Network fault monitoring method and system based on data monitoring
By building a data monitoring system for network fault monitoring, the problem of insufficient detection accuracy in traditional methods has been solved, enabling accurate judgment and timely handling of network faults, and improving the stability and reliability of network operation.
Patent Information
- Application Number
- CN202411408554.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-10
- Publication Date
- 2025-11-25
- Estimated Expiration
- 2044-10-10
AI Technical Summary
Traditional network fault monitoring methods rely on human experience, which makes it difficult to cover all possible fault scenarios and affects the accuracy of fault detection.
A data-based network fault monitoring system is adopted, including data acquisition, preprocessing, anomaly detection and feedback modules. Anomaly detection is performed by constructing a two-dimensional coordinate system to determine the abnormality of network data sample points and issue a red alarm to remind relevant personnel to handle the situation.
It improves the real-time performance and accuracy of network fault monitoring, enabling precise judgment of whether a network fault has occurred and ensuring the stable operation of the network.
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Figure CN119211073B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing, specifically to a network fault monitoring method and system based on data monitoring. Background Technology
[0002] With the rapid development of the Internet, the scale and complexity of networks are constantly increasing. Enterprises, factories, and individuals all rely on networks for daily business and communication. Therefore, ensuring network reliability and performance has become crucial. During network operation, monitoring data is the foundation for ensuring stable and efficient network operation. By monitoring and analyzing network data in real time, network faults can be detected and handled in a timely manner, ensuring the continuity of network services and user experience. Traditional network fault monitoring methods rely on human experience, which makes it difficult to cover all possible fault situations and affects the accuracy of fault detection.
[0003] How to perform anomaly detection on standard network data, determine whether there are abnormal standard network data sample points, and then determine whether there is a network fault, so as to ensure the accuracy of fault detection, is a problem we need to solve. To this end, we now provide a network fault monitoring method and system based on data monitoring. Summary of the Invention
[0004] To address the aforementioned technical problems, the present invention aims to provide a network fault monitoring method and system based on data monitoring.
[0005] The objective of this invention can be achieved through the following technical solution: a network fault monitoring system based on data monitoring, including a monitoring center, wherein the monitoring center is communicatively connected to a data acquisition module, a data preprocessing module, an anomaly detection module, and a feedback module;
[0006] The data acquisition module is used to collect network data;
[0007] The data preprocessing module is used to preprocess the collected network data to obtain standard network data;
[0008] The anomaly detection module is used to perform anomaly detection on the obtained standard network data to determine whether there are abnormal standard network data sample points.
[0009] The feedback module is used to provide feedback based on the judgment result.
[0010] Furthermore, the process by which the data acquisition module acquires network data includes:
[0011] The data acquisition module consists of several data acquisition terminals. The data acquisition terminals are configured to generate corresponding configuration channels. The corresponding servers are linked through the generated configuration channels. The data acquisition terminals acquire network data from the servers through the configuration channels and send the acquired network data to the data preprocessing module.
[0012] Furthermore, the data preprocessing module performs preprocessing on the collected network data, including:
[0013] The data preprocessing module includes a data scanning unit and a data preprocessing unit. The data scanning unit scans the collected network data to obtain the status of the network data. The status of the network data includes duplicate status, missing status, and normal status. If the network data is in a duplicate status, it is sent to the data preprocessing unit, which deletes the duplicate network data. If the network data is in a missing status, it is sent to the data preprocessing unit, which deletes the missing network data. If the network data is in a normal status, no processing is performed. After preprocessing, the preprocessed network data is recorded as standard network data.
[0014] Furthermore, the anomaly detection module performs anomaly detection on the acquired standard network data, including:
[0015] A two-dimensional coordinate system is constructed, the obtained standard network data is quantized, and the quantization result is mapped into the coordinate system to obtain the corresponding standard network data sample points;
[0016] Randomly select k standard network data sample points, k∈(a,b), where k, a, and b are all positive integers. Record the selected k standard network data sample points as the standard network data population center points. One standard network data population center point represents one population.
[0017] Obtain the straight-line distance and tri-line distance between each standard network data sample point and the centroid of each standard network data population;
[0018] Based on the straight-line distance and tri-line distance between each standard network data sample point and each standard network data population center point, the allocation distance between each standard network data sample point and each standard network data population center point is obtained.
[0019] Compare the allocation distance between each standard network data sample point and the centroid of each standard network data population, and assign each standard network data sample point to the population containing the standard network data population centroid with the smallest allocation distance;
[0020] Obtain the average of the x-coordinates and y-coordinates of the standard network data sample points and the standard network data population center point within each population. Use the coordinates formed by the average of the x-coordinates and y-coordinates of the standard network data sample points and the standard network data population center point as the new standard network data population center point.
[0021] Obtain the distribution distance between standard network data sample points and the new standard network data population center point. Compare the distribution distance between each standard network data sample point and each new standard network data population center point. Assign each standard network data sample point to the population containing the new standard network data population center point with the smallest distribution distance. Obtain the average x-coordinate and y-coordinate of the standard network data sample point and the new standard network data population center point within each population. Use the coordinate formed by the average x-coordinate and y-coordinate of the standard network data sample point and the new standard network data population center point as the latest standard network data population center point. Continue in this manner until the straight-line distance between the latest standard network data population center point and the previous standard network data population center point is less than or equal to R, where R is a positive number.
[0022] Obtain the distribution distance between each standard network data sample point and other standard network data sample points in its population. Set a distribution distance threshold according to the actual situation. If there is a standard network data sample point whose distribution distance with other standard network data sample points in its population is greater than the distribution distance threshold, then this standard network data sample point is recorded as an abnormal standard network data sample point. If there is no standard network data sample point whose distribution distance with other standard network data sample points in its population is greater than the distribution distance threshold, then there is no abnormal standard network data sample point.
[0023] Furthermore, the process of obtaining the straight-line distance between each standard network data sample point and each standard network data population center point includes:
[0024] Randomly select a standard network data sample point and a standard network data population center point, and record the selected standard network data sample point and standard network data population center point as the first standard network data sample point and the first standard network data population center point, respectively, and obtain the coordinates of the first standard network data sample point and the first standard network data population center point;
[0025] Based on the coordinates of the first standard network data sample points and the first standard network data population center point, the horizontal and vertical distances between the first standard network data sample points and the first standard network data population center point are obtained.
[0026] Based on the horizontal and vertical distances between the first standard network data sample points and the first standard network data population center point, the straight-line distance between the first standard network data sample points and the first standard network data population center point is obtained.
[0027] By analogy, the straight-line distances between the remaining standard network data sample points and the remaining standard network data population center points can be obtained.
[0028] Furthermore, the process of obtaining the trilinear distance between each standard network data sample point and each standard network data population centroid includes:
[0029] Based on the longitudinal distance and the straight-line distance between the first standard network data sample point and the first standard network data population center point, the median distance between the first standard network data sample point and the first standard network data population center point is obtained.
[0030] Based on the intermediate distance, lateral distance, and longitudinal distance between the first standard network data sample points and the first standard network data population center point, the three-line distance between the first standard network data sample points and the first standard network data population center point is obtained.
[0031] Furthermore, the process by which the feedback module provides feedback based on the judgment result includes:
[0032] If abnormal standard network data sample points are found, the network data is abnormal, the network is malfunctioning, a red alert is issued, and relevant personnel are reminded to handle the situation.
[0033] If no abnormal standard network data sample points are found, then the network data is normal and the network is not faulty.
[0034] This invention also discloses a monitoring method for a network fault monitoring system based on data monitoring, comprising the following steps:
[0035] Step 1: Acquire network data and preprocess the acquired network data to obtain standard network data;
[0036] Step 2: Perform anomaly detection on the obtained standard network data to determine whether there are any abnormal standard network data sample points;
[0037] Step 3: Provide feedback based on the judgment results.
[0038] Compared with the prior art, the beneficial effects of the present invention are as follows: It collects network data, preprocesses the collected network data to obtain standard network data, performs anomaly detection on the obtained standard network data, determines whether there are abnormal standard network data sample points, and provides feedback based on the judgment result. If abnormal standard network data sample points exist, the network data is abnormal and the network is faulty; otherwise, the network data is normal and the network is not faulty. This allows for precise judgment of whether a network fault has occurred, thereby improving the real-time performance and accuracy of network fault monitoring. Attached Figure Description
[0039] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation
[0040] like Figure 1 As shown, the network fault monitoring system based on data monitoring includes a monitoring center, which is communicatively connected to a data acquisition module, a data preprocessing module, an anomaly detection module, and a feedback module.
[0041] The data acquisition module is used to collect network data;
[0042] It should be further explained that, in the specific implementation process, the data acquisition module's process of acquiring network data includes:
[0043] The data acquisition module consists of several data acquisition terminals. The data acquisition terminals are configured to generate corresponding configuration channels. The corresponding servers are linked through the generated configuration channels. The data acquisition terminals acquire network data from the servers through the configuration channels and send the acquired network data to the data preprocessing module.
[0044] The data preprocessing module is used to preprocess the collected network data to obtain standard network data;
[0045] It should be further explained that, in specific implementation, the data preprocessing module performs the following preprocessing process on the collected network data:
[0046] The data preprocessing module includes a data scanning unit and a data preprocessing unit. The data scanning unit scans the collected network data to obtain the status of the network data. The status of the network data includes duplicate status, missing status, and normal status. If the network data is in a duplicate status, it is sent to the data preprocessing unit, which deletes the duplicate network data. If the network data is in a missing status, it is sent to the data preprocessing unit, which deletes the missing network data. If the network data is in a normal status, no processing is performed. After preprocessing, the preprocessed network data is recorded as standard network data.
[0047] The anomaly detection module is used to perform anomaly detection on the obtained standard network data to determine whether there are abnormal standard network data sample points.
[0048] It should be further explained that, in the specific implementation process, the anomaly detection module performs anomaly detection on the obtained standard network data, including:
[0049] A two-dimensional coordinate system is constructed, the obtained standard network data is quantized, and the quantization result is mapped into the coordinate system to obtain the corresponding standard network data sample points;
[0050] Randomly select k standard network data sample points, k∈(a,b), where k, a, and b are all positive integers. Record the selected k standard network data sample points as the standard network data population center points. One standard network data population center point represents one population.
[0051] Obtain the straight-line distance between each standard network data sample point and the centroid of each standard network data population;
[0052] It should be further explained that, in the specific implementation process, the process of obtaining the straight-line distance between each standard network data sample point and each standard network data population center point includes:
[0053] Randomly select a standard network data sample point and a standard network data population center point. Record the selected standard network data sample point and standard network data population center point as the first standard network data sample point and the first standard network data population center point, respectively. Obtain the coordinates of the first standard network data sample point and the first standard network data population center point, and record the obtained coordinates of the first standard network data sample point and the first standard network data population center point as (x1, y1) and (x2, y2), respectively.
[0054] Obtain the lateral distance between the first standard network data sample point and the first standard network data population center point. Subtract the x-coordinate of the first standard network data sample point from the x-coordinate of the first standard network data population center point, and take the absolute value of the subtraction result to obtain the lateral distance between the first standard network data sample point and the first standard network data population center point. Record the obtained lateral distance between the first standard network data sample point and the first standard network data population center point as H.
[0055] in, ;
[0056] Obtain the longitudinal distance between the first standard network data sample point and the first standard network data population center point. Subtract the ordinate of the first standard network data sample point from the ordinate of the first standard network data population center point, and take the absolute value of the subtraction result to obtain the longitudinal distance between the first standard network data sample point and the first standard network data population center point. Record the obtained longitudinal distance between the first standard network data sample point and the first standard network data population center point as Z.
[0057] in, ;
[0058] Based on the horizontal and vertical distances between the obtained first standard network data sample points and the first standard network data population center point, the straight-line distance between the first standard network data sample points and the first standard network data population center point is obtained, and the straight-line distance between the obtained first standard network data sample points and the first standard network data population center point is denoted as J;
[0059] in, ;
[0060] By analogy, the straight-line distances between the remaining standard network data sample points and the remaining standard network data population center points can be obtained;
[0061] Obtain the trilinear distance between each standard network data sample point and the centroid of each standard network data population;
[0062] It should be further explained that, in the specific implementation process, the process of obtaining the trilinear distance between each standard network data sample point and each standard network data population center point includes:
[0063] Obtain the intermediate distance between the first standard network data sample point and the center point of the first standard network data population. Based on the longitudinal distance and the straight-line distance between the first standard network data sample point and the center point of the first standard network data population, obtain the intermediate distance between the first standard network data sample point and the center point of the first standard network data population. Record the obtained intermediate distance between the first standard network data sample point and the center point of the first standard network data population as N.
[0064] in, ;
[0065] Based on the intermediate distance, lateral distance, and longitudinal distance between the obtained first standard network data sample points and the first standard network data population center point, the three-line distance between the first standard network data sample points and the first standard network data population center point is obtained, and the obtained three-line distance between the first standard network data sample points and the first standard network data population center point is denoted as SX;
[0066] in, ;
[0067] By analogy, the three-line distances between the remaining standard network data sample points and the remaining standard network data population center points are obtained;
[0068] Obtain the distribution distance between each standard network data sample point and each standard network data population centroid;
[0069] It should be further explained that, in the specific implementation process, the process of obtaining the allocation distance between each standard network data sample point and each standard network data population centroid includes:
[0070] Based on the trilinear distance and straight-line distance between the obtained first standard network data sample points and the first standard network data population center point, the allocation distance between the first standard network data sample points and the first standard network data population center point is obtained, and the allocation distance between the obtained first standard network data sample points and the first standard network data population center point is denoted as FP;
[0071] in, ;
[0072] By analogy, the distribution distance between the remaining standard network data sample points and the remaining standard network data population center points can be obtained;
[0073] Compare the allocation distance between each standard network data sample point and the centroid of each standard network data population, and assign each standard network data sample point to the population containing the standard network data population centroid with the smallest allocation distance;
[0074] Calculate the average of the x-coordinates and y-coordinates of the standard network data sample points and the standard network data population center point within each population, and use the coordinates formed by the average of the x-coordinates and y-coordinates of the standard network data sample points and the standard network data population center point as the new standard network data population center point.
[0075] Calculate the distribution distance between standard network data sample points and the new standard network data population center point. Compare the distribution distance between each standard network data sample point and each new standard network data population center point. Assign each standard network data sample point to the population containing the new standard network data population center point with the smallest distribution distance. Calculate the average x-coordinate and y-coordinate of the standard network data sample point and the new standard network data population center point within each population. Use the coordinates formed by the average x-coordinate and y-coordinate of the standard network data sample point and the new standard network data population center point as the latest standard network data population center point. Repeat this process for several rounds until the straight-line distance between the latest standard network data population center point and the previous standard network data population center point is less than or equal to R, where R is a positive number.
[0076] Calculate the distribution distance between each standard network data sample point and other standard network data sample points in its population. Set a distribution distance threshold according to the actual situation. If there is a standard network data sample point whose distribution distance with other standard network data sample points in its population is greater than the distribution distance threshold, then this standard network data sample point is recorded as an abnormal standard network data sample point. If there is no standard network data sample point whose distribution distance with other standard network data sample points in its population is greater than the distribution distance threshold, then there is no abnormal standard network data sample point.
[0077] The feedback module is used to provide feedback based on the judgment result;
[0078] It should be further explained that, in the specific implementation process, the feedback module's feedback process based on the judgment result includes:
[0079] If abnormal standard network data sample points are found, the network data is abnormal, the network is malfunctioning, a red alert is issued, and relevant personnel are reminded to handle the situation.
[0080] If no abnormal standard network data sample points are found, then the network data is normal and the network is not faulty.
[0081] This invention also discloses a monitoring method for a network fault monitoring system based on data monitoring, comprising the following steps:
[0082] Step 1: Acquire network data and preprocess the acquired network data to obtain standard network data;
[0083] Step 2: Perform anomaly detection on the obtained standard network data to determine whether there are any abnormal standard network data sample points;
[0084] Step 3: Provide feedback based on the judgment results.
[0085] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.
Claims
1. A network fault monitoring system based on data monitoring, comprising a monitoring center, characterized in that, The monitoring center is communicatively connected with a data acquisition module, a data preprocessing module, an anomaly detection module and a feedback module; The data acquisition module is configured to acquire network data; The data preprocessing module is configured to preprocess the acquired network data to obtain standard network data; The anomaly detection module is configured to detect anomalies in the obtained standard network data to determine whether there are abnormal standard network data sample points; The feedback module is configured to provide feedback based on the determination result; The data preprocessing module is provided with a data scanning unit and a data preprocessing unit. The data scanning unit is configured to scan the acquired network data to obtain the state of the network data, which includes a repeated state, a missing state and a normal form state. If the state of the network data is the repeated state, the network data is sent to the data preprocessing unit, which deletes the repeated network data. If the state of the network data is the missing state, the network data is sent to the data preprocessing unit, which deletes the missing network data. If the state of the network data is the normal form state, the network data is not processed. After preprocessing, the preprocessed network data is recorded as standard network data; The anomaly detection module detects anomalies in the obtained standard network data, including: A two-dimensional coordinate system is constructed, the obtained standard network data is quantized, and the quantized results are mapped in the coordinate system to obtain corresponding standard network data sample points; k standard network data sample points are randomly selected, k∈(a, b), k, a and b are positive integers, the selected k standard network data sample points are recorded as standard network data population center points, and one standard network data population center point represents one population; The straight-line distance between each standard network data sample point and each standard network data population center point is obtained; The process of obtaining the straight-line distance between each standard network data sample point and each standard network data population center point includes: Randomly selecting a standard network data sample point and a standard network data population center point, denoting the selected standard network data sample point and the standard network data population center point as a first standard network data sample point and a first standard network data population center point respectively, obtaining coordinates of the first standard network data sample point and the first standard network data population center point, and denoting the obtained coordinates of the first standard network data sample point and the first standard network data population center point as (x1, y1) and (x2, y2) respectively. x1 , y1 ) and (x2, y2). x2 , y2 ); The horizontal coordinate of the first standard network data sample point is subtracted from the horizontal coordinate of the first standard network data population center point, and the absolute value of the result is obtained to obtain the horizontal distance between the first standard network data sample point and the first standard network data population center point, which is recorded as H; wherein ; The vertical coordinate of the first standard network data sample point is subtracted from the vertical coordinate of the first standard network data population center point, and the absolute value of the result is obtained to obtain the vertical distance between the first standard network data sample point and the first standard network data population center point, which is recorded as Z; wherein ; According to the horizontal distance and the vertical distance between the first standard network data sample point and the first standard network data population center point, a straight line distance between the first standard network data sample point and the first standard network data population center point is obtained, and the obtained straight line distance between the first standard network data sample point and the first standard network data population center point is recorded as J; wherein ; Similarly, the straight line distances between the remaining standard network data sample points and the remaining standard network data population center points are obtained; The three-line distance between each standard network data sample point and each standard network data population center point is obtained; The process of obtaining the three-line distance between each standard network data sample point and each standard network data population center point includes: According to the vertical distance and the straight line distance between the first standard network data sample point and the first standard network data population center point, an intermediate distance between the first standard network data sample point and the first standard network data population center point is obtained, and the obtained intermediate distance between the first standard network data sample point and the first standard network data population center point is recorded as N; wherein ; According to the obtained intermediate distance, the horizontal distance and the vertical distance between the first standard network data sample point and the first standard network data population center point, a three-line distance between the first standard network data sample point and the first standard network data population center point is obtained, and the obtained three-line distance between the first standard network data sample point and the first standard network data population center point is recorded as SX; wherein ; Similarly, the three-line distances between the remaining standard network data sample points and the remaining standard network data population center points are obtained; The allocation distance between each standard network data sample point and each standard network data population center point is obtained; The process of obtaining the allocation distance between each standard network data sample point and each standard network data population center point includes: According to the obtained three-line distance and the straight line distance between the first standard network data sample point and the first standard network data population center point, an allocation distance between the first standard network data sample point and the first standard network data population center point is obtained, and the obtained allocation distance between the first standard network data sample point and the first standard network data population center point is recorded as FP; wherein ; Similarly, the allocation distances between the remaining standard network data sample points and the remaining standard network data population center points are obtained; The allocation distance between each standard network data sample point and each standard network data population center point is compared, and each standard network data sample point is allocated to the population in which the standard network data population center point with the smallest allocation distance is located; The average values of the horizontal coordinates and the vertical coordinates of the standard network data sample points and the standard network data population center points in each population are calculated, and the coordinates composed of the average values of the horizontal coordinates and the vertical coordinates of the standard network data sample points and the standard network data population center points are taken as new standard network data population center points; The assignment distance between each standard network data sample point and each new standard network data population center point is calculated, the assignment distance between each standard network data sample point and each new standard network data population center point is compared, each standard network data sample point is assigned to a population in which the new standard network data population center point with the minimum assignment distance is located, the average value of the horizontal and vertical coordinates of the standard network data sample points and the new standard network data population center points in each population is calculated, and the coordinates composed of the average value of the horizontal and vertical coordinates of the standard network data sample points and the new standard network data population center points are taken as the latest standard network data population center point; the above calculation is repeated for several rounds until the straight-line distance between the latest standard network data population center point and the previous standard network data population center point is less than or equal to R, R being a positive number; The assignment distance between each standard network data sample point and other standard network data sample points in the population in which the standard network data sample point is located is calculated, and an assignment distance threshold is set; if there is a standard network data sample point with an assignment distance greater than the assignment distance threshold from other standard network data sample points in the population in which the standard network data sample point is located, the standard network data sample point is recorded as an abnormal standard network data sample point; if there is no standard network data sample point with an assignment distance greater than the assignment distance threshold from other standard network data sample points in the population in which the standard network data sample point is located, there is no abnormal standard network data sample point. The feedback process of the feedback module according to the judgment result comprises: If there is an abnormal standard network data sample point, the network data is abnormal, the network has a fault, a red alarm is issued, and relevant personnel are reminded to handle; If there is no abnormal standard network data sample point, the network data is normal, and the network has no fault.
2. The network fault monitoring system based on data monitoring according to claim 1, characterized in that, The data collection process of the data collection module comprises: The data collection module is composed of a plurality of data collection terminals, the data collection terminals are configured, corresponding configuration channels are generated, the corresponding servers are linked through the generated configuration channels, the data collection terminals acquire network data in the servers through the configuration channels, and the acquired network data is sent to the data preprocessing module.
3. The method for network fault monitoring based on data monitoring, particularly applied to the system for network fault monitoring based on data monitoring according to any one of claims 1 to 2, characterized in that, The method comprises the following steps: Step 1: acquire network data, and pre-process the acquired network data to obtain standard network data; Step 2: detect the acquired standard network data for abnormality, and judge whether there is an abnormal standard network data sample point; Step 3: feedback according to the judgment result.
Citation Information
Patent Citations
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CN117909870A