CN112817823A - Network state monitoring method, device and medium - Google Patents
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Abstract
The application discloses a network state monitoring method, a network state monitoring device and a network state monitoring medium, wherein the method comprises the steps of establishing algorithm models corresponding to different network state events, and calculating prediction data according to monitoring data by utilizing the algorithm models after network data of a current network are obtained. Due to the fact that algorithm models corresponding to different network state events are established in advance, all network state events can be judged and processed, current network data can be judged in an all-around mode, the problem that serious network accidents occur due to the fact that network management personnel judge only by experience and cannot judge various network states at the same time is solved, and stability and accuracy of a network are improved. In addition, the network state monitoring device and the network state monitoring medium provided by the application correspond to the network state monitoring method, and the effect is the same as that of the network state monitoring method.
Description
Technical Field
The present application relates to the field of network maintenance technologies, and in particular, to a method, an apparatus, and a medium for monitoring a network status.
Background
With the continuous enlargement of the current plant size, the network size required by the plant is larger and larger, and since all data of the industrial production system is transmitted depending on the network, once the network fails, such as data delay or even data loss, immeasurable loss can be caused to the production.
At present, a monitoring method of a network state mainly queries working states of a plurality of network devices and displays the working states to a network administrator, and the network administrator performs judgment and processing. Because the network scale is continuously enlarged and the capability of a network administrator is limited, the network administrator can only judge the network state by the existing experience, and meanwhile, the network administrator needs to process a large amount of network data at the same time and cannot consider judging various types of network states, so that the change of the network state cannot be accurately and timely found, serious network accidents are caused, and the stability and the accuracy of the network are reduced.
Therefore, how to improve the stability and accuracy of the network is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
The application aims to provide a network state monitoring method for improving the stability and accuracy of a network. The application aims to further provide a network state monitoring device and a medium.
In order to solve the above technical problem, the present application provides a network status monitoring method, including:
establishing algorithm models corresponding to different network state events;
acquiring current network data;
and calculating each prediction data according to the network data by using each algorithm model.
Preferably, the establishing of the algorithm models corresponding to different network state events specifically includes:
acquiring original data corresponding to each network state event;
acquiring each characteristic data corresponding to each original data;
and establishing the algorithm model corresponding to the network state event according to each characteristic data.
Preferably, after calculating each prediction data according to the network data by using each algorithm model, the method further includes:
determining a control instruction corresponding to each prediction data according to the corresponding relation between each prediction data of each algorithm model and each control instruction;
and sending the control instruction to the node so that the node can adjust the network parameters according to the control instruction.
Preferably, before the establishing the algorithm model corresponding to the network state event according to each feature data, the method further includes:
and dividing corresponding characteristic data according to preset value intervals corresponding to different characteristic data, and marking.
Preferably, the establishing of the algorithm model corresponding to the network state event according to each feature data specifically includes:
and establishing the algorithm model corresponding to the network state event according to each feature data and the corresponding mark.
Preferably, before obtaining each feature data corresponding to each original data, the method further includes:
performing de-duplication processing on each original data;
formatting each original data subjected to the de-duplication processing into a matrix;
calculating a distance value between any two groups of the matrixes;
and when the distance value is not less than a first preset threshold value, removing the corresponding original data.
Preferably, after the algorithm model corresponding to the network state event is established according to each feature data, the method further includes:
acquiring verification data corresponding to each network state event and each actual monitoring result;
calculating each output result according to the verification data by using each algorithm model;
calculating each comprehensive evaluation index according to each actual monitoring result and each output result;
and adjusting the preset value range under the condition that the comprehensive evaluation index is not greater than a second preset threshold value.
Preferably, the sending the control instruction to the node so that the node adjusts the network parameter according to the control instruction specifically includes:
the control instruction is packaged through an SNMP protocol;
and sending the encapsulated control instruction to a node so that the node can adjust the network parameters according to the encapsulated control instruction.
In order to solve the above technical problem, the present application further provides a network status monitoring apparatus, including:
the first establishing module is used for establishing algorithm models corresponding to different network state events;
the first acquisition module is used for acquiring current network data;
and the first calculation module is used for calculating each prediction data according to the network data by using each algorithm model.
In order to solve the above technical problem, the present application further provides a network status monitoring apparatus, including:
a memory for storing a computer program;
a processor for implementing the steps of the network condition monitoring method as described above when executing the computer program.
In order to solve the above technical problem, the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the network status monitoring method as described above.
According to the network state monitoring method, algorithm models corresponding to different network state events are established, and after network data of the current network are obtained, each prediction data is calculated according to the monitoring data by using each algorithm model. Due to the fact that algorithm models corresponding to different network state events are established in advance, all network state events can be judged and processed, current network data can be judged in an all-around mode, the problem that serious network accidents occur due to the fact that network management personnel judge only by experience and cannot judge various network states at the same time is solved, and stability and accuracy of a network are improved.
In addition, the network state monitoring device and the network state monitoring medium provided by the application correspond to the network state monitoring method, and the effect is the same as that of the network state monitoring method.
Drawings
In order to more clearly illustrate the embodiments of the present application, the drawings needed for the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a network status monitoring method according to an embodiment of the present application;
fig. 2 is a flowchart of a first algorithm model for establishing correspondence between different network state events according to an embodiment of the present application;
fig. 3 is a flowchart of a second algorithm model for establishing correspondence between different network state events according to an embodiment of the present application;
fig. 4 is a flowchart of a third algorithm model for establishing correspondence between different network state events according to the embodiment of the present application;
fig. 5 is a schematic structural diagram of a network status monitoring apparatus according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of another network status monitoring apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the present application.
The core of the application is to provide a network state monitoring method for improving the stability and accuracy of a network. The core of the application is also to provide a network state monitoring device and medium.
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings.
Fig. 1 is a flowchart of a network status monitoring method according to an embodiment of the present application. As shown in fig. 1, the method includes:
s10: and establishing algorithm models corresponding to different network state events.
In the embodiment of the present application, the different network status events may include, but are not limited to, a network congestion event, a network storm event, a node failure event, and the like.
It can be understood that, in a specific implementation, the algorithm model may be established according to the requirement of the user, for example, when the user only concerns a network congestion event caused by network congestion, the algorithm model corresponding to the network congestion event may be established only; when a user pays attention to network congestion related to network storm, two corresponding algorithm models can be established for subsequent use; when the user focuses on other types of network state events (such as network attack events), an algorithm model corresponding to the network attack events can be established. The method for establishing the algorithm model corresponding to different network state events is the same, and the detailed method for establishing the algorithm model will be described in detail below.
S11: current network data is acquired.
It should be noted that the network data is obtained through a monitoring network, where the network data may include an Internet Protocol (IP) address of a node, a working mode, a port state, ingress/egress traffic, an ingress/egress packet loss number, an ingress/egress error message, an ingress/egress unicast packet, an ingress/egress broadcast packet, an ingress/egress multicast packet, an egress speed limit, and the like. It is understood that, in the implementation, the above mentioned network data may be added or subtracted according to the requirement of the user and the corresponding used algorithm model.
S12: and calculating each prediction data according to the network data by using each algorithm model.
After each algorithm model is established, each algorithm model is downloaded to a network model service, and the network state is judged by using each algorithm model according to network data sent by a network state monitoring service. It can be understood that when multiple algorithm models exist in the network model service, calculation and judgment can be performed sequentially one by one, and calculation and judgment can also be performed by multiple algorithm models simultaneously. The prediction data of each algorithm model is specifically a judgment result representing yes or no in order to facilitate the network manager to check the prediction data of each algorithm model.
According to the network state monitoring method provided by the embodiment of the application, algorithm models corresponding to different network state events are established, and after the network data of the current network are obtained, each prediction data is calculated according to the monitoring data by using each algorithm model. Due to the fact that algorithm models corresponding to different network state events are established in advance, all network state events can be judged and processed, current network data can be judged in an all-around mode, the problem that serious network accidents occur due to the fact that network management personnel judge only by experience and cannot judge various network states at the same time is solved, and stability and accuracy of a network are improved.
Fig. 2 is a flowchart of a first algorithm model for establishing correspondence between different network state events according to an embodiment of the present application. As shown in fig. 2, on the basis of the above embodiment, the method for establishing each algorithm model includes:
s20: and acquiring original data corresponding to each network state event.
In the embodiment of the application, a network structure similar to an application scene is built, a network data packet is built through a package sending tool, and after different network state events are manufactured, original data corresponding to the network state events are collected.
In order to make those skilled in the art better understand the step of collecting the raw data corresponding to each network status event, the following example will be described in detail.
When the network state event is a network congestion event, a nonexistent IP address data packet can be constructed through a data packet setting tool, and original data corresponding to the network congestion event is collected by changing the sending rate of the IP address data packet. Wherein the raw data comprises: the packet loss rate of a port, the average delay time of each 1000 packets, the egress traffic, the Central Processing Unit (CPU) load of a node, the rate of sending IP address packets, and the like.
When the network state event is a network storm event, a broadcast packet or a loop can be constructed through a data packet setting tool, and original data corresponding to the network storm event is collected by adjusting the sending rate of the broadcast packet or the data volume of the broadcast packet in the loop under different network scales. Wherein the raw data comprises: the number of broadcast packet transmissions, the number of received packets, the average change frequency of the Media Access Control (MAC) address table of all nodes every 1 second, 10 seconds, 20 seconds, and the changed conditions.
It should be noted that, in a specific implementation, all the raw data collected may be used to train and build a corresponding algorithm model, or a part of the raw data may be used to train and build a corresponding algorithm model, and the rest of the raw data may be used to verify and test the algorithm model, so as to detect whether the algorithm model can be successfully used.
S21: and acquiring each characteristic data corresponding to each original data.
S22: and establishing an algorithm model corresponding to the network state event according to the characteristic data.
In order to reduce the amount of calculation for building the algorithm model corresponding to the network state event, as a preferred embodiment, S22 further includes: and dividing corresponding characteristic data according to preset value intervals corresponding to different characteristic data, and marking. S22 specifically includes: and establishing an algorithm model corresponding to the network state event according to the characteristic data and the corresponding marks.
The algorithm model formula is shown as follows:
wherein x is the set of raw data collected at each moment, aiI-th characteristic data, y, of raw data collected at a timejIs aiThe feature data corresponds to a label, η is a mean value of each feature data, and σ is a standard deviation of each feature data.
According to the network state monitoring method provided by the embodiment of the application, the algorithm model corresponding to each network state event is established through the characteristic data in the original data, so that the influence result of different characteristic parameters in the original data on the final network state event can be quantized, and the accuracy of monitoring the network state can be further improved through the established algorithm model.
On the basis of the above embodiment, after S12, the method further includes:
and determining a control instruction corresponding to each predicted data according to the corresponding relation between each predicted data of each algorithm model and each control instruction, and sending the control instruction to the node so that the node can adjust the network parameters according to the control instruction.
In the embodiment of the present application, in order to further increase the processing speed of the network state event and optimize the processing method of the network state time, as a preferred embodiment, the control instruction is sent to the node, so that the node adjusts the network parameter according to the control instruction specifically as follows: and encapsulating the control instruction through a Simple Network Management Protocol (SNMP), and sending the encapsulated control instruction to the node so that the node can adjust Network parameters according to the encapsulated control instruction.
The control command may be set in advance by a user. For example, for a network storm event, the user may preset: when the prediction data calculated by the algorithm model corresponding to the network storm event indicates that the network storm currently exists, the control instruction may be to control 4 ports of the node to reduce the network speed in batches until the prediction data calculated by the algorithm model indicates that the network storm disappears.
According to the network state monitoring method provided by the embodiment of the application, the corresponding relation between each prediction data of each algorithm model and each control instruction is preset, the prediction data calculated by each algorithm model can be processed in time through the control instruction, the speed of processing the network state time is improved, and meanwhile, the workload of network management personnel is reduced.
Fig. 3 is a flowchart of a second algorithm model for establishing correspondence between different network state events according to an embodiment of the present application. As shown in fig. 3, on the basis of the above embodiment, before S21, the method further includes:
s30: and performing de-duplication processing on each original data.
S31: and formatting each original data after the de-duplication processing into a matrix.
S32: the distance values between any two sets of matrices are calculated.
S33: and judging whether the distance value is smaller than a first preset threshold value, if so, entering S21, and otherwise, entering S34.
S34: the corresponding original data is removed and S21 is entered.
It should be noted that the formula for calculating the distance value between any two sets of matrices is as follows:
a, B represents two arbitrarily selected matrixes among the matrixes corresponding to all the sets of original data, xiIs the eigenvalue corresponding to the A-set matrix, yiAnd the characteristic values corresponding to the B group matrix.
It is understood that whether error data exists in the original data can be determined by determining whether the distance value is smaller than a first preset threshold. According to the method provided by the embodiment of the application, repeated original data and wrong original data are removed, so that the algorithm model can be further perfected, and the accuracy of monitoring the network state is further improved.
Fig. 4 is a flowchart of a third algorithm model for establishing correspondence between different network state events according to an embodiment of the present application. As shown in fig. 4, on the basis of the above embodiment, after S22, the method includes:
s40: and acquiring verification data corresponding to each network state event and each actual monitoring result.
In the embodiment of the present application, the verification data may be collected again according to each network state event, or may be obtained from unused original data.
S41: and calculating each output result according to the verification data by using each algorithm model.
S42: and calculating each comprehensive evaluation index according to each actual monitoring result and each output result.
Table 1 is a statistical table of output results and actual monitoring results provided in the embodiments of the present application. Taking the network storm event as an example, a positive sample indicates that the network storm event occurs, and a negative sample indicates that the network storm event does not occur. TP represents the number of positive samples and the actual monitoring result is a positive sample, FP represents the number of positive samples and the actual monitoring result is a negative sample, FN represents the number of negative samples and the actual monitoring result is a positive sample, TN represents the number of negative samples and the actual monitoring result is a negative sample, P represents the number of positive samples in the actual monitoring result, and N represents the number of negative samples in the actual monitoring result.
TABLE 1
| Number of positive samples output | Number of negative samples output | Total of | |
| Actual number of positive samples | TP | FN | P |
| Number of actual negative samples | FP | TN | N |
Then, the calculation formula of the comprehensive evaluation index is as follows:
wherein F represents the comprehensive evaluation index value of the algorithm model, and the actual meanings of TN, FP, TP, and FN are shown in the above table, which is not repeated here.
S43: and judging whether the comprehensive evaluation index is larger than a second preset threshold value, if so, ending, otherwise, entering S44.
S44: and adjusting the preset value intervals in the step of dividing the corresponding characteristic data according to the preset value intervals corresponding to the different characteristic data and marking the preset value intervals.
It can be understood that, after the preset value-taking interval is adjusted, the step of establishing the algorithm model is re-entered to update the algorithm model, and the step of S40 is entered until the comprehensive evaluation index value of the updated algorithm model is greater than the second preset threshold value.
It should be noted that the value of the second preset threshold is not specifically required, and is matched with the user requirement, and in the specific implementation, 0.7 may be selected as a reference for the second preset threshold.
According to the network state monitoring method provided by the embodiment of the application, the established algorithm model is verified through the verification data and the actual monitoring result, so that the accuracy of the algorithm model can be ensured, when the verified comprehensive evaluation index value is not greater than the second preset threshold value, the algorithm model can be updated and optimized by adjusting the preset value-taking interval, the calculation accuracy of the algorithm model is further improved and ensured, and the accuracy of network state monitoring is further improved.
In the foregoing embodiments, detailed descriptions are given to a network status monitoring method, and the present application also provides embodiments corresponding to a network status monitoring apparatus. It should be noted that the present application describes the embodiments of the apparatus portion from two perspectives, one from the perspective of the function module and the other from the perspective of the hardware.
Fig. 5 is a schematic structural diagram of a network status monitoring apparatus according to an embodiment of the present application. As shown in fig. 5, the apparatus includes, based on the angle of the function module:
the first establishing module 10 is configured to establish algorithm models corresponding to different network state events.
The first obtaining module 12 is configured to obtain current network data.
And a first calculation module 13, configured to calculate each prediction data according to the network data by using each algorithm model.
Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.
As a preferred embodiment, the method further comprises the following steps:
and the acquisition module is used for acquiring the original data corresponding to each network state event.
And the second acquisition module is used for acquiring each feature data corresponding to each original data.
And the second establishing module is used for establishing an algorithm model corresponding to the network state event according to the characteristic data.
Further comprising:
and the determining module is used for determining the control instruction corresponding to each prediction data according to the corresponding relation between each prediction data of each algorithm model and each control instruction.
And the first sending module is used for sending the control instruction to the node so that the node can adjust the network parameters according to the control instruction.
Further comprising:
and the division marking module is used for dividing the corresponding characteristic data according to the preset value intervals corresponding to the different characteristic data and marking the characteristic data.
And the third establishing module is used for establishing an algorithm model corresponding to the network state event according to the characteristic data and the corresponding marks.
Further comprising:
and the first processing module is used for performing de-duplication processing on each original data.
And the formatting module is used for formatting each original data after the de-duplication processing into a matrix.
And the second calculation module is used for calculating the distance value between any two groups of matrixes.
And the removing module is used for removing the corresponding original data when the distance value is not less than the first preset threshold value.
Further comprising:
and the third acquisition module is used for acquiring verification data corresponding to each network state event and each actual monitoring result.
And the third calculation module is used for calculating each output result according to the verification data by using each algorithm model.
And the fourth calculation module is used for calculating each comprehensive evaluation index according to each actual monitoring result and each output result.
And the adjusting module is used for adjusting the preset value-taking interval under the condition that the comprehensive evaluation index is not greater than the second preset threshold value.
Further comprising:
and the second processing module is used for packaging the control instruction through the SNMP protocol.
And the second sending module is used for sending the encapsulated control instruction to the node so that the node can adjust the network parameter according to the encapsulated control instruction.
The network state monitoring device provided by the embodiment of the application establishes the algorithm models corresponding to different network state events, and after the network data of the current network are obtained, calculates each prediction data according to the monitoring data by using each algorithm model. Due to the fact that algorithm models corresponding to different network state events are established in advance, all network state events can be judged and processed, current network data can be judged in an all-around mode, the problem that serious network accidents occur due to the fact that network management personnel judge only by experience and cannot judge various network states at the same time is solved, and stability and accuracy of a network are improved.
Fig. 6 is a schematic structural diagram of another network status monitoring apparatus according to an embodiment of the present application. As shown in fig. 6, the apparatus includes, from the perspective of the hardware configuration:
a memory 20 for storing a computer program;
a processor 21 for implementing the steps of the network condition monitoring method in the above embodiments when executing the computer program.
The processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 21 may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 21 may be integrated with a Graphics Processing Unit (GPU) which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 21 may further include an Artificial Intelligence (AI) processor for processing computational operations related to machine learning.
The memory 20 may include one or more computer-readable storage media, which may be non-transitory. Memory 20 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used for storing the following computer program 201, wherein after being loaded and executed by the processor 21, the computer program can implement the relevant steps of the network status monitoring method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202, data 203, and the like, and the storage manner may be a transient storage manner or a permanent storage manner. Operating system 202 may include, among others, Windows, Unix, Linux, and the like. Data 203 may include, but is not limited to, data involved in network condition monitoring methods, and the like.
In some embodiments, the network status monitoring device may further include a display 22, an input/output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.
Those skilled in the art will appreciate that the configuration shown in fig. 6 does not constitute a limitation of the network condition monitoring apparatus and may include more or fewer components than those shown.
The network state monitoring device provided by the embodiment of the application comprises a memory and a processor, and when the processor executes a program stored in the memory, the following method can be realized: and establishing algorithm models corresponding to different network state events, and calculating each prediction data according to the monitoring data by using each algorithm model after acquiring the network data of the current network. Due to the fact that algorithm models corresponding to different network state events are established in advance, all network state events can be judged and processed, current network data can be judged in an all-around mode, the problem that serious network accidents occur due to the fact that network management personnel judge only by experience and cannot judge various network states at the same time is solved, and stability and accuracy of a network are improved.
Finally, the application also provides a corresponding embodiment of the computer readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps as set forth in the above-mentioned method embodiments.
It is to be understood that if the method in the above embodiments is implemented in the form of software functional units and sold or used as a stand-alone product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the methods described in the embodiments of the present application, or all or part of the technical solutions. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The computer readable storage medium provided by the embodiment of the application, the medium is stored with a computer program, and when the computer program is executed by a processor, the following method can be realized: and establishing algorithm models corresponding to different network state events, and calculating each prediction data according to the monitoring data by using each algorithm model after acquiring the network data of the current network. Due to the fact that algorithm models corresponding to different network state events are established in advance, all network state events can be judged and processed, current network data can be judged in an all-around mode, the problem that serious network accidents occur due to the fact that network management personnel judge only by experience and cannot judge various network states at the same time is solved, and stability and accuracy of a network are improved.
The method, the device and the medium for monitoring the network state provided by the present application are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for monitoring network conditions, comprising:
establishing algorithm models corresponding to different network state events;
acquiring current network data;
and calculating each prediction data according to the network data by using each algorithm model.
2. The method according to claim 1, wherein the establishing of the algorithm models corresponding to different network state events specifically comprises:
acquiring original data corresponding to each network state event;
acquiring each characteristic data corresponding to each original data;
and establishing the algorithm model corresponding to the network state event according to each characteristic data.
3. The method of claim 1, wherein after calculating the prediction data from the network data using the algorithm models, the method further comprises:
determining a control instruction corresponding to each prediction data according to the corresponding relation between each prediction data of each algorithm model and each control instruction;
and sending the control instruction to the node so that the node can adjust the network parameters according to the control instruction.
4. The method according to claim 2, wherein before establishing the algorithmic model corresponding to the network status event according to each of the characteristic data, the method further comprises:
dividing corresponding characteristic data according to preset value intervals corresponding to different characteristic data, and marking;
the establishing of the algorithm model corresponding to the network state event according to each feature data is specifically:
and establishing the algorithm model corresponding to the network state event according to each feature data and the corresponding mark.
5. The network status monitoring method according to claim 2 or 4, wherein before obtaining each feature data corresponding to each original data, the method further comprises:
performing de-duplication processing on each original data;
formatting each original data subjected to the de-duplication processing into a matrix;
calculating a distance value between any two groups of the matrixes;
and when the distance value is not less than a first preset threshold value, removing the corresponding original data.
6. The method according to claim 4, wherein after the establishing the algorithmic model corresponding to the network status event according to each of the characteristic data, the method further comprises:
acquiring verification data corresponding to each network state event and each actual monitoring result;
calculating each output result according to the verification data by using each algorithm model;
calculating each comprehensive evaluation index according to each actual monitoring result and each output result;
and adjusting the preset value range under the condition that the comprehensive evaluation index is not greater than a second preset threshold value.
7. The method according to claim 3, wherein the sending the control instruction to the node so that the node adjusts the network parameter according to the control instruction specifically comprises:
the control instruction is packaged through an SNMP protocol;
and sending the encapsulated control instruction to a node so that the node can adjust the network parameters according to the encapsulated control instruction.
8. A network condition monitoring apparatus, comprising:
the first establishing module is used for establishing algorithm models corresponding to different network state events;
the first acquisition module is used for acquiring current network data;
and the first calculation module is used for calculating each prediction data according to the network data by using each algorithm model.
9. A network condition monitoring apparatus, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the network condition monitoring method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the network status monitoring method according to any one of claims 1 to 7.
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