Automatic configuration method of network equipment server

Abstract

The invention discloses an automatic configuration method of a network equipment server. The automatic configuration method comprises the following steps: acquiring data; collecting historical fault data; acquiring equipment state data; acquiring external network environment information; data preprocessing: carrying out standardization processing on the collected data; training a fault prediction model; fault prediction; intelligent automatic configuration optimization: automatically adjusting the configuration of the network equipment; early warning and repairing faults; and reporting faults. According to the invention, a new automatic method is provided for management of network equipment by combining fault prediction and intelligent automatic configuration optimization technologies. Through accurate fault prediction, real-time equipment state monitoring and intelligent flow routing optimization, the reliability and management efficiency of network equipment can be remarkably improved, the influence of equipment faults on network performance is reduced, and the continuity and high availability of network services are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of network server deployment, and in particular, to an automatic configuration method for network device servers. Background Art

[0002] With the rapid development of informatization and the popularization of the Internet, network devices play a crucial role in enterprise and personal network architectures. Network devices, such as routers, switches, servers, etc., are responsible for ensuring the efficiency and reliability of data transmission. However, with the increase in the number of devices and the complexity of the network architecture, the stability and high availability of network devices have become an important challenge in network management.

[0003] Currently, the management and configuration of most network devices still rely on traditional manual monitoring and manual configuration methods. Administrators usually rely on regularly checking device status, viewing system logs, and performing manual troubleshooting to maintain the stable operation of devices. However, this method not only places a heavy burden on the work of administrators, but also has a long response time when device failures occur, which may lead to service interruptions or network performance degradation. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title, but such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides an automatic configuration method for network device servers, including:

[0007] Data collection; collection of historical fault data: extracting historical data from the historical logs of network devices; collection of device status data: real-time collection of the operating status of each network device; collection of external network environment information: collecting external network environment information;

[0008] Data preprocessing: performing standardized processing on the collected data;

[0009] Fault prediction model training: training the prediction model using historical fault data and device status data;

[0010] Fault prediction: predicting the status of a device in a future period through the real-time collected device status information;

[0011] Intelligent Automatic Configuration Optimization: When it is predicted that a certain device may malfunction, the system will automatically adjust the configuration of network devices, rerouting traffic from the device about to fail to healthy devices;

[0012] Fault Early Warning and Repair: Repair through automated tools, and if recovery fails, issue an alarm and notify the staff;

[0013] Fault Report: Record fault information and regularly generate detailed reports on fault prediction, configuration adjustment, and recovery operations for network administrators to review, ensuring the long-term stability and security of the configuration.

[0014] As a preferred solution of the automatic configuration method for a network device server described in the present invention, wherein: The specific method of the data standardization process is as follows:

[0015]

[0016] Wherein: X is the original data; X min and X max are the minimum and maximum values of the data respectively; X new is the normalized data.

[0017] As a preferred solution of the automatic configuration method for a network device server described in the present invention, wherein: The fault prediction model algorithm is as follows:

[0018]

[0019] Wherein: t 0 is the current time point; t 1 is the future prediction time point; N is the number of device status parameters; α i is the weight coefficient of the i-th device status parameter; f state (S i (t)) is the device status function; λ i is the decay factor of the i-th device status; M is the number of external environmental factors; β j is the weight coefficient of the j-th external environmental factor z j (t); L is the number of historical fault data and other relevant data involved in the j-th external factor; γ k is the weight coefficient of the k-th historical fault data; h k (z j (t)) is the external environment influence function; z j (t) is the time change parameter of the j-th external environmental factor; δ j is the exponential adjustment coefficient of the external environment influence function.

[0020] As a preferred embodiment of the automatic configuration method for a network device server according to the present invention, where: the f state (S i (t)) has the following specific algorithm:

[0021] f state (S i (t)) = s i (t) 2 ·log(s i (t) + 1).

[0022] As a preferred embodiment of the automatic configuration method for a network device server according to the present invention, where: the h k (z j (t)) has the following specific algorithm:

[0023]

[0024] σ k is the adjustment parameter for environmental data.

[0025] As a preferred embodiment of the automatic configuration method for a network device server according to the present invention, where: the P fail (t 0 , t 1 ) has the following specific value range:

[0026] 0 ≤ P fail (t 0 , t 1 ) < 0.1: Normal state, the device is running well;

[0027] 0.1 ≤ P fail (t 0 , t 1 ) < 0.3: Low risk, the device has a certain risk, but does not require immediate intervention, continue to monitor;

[0028] 0.3 ≤ P fail (t 0 , t 1 ) < 0.7: Medium risk, the device has a relatively large risk of failure, strengthen monitoring and make adjustments;

[0029] 0.7 ≤ P fail (t 0 , t 1 ) ≤ 1.0: High risk, the device is about to fail, immediate measures should be taken and the device should be adjusted.

[0030] As a preferred solution of the automatic configuration method for a network device server according to the present invention, wherein: the training of the prediction model is carried out by using historical data for prediction and comparing with the actual situation of the device. When the error probability exceeds a preset threshold, the parameters in the prediction model are gradually adjusted until the requirements are met.

[0031] In a second aspect, an embodiment of the present invention further provides an automatic configuration system for a network device server, specifically including:

[0032] A data acquisition module: including a historical data acquisition unit, a device status data acquisition unit, and an external network environment information acquisition unit;

[0033] A data preprocessing module: performing standardization processing on the collected data;

[0034] A fault prediction model training module: training the prediction model based on historical fault data and device status data to optimize the fault prediction model;

[0035] A fault prediction and analysis module: predicting device faults based on real-time collected device status information and historical data;

[0036] An intelligent automatic configuration optimization module: when it is predicted that the device may have a fault, the system automatically adjusts the configuration of the network device;

[0037] A fault repair and warning module: when the device has a fault, the system repairs it through an automated tool; if it cannot be repaired, the system will send an alarm and notify the staff;

[0038] A fault report generation module: recording all fault information and regularly generating detailed reports on fault prediction, configuration adjustment, and recovery operations.

[0039] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and wherein: when the computer program is executed by the processor, it implements any step of the automatic configuration method for a network device server as described in the first aspect of the present invention.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and wherein: when the computer program is executed by the processor, it implements any step of the automatic configuration method for a network device server as described in the first aspect of the present invention.

[0041] Advantages of the present invention:

[0042] The present invention provides a new automated method for the management of network devices by combining fault prediction and intelligent automatic configuration optimization technologies. Through accurate fault prediction, real-time device status monitoring, and intelligent traffic routing optimization, it can significantly improve the reliability and management efficiency of network devices, reduce the impact of device failures on network performance, and enhance the continuity and high availability of network services. At the same time, by combining historical fault data with real-time device status data, the present invention can train a more accurate prediction model and improve the accuracy of its prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0044] Figure 1 is a flowchart of the working process of an automatic configuration method for a network device server proposed by the present invention;

[0045] Figure 2 is a system architecture diagram of an automatic configuration system for a network device server proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the drawings of the specification.

[0047] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0048] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0049] Referring to Figure 1-2 , the present invention provides an automatic configuration method for a network device server, including:

[0050] Data collection; Collection of historical fault data: Extract historical data from the historical logs of network devices, including data such as device downtime, performance degradation, and increased error rate; Collection of device status data: Real-time collection of the operating status of each network device, including indicators such as CPU occupancy, memory usage, network bandwidth, latency, and packet loss rate; Collection of external network environment information: Collect external network environment information, such as link quality, network congestion situation, attack detection data, etc.;

[0051] Data preprocessing: Perform standardization processing on the collected data;

[0052] Fault prediction model training: Use historical fault data and device status data to train the prediction model;

[0053] Fault prediction: Predict the status of the device in the future for a period of time through the real-time collected device status information;

[0054] Intelligent automatic configuration optimization: When it is predicted that a certain device may fail, the system will automatically adjust the configuration of the network device and reroute the traffic from the device about to fail to a healthy device;

[0055] Fault warning and repair: Use automated tools for repair such as restarting the device and restoring network services. If it cannot be restored, an alarm will be issued and the staff will be notified;

[0056] Fault report: Record the fault information and regularly generate a detailed report on fault prediction, configuration adjustment, and recovery operations for the network administrator to review to ensure the long-term stability and security of the configuration.

[0057] Among them, the specific method of data standardization processing is as follows:

[0058]

[0059] Among them: X is the original data; X min and X max are the minimum and maximum values of the data respectively; X new is the normalized data, and all feature values are scaled to the same range to make the dimensions of different features consistent and avoid some features having too much influence on the training of the model.

[0060] Furthermore, the fault prediction model algorithm is as follows:

[0061]

[0062] Among them: t 0 is the current time point; t 1 is the future prediction time point; N is the number of device status parameters; α i is the weight coefficient of the i-th device status parameter; fstate (S i (t)) is the device status function, which is used to describe the impact of device status parameters on the occurrence of faults; λ i is the attenuation factor of the i-th device status, which determines the decay rate of the impact of this status on the fault risk over time; M is the number of external environmental factors; β j is the weight coefficient of the j-th external environmental factor z j (t); L is the number of historical fault data and other relevant data involved in the j-th external factor; γ k is the weight coefficient of the k-th historical fault data, indicating the degree of influence of this historical fault on fault prediction; h k (z j (t)) is the external environment impact function; z j (t) is the time variation parameter of the j-th external environmental factor; δ j is the exponential adjustment coefficient of the external environment impact function, which is used to control the impact intensity of historical fault data or external environment on fault prediction.

[0063] Furthermore, the specific algorithm of f state (S i (t)) is as follows:

[0064] f state (S i (t)) = s i (t) 2 ·log(s i (t)+1).

[0065] Furthermore, the specific algorithm of h k (z j (t)) is as follows:

[0066]

[0067] σ k is the adjustment parameter of environmental data.

[0068] Furthermore, the specific value range of P fail (t 0 , t 1 ) is as follows:

[0069] 0 ≤ P fail (t 0 , t 1 ) < 0.1: Normal state, the device is running well;

[0070] 0.1 ≤ P fail (t 0 , t 1) < 0.3: Low risk. The device has certain risks, but immediate intervention is not required. Continuously monitor;

[0071] 0.3 ≤ P fail (t 0 ,t 1 ) < 0.7: Medium risk. The device has a relatively high risk of failure. Strengthen monitoring and make adjustments;

[0072] 0.7 ≤ P fail (t 0 ,t 1 ) ≤ 1.0: High risk. The device is about to fail. Immediate measures should be taken, and the device should be adjusted. Based on the prediction results of the prediction model, different countermeasures should be taken according to different predicted values of the failure probability.

[0073] Furthermore, the training of the prediction model is carried out by using historical data for prediction and comparing with the actual situation of the device. When the error probability exceeds the preset threshold, the parameters in the prediction model are gradually adjusted until they meet the requirements. The prediction model is regularly trained with historical data to ensure the accuracy of its prediction.

[0074] This embodiment also provides an automatic configuration system for a network device server, specifically including:

[0075] Data acquisition module: It includes a historical data acquisition unit, a device status data acquisition unit, and an external network environment information acquisition unit. This module is responsible for collecting and processing relevant data of network devices in real time;

[0076] Data preprocessing module: Standardize the collected data to ensure the consistency and usability of the data;

[0077] Fault prediction model training module: Based on historical fault data and device status data, train the prediction model to optimize the fault prediction model;

[0078] Fault prediction and analysis module: Based on the real-time collected device status information and historical data, predict device failures;

[0079] Intelligent automatic configuration optimization module: When it is predicted that the device may fail, the system automatically adjusts the configuration of the network device, optimizes traffic scheduling, and reroutes the traffic from the device about to fail to a healthy device;

[0080] Fault repair and warning module: When the device fails, the system will repair it through automated tools, such as restarting the device, restoring network services, etc.; if it cannot be repaired, the system will send an alarm and notify the staff;

[0081] Fault Report Generation Module: Records all fault information and periodically generates detailed reports on fault prediction, configuration adjustment, and recovery operations for review by network administrators.

[0082] This embodiment also provides a computer device applicable to the situation of an automatic configuration method for a network device server, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement an automatic configuration method for a network device server as proposed in the above embodiment.

[0083] This computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, a touchpad, or a mouse, etc.

[0084] This embodiment also provides a storage medium on which a computer program is stored, and when the program is executed by a processor, it implements an automatic configuration method for a network device server as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0085] In summary, the present invention provides a new automated method for the management of network devices by combining fault prediction and intelligent automatic configuration optimization technologies. Through accurate fault prediction, real-time device status monitoring, and intelligent traffic routing optimization, it can significantly improve the reliability and management efficiency of network devices, reduce the impact of device failures on network performance, and enhance the continuity and high availability of network services. At the same time, by combining historical fault data with real-time device status data, the present invention can train a more accurate prediction model and improve the accuracy of its prediction.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An automatic configuration method for a network device server, characterized in that: include: Data collection; Collection of historical fault data: extract historical data from the historical logs of network devices; Collection of equipment status data: real-time collection of the operating status of each network device; Collection of external network environment information: collect external network environment information; Data preprocessing: standardize the collected data; Fault prediction model training: Use historical fault data and equipment status data to train the prediction model; Fault prediction: predict the status of the equipment in the future through real-time collected equipment status information; Intelligent automatic configuration optimization: When a device is predicted to fail, the system will automatically adjust the configuration of network devices and reroute traffic from the failing device to the healthy device; Fault warning and repair: Repair is performed through automated tools. If the fault cannot be restored, an alarm is issued and the staff is notified. Fault reporting: Fault information is recorded and detailed reports of fault prediction, configuration adjustment and recovery operations are regularly generated for review by network administrators to ensure the long-term stability and security of the configuration.

2. The automatic configuration method of a network device server according to claim 1, characterized in that: The specific method of data standardization processing is as follows: Among them: X is the original data; X min and X max are the minimum and maximum values ​​of the data respectively; X new is the normalized data.

3. The automatic configuration method of a network device server according to claim 2, characterized in that: The fault prediction model algorithm is as follows: Where: t0 is the current time point; t1 is the predicted time point in the future; N is the number of device state parameters; α i The weight coefficient of the i-th device state parameter; f state (S i (t)) is the device state function; λ i is the attenuation factor of the i-th device state; M is the number of external environmental factors; β j is the jth external environmental factor z j (t); L is the number of historical failure data and other related data involved in the jth external factor; γ k is the weight coefficient of the kth historical fault data; h k (z j (t)) is the influence function of the external environment; z j (t) is the time variation parameter of the jth external environmental factor; δ j is the exponential adjustment coefficient of the external environment impact function.

4. The automatic configuration method of a network device server according to claim 3, characterized in that: The f state (S i (t)) The specific algorithm is as follows: f state (S i (t))=s i (t) 2 ·log(s i (t)+1)。 5. The automatic configuration method of a network device server according to claim 4, characterized in that: The h k (z j (t)) The specific algorithm is as follows: σ k Tuning parameters for environmental data.

6. The automatic configuration method of a network device server according to claim 5, characterized in that: The P fail The specific value range of (t0, t1) is as follows: 0≤P fail (t0, t1)<0.1: normal state, the equipment operates well; 0.1≤P fail (t0, t1) < 0.3: low risk, the equipment has some risk, but no immediate intervention is required, and continuous monitoring is required; 0.3≤P fail (t0, t1) < 0.7: Medium risk, the equipment has a high risk of failure, strengthen monitoring and make adjustments; 0.7≤P fail (t0, t1)≤1.0: High risk, the equipment is about to fail, and immediate measures should be taken to adjust the equipment.

7. The automatic configuration method of a network device server according to claim 6, characterized in that: The prediction model is trained by using historical data for prediction and comparing it with the actual situation of the equipment. When the error probability exceeds a preset threshold, the parameters in the prediction model are gradually adjusted until they meet the requirements.

8. An automatic configuration system for a network device server, based on an automatic configuration method for a network device server according to claims 1-7, characterized in that: Specifically include: Data collection module: including historical data collection unit, equipment status data collection unit and external network environment information collection unit; Data preprocessing module: standardize the collected data; Fault prediction model training module: Based on historical fault data and equipment status data, the prediction model is trained to optimize the fault prediction model; Fault prediction and analysis module: predicts equipment failures based on real-time collected equipment status information and historical data; Intelligent automatic configuration optimization module: When a possible device failure is predicted, the system automatically adjusts the configuration of the network device; Fault repair and early warning module: When a device fails, the system will repair it through automated tools; if it cannot be repaired, the system will send an alarm and notify the staff; Fault report generation module: records all fault information and regularly generates detailed reports on fault prediction, configuration adjustments, and recovery operations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the automatic configuration method of a network device server described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the automatic configuration method of a network device server described in any one of claims 1-7 are implemented.

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