Enterprise seat network equipment monitoring management method and system

Abstract

The invention relates to the field of network equipment monitoring, and discloses an enterprise seat network equipment monitoring management method and system, and the method comprises the following steps: S1, collecting the operation data, including but not limited to network delay, packet loss rate, bandwidth utilization rate and connection disconnection rate, of network equipment in real time through a sensor integrated in the network equipment or special monitoring software, therefore, the network connection state can be monitored in real time. S2, analyzing the operation data collected in real time, once the abnormal risk estimated value of the network wind equipment exceeds a preset risk range, immediately triggering an early warning signal, and notifying a manager through a mail, a short message or a system notification mode; s3, further fault analysis is carried out on the network equipment which triggers the early warning, and the root of the problem is positioned; s4, for a common network fault, presetting an automatic recovery script or starting a standby network device; and meanwhile, network equipment configuration and important data are backed up regularly.

Description

Technical Field

[0001] The present invention relates to the field of network equipment monitoring, and in particular to a method and system for monitoring and managing enterprise seat network equipment. Background Art

[0002] With the rapid development of information technology, enterprise agent systems are playing an increasingly important role in customer service, sales support, etc. However, the stable operation of the agent system is inseparable from the support of network equipment, and the monitoring and management of network equipment has become the key to ensure the normal operation of the agent system.

[0003] Since agent work is highly dependent on network connection, any network fluctuation or interruption will directly affect agent work efficiency and customer experience. Therefore, how to monitor the network connection status in real time and ensure the stability of the network is an important issue that needs to be solved in agent network equipment monitoring and management.

[0004] At present, many enterprises have many deficiencies in the monitoring and management of agent network equipment. Some enterprises have backward monitoring methods and cannot grasp the operating status of network equipment in real time, resulting in failure to detect and handle failures in time, affecting the normal use of the agent system. In addition, some enterprises lack a complete monitoring and management mechanism, and the maintenance and management of network equipment are carried out only based on experience, which lacks scientificity and systematicness, and easily leads to frequent equipment failures. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for monitoring and managing enterprise agent network equipment to solve the technical problem that some enterprises have backward monitoring means and cannot grasp the operating status of network equipment in real time, resulting in failure to detect and handle failures in time, affecting the normal use of the agent system.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for monitoring and managing enterprise seat network equipment includes the following steps:

[0008] S1. Real-time network status monitoring: Through sensors integrated in network devices or specialized monitoring software, real-time collection of network device operation data, including but not limited to network delay, packet loss rate, bandwidth utilization rate, and connection disconnection rate, to achieve real-time monitoring of network connection status;

[0009] S2. Abnormal detection and early warning: Analyze the real-time collected operation data. Once the abnormal risk assessment of network wind equipment exceeds the preset risk range, the early warning signal is immediately triggered and the management personnel are notified by email, SMS or system notification to ensure that the fault can be detected quickly;

[0010] S3, Fault diagnosis and location: Further fault analysis is performed on the network equipment that triggers the warning to locate the root cause of the problem;

[0011] S4. Automated recovery and backup: For common network failures, preset automated recovery scripts or enable backup network devices to minimize service interruption time; at the same time, regularly back up network device configurations and important data to ensure that the system can be quickly restored in the event of a serious failure.

[0012] As a further technical solution, the method for analyzing the real-time collected operation data is:

[0013] Substitute the collected network delay La, packet loss rate PLR, bandwidth utilization BU, and connection disconnection rate CS into the formula:

[0014] Calculate and obtain the abnormal risk estimation SSC of the current network equipment;

[0015] Wherein, n is the total number of samplings at preset intervals, i is the i-th sampling, x0 is the reference value, ω1, ω2, ω3, and ω4 are weight coefficients.

[0016] As a further technical solution, S2 includes:

[0017] The abnormal risk valuation SSC of the current network device is compared with the preset risk range [SSC min , SSC max ] for comparison;

[0018] If SSC>SSC max , then the current network device is judged to be high risk;

[0019] If SSC∈[SSC min , SSC max ], the current network device is judged to be of medium risk;

[0020] If SSC <SSC max , the current network device is judged to be low risk.

[0021] As a further technical solution, the S2 further includes:

[0022] Immediately trigger warning signals for network devices that are judged to be high-risk;

[0023] For network devices judged to be at medium risk, wait for early warning signals to be triggered;

[0024] Then, by obtaining the cumulative change F of the current network device in multiple historical monitoring cycles SSC , compared with the preset warning value F0;

[0025] If F SSC ≥F0, the current network equipment is judged to have a deterioration trend and an early warning signal is triggered immediately; otherwise, no early warning signal is triggered;

[0026] For network devices judged to be low-risk, no warning signal will be triggered.

[0027] As a further technical solution, the cumulative change F of the current network device under multiple historical monitoring cycles SSC The calculation formula is:

[0028]

[0029] Where N is the number of monitoring cycles, k is the kth monitoring cycle, is the weight factor corresponding to the kth monitoring period, θ1 and θ2 are preset proportional coefficients, and t a ,t b are the start time and end time of any monitoring cycle, SSC max 、SSC min are the maximum and minimum values ​​of abnormal risk valuation within the monitoring period, respectively. is the mean of the abnormal risk valuation within the monitoring period, SSC(t) is the curve of the abnormal risk valuation changing with time within the monitoring period, and SSC0(t) is the reference curve.

[0030] As a further technical solution, S3 includes:

[0031] S31, the time of triggering the warning signal is T j , trace back the preset interval Δt to get the starting time T j-Δt ;

[0032] S32, establishing a coordinate system with time as the horizontal coordinate and specific values ​​of network delay La, packet loss rate PLR, bandwidth utilization rate BU, and connection disconnection rate CS as comprehensive coordinates;

[0033] S33, plot network delay La, packet loss rate PLR, bandwidth utilization BU, and connection disconnection rate CS at T j-Δt ~T j The respective change curves with time La(t), PLR(t), BU(t), CS(t);

[0034] S34, obtaining reference change curves La0(t), PLR0(t), BU0(t), and CS0(t) corresponding to each parameter based on the big data;

[0035] S35. Obtain the area S of the region above La(t) and below La0(t) La, obtain the area S enclosed by the area above PLR(t) and below PLR0(t) PLR , get the area S enclosed by the area below BU(t) and above BU0(t) BU , the area S enclosed by the area above CS(t) and below CS0(t) CS ;

[0036] S36, S La With the preset threshold S Lath Compare, if S La -S Lath ≥0, the positioning network is congested or the network equipment performance is insufficient;

[0037] S PLR With the preset threshold S PLRth Compare, if S PLR -S PLRth ≥0, the network is congested or the network equipment is faulty;

[0038] S BU With the preset threshold S BUth Compare, if S BU -S BUth ≥0, then the network is congested or attacked;

[0039] S CS With the preset threshold S CSth Compare, if S CS -S CSth ≥0, the network device load is too high or the network device is faulty.

[0040] As a further technical solution, the S3 further includes:

[0041] S37, obtain the network traffic G of the network device that triggers the warning, and compare G with the preset network traffic upper limit G th Compare, if G≥G th , it is determined that the current network device is in a network congestion state.

[0042] An enterprise agent network equipment monitoring and management system, the system is used to use the enterprise agent network equipment monitoring and management method.

[0043] Beneficial effects of the present invention:

[0044] (1) The present invention analyzes the operating data collected in real time. Once the abnormal risk assessment of the network risk equipment exceeds the preset risk range, an early warning signal is immediately triggered, and the management personnel are notified by email, text message or system notification to ensure that the fault can be quickly detected. Subsequently, further fault analysis is performed on the network equipment that triggered the early warning to locate the root cause of the problem. The present invention can timely discover and warn when an abnormality occurs in the network equipment, and thus timely repair and maintain the abnormal network equipment and the network equipment predicted to have abnormal tendencies, so as to improve the overall work efficiency of the seats and customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] Figure 1 It is a diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] See also Figure 1 As shown, the present invention is a method for monitoring and managing enterprise seat network equipment, comprising the following steps:

[0049] S1. Real-time network status monitoring: Through sensors integrated in network devices or specialized monitoring software, real-time collection of network device operation data, including but not limited to network delay, packet loss rate, bandwidth utilization rate, and connection disconnection rate, to achieve real-time monitoring of network connection status;

[0050] S2. Abnormal detection and early warning: Analyze the real-time collected operation data. Once the abnormal risk assessment of network wind equipment exceeds the preset risk range, the early warning signal is immediately triggered and the management personnel are notified by email, SMS or system notification to ensure that the fault can be detected quickly;

[0051] S3, Fault diagnosis and location: Further fault analysis is performed on the network equipment that triggered the warning to locate the root cause of the problem;

[0052] S4. Automated recovery and backup: For common network failures, preset automated recovery scripts or enable backup network devices to minimize service interruption time; at the same time, regularly back up network device configurations and important data to ensure that the system can be quickly restored in the event of a serious failure; this step uses existing backup technology, and the specific automated recovery scripts or enabling backup network devices are existing technologies that can achieve the purpose, so they will not be elaborated on here.

[0053] In this embodiment, the operation data of the network equipment is first collected in real time through sensors or special monitoring software integrated in the network equipment, including but not limited to network delay, packet loss rate, bandwidth utilization rate, and connection disconnection rate, so as to realize real-time monitoring of the network connection status; the operation data collected in real time is then analyzed, and once the abnormal risk valuation of the network risk equipment exceeds the preset risk range, an early warning signal is immediately triggered, and the management personnel are notified by email, SMS or system notification to ensure that the fault can be quickly detected, and then the network equipment that triggers the early warning is further analyzed to locate the root cause of the problem, and finally, for common network failures, automatic recovery scripts are preset or backup network equipment is enabled to minimize service interruption time; at the same time, the network equipment configuration and important data are regularly backed up to ensure that the system can be quickly restored in the event of a serious failure; through the above technical solution, it is possible to timely discover and warn when an abnormality occurs in the network equipment, so as to timely repair and maintain the abnormal network equipment and the network equipment predicted to have abnormal tendencies, so as to improve the overall work efficiency of the agents and customer experience.

[0054] The method for analyzing the real-time collected operation data is:

[0055] Substitute the collected network delay La, packet loss rate PLR, bandwidth utilization BU, and connection disconnection rate CS into the formula:

[0056] Calculate and obtain the abnormal risk estimation SSC of the current network equipment;

[0057] Wherein, n is the total number of samplings at preset intervals, i is the ith sampling, x0 is the reference value, ω1, ω2, ω3, and ω4 are weight coefficients determined based on historical data analysis.

[0058] In this embodiment, a method for analyzing real-time collected operation data is provided. Specifically, by substituting multiple operation parameters into the formula The abnormal risk valuation SSC of the current network equipment is calculated. Obviously, (ω1La) i *(ω2PLR) i *(1-ω3BU) i *(ω4CS) iThe larger the network delay La, packet loss rate PLR, and connection disconnection rate CS, the worse the network device operation status is, and the smaller the bandwidth utilization BU is, the lower the resource utilization of the network device is, and therefore the worse the operation status is. Then through (ω1La) i *(ω2PLR) i *(1-ω3BU) i *(ω4CS) i The standard deviation from the corresponding reference value x0 reflects the fluctuation of the real-time operating parameters of the network equipment. Obviously, the greater the fluctuation, the greater the abnormal risk of the current network equipment. Therefore, an assessment model for the abnormal risk of each network device is established through the above technical solution to achieve the purpose of accurately assessing the abnormal risk and improving the efficiency of subsequent early warning response.

[0059] The S2 includes:

[0060] The abnormal risk valuation SSC of the current network device is compared with the preset risk range [SSC min , SSC max ] for comparison;

[0061] If SSC>SSC max , then the current network device is judged to be high risk;

[0062] If SSC∈[SSC min , SSC max ], the current network device is judged to be of medium risk;

[0063] If SSC <SSC max , the current network device is judged to be low risk.

[0064] The S2 further includes:

[0065] Immediately trigger warning signals for network devices that are judged to be high-risk;

[0066] For network devices judged to be at medium risk, wait for early warning signals to be triggered;

[0067] Then, by obtaining the cumulative change F of the current network device in multiple historical monitoring cycles SSC , compared with the preset warning value F0;

[0068] If F SSC ≥F0, the current network equipment is judged to have a deterioration trend and an early warning signal is triggered immediately; otherwise, no early warning signal is triggered;

[0069] For network devices judged to be low-risk, no warning signal will be triggered.

[0070] In this embodiment, the above process realizes a clear and accurate classification of network equipment risks, provides a priority for timely maintenance after the subsequent early warning signal is triggered, thereby minimizing the impact on the work of the seat; at the same time, the medium-risk network equipment is classified by the cumulative change F under multiple historical monitoring cycles. SSC , predict its subsequent status, and issue a timely warning if a deterioration trend occurs, to prevent the expansion of losses caused by the failure to timely troubleshoot potential failures of network equipment.

[0071] The cumulative change F of the current network device in multiple historical monitoring cycles SSC The calculation formula is:

[0072]

[0073] Where N is the number of monitoring cycles, k is the kth monitoring cycle, is the weight factor corresponding to the kth monitoring period, determined based on experimental data analysis, θ1 and θ2 are preset proportional coefficients, formulated based on historical data and experimental data, t a ,t b are the start time and end time of any monitoring cycle, SSC max 、SSC min are the maximum and minimum values ​​of abnormal risk valuation within the monitoring period, respectively. is the mean of the abnormal risk valuation within the monitoring period, SSC(t) is the curve of the abnormal risk valuation changing with time within the monitoring period, and SSC0(t) is the reference curve.

[0074] In this embodiment, the calculation F SSC The specific process is expressed as

[0075] Obviously, if the deviation of |SSC(t)-SSC0(t)| in each monitoring cycle is larger, it means that the stability of the network device is poor. That is, the difference between the median and mean of the abnormal risk assessment value. The larger the value is, the greater the fluctuation of the abnormal risk valuation of the network equipment is. Therefore, the comprehensive analysis can more accurately reflect the cumulative changes of the network equipment, so as to achieve accurate prediction of subsequent development trends and improve the prediction accuracy.

[0076] The S3 includes:

[0077] S31, the time of triggering the warning signal is T j , trace back the preset interval Δt to get the starting time T j-Δt ;

[0078] S32, establishing a coordinate system with time as the horizontal coordinate and specific values ​​of network delay La, packet loss rate PLR, bandwidth utilization rate BU, and connection disconnection rate CS as comprehensive coordinates;

[0079] S33, plot network delay La, packet loss rate PLR, bandwidth utilization BU, and connection disconnection rate CS at T j-Δt ~T j The respective change curves with time La(t), PLR(t), BU(t), CS(t);

[0080] S34, obtaining reference change curves La0(t), PLR0(t), BU0(t), and CS0(t) corresponding to each parameter based on the big data;

[0081] S35. Obtain the area S of the region above La(t) and below La0(t) La , obtain the area S enclosed by the area above PLR(t) and below PLR0(t) PLR , get the area S enclosed by the area below BU(t) and above BU0(t) BU , the area S enclosed by the area above CS(t) and below CS0(t) CS ;

[0082] S36, S La With the preset threshold S Lath Compare, if S La -S Lath ≥0, the positioning network is congested or the network equipment performance is insufficient;

[0083] S PLR With the preset threshold S PLRth Compare, if S PLR -S PLRth ≥0, the network is congested or the network equipment is faulty;

[0084] S BU With the preset threshold S BUth Compare, if S BU -S BUth ≥0, then the network is congested or attacked;

[0085] S CS With the preset threshold S CSth Compare, if S CS -S CSth ≥0, the network device load is too high or the network device is faulty.

[0086] The S3 further includes:

[0087] S37, obtain the network traffic G of the network device that triggers the warning, and compare G with the preset network traffic upper limit G th Compare, if G≥G th , it is determined that the current network device is in a network congestion state.

[0088] In this embodiment, the area enclosed between the actual change curve and the reference change curve of each parameter in the coordinate system is calculated. Since the network delay La, packet loss rate PLR, and connection disconnection rate CS exceed the reference curve, it means that the operating state of the network device is worse. Therefore, the larger the area enclosed by the bottom of the three parameter change curves and the bottom of each reference curve, the greater the problem of the parameter, that is, the higher the abnormality risk. The bandwidth utilization rate BU is lower than the reference curve, which means that the resource utilization rate of the network device is low, that is, the operating state is worse. Therefore, the larger the area enclosed by the top of the parameter and the bottom of the reference curve, the greater the problem of the parameter, that is, the higher the abnormality risk. Then, the root cause of the problem is quickly determined according to the subsequent comparison process, and the network congestion problem is quickly determined or eliminated through real-time comparison of network traffic, so as to comprehensively realize the rapid and accurate positioning of the problem.

[0089] An enterprise agent network equipment monitoring and management system, the system is used to use the enterprise agent network equipment monitoring and management method.

[0090] It should be noted that the calculation formulas and various parameters involved in the calculation in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0091] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for monitoring and managing enterprise seat network equipment, characterized in that: The steps include: S1. Real-time network status monitoring: Through sensors integrated in network devices or specialized monitoring software, the operation data of network devices are collected in real time, including but not limited to network delay, packet loss rate, bandwidth utilization rate, and connection disconnection rate, so as to achieve real-time monitoring of network connection status; S2. Abnormal detection and early warning: Analyze the real-time collected operation data. Once the abnormal risk assessment of network wind equipment exceeds the preset risk range, the early warning signal is immediately triggered and the management personnel are notified by email, SMS or system notification; S3, Fault diagnosis and location: Further fault analysis is performed on the network equipment that triggers the warning to locate the root cause of the problem; S4. Automated recovery and backup: For common network failures, preset automated recovery scripts or enable backup network devices; at the same time, regularly back up network device configurations and important data.

2. The enterprise agent network equipment monitoring and management method according to claim 1 is characterized in that: The method for analyzing the real-time collected operation data is: Substitute the collected network delay La, packet loss rate PLR, bandwidth utilization BU, and connection disconnection rate CS into the formula: Calculate and obtain the abnormal risk estimation SSC of the current network equipment; Wherein, n is the total number of samplings at preset intervals, i is the i-th sampling, x0 is the reference value, ω1, ω2, ω3, and ω4 are weight coefficients.

3. The enterprise agent network equipment monitoring and management method according to claim 2 is characterized in that: The S2 includes: The abnormal risk valuation SSC of the current network device is compared with the preset risk range [SSC min , SSC max ] for comparison; If SSC>SSC max , then the current network device is judged to be high risk; If SSC∈[SSC min , SSC max ], the current network device is judged to be of medium risk; If SSC <SSC max , the current network device is judged to be low risk.

4. The enterprise agent network equipment monitoring and management method according to claim 3 is characterized in that: The S2 further includes: Immediately trigger warning signals for network devices that are judged to be high-risk; For network devices judged to be at medium risk, wait for early warning signals to be triggered; Then, by obtaining the cumulative change F of the current network device in multiple historical monitoring cycles SSC , compared with the preset warning value F0; If F SSC ≥F0, the current network equipment is judged to have a deterioration trend and an early warning signal is triggered immediately; otherwise, no early warning signal is triggered; For network devices judged to be low-risk, no warning signal will be triggered.

5. The enterprise agent network equipment monitoring and management method according to claim 4 is characterized in that: The cumulative change F of the current network device in multiple historical monitoring cycles SSC The calculation formula is: Where N is the number of monitoring cycles, k is the kth monitoring cycle, is the weight factor corresponding to the kth monitoring period, θ1 and θ2 are preset proportional coefficients, and t a ,t b are the start time and end time of any monitoring cycle, SSC max 、SSC min are the maximum and minimum values ​​of abnormal risk valuation within the monitoring period, respectively. is the mean of the abnormal risk valuation within the monitoring period, SSC(t) is the curve of the abnormal risk valuation changing with time within the monitoring period, and SSC0(t) is the reference curve.

6. The enterprise agent network equipment monitoring and management method according to claim 1 is characterized in that: The S3 includes: S31, the time of triggering the warning signal is T j , trace back the preset interval Δt to get the starting time T j-Δt ; S32, establishing a coordinate system with time as the horizontal coordinate and specific values ​​of network delay La, packet loss rate PLR, bandwidth utilization rate BU, and connection disconnection rate CS as comprehensive coordinates; S33, plot network delay La, packet loss rate PLR, bandwidth utilization BU, and connection disconnection rate CS at T j-Δt ~T j The respective change curves with time La(t), PLR(t), BU(t), CS(t); S34, obtaining reference change curves La0(t), PLR0(t), BU0(t), and CS0(t) corresponding to each parameter based on the big data; S35. Obtain the area S of the region above La(t) and below La0(t) La , obtain the area S enclosed by the area above PLR(t) and below PLR0(t) PLR , get the area S enclosed by the area below BU(t) and above BU0(t) BU , the area S enclosed by the area above CS(t) and below CS0(t) CS ; S36, S La With the preset threshold S Lath Compare, if S La -S Lath ≥0, the positioning network is congested or the network equipment performance is insufficient; S PLR With the preset threshold S PLRth Compare, if S PLR -S PLRth ≥0, the network is congested or the network equipment is faulty; S BU With the preset threshold S BUth Compare, if S BU -S BUth ≥0, then the network is congested or attacked; S CS With the preset threshold S CSth Compare, if S CS -S CSth ≥0, the network device load is too high or the network device is faulty.

7. The enterprise agent network equipment monitoring and management method according to claim 6 is characterized in that: The S3 further includes: S37, obtain the network traffic G of the network device that triggers the warning, and compare G with the preset network traffic upper limit G th Compare, if G≥G th , it is determined that the current network device is in a network congestion state.

8. An enterprise agent network equipment monitoring and management system, characterized in that: The system is used to use the enterprise agent network equipment monitoring and management method described in any one of claims 1-7.

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