CN116436815A - Processing method for monitoring network equipment - Google Patents

Abstract

The embodiment of the invention relates to a processing method for monitoring network equipment, which comprises the following steps: the monitoring server periodically sends a first device query instruction to each first network device; the instruction sending time is recorded as the first server time; receiving first equipment response data returned by each first network equipment; and recording the data receiving time as a second server time; the first record is formed by the first server time, the second server time and the response data of the first equipment and is stored in a first record list; performing instruction overtime state analysis according to the latest first record in the first record list; analyzing the running state of the equipment according to the latest first record in the first record list; and predicting the running state of the equipment according to all the first records in the last appointed time period in the first record list. The invention can make up for the technical defects of insufficient monitoring range, single monitoring content and the like in the conventional scheme.

Description

Processing method for monitoring network equipment

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a processing method for monitoring a network device.

Background

With the development of informatization construction, the application of information networks has been advanced into various industries. In order to ensure that each network can stably and effectively operate, a set of corresponding operation and maintenance monitoring schemes are conventionally configured for each network. Currently, conventional monitoring schemes only monitor traffic or load bottlenecks of network-centric devices (such as disk arrays, firewalls, servers, and databases), do not monitor any network devices across the network (such as disk arrays, routers, switches, firewalls, servers, and databases), and do not collect all operational status of the monitored devices.

Disclosure of Invention

The invention aims at overcoming the defects of the prior art and provides a processing method, electronic equipment and a computer readable storage medium for monitoring network equipment, wherein a monitoring server periodically collects time, CPU (central processing unit) utilization rate, memory utilization rate, QOS parameter sets and inflow/outflow data flow of any network equipment, carries out real-time instruction timeout and running state analysis according to the latest collection result, and carries out state prediction according to the historical collection result by using an artificial intelligent model. The invention can make up for the technical defects of insufficient monitoring range, single monitoring content and the like in the conventional scheme, can monitor a plurality of running states of any network equipment in real time, and can predict future state trend of any network equipment.

To achieve the above object, a first aspect of an embodiment of the present invention provides a processing method for monitoring a network device, where the method includes:

the monitoring server periodically sends a first device query instruction to each first network device; recording the instruction sending time of the first equipment query instruction as corresponding first server time; receiving first equipment response data returned by each first network equipment; and recording the data receiving time of the first equipment response data as the corresponding second server time; the first server time, the second server time and the first equipment response data form corresponding first records which are stored in a corresponding first record list;

performing instruction overtime state analysis according to the latest first record in the first record list to generate a corresponding first analysis result and displaying the first analysis result;

analyzing the running state of the equipment according to the latest first record in the first record list to generate a corresponding second analysis result and displaying the second analysis result;

and predicting the running state of the equipment according to all the first records in the latest appointed time period in the first record list to generate a corresponding first prediction result and displaying the first prediction result.

Preferably, the monitoring server sends an instruction to each of the first network devices based on SNMP protocol and receives response data of each of the first network devices.

Preferably, the first record list includes a plurality of the first records; the first record includes the first server time, the second server time, and the first device response data; the first device response data comprises a first device IP address, a first device name, a first device type, a first device time, a second device time, a first device CPU utilization rate, a first device memory utilization rate, a first device QOS parameter set, a first device inflow flow and a first device outflow flow; the first equipment type comprises a disk array, a router, a switch, a firewall, a server and a database; the first device QOS parameter set includes a plurality of first QOS parameters; the first QOS parameter includes a first parameter name and a first parameter value.

Preferably, the method further comprises:

when the first network equipment receives the first equipment inquiry instruction sent by the monitoring server, recording the instruction receiving time as the corresponding first equipment time; the preset equipment IP address, equipment name and equipment type are obtained from the local area as the corresponding first equipment IP address, first equipment name and first equipment type; counting the current CPU utilization rate of the equipment to generate a corresponding CPU utilization rate of the first equipment; and the current memory utilization rate of the equipment is counted to generate the corresponding memory utilization rate of the first equipment; evaluating QOS parameters of the equipment to generate a corresponding QOS parameter set of the first equipment; the inflow and outflow data flow accumulated by the equipment on the same day is counted to obtain the corresponding inflow flow of the first equipment and the corresponding outflow flow of the first equipment; the first equipment IP address, the first equipment name, the first equipment type, the first equipment time, the first equipment CPU utilization rate, the first equipment memory utilization rate, the first equipment QOS parameter set, the first equipment inflow flow and the first equipment outflow flow are obtained to form corresponding first acquired data; acquiring primary equipment system time as the corresponding second equipment time after the first acquired data are acquired; and the second equipment time and the first acquired data form corresponding first equipment response data to be returned to the monitoring server.

Preferably, the step of analyzing the overtime state of the instruction according to the latest first record in the first record list to generate and display a corresponding first analysis result specifically includes:

extracting the latest first record in the first record list to serve as a corresponding current record; extracting the first server time, the second server time, the first equipment time and the second equipment time which are recorded currently as corresponding first time, second time, third time and fourth time;

calculating the absolute values of the time differences of the first time and the second time to generate corresponding first time differences; calculating the absolute values of the time differences of the first time and the third time to generate a corresponding second time difference; calculating the absolute values of the time differences of the second time and the fourth time to generate a corresponding third time difference;

identifying whether the first time difference exceeds a preset first time difference threshold; if yes, setting the corresponding first information as preset instruction sending and feedback total time-out information; if not, setting the corresponding first information to be empty;

identifying whether the second time difference exceeds a preset second time difference threshold; if yes, setting the corresponding second information as preset command sending duration timeout information; if not, setting the corresponding second information to be empty;

Identifying whether the third time difference exceeds a preset third time difference threshold; if yes, setting the corresponding third information as preset instruction feedback receiving time timeout information; if not, setting the corresponding third information to be empty;

identifying whether the first, second and third information are all empty; if yes, setting the corresponding first analysis information as a preset instruction to send and feed back normal information; if not, the first analysis information corresponding to the first, second and third information is formed;

and the first analysis result corresponding to the first equipment IP address, the first equipment name, the first equipment type and the obtained first analysis information are formed and displayed.

Preferably, the analyzing the running state of the device according to the latest first record in the first record list to generate and display a corresponding second analysis result specifically includes:

extracting the first record with the latest time from the first record list as a corresponding current record; extracting the first device IP address, the first device name, the first device type, the first device CPU usage rate, the first device memory usage rate, the first device QOS parameter set, the first device inflow rate and the first device outflow rate recorded currently as corresponding current device IP addresses, current device names, current device types, current device CPU usage rates, current device memory usage rates, current device QOS parameter sets, current device inflow rates and current device outflow rates;

Identifying whether the CPU utilization rate of the current equipment exceeds a preset CPU utilization rate warning threshold; if yes, setting the corresponding fourth information as preset excessive alarm information of equipment CPU resource occupation; if not, setting the corresponding fourth information to be null;

identifying whether the current equipment memory usage exceeds a preset memory usage warning threshold; if yes, setting the corresponding fifth information as preset excessive alarm information of the memory resource occupation of the equipment; if not, setting the corresponding fifth information to be null;

identifying whether the first parameter values of all the first QOS parameters of the current device QOS parameter set meet respective corresponding first parameter threshold ranges; if yes, setting the corresponding sixth information to be null; if not, extracting the first parameter names of the first QOS parameters, of which the first parameter values do not meet the corresponding first parameter threshold range, in the current device QOS parameter set to form a corresponding first parameter name sequence, and forming corresponding sixth information by preset device QOS parameter abnormality alarm information and the first parameter name sequence;

Identifying whether the current equipment inflow flow exceeds a preset equipment access flow threshold; if yes, setting the corresponding seventh information as preset equipment access load overload alarm information; if not, setting the corresponding seventh information to be empty;

identifying whether the current equipment type is a disk array, a server or a database; if the current equipment type is a disk array, a server or a database, identifying whether the current equipment outflow exceeds a preset equipment sending flow threshold, if so, setting corresponding eighth information as preset equipment large data volume access alarm information, and if not, setting the corresponding eighth information as null; if the current equipment type is not a disk array, a server or a database, setting the corresponding eighth information to be null;

identifying whether the current equipment type is a switch; if the current equipment type is a switch, calculating the flow ratio of the current equipment outflow flow to the current equipment inflow flow to generate a corresponding first forwarding proportion= (current equipment outflow flow/current equipment inflow flow) = 100%, identifying whether the first forwarding proportion is lower than a preset forwarding proportion warning threshold value, if so, setting corresponding ninth information as preset equipment forwarding efficiency low warning information, and if not, setting the corresponding ninth information as null; if the current equipment type is not the switch, setting the corresponding ninth information to be null;

Identifying whether the fourth, fifth, sixth, seventh, eighth and ninth information is all empty; if yes, setting the corresponding second analysis information as preset equipment running state normal information; if not, the fourth, fifth, sixth, seventh, eighth and ninth information forms corresponding second analysis information;

and the corresponding second analysis result is formed by the current equipment IP address, the current equipment name, the current equipment type and the obtained second analysis information and displayed.

Preferably, the predicting the running state of the device according to all the first records in the last specified period in the first record list to generate and display a corresponding first prediction result specifically includes:

extracting the first equipment IP address, the first equipment name and the first equipment type of any first record in the first record list as a corresponding current equipment IP address, a corresponding current equipment name and a corresponding current equipment type;

extracting all the first records in the first record list within the latest appointed time period, and sequencing the first records in time sequence to generate a corresponding first record sequence;

Extracting the first server time, the second server time, the first device time, the second device time, the first device CPU usage rate, the first device memory usage rate, the first device QOS parameter set, the first device inflow flow and the first device outflow flow of each first record in the first record sequence to form corresponding first data vectors; and forming a corresponding first data tensor by all the obtained first data vectors;

inputting the first data tensor into a preset running state classification prediction model to perform running state classification prediction processing to obtain a corresponding first prediction vector; the first prediction vector includes a plurality of first classification probabilities; each first classification probability corresponds to a preset classification type;

forming corresponding first-type prediction information by each first classification probability and the corresponding classification type; and the corresponding first prediction results are formed and displayed by all the obtained first type of prediction information.

A second aspect of an embodiment of the present invention provides an electronic device, including: memory, processor, and transceiver;

The processor is coupled to the memory, and reads and executes the instructions in the memory to implement the method of the first aspect;

the transceiver is coupled to the processor and is controlled by the processor to transmit and receive messages.

A third aspect of the embodiments of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the method of the first aspect.

The embodiment of the invention provides a processing method for monitoring network equipment, electronic equipment and a computer readable storage medium, wherein a monitoring server periodically collects time, CPU (Central processing Unit) utilization rate, memory utilization rate, QOS parameter sets and inflow/outflow data flow of any network equipment, carries out real-time instruction timeout and running state analysis according to the latest collection result, and carries out state prediction according to the historical collection result by using an artificial intelligent model. The invention can monitor a plurality of running states of any network equipment in real time and predict the future state trend of any network equipment, thereby effectively compensating the technical defects of insufficient monitoring range, single monitoring content and the like in the conventional scheme.

Drawings

Fig. 1 is a schematic diagram of a processing method for monitoring a network device according to a first embodiment of the present invention;

fig. 2 is a schematic structural diagram of an electronic device according to a second embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Fig. 1 is a schematic diagram of a processing method for monitoring network equipment according to a first embodiment of the present invention, where as shown in fig. 1, the method mainly includes the following steps:

step 1, a monitoring server periodically sends a first device query instruction to each first network device; the instruction sending time of the first equipment query instruction is recorded as corresponding first server time; receiving first equipment response data returned by each first network equipment; and recording the data receiving time of the first equipment response data as the corresponding second server time; the first server time, the second server time and the first equipment response data form corresponding first records which are stored in a corresponding first record list;

The monitoring server sends instructions to each first network device based on an SNMP protocol and receives response data of each first network device;

the first record list comprises a plurality of first records; the first record includes a first server time, a second server time, and first device response data; the first device response data includes a first device IP address, a first device name, a first device type, a first device time, a second device time, a first device CPU utilization, a first device memory utilization, a first device QOS parameter set, a first device ingress traffic, and a first device egress traffic; the first equipment type comprises a disk array, a router, a switch, a firewall, a server and a database; the first device QOS parameter set includes a plurality of first QOS parameters; the first QOS parameter includes a first parameter name and a first parameter value; here, the first parameter names include at least parameter names capable of reflecting information of various message types (voice, video, fax, data, etc.), such as delay parameters of voice (video, fax, data, etc.), jitter parameters of voice (video, fax, data, etc.), packet loss rate parameters of voice (video, fax, data, etc.), packet error rate parameters of voice (video, fax, data, etc.), and the like.

It should be noted that, when the first network device in the embodiment of the present invention receives the first device query instruction sent by the monitoring server, the corresponding processing steps are as follows:

recording the instruction receiving time as corresponding first equipment time; the method comprises the steps that a preset equipment IP address, equipment name and equipment type are obtained locally and serve as a corresponding first equipment IP address, first equipment name and first equipment type; counting the current CPU utilization rate of the equipment to generate a corresponding first equipment CPU utilization rate; and the current memory utilization rate of the equipment is counted to generate a corresponding first equipment memory utilization rate; the QOS parameters of the equipment are evaluated to generate a corresponding first equipment QOS parameter set; the inflow and outflow data flow accumulated by the equipment on the same day is counted to obtain corresponding first equipment inflow flow and first equipment outflow flow; the method comprises the steps of obtaining a first equipment IP address, a first equipment name, a first equipment type, first equipment time, a first equipment CPU (central processing unit) utilization rate, a first equipment memory utilization rate, a first equipment QOS parameter set, a first equipment inflow flow and a first equipment outflow flow to form corresponding first acquisition data; acquiring primary equipment system time as corresponding second equipment time after acquiring the first acquired data; and the second equipment time and the first acquired data form corresponding first equipment response data which are sent back to the monitoring server.

Step 2, analyzing the overtime state of the instruction according to the latest first record in the first record list to generate a corresponding first analysis result and displaying the first analysis result;

the method specifically comprises the following steps: step 21, extracting the latest first record in the first record list as the corresponding current record; extracting the first server time, the second server time, the first equipment time and the second equipment time which are recorded currently as corresponding first time, second time, third time and fourth time;

step 22, calculating the absolute value of the time difference between the first time and the second time to generate a corresponding first time difference; calculating the absolute values of the time differences of the first time and the third time to generate a corresponding second time difference; calculating the absolute values of the time differences of the second time and the fourth time to generate a corresponding third time difference;

step 23, identifying whether the first time difference exceeds a preset first time difference threshold; if yes, setting the corresponding first information as preset instruction sending and feedback total time-out information; if not, setting the corresponding first information to be empty;

here, the first time difference threshold is a preset time difference threshold constant greater than zero;

Step 24, identifying whether the second time difference exceeds a preset second time difference threshold; if yes, setting the corresponding second information as preset command sending duration timeout information; if not, setting the corresponding second information to be empty;

here, the second time difference threshold is a preset time difference threshold constant greater than zero;

step 25, identifying whether the third time difference exceeds a preset third time difference threshold; if yes, setting the corresponding third information as preset instruction feedback receiving time timeout information; if not, setting the corresponding third information to be null;

here, the third time difference threshold is a preset time difference threshold constant greater than zero;

step 26, identifying whether the first, second and third information are all empty; if yes, setting the corresponding first analysis information as a preset instruction to send and feed back normal information; if not, the first, second and third information form corresponding first analysis information;

and step 27, forming and displaying a corresponding first analysis result by the first equipment IP address, the first equipment name, the first equipment type and the obtained first analysis information which are recorded currently.

Step 3, analyzing the running state of the equipment according to the latest first record in the first record list to generate a corresponding second analysis result and displaying the second analysis result;

the method specifically comprises the following steps: step 31, extracting the first record with the latest time in the first record list as the corresponding current record; extracting a first equipment IP address, a first equipment name, a first equipment type, a first equipment CPU utilization rate, a first equipment memory utilization rate, a first equipment QOS parameter set, a first equipment inflow flow and a first equipment outflow flow which are recorded currently as corresponding current equipment IP addresses, current equipment names, current equipment types, current equipment CPU utilization rates, current equipment memory utilization rates, current equipment QOS parameter sets, current equipment inflow flow and current equipment outflow flow;

step 32, identifying whether the CPU usage rate of the current device exceeds a preset CPU usage rate warning threshold; if yes, setting the corresponding fourth information as preset excessive alarm information of equipment CPU resource occupation; if not, setting the corresponding fourth information to be null;

here, the CPU usage alert threshold is a proportionality threshold constant with a preset value range between [0,1 ];

Step 33, identifying whether the memory usage of the current device exceeds a preset memory usage alert threshold; if yes, setting the corresponding fifth information as preset excessive alarm information of the memory resource occupation of the equipment; if not, setting the corresponding fifth information to be null;

here, the memory usage alert threshold is a preset proportional threshold constant with a value range between [0,1 ];

step 34, identifying whether the first parameter values of all the first QOS parameters of the QOS parameter set of the current device meet the respective corresponding first parameter threshold ranges; if yes, setting the corresponding sixth information to be null; if not, extracting first parameter names of first QOS parameters of which the first parameter values do not meet the corresponding first parameter threshold range from the current device QOS parameter set to form a corresponding first parameter name sequence, and forming corresponding sixth information by preset device QOS parameter abnormality alarm information and the first parameter name sequence;

here, a QOS parameter threshold range set is preset on the monitoring server according to the embodiment of the present invention, where the QOS parameter threshold range set is composed of a plurality of first parameter threshold ranges, and each first parameter threshold range corresponds to a first QOS parameter;

Step 35, identifying whether the inflow flow of the current device exceeds a preset device access flow threshold; if yes, setting the corresponding seventh information as preset equipment access load overload alarm information; if not, setting the corresponding seventh information to be null;

here, the device access flow threshold is a preset flow threshold constant;

step 36, identifying whether the current device type is a disk array, a server or a database; if the current equipment type is a disk array, a server or a database, identifying whether the current equipment outflow exceeds a preset equipment transmission flow threshold, if so, setting corresponding eighth information as preset equipment large data volume access alarm information, and if not, setting the corresponding eighth information as null; if the current equipment type is not a disk array, a server or a database, setting corresponding eighth information to be null;

here, the device sends the traffic threshold as a preset traffic threshold constant;

step 37, identifying whether the current device type is a switch; if the current equipment type is a switch, calculating the flow ratio of the current equipment outflow flow to the current equipment inflow flow to generate a corresponding first forwarding proportion= (current equipment outflow flow/current equipment inflow flow) = 100%, identifying whether the first forwarding proportion is lower than a preset forwarding proportion warning threshold value, if so, setting corresponding ninth information as preset equipment forwarding efficiency too low warning information, and if not, setting corresponding ninth information as null; if the current equipment type is not the switch, setting the corresponding ninth information to be null;

The forwarding proportion warning threshold is a preset proportion threshold constant with the value range of 0 and 1;

step 38, identifying whether the fourth, fifth, sixth, seventh, eighth and ninth information is all empty; if yes, setting the corresponding second analysis information as preset equipment running state normal information; if not, the fourth, fifth, sixth, seventh, eighth and ninth information form corresponding second analysis information;

and step 39, forming and displaying a corresponding second analysis result by the IP address of the current equipment, the name of the current equipment, the type of the current equipment and the obtained second analysis information.

Step 4, predicting the running state of the equipment according to all the first records in the latest appointed time period in the first record list to generate a corresponding first prediction result and displaying the first prediction result;

the method specifically comprises the following steps: step 41, extracting a first device IP address, a first device name, and a first device type of any first record in the first record list as a corresponding current device IP address, a current device name, and a current device type;

step 42, extracting all the first records in the first record list within the latest appointed time period, and sorting the first records in time sequence to generate a corresponding first record sequence;

Step 43, extracting the first server time, the second server time, the first device time, the second device time, the first device CPU usage, the first device memory usage, the first device QOS parameter set, the first device inflow traffic and the first device outflow traffic of each first record in the first record sequence to form a corresponding first data vector; and forming corresponding first data tensors by all the obtained first data vectors;

step 44, inputting the first data tensor into a preset running state classification prediction model to perform running state classification prediction processing to obtain a corresponding first prediction vector; wherein the first predictive vector includes a plurality of first classification probabilities; each first classification probability corresponds to a preset classification type;

here, the running state classification prediction model of the embodiment of the present invention may predict risk types that may occur in a current network device at a future time based on a section of latest historical data of the device, that is, a first record sequence, and takes each risk type as a classification type and assigns a corresponding possible probability, that is, a first classification probability, to each classification type when the prediction is output;

It should be noted that, the running state classification prediction model in the embodiment of the present invention is an artificial intelligent prediction model implemented based on a classifier model, and the specific implementation manner of the classifier model in the embodiment of the present invention is various, where one is implemented based on an SVM model structure, one is implemented based on an MLP network structure, one is implemented based on a random forest model structure, and also can be implemented based on other neural networks or algorithm models capable of implementing classification prediction; before the running state classification prediction model is used, the model needs to be trained based on enough historical data-risk type labels;

it should also be noted that, the risk types predictable by the running risk classification prediction model according to the embodiment of the present invention include: delay risk, packet loss risk, blocking risk, network disconnection risk, excessive resource loss risk and downtime risk; if the trend of the time difference absolute value between the first server time and the second server time over time is an increasing trend, or the trend of the time difference absolute value between the first server time and the first device time over time is an increasing trend, or the probability of the predicted delay risk is increased if the trend of the time difference absolute value between the second server time and the second device time over time is an increasing trend, in the first data tensor; if the trend of the absolute value of the time difference between the first server time and the first equipment time along with the time is an increasing trend, the predicted probability of the packet loss risk is increased; if the trend of the absolute value of the time difference between the first equipment time and the second equipment time along with the time is an increasing trend, the predicted probability of blocking risk is increased; if the number of times that the absolute value of the time difference between the first server time and the second server time exceeds the preset maximum time delay threshold is larger, the predicted probability of the network disconnection risk is larger; if the trend of the voice (video, fax, data, etc.) delay parameter of the first device QOS parameter set over time is an increasing trend, or if the voice (video, fax, data, etc.) jitter parameter is an increasing trend, the probability of the predicted delay risk increases, if the trend of the voice (video, fax, data, etc.) packet loss rate parameter of the first device QOS parameter set over time is an increasing trend, the probability of the predicted packet loss risk increases, or if the trend of the voice (video, fax, data, etc.) packet error rate parameter over time is an increasing trend, the probability of the predicted packet loss risk, resource excessive loss risk, downtime risk increases; if the trend of the CPU utilization rate of the first device over time is an increasing trend or the trend of the memory utilization rate of the first device over time is a decreasing trend, the predicted probability of excessive resource loss risk and downtime risk is increased;

Step 45, forming corresponding first-type prediction information by each first classification probability and the corresponding classification type; and all the obtained first type of prediction information forms a corresponding first prediction result and is displayed.

Fig. 2 is a schematic structural diagram of an electronic device according to a second embodiment of the present invention. The electronic device may be the aforementioned terminal device or server, or may be a terminal device or server connected to the aforementioned terminal device or server for implementing the method of the embodiment of the present invention. As shown in fig. 2, the electronic device may include: a processor 301 (e.g., a CPU), a memory 302, a transceiver 303; the transceiver 303 is coupled to the processor 301, and the processor 301 controls the transceiving actions of the transceiver 303. The memory 302 may store various instructions for performing the various processing functions and implementing the processing steps described in the method embodiments previously described. Preferably, the electronic device according to the embodiment of the present invention further includes: a power supply 304, a system bus 305, and a communication port 306. The system bus 305 is used to implement communication connections between the elements. The communication port 306 is used for connection communication between the electronic device and other peripheral devices.

The system bus 305 referred to in fig. 2 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The system bus may be classified into an address bus, a data bus, a control bus, and the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus. The communication interface is used to enable communication between the database access apparatus and other devices (e.g., clients, read-write libraries, and read-only libraries). The Memory may comprise random access Memory (Random Access Memory, RAM) and may also include Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory.

The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a graphics processor (Graphics Processing Unit, GPU), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

It should be noted that, the embodiments of the present invention also provide a computer readable storage medium, where instructions are stored, when the computer readable storage medium runs on a computer, to cause the computer to perform the method and the process provided in the above embodiments.

The embodiment of the invention also provides a chip for running the instructions, and the chip is used for executing the processing steps described in the embodiment of the method.

The embodiment of the invention provides a processing method for monitoring network equipment, electronic equipment and a computer readable storage medium, wherein a monitoring server periodically collects time, CPU (Central processing Unit) utilization rate, memory utilization rate, QOS parameter sets and inflow/outflow data flow of any network equipment, carries out real-time instruction timeout and running state analysis according to the latest collection result, and carries out state prediction according to the historical collection result by using an artificial intelligent model. The invention can monitor a plurality of running states of any network equipment in real time and predict the future state trend of any network equipment, thereby effectively compensating the technical defects of insufficient monitoring range, single monitoring content and the like in the conventional scheme.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of function in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (9)

1. A method of processing for monitoring a network device, the method comprising:

the monitoring server periodically sends a first device query instruction to each first network device; recording the instruction sending time of the first equipment query instruction as corresponding first server time; receiving first equipment response data returned by each first network equipment; and recording the data receiving time of the first equipment response data as the corresponding second server time; the first server time, the second server time and the first equipment response data form corresponding first records which are stored in a corresponding first record list;

performing instruction overtime state analysis according to the latest first record in the first record list to generate a corresponding first analysis result and displaying the first analysis result;

analyzing the running state of the equipment according to the latest first record in the first record list to generate a corresponding second analysis result and displaying the second analysis result;

and predicting the running state of the equipment according to all the first records in the latest appointed time period in the first record list to generate a corresponding first prediction result and displaying the first prediction result.

2. The method of claim 1, wherein the step of monitoring the network device,

the monitoring server sends instructions to each first network device based on an SNMP protocol and receives response data of each first network device.

3. The method of claim 1, wherein the step of monitoring the network device,

the first record list comprises a plurality of first records; the first record includes the first server time, the second server time, and the first device response data; the first device response data comprises a first device IP address, a first device name, a first device type, a first device time, a second device time, a first device CPU utilization rate, a first device memory utilization rate, a first device QOS parameter set, a first device inflow flow and a first device outflow flow; the first equipment type comprises a disk array, a router, a switch, a firewall, a server and a database; the first device QOS parameter set includes a plurality of first QOS parameters; the first QOS parameter includes a first parameter name and a first parameter value.

4. A method of processing for monitoring a network device according to claim 3, further comprising:

When the first network equipment receives the first equipment inquiry instruction sent by the monitoring server, recording the instruction receiving time as the corresponding first equipment time; the preset equipment IP address, equipment name and equipment type are obtained from the local area as the corresponding first equipment IP address, first equipment name and first equipment type; counting the current CPU utilization rate of the equipment to generate a corresponding CPU utilization rate of the first equipment; and the current memory utilization rate of the equipment is counted to generate the corresponding memory utilization rate of the first equipment; evaluating QOS parameters of the equipment to generate a corresponding QOS parameter set of the first equipment; the inflow and outflow data flow accumulated by the equipment on the same day is counted to obtain the corresponding inflow flow of the first equipment and the corresponding outflow flow of the first equipment; the first equipment IP address, the first equipment name, the first equipment type, the first equipment time, the first equipment CPU utilization rate, the first equipment memory utilization rate, the first equipment QOS parameter set, the first equipment inflow flow and the first equipment outflow flow are obtained to form corresponding first acquired data; acquiring primary equipment system time as the corresponding second equipment time after the first acquired data are acquired; and the second equipment time and the first acquired data form corresponding first equipment response data to be returned to the monitoring server.

5. The method for monitoring network devices according to claim 3, wherein the analyzing the command timeout status according to the latest first record in the first record list generates and displays a corresponding first analysis result, specifically including:

extracting the latest first record in the first record list to serve as a corresponding current record; extracting the first server time, the second server time, the first equipment time and the second equipment time which are recorded currently as corresponding first time, second time, third time and fourth time;

calculating the absolute values of the time differences of the first time and the second time to generate corresponding first time differences; calculating the absolute values of the time differences of the first time and the third time to generate a corresponding second time difference; calculating the absolute values of the time differences of the second time and the fourth time to generate a corresponding third time difference;

identifying whether the first time difference exceeds a preset first time difference threshold; if yes, setting the corresponding first information as preset instruction sending and feedback total time-out information; if not, setting the corresponding first information to be empty;

Identifying whether the second time difference exceeds a preset second time difference threshold; if yes, setting the corresponding second information as preset command sending duration timeout information; if not, setting the corresponding second information to be empty;

identifying whether the third time difference exceeds a preset third time difference threshold; if yes, setting the corresponding third information as preset instruction feedback receiving time timeout information; if not, setting the corresponding third information to be empty;

identifying whether the first, second and third information are all empty; if yes, setting the corresponding first analysis information as a preset instruction to send and feed back normal information; if not, the first analysis information corresponding to the first, second and third information is formed;

and the first analysis result corresponding to the first equipment IP address, the first equipment name, the first equipment type and the obtained first analysis information are formed and displayed.

6. The method for monitoring network devices according to claim 3, wherein the analyzing the running state of the device according to the latest first record in the first record list to generate and display a corresponding second analysis result specifically includes:

Extracting the first record with the latest time from the first record list as a corresponding current record; extracting the first device IP address, the first device name, the first device type, the first device CPU usage rate, the first device memory usage rate, the first device QOS parameter set, the first device inflow rate and the first device outflow rate recorded currently as corresponding current device IP addresses, current device names, current device types, current device CPU usage rates, current device memory usage rates, current device QOS parameter sets, current device inflow rates and current device outflow rates;

identifying whether the CPU utilization rate of the current equipment exceeds a preset CPU utilization rate warning threshold; if yes, setting the corresponding fourth information as preset excessive alarm information of equipment CPU resource occupation; if not, setting the corresponding fourth information to be null;

identifying whether the current equipment memory usage exceeds a preset memory usage warning threshold; if yes, setting the corresponding fifth information as preset excessive alarm information of the memory resource occupation of the equipment; if not, setting the corresponding fifth information to be null;

Identifying whether the first parameter values of all the first QOS parameters of the current device QOS parameter set meet respective corresponding first parameter threshold ranges; if yes, setting the corresponding sixth information to be null; if not, extracting the first parameter names of the first QOS parameters, of which the first parameter values do not meet the corresponding first parameter threshold range, in the current device QOS parameter set to form a corresponding first parameter name sequence, and forming corresponding sixth information by preset device QOS parameter abnormality alarm information and the first parameter name sequence;

identifying whether the current equipment inflow flow exceeds a preset equipment access flow threshold; if yes, setting the corresponding seventh information as preset equipment access load overload alarm information; if not, setting the corresponding seventh information to be empty;

identifying whether the current equipment type is a disk array, a server or a database; if the current equipment type is a disk array, a server or a database, identifying whether the current equipment outflow exceeds a preset equipment sending flow threshold, if so, setting corresponding eighth information as preset equipment large data volume access alarm information, and if not, setting the corresponding eighth information as null; if the current equipment type is not a disk array, a server or a database, setting the corresponding eighth information to be null;

Identifying whether the current equipment type is a switch; if the current equipment type is a switch, calculating the flow ratio of the current equipment outflow flow to the current equipment inflow flow to generate a corresponding first forwarding proportion= (current equipment outflow flow/current equipment inflow flow) = 100%, identifying whether the first forwarding proportion is lower than a preset forwarding proportion warning threshold value, if so, setting corresponding ninth information as preset equipment forwarding efficiency low warning information, and if not, setting the corresponding ninth information as null; if the current equipment type is not the switch, setting the corresponding ninth information to be null;

identifying whether the fourth, fifth, sixth, seventh, eighth and ninth information is all empty; if yes, setting the corresponding second analysis information as preset equipment running state normal information; if not, the fourth, fifth, sixth, seventh, eighth and ninth information forms corresponding second analysis information;

and the corresponding second analysis result is formed by the current equipment IP address, the current equipment name, the current equipment type and the obtained second analysis information and displayed.

7. The method for processing network device monitoring according to claim 3, wherein predicting the device running state according to all the first records in the last specified period in the first record list to generate and display a corresponding first prediction result specifically includes:

extracting the first equipment IP address, the first equipment name and the first equipment type of any first record in the first record list as a corresponding current equipment IP address, a corresponding current equipment name and a corresponding current equipment type;

extracting all the first records in the first record list within the latest appointed time period, and sequencing the first records in time sequence to generate a corresponding first record sequence;

extracting the first server time, the second server time, the first device time, the second device time, the first device CPU usage rate, the first device memory usage rate, the first device QOS parameter set, the first device inflow flow and the first device outflow flow of each first record in the first record sequence to form corresponding first data vectors; and forming a corresponding first data tensor by all the obtained first data vectors;

Inputting the first data tensor into a preset running state classification prediction model to perform running state classification prediction processing to obtain a corresponding first prediction vector; the first prediction vector includes a plurality of first classification probabilities; each first classification probability corresponds to a preset classification type;

forming corresponding first-type prediction information by each first classification probability and the corresponding classification type; and the corresponding first prediction results are formed and displayed by all the obtained first type of prediction information.

8. An electronic device, comprising: memory, processor, and transceiver;

the processor being operative to couple with the memory, read and execute instructions in the memory to implement the method of any one of claims 1-7;

the transceiver is coupled to the processor and is controlled by the processor to transmit and receive messages.

9. A computer readable storage medium storing computer instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1-7.

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