 is a schematic diagram of a network health check system according to an embodiment of the present disclosure;

[0018] FIG. 7 is a block diagram of a network health check device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] In order for those skilled in the art to better understand the technical solutions of the present disclosure, the communication sensing data processing method and the computer readable storage medium provided by the embodiments of the present disclosure will be described in detail below with reference to the drawings.

[0020] The embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings, but the embodiments shown can be embodied in different forms and the present disclosure should not be construed as being limited to the embodiments set forth herein. Rather, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to fully enable a person skilled in the art to understand the scope of the present disclosure.

[0021] The accompanying drawings of the embodiments of the present disclosure are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification, which together with the detailed embodiments, serve to explain the present disclosure and do not constitute a limitation of the present disclosure. The above and other features and advantages will become more apparent to those skilled in the art by referring to the detailed embodiments described below with reference to the accompanying drawings.

[0022] The present disclosure can be described with reference to plan views and / or cross-sectional views by idealized illustrations of the present disclosure. Thus, the example illustrations can vary depending on the manufacturing technology and / or tolerances.

[0023] The embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0024] The terms used in the present disclosure are only used to describe specific embodiments, and are not intended to limit the present disclosure. As used in the present disclosure, the term "and / or" includes any and all combinations of one or more of the associated listed items. As used in the present disclosure, the singular forms "a" and "the" are also intended to include the plural forms unless the context clearly indicates otherwise. As used in the present disclosure, the terms "comprise", "made of" designate the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0025] Unless otherwise defined, all terms used in the present disclosure, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal sense unless expressly so defined in the present disclosure.

[0026] With the development of communication networks, the network networking complexity is increasing, and the checking work for network operation health is gradually complex. At present, although some periodic network checking actions exist in some communication networks, too much manual intervention is still required, the skills of the operation and maintenance personnel are required, and the checking period is too long, some communication network problems cannot be discovered in time, and it is difficult to guarantee the stability of the communication network operation.

[0027] The embodiments of the present disclosure extract original network data from the network layer, and extract key data from the original network data, guaranteeing the accuracy and effectiveness of the data; one or more preset business systems perform respective network service exception judgments based on the key data, and determine a network health checking result based on the network service exception judgment results output by the one or more business systems, realizing automatic network health checking without human intervention, and quickly and efficiently completing network health checking, realizing cost reduction and efficiency improvement, facilitating the operation and maintenance personnel to timely process the discovered problems, guaranteeing the stability of network operation, and realizing network healthy operation.

[0028] The network health check method of the embodiments of the present disclosure can be applied to any terminal device, which can include but is not limited to: a vehicle-mounted device, a user equipment (UE), a mobile device, a computing device, a wearable device, etc., such as but not limited to a cellular phone, a cordless phone, a personal digital assistant (PDA), a portable computer, etc. The network health check method can be implemented by a processor invoking computer readable program instructions stored in a memory, or can be implemented by a server.

[0029] The network health check method of the embodiments of the present disclosure can be applied to, but is not limited to, network health check of a wireless communication network (for example, a wireless commercial communication network).

[0030] The embodiments of the present disclosure will be described in detail below.

[0031] The embodiments of the present disclosure provide a network health check method, as shown in FIG. 1 and FIG. 2, which includes steps S11-S13.

[0032] In step S11, raw network data is extracted from a network layer, and key data is obtained based on the raw network data.

[0033] In the embodiments of the present disclosure, the key data can refer to service data of a network health check service performed by one or more preset service systems.

[0034] In the embodiments of the present disclosure, extracting the raw network data from the network layer can include: in the form of command interaction, extracting the raw network data from the network layer by a preset remote access protocol.

[0035] In the embodiments of the present disclosure, the network layer can include any network in a wireless communication network, and the network layer can include any one or more of the following: a wireless communication core network, a transport network, and an access network.

[0036] In the embodiments of the present disclosure, the network layer can include various hardware in the field of wireless communication, and can also include network management software in the field of wireless communication. For example, including but not limited to: UME (Unified Management Expert), EMS (Element Management System), base stations and network devices (such as controllers and other network devices), etc.

[0037] In the embodiments of the present disclosure, the remote access protocol can include, but is not limited to, any one or more of the following: SSH, Telnet and Syslog. The SSH (Secure Shell) is a network protocol, mainly used for secure remote login and file transfer; the Telnet is a network protocol, used for remote login to another computer and execute commands; and the Syslog is a standard for passing logging messages over Internet Protocol (TCP / IP) networks.

[0038] In the embodiments of the present disclosure, the key data can be extracted from the original network data by identifying keywords, or can be calculated based on the original network data, and the details of the key data are not limited herein, and can be defined according to different original network data, different network health check business requirements, etc.

[0039] In the embodiments of the present disclosure, for example, for an embodiment of detecting the user call success rate (percentage) in network performance, the extracted original network data can be user call data in a certain period (for example, a week or a month), and the key data can be the user call success rate calculated based on the user call success data extracted from the user call data.

[0040] In the embodiments of the present disclosure, for example, for an embodiment of base station link fault checking in fault checking, the extracted original network data can be base station transmission link operation data in a certain period (for example, a day, a week or a month), and the key data can be the number of base station transmission links whose link congestion duration exceeds a preset duration threshold, which is extracted based on the base station transmission link operation data.

[0041] In the embodiments of the present disclosure, for example, for an embodiment of high-load network element checking in load evaluation, the extracted original network data can be network element load data in a certain period (for example, a day or a week), and the key data can be the number of network elements whose load exceeds a preset load threshold, which is extracted based on the network element load data.

[0042] In the embodiments of the present disclosure, after obtaining the original network data, the original network data can be preprocessed (for example, can include but is not limited to data cleaning and data screening), the key data is obtained based on the preprocessed original network data, and data filtering, data storage and necessary log recording in the entire data processing process are completed. The data processing can include, but is not limited to, the above-mentioned data cleaning, data screening, data extraction, data filtering and data storage.

[0043] In step S12, the one or more preset business systems respectively perform network business exception judgment based on the key data.

[0044] In the embodiments of the present disclosure, one or more service systems can be established in advance according to different service requirements, different service systems perform different types of network health check services respectively, and any software and hardware related to the network health check service are checked accordingly to determine network service abnormalities.

[0045] In the embodiments of the present disclosure, the network health check service can include, but is not limited to, any one or more of the following: performance check, fault check, load check (such as high load check), running state check, disaster recovery backup check, mains and backup battery check, in-service and delisting equipment check, software and hardware version check, and end-to-end link quality check.

[0046] In the embodiments of the present disclosure, each network health check service described above can be performed by one or more preset service groups, and the detailed services of the service group are not limited here, and any one or more existing service groups can be selected to form a corresponding service group according to requirements. For example, it can include, but is not limited to, service group A (which can include service A1, service A2, …, service An, n is a positive integer), service group B (which can include service B1, service B2, …, service Bm, m is a positive integer), service group C (which can include service C1, service C2, …, service Ck, k is a positive integer).

[0047] In the embodiments of the present disclosure, the network service abnormality determination based on the key data by the one or more preset service systems includes: for any one of the service systems, performing the following operations: comparing the key data with at least one preset level of data threshold, and determining the network health degree according to the comparison result.

[0048] In the embodiments of the present disclosure, one or more data thresholds of different levels can be set in each service system according to service requirements in advance, and each level of data threshold can correspond to a network health degree.

[0049] In the embodiments of the present disclosure, the data threshold can be based on experience parameters formed by network experts, and the experience parameters corresponding to the data threshold can be modified accordingly for different networks to achieve better personalized adaptation.

[0050] In the embodiments of the present disclosure, the network health degree can include, but is not limited to, being represented in the form of scoring. For example, the network health degree can be determined in the form of scoring with a full score of 100. For example, the score of the network health degree corresponding to the numerical threshold of the first level can be set to 60 or below, the score of the network health degree corresponding to the numerical threshold of the second level can be set to 80 or below, and the score of the network health degree corresponding to the numerical threshold of the third level can be set to 100 or below. That is, when the key data does not meet the numerical threshold of the first level, the network health degree can be scored as 60 or below; when the key data meets the numerical threshold of the first level and does not meet the numerical threshold of the second level, the network health degree can be scored as 60 or above and 80 or below; when the key data meets the numerical threshold of the first level and the numerical threshold of the second level and does not meet the numerical threshold of the third level, the network health degree can be scored as 80 or above and 100 or below. When the key data meets the numerical threshold of the first level, the numerical threshold of the second level and the numerical threshold of the third level, the network health degree can be scored as 100.

[0051] In the embodiments of the present disclosure, determining the network health degree according to the comparison result includes: when the key data meets the data threshold of any one level, comparing the key data with the data threshold of the next level until the key data meets the data threshold of all levels, and determining the network health degree as completely healthy.

[0052] In the embodiments of the present disclosure, when there is only one level of data threshold, the network health degree can be directly determined according to whether the key data meets the data threshold of this level, that is, if the key data meets the data threshold of this level, it can be determined that the network of the business type corresponding to the current business system is healthy, and a normal detail corresponding to the business system can be generated. If the key data does not meet the data threshold of this level, it can be determined that the network of the business type corresponding to the current business system is not healthy.

[0053] In the embodiments of the present disclosure, when there are multiple levels of data thresholds, the key data can be compared with the data thresholds of each level from the lowest level, and when the key data meets the data threshold of each level, it can be determined that the network health degree of the business type corresponding to the business system is completely healthy, which can be represented in the form of scoring as 100, and a normal detail corresponding to the business system can be generated.

[0054] In the embodiments of the present disclosure, for example, for the business system 1, assuming that it is used for performing the business X, the first data threshold, the second data threshold and the third data threshold can be set, and the levels of the first data threshold, the second data threshold and the third data threshold increase gradually, the key data can be compared with the first data threshold first, in the case that the first data threshold is met, the key data can be compared with the second data threshold, in the case that the second data threshold is met, the key data can be compared with the third data threshold, if the key data also meets the third data threshold, it means that the key data meets all the data thresholds of the business system 1, and it can be determined that the network health degree corresponding to the business X in the business system 1 is completely healthy, that is, 100 points.

[0055] In the embodiments of the present disclosure, according to the comparison result, the network health degree can also be determined, and in the case that the key data does not meet any one of the data thresholds of at least one level, the network abnormal business type corresponding to the unmet data threshold can be obtained, the abnormal reason can be determined based on the network abnormal business type, and the network health degree can be determined according to the abnormal reason.

[0056] In the embodiments of the present disclosure, in the case that the data thresholds include multiple levels, the key data can be compared with the data thresholds of each level gradually from the lowest level, and in the case that the key data does not meet any one level of data threshold, the network abnormal business type corresponding to the unmet data threshold can be determined, and the abnormal reason can be determined automatically, and the network health degree can be determined according to the abnormal reason.

[0057] In the embodiments of the present disclosure, for example, for the business system 2, assuming that it is used for performing the business Y, the fourth data threshold, the fifth data threshold and the sixth data threshold can be set, and the levels of the fourth data threshold, the fifth data threshold and the sixth data threshold increase gradually, the key data can be compared with the fourth data threshold first, in the case that the fourth data threshold is met, the key data can be compared with the fifth data threshold, in the case that the key data does not meet the fourth data threshold, the network abnormal business type (the business Y) corresponding to the fourth data threshold can be determined, and the abnormal reason can be determined for the network abnormal business type (the business Y), and the network health degree corresponding to the business Y can be obtained.

[0058] In the embodiments of the present disclosure, for example, for the business system 3, assuming that business Z is executed, the seventh data threshold, the eighth data threshold and the ninth data threshold can be set, and the levels of the seventh data threshold, the eighth data threshold and the ninth data threshold increase gradually, the key data can be compared with the seventh data threshold first, in the case that the key data meets the seventh data threshold, the key data can be compared with the eighth data threshold, in the case that the key data meets the eighth data threshold, the key data can be compared with the ninth data threshold, in the case that the key data does not meet the ninth data threshold, the network abnormal business type (business Z) corresponding to the ninth data threshold can be determined, and the abnormal reason is determined for the network abnormal business type (business Z), and the network health degree corresponding to the business Z is obtained.

[0059] In the embodiments of the present disclosure, the user call success rate (percentage) in network performance is taken as an example to illustrate the embodiments of the present disclosure, wherein the closer to the 100% index, the better, specifically, the data threshold I of the first level is “the day-level index reaches 99.7% for three consecutive days”, the data threshold II of the second level is “the hour-level index reaches 99.5% for 24 consecutive hours”, and the data threshold III of the third level is “the fluctuation of the hour-level index is less than 2% for 24 consecutive hours”. If the data thresholds of the above three levels are all met, it indicates that the network health degree is 100 points, and the network normal details can be automatically generated, such as “the user call success rate index is stable and has no fluctuation”. If any one of the data thresholds of the above three levels is not met, the network abnormal business type is determined, and the abnormal details are automatically drilled down. For example, if the data threshold III of the third level is not met, the network abnormal business type is “the user call success rate index is abnormal, and the abnormal reason is that the fluctuation of the hour-level index is too large”, and the abnormal reason is “the main root cause 1 is that the transmission packet loss rate is too high, accounting for 86%, the main root cause 2 is that the cell service is withdrawn, accounting for 13%, and the other reasons account for 1%”. For the above abnormal situation, the network health degree can be given as 90 points.

[0060] In the embodiments of the present disclosure, the network abnormal business type and the abnormal reason described above can form the abnormal details, for example, “the user call success rate index is abnormal, and the abnormal reason is that the fluctuation of the hour-level index is too large; the main root cause 1 is that the transmission packet loss rate is too high, accounting for 86%, the main root cause 2 is that the cell service is withdrawn, accounting for 13%, and the other reasons account for 1%”; and the network health degree is 90 points.

[0061] In the embodiments of the present disclosure, the network service abnormality judgment based on the key data by the one or more preset service systems respectively can further include: inputting the key data into a service abnormality judgment model corresponding to the service system; and performing network service abnormality judgment based on the key data by the service abnormality judgment model, wherein the service abnormality judgment model is a model obtained by training a preset first neural network with feature data labeled with a network health label as training data, and the network health label indicates the network health degree of the labeled data.

[0062] In the embodiments of the present disclosure, the network service abnormality judgment performed by each service system can also be implemented by a pre-trained service abnormality judgment model. For different service systems, corresponding service abnormality judgment models can be set respectively, and the service abnormality judgment models used by different service systems can implement different service abnormality judgment functions.

[0063] In the embodiments of the present disclosure, for each service abnormality judgment model, a large amount of original network data can be collected in advance, for example, a large amount of operator data can be collected, and for the current service system, a large amount of key data corresponding to the original network data can be extracted, the key data is labeled, and the labeled key data is used as training data to iteratively train a pre-set first neural network. When the first loss value of the first neural network meets the first preset requirement, it is confirmed that the service abnormality judgment model is successfully trained. The network service abnormality judgment can be directly performed on the received key data based on the service abnormality judgment model, and the network service abnormality judgment result is output.

[0064] In the embodiments of the present disclosure, the detailed structure of the first neural network is not limited here, and can be defined by itself according to different needs, and a suitable neural network structure can be selected from the existing neural network structures.

[0065] In step S13, the network health check result is determined based on the network service abnormality judgment result output by the one or more service systems.

[0066] In the embodiments of the present disclosure, the network health check result is determined based on the network service abnormality judgment result output by the one or more service systems, which can include: summarizing the network health degrees output by the one or more service systems, and generating a health check report about the one or more service systems.

[0067] In the embodiments of the present disclosure, when there are multiple service systems, each service system can perform its own network service abnormality judgment scheme in parallel, and output the network health degree of the corresponding service after completing the network service abnormality judgment. The normal details or abnormal details corresponding to the network health degree can also be output.

[0068] In the embodiments of the present disclosure, the network health degree output by one or more business systems is summarized, and a health check report about the one or more business systems is generated based on the network health degree of the one or more business systems, the normal details or the abnormal details corresponding to the network health degree of each business system, and the network health degree of the entire network corresponding to all business systems, which can include: performing weighted calculation on the network health degree of the different business systems based on the weights of the different business systems and the network health degree of the business system, to obtain the network health degree of the entire network corresponding to all business systems; and generating the health check report based on the network health degree of the entire network and the normal details or the abnormal details corresponding to the network health degree output by each business system.

[0069] In the embodiments of the present disclosure, after determining the network health check result based on the network service abnormality judgment result output by one or more business systems, the network health check method can further include: displaying the network health check result through a preset display interface and / or display platform.

[0070] In the embodiments of the present disclosure, the health check report described above can be displayed on the preset display interface and / or display platform.

[0071] In the embodiments of the present disclosure, the display platform can include but is not limited to a mobile applet, a PC (personal computer) browser, a mail system (which can push emails), and the like.

[0072] In the embodiments of the present disclosure, the display interface can display the overall overview of the network health and the evaluation of each business system. The display interface can be divided into but is not limited to three parts: a menu part, a title part, and a detail part.

[0073] In the embodiments of the present disclosure, the menu part can include the overall overview and the click icons of each business system, and the menu part can be used by a user to select the network health report of the entire network or the network health report of each business system to be browsed. By clicking the click icon of the overall overview, the overall overview of the network health report of the entire network can be displayed on the display interface, and by clicking the click icon of a certain business system, the network health report corresponding to the business system can be displayed on the display interface.

[0074] In the embodiments of the present disclosure, the click icons of each business system can include but are not limited to the following charts: performance check, fault check, high load check, running state check, disaster recovery backup check, mains and backup battery check, in-force and delisting check, software and hardware version check, end-to-end link quality check, and the like.

[0075] In the embodiments of the present disclosure, the title part refers to the title of the overall overview or each business system displayed in the block corresponding to the overall overview and each business system in the area outside the menu part, which is used to clearly indicate the display position of the overall overview and each business system.

[0076] In the embodiments of the present disclosure, the detail part refers to the detailed network health report content corresponding to each title part respectively.

[0077] In the embodiments of the present disclosure, as shown in FIG. 3, taking the overall overview as an example, the overall achievement situation can be viewed in the detail part, and the network health degree score value is provided, and the network health degree can display different colors according to different score segments to give intuitive prompt to the user. For example, higher than 90 points is green, 80-90 points is light green, 70-80 points is orange, 60-70 points is yellow, and lower than 60 points is red. If the current displayed network health degree of the overall is 95 points, the green color is displayed accordingly.

[0078] In the embodiments of the present disclosure, as shown in FIG. 3, taking the overall overview as an example, the abnormal details (or abnormal events) of the overall can be displayed in the form of a list in the detail part, which can include but is not limited to: network abnormal service type (which can be referred to as abnormal type), severity, event description, and occurrence time, etc. For example: abnormal service type: index analysis, severity: medium, event description: F0 call establishment success rate is lower than the normal value, occurrence time: 1 hour ago; abnormal type: fault analysis, severity: high, event description: 193 base station transmission link is abnormal, occurrence time: 23 hours ago; abnormal type: high load network element, severity: low, event description: 8 sites user load is high, occurrence time: 8 hours ago.

[0079] In the embodiments of the present disclosure, as shown in FIG. 3, taking the overall overview as an example, different blocks can be divided for different business systems in the detail part, and the main details content in the network health degree and the corresponding abnormal details output for each business system in the respective blocks are displayed. For example, the key information of each business system analysis situation is displayed, such as the key indicators of performance check, the key alarms of fault check, the balance situation of high load check, etc.

[0080] In the embodiments of the present disclosure, for example, the fault check can include the TOP10 (top 10) alarms determined for a plurality of check targets. For example, the TOP alarms of the first check target include: 1, 160 times of transmission unreachable, 2, 1.88 of excessive standing wave ratio, …; the TOP alarms of the second check target include: 1, 360 times of network element disconnection, 2, 1.89 of excessive standing wave ratio, ….

[0081] In the embodiments of the present disclosure, for example, the high-load check can include the determined network element load, peak load and average load of a plurality of network elements (such as network element 1, network element 2 and network element 3). For example, network element 1 has a load of 80%, a peak load of 95% and an average load of 75%; network element 2 has a load of 85%, a peak load of 98% and an average load of 79%; and network element 3 has a load of 88%, a peak load of 98% and an average load of 82%.

[0082] In the embodiments of the present disclosure, for the currently displayed health check report, an export function and a refresh function can also be set.

[0083] In the embodiments of the present disclosure, before extracting the original network data from the network layer, the network health check method can further include: determining the selected network health check mode; and in the case that the selected network health check mode is an automatic mode, entering the process of extracting the original network data from the network layer.

[0084] In the embodiments of the present disclosure, as shown in FIG. 4, before the network health check is performed, a start interface of the network health check task can be provided to the user, and a plurality of network health check modes can be set in the start interface, for example, a manual mode and an automatic mode can be included, the automatic mode means that the network health check is automatically performed based on steps S11 to S13 in the above-mentioned scheme of the embodiments of the present disclosure, and the network health check result is output. The network health check scheme of the embodiments of the present disclosure can be implemented as a control template.

[0085] In the embodiments of the present disclosure, after the network health check result is determined based on the network service exception judgment result output by one or more service systems, the network health check method can further include: determining whether the network health check result is normal; in the case that the network health check result is determined to be normal, issuing a notification that the network health check result is normal; in the case that the network health check result is determined to be abnormal, notifying whether a false alarm phenomenon occurs, in the case that it is confirmed that the false alarm phenomenon occurs, re-performing the network health check, and in the case that it is confirmed that the false alarm phenomenon does not occur, issuing a processing notification.

[0086] In the embodiments of the present disclosure, after obtaining the network health check result, in order to ensure the validity of the network health check result, it can be firstly judged whether the network health check result is normal. If it is judged that the network health check result is normal, a notification that the network health check result is normal can be sent to notify the associated personnel to close the current network health check mode, and the network health check task is ended. If it is judged that the network health check result is not normal, it can be firstly judged whether the network health check result is a false alarm. If it is a false alarm, the network health check can be performed again, that is, the associated personnel is notified to restart the network health check mode to re-execute the network health check task. If it is not a false alarm, the associated personnel needs to be notified to handle in time.

[0087] In the embodiments of the present disclosure, whether the network health check result is normal can be judged by checking whether the result data is abnormal. If the result data obviously exceeds the preset numerical range and obviously does not conform to the common sense, it can be confirmed that the network health check result is not normal.

[0088] In the embodiments of the present disclosure, whether the network health check result is normal can include: inputting the network health check result into a preset result judgment model; judging whether the network health check result is normal by the result judgment model; the result judgment model is a model obtained by training a preset second neural network with network health check results labeled with result judgment labels as training data under the condition that a second loss value meets a second preset requirement; the result judgment label indicates whether the labeled network health check result is normal.

[0089] In the embodiments of the present disclosure, the result judgment model trained in advance can also be used to judge whether the network health check result is normal.

[0090] In the embodiments of the present disclosure, for the above-mentioned result judgment model, a large amount of network health check results (such as network health check reports) can be collected in advance, and these network health check results are labeled (labeled whether the network health check result is normal). The network health check result after labeling is used as training data to iteratively train a preset second neural network until the second loss value of the second neural network meets the second preset requirement, and then it is confirmed that the result judgment model is successfully trained. The result judgment model can be used to directly judge whether the received network health check result is normal, and the judgment result is output.

[0091] In the embodiments of the present disclosure, the detailed structure of the second neural network is not limited here, and can be defined by itself according to different needs. In the existing neural network structure, a suitable neural network structure can be selected.

[0092] In the embodiments of the present disclosure, the associated personnel generally refers to the operation and maintenance personnel of the communication network, and the network health degree check result (or report) can be referred to for processing and tracking the network problems involved.

[0093] In the embodiments of the present disclosure, through the automatic mode, the network health degree check can be periodically completed by using the solutions of the embodiments of the present disclosure automatically, and the software judgment can be manually participated in when necessary to perform manual intervention, so as to avoid the abnormality of the network health degree check result.

[0094] In the embodiments of the present disclosure, the embodiments of the present disclosure at least have the following advantages:

[0095] 1. The solutions of the embodiments of the present disclosure automatically extract the key data in the network (for example, a wireless commercial communication network), improve the efficiency and accuracy of the health degree check report, summarize, refine, analyze, evaluate and give the check conclusion of the extracted key information, and transmit the check conclusion and the key information to the network operation and maintenance personnel in real time, so as to achieve the purpose of automatically maintaining the healthy operation of the network.

[0096] 2. The solutions of the embodiments of the present disclosure are automatically executed without human intervention, can quickly and efficiently complete the check, realize cost reduction and efficiency improvement, and facilitate the operation and maintenance personnel to timely process the problems found, so as to ensure the healthy operation of the network.

[0097] 3. In many network construction scenarios such as network addition, network relocation, network upgrade and the like, the network health check function of the embodiments of the present disclosure provides a visual solution, clearly and efficiently judges and displays the problems by means of graphical means, so that the user can quickly locate the network problems, liberate manpower to the maximum extent, and quickly and continuously improve the network health degree.

[0098] 4. The embodiments of the present disclosure can integrate the network health detection tool (for example, a software tool based on the network health detection method of the embodiments of the present disclosure) into the daily maintenance work of the communication network, realize the digital penetration of the network health degree management, analysis and tracing, and realize the collaboration and efficiency of the discovery, reminding, processing and problem closing of the network health problems.

[0099] The embodiments of the present disclosure also provide a network health check system 100, as shown in FIGS. 5 and 6, which can include a network layer 101, a data layer 102 and a service layer 103.

[0100] The data layer 102 can be configured to extract original network data from the network layer 101, and obtain key data based on the original network data.

[0101] The service layer 103 can be configured to perform network service anomaly judgment based on the key data by one or more preset service systems respectively, wherein different service systems perform different types of network health check services.

[0102] The service layer 103 can also be configured to determine the network health check result based on the network service anomaly judgment result output by the one or more service systems.

[0103] In the embodiments of the present disclosure, the data layer 102 can also be configured to perform data filtering and data storage on the key data, and log the data processing process; the data processing includes data extraction, data filtering and data storage on the key data.

[0104] In the embodiments of the present disclosure, the network health check system 100 can further include an application layer 104.

[0105] The application layer 104 can be configured to provide a service group for supporting the data processing of the service layer 103.

[0106] In the embodiments of the present disclosure, the network health check system 100 can further include a user layer 105.

[0107] The user layer 105 can be configured to implement the management of the network health check process by the user and the query and processing of the network health check result. The processing of the network health check result includes but is not limited to editing (for example, deleting, modifying) and / or exporting.

[0108] In the embodiments of the present disclosure, the network layer 105 includes any one or more of the following: a wireless communication core network, a transmission network and an access network.

[0109] In the embodiments of the present disclosure, any of the foregoing embodiments of the network health check method is applicable to the network health check system, and will not be repeated here.

[0110] The embodiments of the present disclosure also provide a network health check device 200, as shown in FIG. 7, which can include one or more processors 201; a memory 202 having one or more computer programs stored thereon, when the one or more computer programs are executed by the one or more processors 201, the one or more processors 201 implement the network health check method.

[0111] The embodiments of the present disclosure also provide a computer readable storage medium having a computer program stored thereon, when the computer program is executed by a processor, the network health check method is implemented.

[0112] The embodiments of the present disclosure further provide a computer program product comprising a computer program which, when executed by a processor, implements the network health check method.

[0113] Those skilled in the art can understand that all or some of the function modules / units disclosed above can be implemented as software, firmware, hardware and appropriate combinations thereof.

[0114] In the hardware implementation, the division between the function modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component can have multiple functions, or one function or step can be executed by several physical components in cooperation.

[0115] Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit (CPU), a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on computer readable media, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As is well known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), FLASH memory or other solid state memory; compact disc read-only memory (CD-ROM), digital versatile discs (DVDs) or other optical disk storage; magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices; any other medium that can be used to store the desired information and that can be accessed by a computer. Furthermore, it is common knowledge to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and can include any information delivery media.

[0116] The present disclosure has disclosed example embodiments, and although specific terms are employed, they are used in the broadest sense only and should not be construed to limit the disclosure. In some instances, it will be apparent to those skilled in the art that features, characteristics or / and elements described in connection with a particular embodiment can be used in conjunction with other embodiments unless otherwise explicitly stated. Therefore, those skilled in the art will appreciate that various modifications can be made to the described embodiments without departing from the scope of the present disclosure as set forth in the appended claims.

Claims

1. A network health check method, comprising: extracting original network data from a network layer, and obtaining key data based on the original network data; performing network service abnormality judgment based on the key data by one or more preset service systems respectively, wherein different service systems perform different types of network health check services respectively; and determining a network health check result based on network service abnormality judgment results output by the one or more service systems.

2. The network health check method of claim 1, wherein, extracting original network data from a network layer comprises: extracting the original network data from the network layer in a preset remote access protocol through command interaction.

3. The network health check method of claim 1, wherein, performing network service abnormality judgment based on the key data by one or more preset service systems respectively comprises: for any one of the service systems, performing the following operations: comparing the key data with at least one preset level of data threshold, and determining a network health degree according to a comparison result.

4. The network health check method of claim 3, wherein, determining the network health degree according to the comparison result comprises: in a case where the key data meets any one level of data threshold, comparing the key data with a next level of data threshold until the key data meets all levels of data threshold, and determining the network health degree as completely healthy.

5. The network health check method of claim 3, wherein, determining the network health degree according to the comparison result comprises: in a case where the key data does not meet any one of the at least one level of data threshold, obtaining a network abnormal service type corresponding to an unmet data threshold, and determining an abnormal reason based on the network abnormal service type, and determining the network health degree according to the abnormal reason.

6. The network health check method of claim 1, wherein, performing network service abnormality judgment based on the key data by one or more preset service systems respectively comprises: inputting the key data into a service abnormality judgment model corresponding to the service system; and performing network service abnormality judgment by the service abnormality judgment model based on the key data, wherein the service abnormality judgment model is a model obtained by training a preset first neural network with feature data labeled with a network health label as training data, and the model is obtained in a case where a first loss value meets a first preset requirement, and wherein the network health label indicates a network health degree of the labeled data.

7. The network health check method of claim 3, wherein, determining the network health check result based on network service abnormality judgment results output by the one or more service systems comprises: summarizing network health degrees output by one or more of the service systems, and generating a health check report about one or more of the service systems.

8. The network health check method of any of claims 1-7, wherein, The network health check service comprises any one or more of the following: performance check, fault check, load check, running state check, disaster recovery backup check, mains and backup battery check, in-service and delisting equipment check, software and hardware version check, and end-to-end link quality check.

9. The network health check method of claim 1, wherein, After determining the network health check result based on network service abnormality judgment results output by the one or more service systems, the network health check method further comprises: displaying the network health check result through a preset display interface and / or display platform.

10. The network health check method of claim 1, wherein, Before extracting the original network data from the network layer, the network health check method further comprises: determining the selected network health check mode; and in the case that the selected network health check mode is an automatic mode, entering the process of extracting the original network data from the network layer.

11. The network health check method of claim 1, wherein, After determining the network health check result based on the network service exception judgment result output by the one or more business systems, the network health check method further comprises: judging whether the network health check result is normal; in the case that the network health check result is determined to be normal, issuing a notification that the network health check result is normal; and in the case that the network health check result is determined to be abnormal, notifying whether a false alarm phenomenon occurs, in the case that the false alarm phenomenon is confirmed to occur, re-performing network health check, in the case that the false alarm phenomenon is confirmed not to occur, issuing a processing notification.

12. The network health check method of claim 11, wherein, judging whether the network health check result is normal, comprising: inputting the network health check result into a preset result judgment model; and judging whether the network health check result is normal by the result judgment model, wherein the result judgment model is a model obtained by training a preset second neural network with network health check results labeled with result judgment labels as training data, in the case that a second loss value meets a second preset requirement; the result judgment label indicates whether the labeled network health check result is normal.

13. A network health check system, comprising: a network layer, a data layer and a business layer, wherein the data layer is configured to extract original network data from the network layer, and obtain key data based on the original network data; the business layer is configured to perform network service exception judgment by one or more preset business systems based on the key data, respectively, wherein different business systems perform different types of network health check services; and the business layer is further configured to determine a network health check result based on the network service exception judgment result output by the one or more business systems.

14. The network health check system of claim 13, wherein, the data layer is further configured to perform data filtering and data storage on the key data, and log records the data processing process, which includes data extraction, data filtering and data storage on the key data.

15. The network health check system of claim 14, further comprising an application layer, wherein the application layer is configured to provide a group of business services for supporting the business layer to complete the data processing.

16. The network health check system of claim 13, further comprising a user layer, wherein the user layer is configured to realize user management of network health check process and query and processing of the network health check result.

17. The network health check system of claim 13, wherein, The network layer comprises any one or more of the following: wireless communication core network, transmission network and access network.

18. A network health check device, comprising: one or more processors; and a memory having stored thereon one or more computer programs that, when executed by the one or more processors, cause the one or more processors to carry out the network health check method of any of claims 1-12.

19. A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the network health check method of any of claims 1-12.

20. A computer program product comprising a computer program which, when executed by a processor, carries out the network health check method of any of claims 1-12.

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