Communication assurance method and apparatus

Abstract

The application relates to the technical field of communication, and provides a communication guarantee method and device. The method comprises the following steps: obtaining target fault alarm data and target key performance indicator data; classifying the target fault alarm data, and obtaining first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data; performing abnormality judgment on the target key performance indicator data to obtain index abnormality data; and processing the first occurrence frequency data, the second occurrence frequency data and the index abnormality data. The communication guarantee method and device provided by the application cover data acquisition, data analysis and data processing, and realize machine automatic operation in the whole process, without manual intervention, so that the omissions caused by manual operation are eliminated, time consumption is reduced, and the quality and efficiency of communication guarantee are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a communication guarantee method and device. BACKGROUND

[0002] With the rapid development of information technology, artificial intelligence technology has gradually become an important pillar in the production and service fields, gradually replacing traditional manual operation.

[0003] Communication guarantee is an important link in network optimization, but the current communication guarantee work still mainly adopts the traditional mode, and the whole process needs manual processing. Since the communication guarantee work involves multiple interfaces, miscellaneous content and complicated process, manual processing is easy to be omitted and needs to consume more time, which reduces the quality and efficiency of communication guarantee. SUMMARY

[0004] The embodiments of the present application provide a communication guarantee method and device to solve the technical problem that the current communication guarantee work still mainly adopts the traditional mode, the whole process needs manual processing, it is easy to be omitted and needs to consume more time, which reduces the quality and efficiency of communication guarantee.

[0005] In a first aspect, the embodiments of the present application provide a communication guarantee method, comprising:

[0006] obtaining target fault alarm data and target key performance indicator data;

[0007] classifying the target fault alarm data, and obtaining first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data;

[0008] performing abnormality judgment on the target key performance indicator data to obtain indicator abnormality data;

[0009] processing the first occurrence frequency data, the second occurrence frequency data and the indicator abnormality data;

[0010] The target fault alarm data is data obtained by preprocessing fault alarm new data, and the target key performance indicator data is data obtained by preprocessing key performance indicator new data.

[0011] In one embodiment, the classifying the target fault alarm data, and obtaining first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data comprises:

[0012] grouping the target fault alarm data according to an alarm level field to obtain target fault alarm data of a plurality of first groups;

[0013] Counting the number of target fault alarm data in each first group to obtain first occurrence frequency data of each alarm level;

[0014] Grouping the target fault alarm data according to the alarm level field and the whether-recovered field to obtain target fault alarm data of a plurality of second groups;

[0015] Counting the number of unrecovered target fault alarm data in each second group to obtain second occurrence frequency data of unrecovered alarm data in each alarm level.

[0016] In one embodiment, the abnormality judgment on the target key performance indicator data to obtain indicator abnormal data comprises:

[0017] If there is a target key performance indicator data exceeding the corresponding degradation threshold, the target key performance indicator data is determined as indicator abnormal data;

[0018] If there are a plurality of target key performance indicator data exceeding the corresponding degradation threshold, the target key performance indicator data with higher weight is determined as indicator abnormal data.

[0019] In one embodiment, the processing of the first occurrence frequency data, the second occurrence frequency data and the indicator abnormal data comprises:

[0020] According to the first occurrence frequency data, the second occurrence frequency data and the indicator abnormal data, identifying indicator serious degradation data and / or burst fault alarm data;

[0021] Pushing the indicator serious degradation data and / or the burst fault alarm data to a professional person in charge and starting emergency support processing;

[0022] Tracking and detecting the result of the emergency support processing;

[0023] If the result shows that the indicator serious degradation data and / or the burst fault alarm data is not solved, the indicator serious degradation data and / or the burst fault alarm data is included in an emergency support library, and after being pushed to the professional person in charge, the step of tracking and detecting the result of the emergency support processing is returned;

[0024] If the result shows that the indicator serious degradation data and / or the burst fault alarm data is solved, the first occurrence frequency data, the second occurrence frequency data and the indicator abnormal data after being solved are subjected to closed-loop detection.

[0025] In one embodiment, the obtaining of target fault alarm data and target key performance indicator data comprises:

[0026] initiating a data acquisition request to a data platform;

[0027] parsing a response sent by the data platform based on the data acquisition request to obtain fault alarm new data and key performance indicator new data;

[0028] preprocessing the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data; or

[0029] taking a screenshot of a key node webpage element of the data platform;

[0030] performing similarity matching between a webpage element to be matched and a webpage element in the screenshot;

[0031] if the similarity is greater than or equal to a similarity threshold, determining that the webpage element in the screenshot is a target webpage element;

[0032] simulating keyboard and mouse operations, clicking a center position coordinate of the target webpage element, and downloading fault alarm new data and key performance indicator new data;

[0033] preprocessing the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data.

[0034] In one embodiment, the preprocessing of the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data includes:

[0035] if the data volume of the fault alarm new data and the key performance indicator new data is greater than or equal to a data volume threshold, a big data processing method is used to preprocess the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data;

[0036] if the data volume of the fault alarm new data and the key performance indicator new data is less than a data volume threshold, a small data processing method is used to preprocess the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data.

[0037] In one embodiment, the preprocessing of the fault alarm new data and the key performance indicator new data using a big data processing method to obtain target fault alarm data and target key performance indicator data includes:

[0038] importing the fault alarm new data and the key performance indicator new data into a MySQL database;

[0039] The fault alarm new data and the key performance indicator new data are merged into new data according to specified fields;

[0040] The new data is arranged according to preset rules and preset logic to obtain target fault alarm data and target key performance indicator data.

[0041] In one embodiment, the small data processing method is used to pre-process the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data, including:

[0042] The file to which the fault alarm new data belongs and the file to which the key performance indicator new data belongs are found through a path name, and the file name of each file is defined as a function;

[0043] The file names in the function are read and traversed, and the files under the file names belonging to the same directory name are concatenated to obtain a plurality of concatenated files;

[0044] The abnormal data and redundant data in the plurality of concatenated files are removed to obtain target fault alarm data and target key performance indicator data in the plurality of concatenated files.

[0045] In a second aspect, an embodiment of the present application provides a communication guarantee device, including:

[0046] A data acquisition module is configured to acquire target fault alarm data and target key performance indicator data;

[0047] A fault alarm data classification module is configured to classify the target fault alarm data and acquire first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data;

[0048] A key performance indicator data anomaly judgment module is configured to perform anomaly judgment on the target key performance indicator data to obtain indicator anomaly data;

[0049] A data processing module is configured to process the first occurrence frequency data, the second occurrence frequency data, and the indicator anomaly data;

[0050] The target fault alarm data is data pre-processed from fault alarm new data, and the target key performance indicator data is data pre-processed from key performance indicator new data.

[0051] In a third aspect, an embodiment of the present application provides an electronic device including a processor and a memory storing a computer program, and the processor implements the steps of the communication guarantee method of the first aspect when executing the program.

[0052] In a fourth aspect, an embodiment of the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the communication guarantee method in the first aspect.

[0053] In a fifth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium, comprising a computer program, which, when executed by a processor, implements the steps of the communication guarantee method in the first aspect.

[0054] The communication guarantee method and device provided by the present application obtain target fault alarm data and target key performance indicator data, classify the target fault alarm data, and obtain first occurrence frequency data of each type of target fault alarm data and second occurrence frequency data of un-recovered alarm data, perform abnormality judgment on the target key performance indicator data to obtain index abnormal data, and process the first occurrence frequency data, the second occurrence frequency data and the index abnormal data. The present application covers data acquisition, data analysis and data processing in the whole process, and realizes machine automatic operation in the whole process without human intervention, eliminates the omissions caused by manual operation, reduces the time consumption, and thus improves the quality and efficiency of communication guarantee. BRIEF DESCRIPTION OF DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0056] Figure 1 is one of the flowcharts of the communication guarantee method provided by the embodiments of the present application;

[0057] Figure 2 is the second flowchart of the communication guarantee method provided by the embodiments of the present application;

[0058] Figure 3 is the third flowchart of the communication guarantee method provided by the embodiments of the present application;

[0059] Figure 4 is the system architecture diagram of the communication guarantee system provided by the embodiments of the present application;

[0060] Figure 5 is the structural schematic diagram of the communication guarantee device provided by the embodiments of the present application;

[0061] Figure 6 is the structural schematic diagram of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0062] For the purposes of the present application, the technical solutions and advantages will be more apparent, the technical solutions in the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0063] Figure 1 is one of the flowcharts of the communication guarantee method provided by the embodiments of the present application. Referring to Figure 1 , the embodiments of the present application provide a communication guarantee method, which can include:

[0064] 101, obtaining target fault alarm data and target key performance indicator data;

[0065] 102, classifying the target fault alarm data, and obtaining first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data;

[0066] 103, performing abnormality judgment on the target key performance indicator data to obtain index abnormality data;

[0067] 104, processing the first occurrence frequency data, the second occurrence frequency data and the index abnormality data.

[0068] The target fault alarm data is data obtained by preprocessing the newly added fault alarm data, and the target key performance indicator data is data obtained by preprocessing the newly added key performance indicator data.

[0069] The communication guarantee includes real-time monitoring and processing of two types of data, i.e. fault alarm and key performance indicator. By obtaining newly added fault alarm data and key performance indicator data at each moment, and preprocessing the data, the target fault alarm data and the target key performance indicator data after processing are analyzed. For the target fault alarm data, the occurrence frequency is selected as the processing target data, and for the target key performance indicator data, the index abnormality data is selected as the processing target data, thereby facilitating subsequent guarantee processing.

[0070] In actual application, there is no strict time sequence relationship between step 102 and step 103; that is, either step can be executed first, and the specific execution sequence is determined according to actual needs, which is not limited here.

[0071] The communication guarantee method provided by the embodiment comprises the following steps: obtaining target fault alarm data and target key performance indicator data, classifying the target fault alarm data, obtaining first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data, performing abnormality judgment on the target key performance indicator data to obtain indicator abnormal data, and processing the first occurrence frequency data, the second occurrence frequency data and the indicator abnormal data. The embodiment process covers data acquisition, data analysis and data processing, and the whole process realizes machine automatic operation without manual intervention, eliminates the omissions caused by manual operation, reduces the time consumption, and thus improves the quality and efficiency of communication guarantee.

[0072] Figure 2 FIG. 2 is a flowchart of a communication guarantee method provided by an embodiment of the application. Referring to FIG. 2, Figure 2 In one embodiment, classifying the target fault alarm data and obtaining the first occurrence frequency data of each category of target fault alarm data and the second occurrence frequency data of un-recovered alarm data can comprise: 201, grouping the target fault alarm data according to the alarm level field to obtain a plurality of first groups of target fault alarm data;

[0073] 202, calculating the number of target fault alarm data in each first group to obtain the first occurrence frequency data of each alarm level;

[0074] 203, grouping the target fault alarm data according to the alarm level field and the recovery field to obtain a plurality of second groups of target fault alarm data;

[0075] 204, calculating the number of un-recovered target fault alarm data in each second group to obtain the second occurrence frequency data of un-recovered alarm data in each alarm level.

[0076] In steps 201 to 202, the target fault alarm data can be grouped according to the alarm level field by using the groupby() function of the pandas library, and the size() function is used to calculate the number of target fault alarm data in each group to obtain the first occurrence frequency data of each alarm level. The alarm level can include emergency, important, secondary and prompt.

[0077] Pandas is an open source data analysis and operation library for Python programming language. It provides high-performance and easy-to-use data structures and data analysis tools, which are very suitable for processing table data and time series data.

[0078] In steps 203 to 204, the target fault alarm data can also be grouped according to the alarm level field and the whether-recovered field by using the groupby() function of the pandas library, at this time, each group includes the alarm level and the whether-recovered field, and the whether-recovered means whether the target fault alarm data at the current moment is improved compared with the last moment. The size() function is used to calculate the number of un-recovered target fault alarm data in each second group, so as to obtain the second occurrence frequency data of the un-recovered alarm data in each alarm level.

[0079] In actual application, there is no strict time sequence relationship between steps 201 and 203; that is, either step can be executed first, which is determined according to actual needs, and the present disclosure does not limit it. However, step 202 must be executed after step 201, and step 204 must be executed after step 203.

[0080] The embodiment classifies and processes the target fault alarm data, filters the occurrence frequency data of each alarm level and the occurrence frequency data of the un-recovered alarm data in each alarm level, which is helpful for subsequent differentiated protection processing of different alarm levels and their occurrence frequencies of the target fault alarm level data, and the occurrence frequencies of the un-recovered alarm data in different alarm levels of the target fault alarm level data, and improves the quality and efficiency of communication protection.

[0081] In one embodiment, the target key performance indicator data is subjected to abnormality judgment to obtain indicator abnormal data, which can include:

[0082] If one target key performance indicator data exceeds the corresponding degradation threshold, the target key performance indicator data is determined as the indicator abnormal data;

[0083] If multiple target key performance indicator data exceeds the corresponding degradation threshold, the target key performance indicator data with higher weight is determined as the indicator abnormal data.

[0084] The key performance indicators can be wireless signal coverage, wireless utilization rate, interference rate, connection rate, drop rate, handover success rate, etc. A corresponding degradation threshold can be set for each indicator, and the if statement is combined with the regular expression to judge the relationship between each target key performance indicator data and its corresponding degradation threshold, so as to filter out the indicator abnormal data, which is the data that needs to be processed first.

[0085] The embodiment filters out the abnormal data through the degradation threshold of each indicator, which is helpful for subsequent targeted protection processing of the abnormal data, and improves the quality and efficiency of communication protection.

[0086] Figure 3Figure 3 is a flowchart of a third embodiment of the communication guarantee method provided in the present application. Refer to Figure 3 In one embodiment, processing the first occurrence frequency data, the second occurrence frequency data, and the index abnormal data can include: 301, identifying index serious degradation data and / or sudden failure alarm data according to the first occurrence frequency data, the second occurrence frequency data, and the index abnormal data;

[0087] 302, pushing the index serious degradation data and / or the sudden failure alarm data to a professional person in charge and starting emergency guarantee processing; 303, tracking and detecting the result of the emergency guarantee processing;

[0088] 304, if the result shows that the index serious degradation data and / or the sudden failure alarm data has not been solved, the index serious degradation data and / or the sudden failure alarm data is included in the emergency guarantee library, and is pushed to the professional person in charge, and then returning to step 303; 305, if the result shows that the index serious degradation data and / or the sudden failure alarm data has been solved, the first occurrence frequency data, the second occurrence frequency data, and the index abnormal data after being solved are detected in a closed loop.

[0089] In step 301, if a certain index abnormal data is data that exceeds the corresponding degradation threshold to reach a preset standard, it can be determined that the index abnormal data is index serious degradation data; if the first occurrence frequency data or the second occurrence frequency data suddenly increases at a certain time, it can be determined that the corresponding target failure alarm data or the un-recovered alarm data is sudden failure alarm data.

[0090] Further, the first occurrence frequency data, the second occurrence frequency data, and the index abnormal data can be integrated into an alarm detail table and an index statistics table using the Pandas data analysis tool and the OpenPyXL library, the openpyxl.styles class is called, and the style of the table is adjusted in detail according to the needs, for example, the color, size, and font of the cell where the important data is located are adjusted accordingly, the adjusted data is written into the fault alarm and performance index text template using the format method, and the template is automatically pushed to the relevant professional person in charge through the GUI (Graphical User Interface) automation program, which facilitates the relevant professional personnel to quickly understand the brief information of the current network status. Wherein, OpenPyXL is a Python library for reading and writing Excel files. It is an open source project that allows users to create, modify, and delete worksheets in Excel files, and can handle cells, styles, charts, pictures, etc.

[0091] The format and content of the push information can be freely set to better meet actual needs. Meanwhile, some default push information templates are provided for selection and can be combined with the Time time processing program to set two modes for pushing:

[0092] Mode 1: Periodic, push at a fixed time.

[0093] The time-related functions provided by the Time time processing program of Python, including time formatting, timestamp calculation, and sleep, are used to set the push time according to actual needs, and to realize automatic push of key information at a fixed time.

[0094] When the set time is reached, the program automatically sends key information to the relevant professional responsible person, including table data and related text prompt information.

[0095] Mode 2: Event type, push in time.

[0096] Based on the particularity that fault alarms may occur at any time, as soon as the automatic detection mechanism captures new fault alarm data, the automatic push function is triggered to push key information to the relevant professional responsible person, to ensure that the sudden fault alarm is handled in time.

[0097] By setting two automatic push modes, work efficiency can be more effectively improved and the timeliness and accuracy of information transmission can be ensured.

[0098] Further, according to the first occurrence frequency data, the second occurrence frequency data, and the index abnormal data, high-frequency fault alarm data belonging to the cell, long-term unsolved high-frequency fault alarm data belonging to the cell, and performance degradation cell can also be sorted out, and according to the problem data type, occurrence frequency, and current state, they are summarized and entered into the high-frequency problem library for key monitoring. Using OpenCV vision library to monitor the cell state, when the problem data appears again, the related historical data is sorted out by the GUI automation program and pushed to the relevant professional responsible person for easy viewing and processing. The cell state is monitored periodically until the problem data is completely solved, and the problem point is removed from the monitoring list, otherwise it will be continuously monitored, making the communication support process more perfect and ensuring the smooth progress of communication support work.

[0099] OpenCV is an open-source computer vision and machine learning software library that provides a wide range of programming interfaces for image and video analysis tasks, including real-time operations.

[0100] In step 303, the OpenCV vision library can be used to track and detect the results of emergency handling.

[0101] In steps 304-305, the data included in the emergency guarantee library can be pushed to the professional person in charge for processing, and according to the feedback processing progress and automatic verification results, it is judged whether the index serious degradation data and / or sudden failure alarm data is solved, and the solved one enters the closed loop detection, and the unsolved one continues to track and monitor.

[0102] For example, transmission flash, super strong interference, etc., trigger emergency guarantee processing, based on the problem severity, shrink the power of the cell, lock and other emergency measures, at the same time, push the information to the relevant professional person in charge, promote the problem processing. Continue to track the problem state, according to the professional person in charge feedback processing progress and automatic verification results, judge whether the problem is solved, the solved one enters the closed loop detection, and the unsolved one continues to track and monitor.

[0103] Further, for the data for closed loop detection, using Pandas data analysis tool combined with OpenCV vision library, the first occurrence frequency data, the second occurrence frequency data and the index abnormal data are realized, and the similar problem data after the index serious degradation data and / or sudden failure alarm data has been solved is identified, and the processing results of the relevant professional person in charge are tracked and detected, if the similar problem data is not solved, it is continuously monitored, if the similar problem data is solved, the similar problem data is closed loop added to the historical problem library, which provides basis for subsequent high frequency problem analysis.

[0104] In addition, in order to ensure the accuracy and real-time of the data, the latest communication state data, fault alarm information, key performance index information, etc. can be obtained regularly and updated to the database, so as to realize real-time state tracking and problem judgment; in addition, for the problem data which is not solved or new abnormal data appears, according to the different situations, the corresponding alarm mechanism is triggered, and the unsolved problem data or new abnormal data is pushed to the relevant professional personnel for processing.

[0105] Further, the embodiment also provides the following schemes with practicability and flexibility:

[0106] Custom threshold: the degradation threshold can be customized according to the actual situation, so as to more flexibly adapt to different environments and needs.

[0107] Automatic adjustment of emergency measures: according to the fault condition and cell index, the emergency measures can be automatically adjusted, such as automatically selecting appropriate lock strategy or power shrink scheme, so as to better cope with different situations and improve the processing efficiency.

[0108] Multi-dimensional information pushing: in addition to pushing the problem data to the professional person in charge, it can also be pushed to other related personnel or system, such as network administrator, system monitoring platform, etc., realizing multi-dimensional information sharing and processing, improving the overall collaborative efficiency.

[0109] Visual monitoring interface: In order to facilitate real-time monitoring and operation, real-time data, alarm information, emergency measure implementation and other key information can also be displayed, and operation buttons or menus can be provided to facilitate manual intervention or adjustment settings.

[0110] In this embodiment, Pandas data analysis tool is used to aggregate tables and OpenPyXL is used to adjust table styles. By adjusting the color of the cells, the key data is more eye-catching, ensuring data accuracy and taking into account the comfort and convenience of viewing the table. In addition, through the fault alarm and performance index text template, the key information prompt in text form is sorted, so that the relevant person in charge can obtain the key information of the alarm in time through the text prompt in the case of not being convenient to open the table.

[0111] Further, the emergency handling mechanism of the embodiment can intelligently identify sudden fault alarm data and performance index serious degradation data, and separate the solved and unsolved problem data, and the unsolved problem data is included in the emergency support library and enters the emergency support state, automatically issues instructions to the backup communication equipment, triggers the temporary support mechanism, and quickly switches to the backup equipment to form a full-flow closed loop without human intervention, thereby ensuring the continuity of communication. The system will continue to track the problem until the emergency state is lifted. Compared with the prior art, the present scheme can more effectively respond to sudden situations and ensure the stability of communication.

[0112] Further, a closed-loop detection link is added in the communication support process, which can intelligently identify problems and determine whether the problem has been solved by creating custom rules or thresholds, and further detects and determines on the basis of emergency support processing to ensure that problem data is handled in a timely manner. The rules set can be used to guide state tracking, problem judgment and index detection operations, making these operations more in line with actual needs and environmental characteristics.

[0113] In summary, the embodiment supplements the monitoring, pushing and closed-loop detection processes on the basis of the data acquisition, analysis and processing of the foregoing embodiments for communication support work, so that the communication support process realizes full-flow automation.

[0114] In one embodiment, obtaining target fault alarm data and target key performance indicator data can include: mode one, initiating a data acquisition request to a data platform, parsing a response sent by the data platform based on the data acquisition request to obtain new fault alarm data and new key performance indicator data, preprocessing the new fault alarm data and the new key performance indicator data to obtain the target fault alarm data and the target key performance indicator data;

[0115] In the method, the data acquisition request can be initiated to the data platform by using an http library, and the response sent by the data platform contains various types of data, which can be parsed by using a regular expression, an Xpath, a Beautiful soup, a CSS, and the like, so as to obtain the fault alarm new data and the key performance indicator new data, and the obtained data can be imported into a MySQL database.

[0116] The Xpath is a powerful XML document query language, which provides rich features and flexibility, so that a user can efficiently extract required information from an XML document.

[0117] The Beautiful soup is a Python library for web crawling, which can parse HTML or XML documents and extract data therefrom.

[0118] The CSS (Cascading Style Sheets) is a computer language used to present the style of an HTML or XML file. Its main function is to enhance the style rendering of the file, so that the web page and application interface are more beautiful, user-friendly and easy to maintain.

[0119] In the second mode, the key node web elements of the data platform are screenshot, the similarity between the to-be-matched web elements and the web elements in the screenshot is matched, if the similarity is greater than or equal to a similarity threshold, the web elements in the screenshot are determined as the target web elements, the center position coordinates of the target web elements are clicked by simulating the keyboard and mouse operations, the fault alarm new data and the key performance indicator new data are downloaded, the target fault alarm data and the target key performance indicator data are obtained by preprocessing the fault alarm new data and the key performance indicator new data. The above process can be realized by using a findImg (icon) function, that is, the screenshot is saved by using a screenshot method of a PyAutoGUI automation tool, then a matchTemplate method of an OpenCV vision library is used to find the web elements related to the to-be-matched web elements (i.e., the web elements specified by an 'icon' parameter) in the screenshot, and then the similarity is matched, so as to obtain the target web elements. If the target web elements are not matched, a certain period of time can be waited for re-trying, and if the target web elements are still not matched after a certain number of times of trying, a prompt information is automatically sent to a related person in charge. It should be noted that the waiting time and the number of times of trying can be adjusted as required, which is not limited herein. In addition, the keyboard and mouse operations can be simulated by using a click method and a press method of the PyAutoGUI automation tool. The PyAutoGUI is a Python tool specially used for GUI automation, which can simulate mouse and keyboard operations, and is suitable for various automation tasks.

[0120] Wherein, for the findImg(icon) function, its running steps can be parsed as follows:

[0121] 1. Define a variable named num to record the waiting time;

[0122] 2. Define a while loop to record the number of attempts;

[0123] 3. Start screenshot using the screenshot method of the PyAutoGUI automation tool, and use the matchTemplate method to match between the screenshot and the web element to be matched;

[0124] 4. Use if statement to judge, use cv2.minMaxLoc() to calculate the similarity, if the similarity is greater than or equal to 90%, it is judged that the web element in the screenshot is the target web element, the center position of the target web element is calculated, and the coordinates (x, y) of this position are returned, if the similarity is less than 90%, the loop is tried and the waiting time is increased, and the time that has been waited is printed; 5. Set the timer through the Time time processing program, if the waiting time is exceeded, send a prompt message to the relevant responsible person automatically, and exit the loop; if it is found that the target data has been downloaded, go to the next loop.

[0125] Since the data needs to be downloaded on multiple platforms, different interface switching is involved to extract relevant data. In order to avoid data loss as much as possible, the above two methods can be used for data collection, and redundant data can be deleted, and difference items can be merged to further improve data integrity.

[0126] In this embodiment, the matchTemplate function of the OpenCV vision library is combined with the Time interval processing program to write a real-time screen image pixel mapping program, which realizes automatic real-time cyclic detection of fault alarm new data and key performance indicator new data fault alarm new data in seconds, solves the problem that manual continuous long-time monitoring is difficult and prone to errors. At the same time, by setting the cyclic detection period, if there is no key performance indicator new data in the period, it is considered that there may be an abnormality in a node, and the node is automatically checked with the network management data source in time, prompting the relevant responsible person to handle.

[0127] In one embodiment, the fault alarm new data and the key performance indicator new data are preprocessed to obtain target fault alarm data and target key performance indicator data, which can include:

[0128] 1. If the data volume of the fault alarm new data and the key performance indicator new data is greater than or equal to the data volume threshold, a big data processing method is used to preprocess the fault alarm new data and the key performance indicator new data, to obtain target fault alarm data and target key performance indicator data.

[0129] In the method, the big data processing method is used to preprocess the fault alarm new data and the key performance indicator new data, to obtain the target fault alarm data and the target key performance indicator data, and the specific steps can be as follows:

[0130] 1.1. Import the fault alarm new data and the key performance indicator new data into a MySQL database.

[0131] 1.2. Merge the fault alarm new data and the key performance indicator new data into new data according to specified fields.

[0132] 1.3. Arrange the new data according to preset rules and preset logic, to obtain the target fault alarm data and the target key performance indicator data.

[0133] In step 1.2, a UNION operator can be used to merge the fault alarm new data and the key performance indicator new data into new data according to specified fields.

[0134] In step 1.3, the new data can be preliminarily processed using a CONCAT_WS, CASE and JOIN statement, and then arranged according to preset rules and preset logic.

[0135] 2. If the data volume of the fault alarm new data and the key performance indicator new data is less than the data volume threshold, a small data processing method is used to preprocess the fault alarm new data and the key performance indicator new data, to obtain target fault alarm data and target key performance indicator data.

[0136] In the method, the small data processing method is used to preprocess the fault alarm new data and the key performance indicator new data, to obtain the target fault alarm data and the target key performance indicator data, and the specific steps can be as follows:

[0137] 2.1. Find the file to which the fault alarm new data belongs and the file to which the key performance indicator new data belongs through a path name, and define the file names of the respective files as functions.

[0138] 2.2. Traverse and read the file names in the functions, and cascade the files under the file names belonging to the same directory name, to obtain a plurality of cascaded files.

[0139] 2.3, remove abnormal data and redundant data in multiple concatenated files to obtain target fault alarm data and target key performance indicator data in multiple concatenated files.

[0140] In step 2.1, the glob method can be called with a wildcard to find the file to which the fault alarm new data belongs and the file to which the key performance indicator new data belongs through the path name, and the file names of each belonging file are returned in the form of list and defined as a function.

[0141] In step 2.2, the Pandas data analysis tool can be used to traverse the file name in the read function, and the concat method is used to concatenate the files under the file name belonging to the same directory name.

[0142] In step 2.3, the isnull, describe, duplicated and other methods can be used to remove abnormal data and redundant data in multiple concatenated files.

[0143] In this embodiment, since the fault alarm new data and the key performance indicator new data contain multiple formats and have different data sizes, it is inconvenient to process the subsequent data, therefore, based on the characteristics that the MySQL database is fast in processing large data and the Pandas data analysis tool is convenient in processing small data, a large data processing scheme and a small data processing scheme are provided for the fault alarm new data and the key performance indicator new data respectively, which greatly improves the preprocessing speed, so that in any data amount scene, efficient preprocessing of the fault alarm new data and the key performance indicator new data can be completed, the data format is unified, the data cleaning is completed, and the subsequent data processing is facilitated.

[0144] Figure 4 FIG. 1 is a communication guarantee system architecture diagram provided by the embodiment of the present application. Referring to Figure 4 The embodiment of the present application provides a communication guarantee system, which is developed based on a server building Python environment, installs third-party tools required by a GUI automation program: PyAutoGUI automation tool, OpenCV vision library and Time time processing program; installs third-party tools required by a data preprocessing program: MySQL database, Pandas data analysis tool and OpenPyXL library. The GUI automation program is a visual interface program, and the data preprocessing program is mainly used for data cleaning.

[0145] The communication guarantee system of the embodiment is based on a server built Python environment, combs a guarantee work process to write an automatic program, and realizes automatic data extraction, intelligent analysis, emergency processing, man-machine interaction, closed-loop detection and other full-process automation and intelligent operation. No manual intervention is required throughout the process, eliminating errors caused by human error, greatly reducing the number of manual requirements and work intensity, and achieving double improvement in quality and efficiency.

[0146] The communication guarantee device provided by the embodiment of the application is described below, and the communication guarantee device described below can be correspondingly referred to the communication guarantee method described above.

[0147] Figure 5 FIG. 1 is a structural schematic diagram of the communication guarantee device provided by the embodiment of the application. Referring to FIG. 1, the communication guarantee device provided by the embodiment of the application can include: Figure 5

[0148] The data acquisition module 501 is configured to acquire target fault alarm data and target key performance indicator data.

[0149] The fault alarm data classification module 502 is configured to classify the target fault alarm data and acquire first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data.

[0150] The key performance indicator data anomaly judgment module 503 is configured to perform anomaly judgment on the target key performance indicator data to obtain indicator anomaly data.

[0151] The data processing module 504 is configured to process the first occurrence frequency data, the second occurrence frequency data and the indicator anomaly data.

[0152] The target fault alarm data is data obtained by preprocessing fault alarm new data, and the target key performance indicator data is data obtained by preprocessing key performance indicator new data.

[0153] The communication guarantee device provided by the embodiment acquires target fault alarm data and target key performance indicator data, classifies the target fault alarm data, acquires first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data, performs anomaly judgment on the target key performance indicator data to obtain indicator anomaly data, and processes the first occurrence frequency data, the second occurrence frequency data and the indicator anomaly data. The embodiment covers data acquisition, data analysis and data processing, and realizes machine automatic operation throughout the process without manual intervention, eliminates omissions caused by manual operation, reduces time consumption, and thus improves the quality and efficiency of communication guarantee.

[0154] ​In one embodiment, the fault alarm data classification module 502 is specifically configured to:

[0155] grouping the target fault alarm data according to the alarm level field to obtain a plurality of first groups of target fault alarm data;

[0156] calculating the number of target fault alarm data in each first group to obtain first occurrence frequency data of each alarm level;

[0157] grouping the target fault alarm data according to the alarm level field and the recovery field to obtain a plurality of second groups of target fault alarm data;

[0158] calculating the number of un-recovered target fault alarm data in each second group to obtain second occurrence frequency data of un-recovered alarm data in each alarm level.

[0159] In one embodiment, the key performance indicator data anomaly judgment module 503 is specifically configured to:

[0160] if there is a target key performance indicator data exceeding the corresponding degradation threshold, determining the target key performance indicator data as indicator abnormal data;

[0161] if there are multiple target key performance indicator data exceeding the corresponding degradation threshold, determining the target key performance indicator data with higher weight as indicator abnormal data.

[0162] In one embodiment, the data processing module 504 is specifically configured to:

[0163] according to the first occurrence frequency data, the second occurrence frequency data and the indicator abnormal data, identifying indicator serious degradation data and / or sudden fault alarm data;

[0164] pushing the indicator serious degradation data and / or the sudden fault alarm data to a professional person in charge and starting emergency support processing;

[0165] tracking and detecting the result of the emergency support processing;

[0166] if the result shows that the indicator serious degradation data and / or the sudden fault alarm data has not been solved, including the indicator serious degradation data and / or the sudden fault alarm data in an emergency support library, pushing to the professional person in charge, and returning to the step of tracking and detecting the result of the emergency support processing;

[0167] if the result shows that the indicator serious degradation data and / or the sudden fault alarm data has been solved, performing closed-loop detection on the first occurrence frequency data, the second occurrence frequency data and the indicator abnormal data after being solved.

[0168] In one embodiment, the data acquisition module 501 is specifically configured to:

[0169] initiate a data acquisition request to a data platform;

[0170] parse a response sent by the data platform based on the data acquisition request to obtain fault alarm new data and key performance indicator new data;

[0171] preprocess the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data; or

[0172] take screenshots of key node web elements of the data platform;

[0173] perform similarity matching between a web element to be matched and a web element in the screenshots;

[0174] if the similarity is greater than or equal to a similarity threshold, determine that the web element in the screenshots is a target web element;

[0175] simulate keyboard and mouse operations, click on the center position coordinates of the target web element, and download the fault alarm new data and the key performance indicator new data;

[0176] preprocess the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data.

[0177] In one embodiment, the data acquisition module 501 is specifically configured to:

[0178] if the data volume of the fault alarm new data and the key performance indicator new data is greater than or equal to a data volume threshold, preprocess the fault alarm new data and the key performance indicator new data using a big data processing method to obtain target fault alarm data and target key performance indicator data;

[0179] if the data volume of the fault alarm new data and the key performance indicator new data is less than the data volume threshold, preprocess the fault alarm new data and the key performance indicator new data using a small data processing method to obtain target fault alarm data and target key performance indicator data.

[0180] In one embodiment, the data acquisition module 501 is specifically configured to:

[0181] import the fault alarm new data and the key performance indicator new data into a MySQL database;

[0182] The fault alarm new data and the key performance indicator new data are merged into new data according to specified fields;

[0183] The new data is arranged according to preset rules and preset logic to obtain target fault alarm data and target key performance indicator data.

[0184] In one embodiment, the data acquisition module 501 is specifically configured to:

[0185] The file to which the fault alarm new data belongs and the file to which the key performance indicator new data belongs are found through a path name, and the file name of each file is defined as a function;

[0186] The file names in the function are read in a loop, and the files under the file names belonging to the same directory name are concatenated to obtain a plurality of concatenated files;

[0187] The abnormal data and redundant data in the plurality of concatenated files are removed to obtain target fault alarm data and target key performance indicator data in the plurality of concatenated files.

[0188] Figure 6 An example of a structural diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device can include a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can invoke the computer program in the memory 630 to execute the steps of the communication assurance method, for example, including:

[0189] Obtaining target fault alarm data and target key performance indicator data;

[0190] Classifying the target fault alarm data and obtaining first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data;

[0191] Abnormality judgment is performed on the target key performance indicator data to obtain indicator abnormal data;

[0192] The first occurrence frequency data, the second occurrence frequency data, and the indicator abnormal data are processed;

[0193] The target fault alarm data is data preprocessed from fault alarm new data, and the target key performance indicator data is data preprocessed from key performance indicator new data.

[0194] Further, the logic instructions in the memory 630 described above can be implemented in the form of software functional units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts of the prior art that make contributions or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0195] In another aspect, the embodiments of the present application also provide a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, so that the computer can execute the steps of the communication guarantee method provided by the above-mentioned embodiments, for example, including:

[0196] obtaining target fault alarm data and target key performance indicator data;

[0197] classifying the target fault alarm data, and obtaining first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data;

[0198] performing abnormality judgment on the target key performance indicator data to obtain indicator abnormal data;

[0199] processing the first occurrence frequency data, the second occurrence frequency data and the indicator abnormal data;

[0200] The target fault alarm data is data obtained by preprocessing fault alarm new data, and the target key performance indicator data is data obtained by preprocessing key performance indicator new data.

[0201] In another aspect, the embodiments of the present application also provide a non-transitory computer readable storage medium, which stores a computer program, and the computer program is used to make the processor execute the steps of the communication guarantee method provided by the above-mentioned embodiments, for example, including:

[0202] obtaining target fault alarm data and target key performance indicator data;

[0203] Classify the target fault alarm data, and obtain first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data;

[0204] Perform abnormality judgment on the target key performance indicator data to obtain indicator abnormality data;

[0205] Process the first occurrence frequency data, the second occurrence frequency data, and the indicator abnormality data;

[0206] The target fault alarm data is data obtained by preprocessing fault alarm new data, and the target key performance indicator data is data obtained by preprocessing key performance indicator new data.

[0207] The non-transitory computer-readable storage medium can be any available medium or data storage device that the processor can access including, but not limited to, a magnetic storage (such as a floppy disk, a hard disk, a magnetic tape, a magneto-optical disk (MO), etc.), an optical storage (such as a CD, a DVD, a BD, a HVD, etc.), and a semiconductor memory (such as a ROM, an EPROM, an EEPROM, a non-volatile memory (NAND FLASH), a solid state disk (SSD)), etc.

[0208] The device embodiments described above are only schematic, wherein the units shown as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement without creative labor.

[0209] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by means of software plus necessary universal hardware platforms, and of course can also be implemented by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0210] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the same; although the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A communication assurance method, characterized in that, The method comprises the following steps: obtaining target fault alarm data and target key performance indicator data; classifying the target fault alarm data, and obtaining first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data; performing abnormality judgment on the target key performance indicator data to obtain index abnormal data; processing the first occurrence frequency data, the second occurrence frequency data and the index abnormal data, comprising: identifying index serious degradation data and / or sudden fault alarm data according to the first occurrence frequency data, the second occurrence frequency data and the index abnormal data; the target fault alarm data is data obtained by preprocessing fault alarm new data, and the target key performance indicator data is data obtained by preprocessing key performance indicator new data.

2. The communication assurance method of claim 1, wherein, The classification of the target fault alarm data and the obtaining of the first occurrence frequency data of each category of target fault alarm data and the second occurrence frequency data of un-recovered alarm data comprise: grouping the target fault alarm data according to the alarm level field to obtain target fault alarm data of multiple first groups; calculating the number of target fault alarm data in each first group to obtain the first occurrence frequency data of each alarm level; grouping the target fault alarm data according to the alarm level field and the recovery field to obtain target fault alarm data of multiple second groups; calculating the number of un-recovered target fault alarm data in each second group to obtain the second occurrence frequency data of un-recovered alarm data in each alarm level.

3. The communication assurance method of claim 1, wherein, The abnormality judgment on the target key performance indicator data to obtain index abnormal data comprises: if one target key performance indicator data exceeds the corresponding degradation threshold, the target key performance indicator data is determined as index abnormal data; if multiple target key performance indicator data exceeds the corresponding degradation threshold, the target key performance indicator data with higher weight is determined as index abnormal data.

4. The communication assurance method of claim 1, wherein, The processing of the first occurrence frequency data, the second occurrence frequency data and the index abnormal data further comprises: pushing the index serious degradation data and / or the sudden fault alarm data to a professional person in charge, and starting emergency support processing; tracking and detecting the results of the emergency support processing; if the results show that the index serious degradation data and / or the sudden fault alarm data have not been solved, the index serious degradation data and / or the sudden fault alarm data are included in the emergency support library, and are pushed to the professional person in charge, and then the step of tracking and detecting the results of the emergency support processing is returned; if the results show that the index serious degradation data and / or the sudden fault alarm data have been solved, the first occurrence frequency data, the second occurrence frequency data and the index abnormal data after being solved are subjected to closed-loop detection.

5. The communication assurance method of claim 1, wherein, The obtaining of target fault alarm data and target key performance indicator data comprises: initiating a data acquisition request to a data platform; Analyzing a response sent by the data platform based on the data acquisition request to obtain fault alarm new data and key performance indicator new data; Preprocessing the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data; or Taking a screenshot of a key node webpage element of a data platform; Matching the webpage element to be matched with the webpage element in the screenshot in similarity; If the similarity is greater than or equal to a similarity threshold, the webpage element in the screenshot is determined to be a target webpage element; Simulating keyboard and mouse operations, clicking the center position coordinates of the target webpage element, and downloading fault alarm new data and key performance indicator new data; Preprocessing the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data.

6. The communication assurance method of claim 5, wherein, The preprocessing of the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data includes: If the data volume of the fault alarm new data and the key performance indicator new data is greater than or equal to a data volume threshold, a big data processing method is used to preprocess the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data; If the data volume of the fault alarm new data and the key performance indicator new data is less than a data volume threshold, a small data processing method is used to preprocess the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data.

7. The communication assurance method of claim 6, wherein, The preprocessing of the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data includes: Importing the fault alarm new data and the key performance indicator new data into a MySQL database; Merging the fault alarm new data and the key performance indicator new data into new data according to specified fields; According to a preset rule and a preset logic, the new data is arranged to obtain target fault alarm data and target key performance indicator data.

8. The communication assurance method of claim 6, wherein, The preprocessing of the fault alarm new data and the key performance indicator new data to obtain target fault alarm data and target key performance indicator data includes: Finding the files to which the fault alarm new data and the key performance indicator new data belong through path names, and defining the file names of the files as functions; Reading the file names in the functions, and concatenating the files under the file names belonging to the same directory name to obtain a plurality of concatenated files; Eliminating abnormal data and redundant data in the plurality of concatenated files to obtain target fault alarm data and target key performance indicator data in the plurality of concatenated files.

9. A communication assurance apparatus, characterized by comprising: It includes: A data acquisition module configured to acquire target fault alarm data and target key performance indicator data. The fault alarm data classification module is configured to classify the target fault alarm data and obtain first occurrence frequency data of each category of target fault alarm data and second occurrence frequency data of un-recovered alarm data. The key performance indicator data anomaly judgment module is configured to perform anomaly judgment on the target key performance indicator data to obtain indicator anomaly data. The data processing module is configured to process the first occurrence frequency data, the second occurrence frequency data, and the indicator anomaly data, including: According to the first occurrence frequency data, the second occurrence frequency data, and the indicator anomaly data, identifying indicator serious degradation data and / or sudden fault alarm data. The target fault alarm data is data obtained by preprocessing fault alarm new data, and the target key performance indicator data is data obtained by preprocessing key performance indicator new data.

10. An electronic device comprising a processor and a memory having a computer program stored therein, characterized in that, The processor executes the computer program to implement the steps of the communication guarantee method of any one of claims 1 to 8.

11. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the communication guarantee method of any one of claims 1 to 8.

12. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the communication guarantee method of any one of claims 1 to 8.

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