A real-time network engineering monitoring and alarm system

Abstract

The present invention discloses a real-time network engineering monitoring and alarm system, which relates to the technical field of network engineering monitoring, including obtaining real-time operation data of various types of data in the network engineering at intervals of preset time length T, analyzing the real-time operation data obtained twice in succession as a group of analysis data, obtaining the real-time trend serial number of various types of data in the network engineering, comparing the real-time trend serial number with the benchmark trend serial number, and judging and generating data trend abnormality identification; by real-time monitoring of the operation trend of various types of data in the network engineering, the real-time trend serial number of various types of data can be calculated in real time, the real-time trend serial number is compared with the benchmark trend serial number to judge whether there is data trend abnormality, real-time monitoring and alarming of the network engineering are performed, and real-time monitoring of the network engineering status is realized, thereby improving the monitoring capability and response speed of the network engineering and helping to ensure the stable operation of the network engineering.

Description

Technical Field

[0001] The invention relates to the technical field of network engineering monitoring, and in particular to a real-time network engineering monitoring alarm system. Background Art

[0002] With the rapid development of science and technology, network technology and applications are constantly being innovated, the network scale is constantly expanding, the network structure is becoming increasingly complex, and the improvement of social living standards has led to higher requirements for the stability and security of network services. Especially in the fields of transportation, banking, government agencies, and residential communities, the demand for network monitoring is more urgent. Network security issues are becoming increasingly prominent, and network attacks, data leaks and other incidents are frequent, causing serious losses to enterprises and individuals. Real-time network engineering monitoring can timely discover and handle network security incidents, reduce risks, and ensure network security;

[0003] However, when monitoring various types of data in network engineering, only its real-time operation data is compared with the preset data threshold, and whether there are any abnormalities in various types of data in the network engineering is judged based on the comparison results and an alarm is issued, but the dynamic characteristics of the data in the network engineering are not monitored, and the abnormalities of the changing trends of various types of data cannot be accurately judged, making the monitoring alarm not accurate, precise and timely, and thus unable to ensure the stable operation of the network engineering, causing risks in the operation of the network engineering. Based on this, a real-time network engineering monitoring and alarm system is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a real-time network engineering monitoring and alarm system, which solves the technical problem that the dynamic characteristics of data in the network engineering are not monitored, the abnormalities of the changing trends of various types of data cannot be accurately judged, the monitoring alarm is not accurate and timely, and the stable operation of the network engineering cannot be guaranteed.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A real-time network engineering monitoring and alarm system comprises the following steps:

[0007] Data acquisition end, obtains the historical operation time, initial operation value and terminal operation value of various network engineering data;

[0008] On the data analysis side, the historical running time, initial running value, and terminal running value of various data in the network project are analyzed to obtain the data trend categories corresponding to each type of data:

[0009] The quasi-trend serial number acquisition end obtains the trend number corresponding to each type of data according to the data trend category corresponding to each type of data, and combines the trend numbers to generate the benchmark trend serial numbers of each type of data in the network engineering;

[0010] The data trend anomaly identification generating end obtains the real-time operation data of various data in the network project at every preset time interval T, and analyzes the real-time operation data obtained twice in succession as a group of analysis data, obtains the real-time trend serial number of various data in the network project, compares the real-time trend serial number with the benchmark trend serial number, and determines and generates the data trend anomaly identification.

[0011] As a further solution of the present invention: the specific method of obtaining the data trend categories corresponding to each type of data is:

[0012] A1: Randomly select one item from each type of data as analysis data;

[0013] A1: Obtain the initial running value, the final running value and the running time of the analysis data in the jth historical running, calculate the difference between the final running value and the initial running value of the analysis data in the jth historical running, and mark the ratio between the difference and the corresponding running time as the variation coefficient Bj corresponding to the analysis data in the jth historical running, where is the label of each historical running number, and j is a positive integer;

[0014] According to the number of coefficients of variation Bj that are equal to 0, determine whether the analyzed data is a flat data type, if not, proceed to step A3;

[0015] A3: Analyze the number of variation coefficients Bj that are greater than 0 and less than 0. According to the analysis results, determine whether the analyzed data is an increasing data type, a decreasing data type, or an unordered data type.

[0016] A04: Repeat the above steps A01-A03 to obtain the data trend categories corresponding to each type of data.

[0017] As a further solution of the present invention: the specific method of judging whether the analyzed data is a flat data type is:

[0018] The number of variation coefficients Bj that are equal to 0 is marked as a. When a is greater than a preset threshold value Y1, the analysis data is marked as a flat data class. When a is less than or equal to the preset threshold value Y1, step A3 is performed.

[0019] As a further solution of the present invention: the specific method of judging whether the analyzed data is an increasing data type, a decreasing data type or an unordered data type is:

[0020] The number of coefficients of variation Bj greater than 0 and less than 0 are marked as X respectively. 正 and X Negative, when the number of positive values ​​in the coefficient of variation Bj is X 正 Greater than negative value X 负When the number of positive values ​​in the coefficient of variation Bj is X, the data trend category of the analyzed data is marked as an increasing data category; 正 Less than negative value X 负 When the number of positive values ​​in the coefficient of variation Bj is X, the data trend category of the analyzed data is marked as a decreasing data category; 正 Equal to negative value X 负 When the quantity is large, the data trend category of the analyzed data is marked as disordered data category.

[0021] As a further solution of the present invention: the specific method of obtaining the trend labels corresponding to each type of data is:

[0022] The trend label of the increasing data class in each type of data is marked as 1, the trend label of the decreasing data class is marked as 2, and the trend label of the flat data class or disordered data class is marked as 0'.

[0023] As a further solution of the present invention: the specific method of generating the benchmark trend serial number of various types of data in the network engineering is:

[0024] Mark each type of data as b according to its corresponding type number, where b refers to different data types and is a positive integer. Sort each type of data in ascending order according to its corresponding type number. At the same time, combine the trend numbers corresponding to each type of data according to the sorting order of each type of data, and then generate the benchmark trend serial number of each type of data in the network engineering.

[0025] As a further solution of the present invention: the specific method of obtaining the real-time trend serial number of various types of data in the network project is:

[0026] At each preset time interval T, the real-time operation data of various types of data in the network project are obtained, and the real-time operation data of various types of data in the network project obtained twice in succession are taken as a group of analysis data, and the real-time operation values ​​of various types of data in the network project in a group of analysis data are marked as Fb 前 and Facebook 后 ; Fb 前 It is represented by the real-time running value of various data obtained in the first of two consecutive data acquisitions, Fb 后 It is represented as the real-time running value of each type of data obtained in the latter of two consecutive data acquisitions. The real-time running time of each type of data in the network engineering in a set of analysis data is marked as Hb 前 and Hb 后 ; Hb 前 It is represented by the real-time running time of each type of data obtained in the previous two consecutive data acquisitions, Hb 后 It is expressed as the real-time running time of each type of data obtained in the latter of two consecutive data acquisitions, and is expressed as Gb=(Fb后 -Fb 前 ) / (Hb 后 -Hb 前 ), calculate the real-time variation coefficient Gb of various data, where T is 10 minutes;

[0027] The trend labels of positive real-time change coefficients in various types of data are marked as 1, those with negative real-time change coefficients are marked as 2, and those with 0 real-time change coefficients are marked as 0'. Various types of data are sorted in ascending order according to their corresponding type numbers. At the same time, the trend labels corresponding to various types of data are combined according to the sorting order of each type of data, so as to generate real-time trend serial numbers for various types of network engineering data.

[0028] As a further solution of the present invention: the specific method of determining and generating the data trend abnormality mark is:

[0029] The real-time trend serial number is compared with the benchmark trend serial number. If they are consistent, no processing is performed. If they are inconsistent, a data trend abnormality mark is generated.

[0030] Beneficial effects of the present invention:

[0031] (1) The present invention, by analyzing the historical operation data of various types of data in network engineering, can deeply explore the law of changes of various types of data in network engineering over time, and then generate benchmark trend serial numbers of various types of data in network engineering, which is helpful to accurately evaluate the change trend of data in network engineering, provide a strong basis for monitoring and early warning, and facilitate real-time monitoring and alarm of network engineering based on the benchmark trend serial numbers of various types of data in network engineering;

[0032] (2) The present invention can calculate the real-time trend serial number of each type of data in real time by real-time monitoring the running trend of each type of data in the network project, compare the real-time trend serial number with the benchmark trend serial number to determine whether there is a data trend anomaly. When the real-time trend serial number is inconsistent with the benchmark trend serial number, it indicates that there is a data trend anomaly, and then generate a data trend anomaly mark, perform real-time monitoring and alarm on the network project, thereby realizing real-time monitoring of the network project status. When the trend serial number of a certain type of data changes, the relevant personnel are notified to handle it in time, and the running status of the network project can be monitored more accurately, thereby improving the monitoring capability and response speed of the network project, and helping to ensure the stable operation of the network project;

[0033] (3) The present invention, when generating a data trend anomaly identification, obtains the type number corresponding to the trend number that is inconsistent between the real-time trend serial number and the reference trend serial number, and outputs it as the abnormal data type, which is beneficial for relevant personnel to conduct targeted inspections of data based on the abnormal data type. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] Figure 1 It is a schematic diagram of the system framework structure of the present invention;

[0036] Figure 2 It is a schematic diagram of the framework structure of the method of the present invention;

[0037] Figure 3 It is a flow chart of the present invention for judging whether the analyzed data is an increasing data type, a decreasing data type or an unordered data type. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1

[0039] See also Figure 1-Figure 3 As shown, the present invention is a real-time network engineering monitoring and alarm system, comprising:

[0040] Data acquisition end: Acquire historical operation data of various data in network engineering, including operation duration, initial operation value and terminal operation value;

[0041] Data analysis end: Analyze the historical operation data of various types of data in the network project, and then obtain the data trend categories and standard operation coefficients corresponding to each type of data. The specific method is as follows:

[0042] A1: Randomly select one item from each type of data as the analysis data;

[0043] A2: Obtain the initial run value, final run value, and run duration of the analyzed data in the jth historical run, where j refers to different historical run times and is a positive integer. Here, j is 60 times from the moment the data is obtained, and the moment the data is obtained is not included in this historical run number. j is a positive integer, 60 ≥ j ≥ 1, and j is used as the number of times corresponding to each historical run number.

[0044] Obtain the difference between the final running value and the initial running value of the analysis data in the jth historical running, and mark the ratio between the difference and the corresponding running time as the variation coefficient Bj corresponding to the analysis data in the jth historical running;

[0045] The number of variation coefficients Bj that are equal to 0 is marked as a. When a is greater than the preset threshold value Y1, it means that the number of variation coefficients Bj that are equal to 0 is relatively large, and the analysis data is marked as a flat data type, and the value of the standard operation coefficient E1 corresponding to the analysis data is marked as 0; when a is less than or equal to the preset threshold value Y1, it means that the number of variation coefficients Bj that are equal to 0 is relatively small, and step A3 is performed. The specific value of the preset threshold value Y1 is formulated by relevant personnel according to actual usage requirements;

[0046] A3: Mark the number of variation coefficients Bj that is greater than 0 and less than 0 as Xpositive and Xnegative, respectively. Xpositive and Xnegative are the number of positive and negative values ​​in the variation coefficient Bj, respectively.

[0047] When the number of positive values ​​X in the coefficient of variation Bj is greater than the number of negative values ​​X, it means that the analysis data increases with time in most of the time, and the mean of the positive values ​​in the coefficient of variation Bj is used as the value of the standard operation coefficient E1 of the analysis data, and the data trend category of the analysis data is marked as the increasing data category;

[0048] When the number of positive values ​​X in the coefficient of variation Bj 正 Less than negative value X 负 When the number is less than 1, it means that the analysis data decreases with the increase of time in most of the time, then the mean of the negative values ​​in the coefficient of variation Bj is used as the value of the standard operation coefficient E1 of the analysis data, and the data trend category of the analysis data is marked as the decreasing data category;

[0049] When the number of positive values ​​X in the coefficient of variation Bj 正 Equal to negative value X 负 When the number is less than 0, it means that the analysis data does not change much or does not change with the increase of time in most of the time, then the value of the standard operation coefficient E1 corresponding to the analysis data is marked as 0, and the data trend category of the analysis data is marked as disordered data category;

[0050] A04: Repeat the above steps A01-A03 to obtain the data trend categories and standard operating coefficient Eb values ​​corresponding to each type of data. b refers to different data types, b is a positive integer, and b is used as the type number corresponding to each type of data.

[0051] Quasi-trend serial number acquisition terminal: sort the various types of data according to their corresponding type numbers in ascending order, and then obtain the trend labels corresponding to the various types of data according to the data trend categories corresponding to the various types of data, and combine the trend labels corresponding to the various types of data to generate the benchmark trend serial numbers of the various types of data in the network engineering. The specific method is:

[0052] The trend number of the increasing data class is marked as 1, the trend number of the decreasing data class is marked as 2, and the trend number of the flat data class or disordered data class is marked as 0', and the various types of data are sorted in ascending order according to the size of the corresponding type number, and the trend numbers corresponding to the various types of data are combined according to the sorting order of the various types of data, so as to generate the benchmark trend serial number of various types of data in the network engineering;

[0053] For example, if the network project contains five types of data, the type numbers of the five types of data are 1, 2, 3, 4 and 5, and the five types of data in the network project are sorted in ascending order according to the size of their corresponding type numbers, when the data with type numbers 1 and 3 are both increasing data types, the data with type numbers 2 and 5 are both decreasing data types, and the data with type number 4 is a flat data type or an unordered data type, then the base trend serial number of each type of data in the network project is 1210'2;

[0054] By analyzing the historical operation data of various types of data in network engineering, we can deeply explore the laws of changes of various types of data in network engineering over time, and then generate benchmark trend serial numbers of various types of data in network engineering, which is helpful to accurately evaluate the changing trends of data in network engineering, provide a strong basis for monitoring and early warning, and facilitate real-time monitoring and alarm of network engineering based on the benchmark trend serial numbers of various types of data in network engineering. Embodiment 2

[0055] As the second embodiment of the present invention, when the present application is specifically implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes a data trend anomaly identification generating end;

[0056] The data trend abnormality identification generating end obtains the real-time operation data of various data in the network project at each preset time interval T, and analyzes the real-time operation data of various data in the network project obtained twice in succession as a group of analysis data, obtains the real-time trend serial number of various data in the network project, compares the real-time trend serial number with the reference trend serial number, and determines and generates the data trend abnormality identification, and the specific method is as follows;

[0057] It should be noted that the real-time operation data includes the real-time operation time and the real-time operation value. The specific value of the preset time T is formulated by relevant staff according to actual needs. Here, the value is 10 minutes;

[0058] At each preset time interval T, the real-time operation data of various types of data in the network project are obtained, and the real-time operation data of various types of data in the network project obtained twice in succession are taken as a group of analysis data, and the real-time operation values ​​of various types of data in the network project in a group of analysis data are marked as Fb前 and Facebook 后 ; Fb 前 It is represented by the real-time running value of various data obtained in the first of two consecutive data acquisitions, Fb 后 It is expressed as the real-time running value of various data obtained in the latter of two consecutive data acquisitions;

[0059] The real-time running time of each type of data in the network engineering in a set of analysis data is marked as Hb 前 and Hb 后 ; Hb 前 It is represented by the real-time running time of each type of data obtained in the previous two consecutive data acquisitions, Hb 后 It is expressed as the real-time running time of each type of data obtained in the latter of two consecutive data acquisitions;

[0060] By Gb = (Fb 后 -Fb 前 ) / (Hb 后 -Hb 前 ), calculate and obtain the real-time variation coefficient Gb of various data;

[0061] The trend labels of the real-time change coefficients of various types of data with positive values ​​are marked as 1, the trend labels of the real-time change coefficients with negative values ​​are marked as 2, and the trend labels of the real-time change coefficients with 0 values ​​are marked as 0'. The data of various types are sorted in ascending order according to the size of the corresponding type numbers. At the same time, the trend labels corresponding to the data of various types are combined according to the sorting order of the data of various types, so as to generate the real-time trend serial numbers of the data of various types of network engineering.

[0062] Compare the real-time trend serial number with the benchmark trend serial number. If they are consistent, no processing will be done. If they are inconsistent, a data trend abnormality mark will be generated.

[0063] By real-time monitoring of the operation trends of various types of data in the network project, the real-time trend serial numbers of various types of data can be calculated in real time, and the real-time trend serial numbers can be compared with the benchmark trend serial numbers to determine whether there are data trend anomalies. When the real-time trend serial number is inconsistent with the benchmark trend serial number, it means that there is a data trend anomaly, and then a data trend anomaly mark is generated to monitor and alarm the network project in real time, thereby realizing real-time monitoring of the network project status. When the trend serial number of a certain type of data changes, the relevant personnel are notified to handle it in time, which can more accurately monitor the operation status of the network project and trigger the alarm mechanism when necessary, thereby improving the monitoring capability and response speed of the network project and helping to ensure the stable operation of the network project. Embodiment 3

[0064] As the third embodiment of the present invention, the present application is different from the first and second embodiments in that, in the present embodiment, when generating a data trend anomaly identification, the type number corresponding to the trend label that is inconsistent between the real-time trend serial number and the benchmark trend serial number is obtained, and it is output as an abnormal data type, which is beneficial for relevant personnel to conduct targeted investigation of the data according to the abnormal data type. Embodiment 4

[0065] As the fourth embodiment of the present invention, when the present application is implemented specifically, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second, third and fourth embodiments.

[0066] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0067] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A real-time network engineering monitoring and alarm system, characterized in that: include: Data acquisition end, obtains the historical operation time, initial operation value and terminal operation value of various network engineering data; On the data analysis side, the historical running time, initial running value, and terminal running value of various data in the network project are analyzed to obtain the data trend categories corresponding to each type of data: The quasi-trend serial number acquisition end obtains the trend number corresponding to each type of data according to the data trend category corresponding to each type of data, and combines the trend numbers to generate the benchmark trend serial numbers of each type of data in the network engineering; The data trend abnormality identification generating end obtains the real-time operation data of various data in the network project at every preset time interval T, and analyzes the real-time operation data obtained twice in succession as a group of analysis data to obtain the real-time trend serial number of various data in the network project, compares the real-time trend serial number with the reference trend serial number, and determines and generates the data trend abnormality identification; The specific method of obtaining the data trend categories corresponding to each type of data is: A1: Randomly select one item from each type of data as analysis data; A1: Obtain the initial running value, the final running value and the running time of the analysis data in the jth historical running, calculate the difference between the final running value and the initial running value of the analysis data in the jth historical running, and mark the ratio between the difference and the corresponding running time as the variation coefficient Bj corresponding to the analysis data in the jth historical running, where is the label of each historical running number, and j is a positive integer; According to the number of coefficients of variation Bj that are equal to 0, determine whether the analyzed data is a flat data type, if not, proceed to step A3; A3: Analyze the number of variation coefficients Bj that are greater than 0 and less than 0. According to the analysis results, determine whether the analyzed data is an increasing data type, a decreasing data type, or an unordered data type. A04: Repeat the above steps A01-A03 to obtain the data trend categories corresponding to each type of data; The specific method to determine whether the analyzed data is a flat data type is: The number of variation coefficients Bj that are equal to 0 is marked as a. When a is greater than a preset threshold value Y1, the analyzed data is marked as a flat data class. When a is less than or equal to the preset threshold value Y1, step A3 is performed. The specific method for determining whether the analyzed data is an increasing data type, a decreasing data type, or an unordered data type is as follows: The number of coefficients of variation Bj greater than 0 and less than 0 are marked as X respectively. 正 and X 负 , when the number of positive values ​​X in the coefficient of variation Bj 正 Greater than negative value X 负 When the number of positive values ​​in the coefficient of variation Bj is X, the data trend category of the analyzed data is marked as an increasing data category; 正 Less than negative value X 负 When the number of positive values ​​in the coefficient of variation Bj is X, the data trend category of the analyzed data is marked as a decreasing data category; 正 Equal to negative value X 负 When the quantity is large, the data trend category of the analyzed data is marked as disordered data category; The specific method of obtaining the trend labels corresponding to each type of data is: The trend labels of the increasing data are marked as 1, the trend labels of the decreasing data are marked as 2, and the trend labels of the flat data or disordered data are marked as 0'; The specific method of generating the benchmark trend serial number of various network engineering data is as follows: Mark each type of data as b according to its corresponding type number, where b refers to different data types and is a positive integer. Sort each type of data in ascending order according to its corresponding type number. Combine the trend numbers corresponding to each type of data according to the sorting order of each type of data, and generate the benchmark trend serial number of each type of data in the network engineering. The specific method of obtaining the real-time trend serial number of various data in network engineering is: At each preset time interval T, the real-time operation data of various types of data in the network project are obtained, and the real-time operation data of various types of data in the network project obtained twice in succession are taken as a group of analysis data, and the real-time operation values ​​of various types of data in the network project in a group of analysis data are marked as Fb 前 and Facebook 后 ; Fb 前 It is represented by the real-time running value of various data obtained in the first of two consecutive data acquisitions, Fb 后 It is represented as the real-time running value of each type of data obtained in the latter of two consecutive data acquisitions. The real-time running time of each type of data in the network engineering in a set of analysis data is marked as Hb 前 and Hb 后 ; Hb 前 It is represented by the real-time running time of each type of data obtained in the previous two consecutive data acquisitions, Hb 后 It is expressed as the real-time running time of each type of data obtained in the latter of two consecutive data acquisitions, and is expressed by Gb=(Fb 后 -Fb 前 ) / (Hb 后 -Hb 前 ), calculate the real-time change coefficient Gb of each type of data, where T is 10 minutes, b refers to different data types, b is a positive integer, and b is used as the type number corresponding to each type of data; The trend labels of positive real-time change coefficients in various types of data are marked as 1, those with negative real-time change coefficients are marked as 2, and those with 0 real-time change coefficients are marked as 0'. Various types of data are sorted in ascending order according to their corresponding type numbers. At the same time, the trend labels corresponding to various types of data are combined according to the sorting order of each type of data, so as to generate real-time trend serial numbers for various types of network engineering data.

2. A real-time network engineering monitoring and alarm system according to claim 1, characterized in that: The specific method for determining and generating data trend abnormality identification is: The real-time trend serial number is compared with the benchmark trend serial number. If they are consistent, no processing is performed. If they are inconsistent, a data trend abnormality mark is generated.

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