CN117750420A - Network analysis method, device and storage medium - Google Patents

Abstract

The application provides a network analysis method, a device and a storage medium, relates to the technical field of communication, and can solve the problem that network performance cannot be automatically and intelligently analyzed and optimize the network performance. The method comprises the following steps: acquiring monitoring data of equipment; the monitoring data comprises network performance data and network running conditions; network performance data includes, but is not limited to: delay, bandwidth, packet loss rate; network operating conditions include, but are not limited to: device behavior, network load; determining network performance parameters and network condition parameters of the device based on the monitoring data; network performance parameters include, but are not limited to: average delay, bandwidth utilization, packet loss; network performance parameters include, but are not limited to: average service usage frequency, average device connection duration; predicting a target network performance of the device based on the network performance parameter and the network condition parameter; network parameters of the device are adjusted based on the target network performance.

Description

Network analysis method, device and storage medium

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to a network analysis method, a device, and a storage medium.

Background

Fifth generation mobile communication technology (5 th generation mobile communication technology,

5G) The satellite private network is a 5G network service provided for the industries of campus, travel, medical treatment and the like, and meets the requirements of the industry by dividing special slices in a mobile network. The rapid development of 5G technology provides tremendous opportunities for ad hoc networks, but also challenges the monitoring, analysis, and management of network performance.

At present, a 5G follow-up private network generally relates to a plurality of devices, frequency bands and network elements, network monitoring and management are quite complex, common network monitoring, analysis and management technology is based on manual operation and management, specific situations among the devices, frequency bands and network elements are difficult to fully consider, network performance problems of the devices cannot be intelligently treated, and a network is optimized.

Disclosure of Invention

The application provides a network analysis method, a device and a storage medium, which are used for solving the problem that the network performance cannot be automatically and intelligently analyzed in a general method and optimizing the network performance.

In order to achieve the above purpose, the present application adopts the following technical scheme:

in a first aspect, the present application provides a network analysis method, the method comprising: acquiring monitoring data of equipment; the monitoring data comprises network performance data and network running conditions; network performance data includes, but is not limited to: delay, bandwidth, packet loss rate; network operating conditions include, but are not limited to: device behavior, network load; determining network performance parameters and network condition parameters of the device based on the monitoring data; network performance parameters include, but are not limited to: average delay, bandwidth utilization, packet loss; network performance parameters include, but are not limited to: average service usage frequency, average device connection duration; predicting a target network performance of the device based on the network performance parameter and the network condition parameter; network parameters of the device are adjusted based on the target network performance.

In one possible implementation, a method of predicting a target network performance of a device based on network performance parameters and network condition parameters, includes: determining a predicted first network performance based on the network performance data and the network performance parameter; determining a predicted second network performance based on the network operating condition and the network condition parameter; the target network performance of the device is predicted based on the first network performance and the second network performance.

In one possible implementation, the predicted first network performance satisfies the following formula: w=f (x|θ); wherein W is predicted first network performance, f is at least one of a preset model or algorithm for processing network performance data, X is network performance data, and θ is a network performance parameter.

In one possible implementation, the predicted second network performance satisfies the following formula: v=α·g (z|Φ); wherein V is predicted second network performance, alpha is a weight coefficient, g is at least one of a model or an algorithm for processing network operation conditions, Z is network operation conditions, and phi is a network condition parameter.

In one possible implementation, a method of adjusting network parameters of a device based on target network performance, includes: inputting the target network performance into a preset decision logic function to obtain corresponding decision logic; based on the decision logic, network parameters of the device are adjusted.

In one possible implementation, after adjusting the network parameters of the device based on the decision logic, further comprises: acquiring current network performance data of equipment; the current network performance data is the network performance data when the equipment operates based on the current network parameters; inputting the current network performance data into a preset feedback function to obtain feedback adjustment logic; based on the feedback adjustment logic, current network parameters of the device are adjusted.

In a second aspect, the present application provides a network analysis apparatus, the apparatus comprising: a communication unit and a processing unit; a communication unit for acquiring monitoring data of the equipment; the monitoring data comprises network performance data and network running conditions; network performance data includes, but is not limited to: delay, bandwidth, packet loss rate; network operating conditions include, but are not limited to: device behavior, network load; a processing unit for determining network performance parameters and network condition parameters of the device based on the monitoring data; network performance parameters include, but are not limited to: average delay, bandwidth utilization, packet loss; network performance parameters include, but are not limited to: average service usage frequency, average device connection duration; the processing unit is also used for predicting the target network performance of the equipment based on the network performance parameters and the network condition parameters; and the processing unit is also used for adjusting the network parameters of the equipment based on the target network performance.

In one possible implementation, the processing unit is specifically configured to: determining a predicted first network performance based on the network performance data and the network performance parameter; determining a predicted second network performance based on the network operating condition and the network condition parameter; the target network performance of the device is predicted based on the first network performance and the second network performance.

In one possible implementation, the predicted first network performance satisfies the following formula: w=f (x|θ); wherein W is predicted first network performance, f is at least one of a preset model or algorithm for processing network performance data, X is network performance data, and θ is a network performance parameter.

In one possible implementation, the predicted second network performance satisfies the following formula: v=α·g (z|Φ); wherein V is predicted second network performance, alpha is a weight coefficient, g is at least one of a model or an algorithm for processing network operation conditions, Z is network operation conditions, and phi is a network condition parameter.

In one possible implementation, the processing unit is specifically configured to: inputting the target network performance into a preset decision logic function to obtain corresponding decision logic; based on the decision logic, network parameters of the device are adjusted.

In one possible implementation, after adjusting the network parameters of the device based on the decision logic, the processing unit is further configured to: acquiring current network performance data of equipment; the current network performance data is the network performance data when the equipment operates based on the current network parameters; inputting the current network performance data into a preset feedback function to obtain feedback adjustment logic; based on the feedback adjustment logic, current network parameters of the device are adjusted.

In a third aspect, the present application provides a network analysis apparatus, the apparatus comprising: a processor and a communication interface; the communication interface is coupled to a processor for running a computer program or instructions to implement the network analysis method as described in any one of the possible implementations of the first aspect and the first aspect.

In a fourth aspect, the present application provides a computer readable storage medium having instructions stored therein which, when run on a terminal, cause the terminal to perform a network analysis method as described in any one of the possible implementations of the first aspect and the first aspect.

In a fifth aspect, the present application provides a computer program product comprising instructions which, when run on a network analysis device, cause the network analysis device to perform a network analysis method as described in any one of the possible implementations of the first aspect and the first aspect.

In a sixth aspect, the present application provides a chip comprising a processor and a communications interface, the communications interface and the processor being coupled, the processor being for running a computer program or instructions to implement a network analysis method as described in any one of the possible implementations of the first aspect and the first aspect.

In particular, the chip provided in the present application further includes a memory for storing a computer program or instructions.

According to the network analysis method, the monitoring data of the equipment can be obtained, the monitoring data are processed to obtain the network performance parameters and the network condition parameters, the target network performance of the equipment is predicted according to the monitoring data and the network performance parameters and the network condition parameters, the network parameters of the equipment are automatically adjusted, the network performance of the equipment is automatically managed through adjustment of the network parameters of the equipment, and further the performance problem of the network can be timely and accurately processed, and the network is optimized.

Drawings

Fig. 1 is a schematic structural diagram of a network analysis device according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of another network analysis device according to an embodiment of the present application;

Fig. 3 is a schematic hardware structure diagram of a communication device according to an embodiment of the present application;

fig. 4 is a schematic hardware structure diagram of a communication device according to the embodiment of the present application;

fig. 5 is a flowchart of a network analysis method according to an embodiment of the present application;

FIG. 6 is a flowchart of another network analysis method according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of another network analysis device according to an embodiment of the present application.

Detailed Description

The network analysis method, the device and the storage medium provided by the embodiment of the application are described in detail below with reference to the accompanying drawings.

The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone.

The terms "first" and "second" and the like in the description and in the drawings are used for distinguishing between different objects or for distinguishing between different processes of the same object and not for describing a particular sequential order of objects.

Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.

It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In the description of the present application, unless otherwise indicated, the meaning of "a plurality" means two or more.

The following explains the terms related to the embodiments of the present application, so as to facilitate the understanding of the reader.

(1) Network slicing technology: network slicing is a new network architecture that provides multiple logical networks on the same shared network infrastructure, each serving a particular business type or industry user. Each network slice can flexibly define own logic topology, reliability and security level so as to meet the differentiated requirements of different businesses, industries or users.

(2) Self-healing network technology: the self-healing network technology is a technology which can automatically recover the carried service from the failure fault in a short time without human intervention, so that a user does not feel that the network has failed. Self-healing network technology cannot automatically remove faults, but only takes protective measures on the network, so that the network has an alternative transmission route.

The foregoing is a simplified description of some of the concepts involved in the embodiments of this application.

Current methods of network monitoring and analysis generally include the following:

performing 5G industry private network wireless test and service quality test through wireless probes deployed in the 5G industry private network, and collecting wireless indexes and service indexes; active testing and passive testing are carried out on the private network of the 5G industry through active and passive integrated probes deployed in the edge cloud platform of the private network of the 5G industry, and wireless indexes, wired network indexes and business indexes of the private network of the 5G industry are collected.

However, the general technology cannot automatically and intelligently analyze the network performance problem and optimize the network performance, and cannot timely and accurately monitor and manage the network performance.

In view of this, the embodiment of the present application provides a network analysis method, where the method may obtain monitoring data of a device, and then process the monitoring data to obtain network performance parameters and network condition parameters, predict target network performance of the device and automatically adjust network parameters of the device according to the monitoring data and the network performance parameters and the network condition parameters, and implement automatic management of network performance of the device by adjusting the network parameters of the device, so as to timely and accurately process performance problems of the network and optimize the network.

Illustratively, the network analysis method is applied to a network monitoring system, in which a network analysis device is included, and fig. 1 shows a schematic structure diagram of a network analysis device 101. The network analysis device 101 includes: a data acquisition module 110, a data transmission module 111, a data analysis module 112, an automated decision module 113, a big data management module 114, an automated monitoring module 115, a cross-boundary management module 116, a network slice management module 117, a security and privacy protection module 118, a normalization module 119.

The data acquisition module 110 includes a monitoring device, and is configured to acquire monitoring data of a network in real time.

Optionally, the monitoring device comprises at least one of: a 5G follow-up private network probe, a network tester and the like.

The data transmission module 111 is configured to transmit the monitoring data collected by the monitoring device to a central server or a cloud platform, so as to perform analysis processing on the monitoring data.

The data analysis module 112 is configured to analyze the monitored data to obtain predicted target network performance.

The automated decision module 113 is configured to process the predicted network performance through a preset algorithm, automatically make a decision, and adjust network parameters based on the decision.

Big data management module 114 is a powerful data management system for handling and storing large amounts of data generated by 5G networks. The big data management module 114 may fragment and sort the data according to the needs of the application.

The automatic monitoring module 115 is configured to continuously collect network data through a monitoring device, ensure healthy operation of a slice and an overall network, and provide a high adaptive capacity for a 5G network, so as to ensure more stable and efficient network service.

In one possible implementation, an automation monitoring module is used to implement automation monitoring and maintenance functions of the device to ensure stability and usability of the device. In the automated monitoring module 117, self-healing network technology is employed to identify and address equipment failures or other network problems. By automated monitoring and maintenance, an organization can ensure that its network and system are always in an optimal state and reduce the risk of human error and delayed response. The probe captures performance metrics such as delay, throughput in real time, thereby enabling network problems to be discovered in the early stages, reducing potential service outages. The automatic maintenance further perfects the system, and can automatically adjust network parameters according to the data collected by the probes or trigger a preset recovery flow when a potential fault is found.

For example, when traffic of a node abnormally increases, the system may automatically reallocate traffic resources or initiate a backup path.

The cross-border management module 116 is configured to establish international standard protocols and to formulate international collaboration mechanisms to address spectrum management and roaming issues so that cross-border 5G-compliance private networks can interoperate and coordinate.

Included in the network slice management module 117 is a network slice management component for monitoring and managing the performance of the different network slices to ensure that they meet the requirements of a particular application.

In one possible implementation, the network slicing technique allows multiple logically independent networks to be deployed on the same physical infrastructure, thereby meeting the specific needs of different applications and services. In such complex environments, operators may acquire network status in real-time and in depth through probes, ensuring that each slice in the network can meet the expected performance and security standards. The network slicing technique also supports dynamic network slicing configuration, and operators can flexibly create, modify or delete slices based on real-time and historical network data, so as to adapt to continuously changing service demands and traffic patterns.

For example, a large public activity may cause a sudden increase in network traffic, the probe may collect network data in different network slices, and the operator may receive the probe's monitoring data and quickly evaluate the situation to adjust the network configuration.

The security and privacy protection module 118 may use the latest privacy and identity verification techniques to protect the detection devices, data transmission and storage.

The normalization module 119 is used to participate in and facilitate international and industry standard formulation to ensure interoperability and compatibility of devices and systems.

In one example, the server that analyzes the monitoring data may be a single server, or may be a server cluster composed of a plurality of servers. In some implementations, the server cluster may also be a distributed cluster.

In addition, the network monitoring system described in the embodiments of the present application is for more clearly describing the technical solution of the embodiments of the present application, and does not constitute a limitation on the technical solution provided in the embodiments of the present application, and those skilled in the art can know that, with the evolution of the network architecture and the appearance of the new network monitoring system, the technical solution provided in the embodiments of the present application is applicable to similar technical problems.

The network analysis device described in the embodiments of the present application is for more clearly describing the technical solution of the embodiments of the present application, and does not constitute a limitation to the technical solution provided in the embodiments of the present application, and as a person of ordinary skill in the art can know, with evolution of a network architecture and appearance of a new network analysis device, the technical solution provided in the embodiments of the present application is equally applicable to similar technical problems.

With reference to fig. 1, the network analysis device 101 includes elements included in the computer apparatus shown in fig. 2, and a hardware configuration of the network analysis device will be described below taking the computer apparatus shown in fig. 2 as an example.

As shown in fig. 2, a central processor (central processing unit, CPU) is used to process data and perform corporate computing the weights of value cells, which are cells with higher user density and high quality network coverage. A program memory (ROM) is used to store the formula algorithm program in each module and the data information of each influencing factor in the high-value base station cell. The data storage (random access memory, RAM) is used to store temporary data generated during the CPU computation. The I/O device refers to various input/output devices such as a mouse, a keyboard, a display, etc., and is used for a user to operate the computer to execute a command or view a calculation result. The communication device refers to various software and hardware capable of interacting data information with other devices, including wireless local area network (wireless fidelity, WIFI), bluetooth, 4/5G baseband and antenna, etc., and is used for acquiring data of each cell and transmitting the data to the server. The interrupt system is used for controlling and stopping the program executed by the central processing unit based on the command input by the input device. The internal bus is used to transmit data information in the various parts.

In connection with fig. 1, the network analysis device 101 includes elements included in the communication device shown in fig. 3 or fig. 4. The hardware configuration of the network analysis device 101 will be described below taking the communication devices shown in fig. 3 and 4 as an example.

Fig. 3 is a schematic hardware structure of a communication device according to an embodiment of the present application. The communication device comprises a processor 21, a memory 22, a communication interface 23, a bus 24. The processor 21, the memory 22 and the communication interface 23 may be connected by a bus 24.

The processor 21 is a control center of the communication device, and may be one processor or a collective term of a plurality of processing elements. For example, the processor 21 may be a general-purpose central processing unit (central processing unit, CPU), or may be another general-purpose processor. Wherein the general purpose processor may be a microprocessor or any conventional processor or the like.

As one example, processor 21 may include one or more CPUs, such as CPU 0 and CPU 1 shown in fig. 3.

Memory 22 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), magnetic disk storage or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

In a possible implementation, the memory 22 may exist separately from the processor 21, and the memory 22 may be connected to the processor 21 by a bus 24 for storing instructions or program code. The processor 21, when calling and executing instructions or program code stored in the memory 22, is capable of implementing the network analysis method provided in the following embodiments of the present invention.

In another possible implementation, the memory 22 may also be integrated with the processor 21.

The communication interface 23 is used for connecting the communication device with other devices through a communication network, such as ethernet, radio access network, wireless local area network (wireless local area networks, WLAN), etc. The communication interface 23 may include a receiving unit for receiving data, and a transmitting unit for transmitting data.

Bus 24 may be an industry standard architecture (industry standard architecture, ISA) bus, an external device interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in fig. 3, but not only one bus or one type of bus.

Fig. 4 shows another hardware configuration of the communication apparatus in the embodiment of the present invention. As shown in fig. 4, the communication device may include a processor 31 and a communication interface 32. The processor 31 is coupled to a communication interface 32.

The function of the processor 31 may be as described above with reference to the processor 21. The processor 31 also has a memory function and can function as the memory 22.

The communication interface 32 is used to provide data to the processor 31. The communication interface 32 may be an internal interface of the communication device or an external interface of the communication device (corresponding to the communication interface 23).

It should be noted that the structure shown in fig. 3 (or fig. 4) does not constitute a limitation of the communication apparatus, and the communication apparatus may include more or less components than those shown in fig. 3 (or fig. 4), or may combine some components, or may be arranged in different components.

Further, actions, terms, etc. referred to between embodiments of the present application may be referred to each other without limitation. In the embodiment of the present application, the name of the message or the name of the parameter in the message, etc. interacted between the devices are only an example, and other names may also be adopted in the specific implementation, and are not limited.

The network analysis method provided in the embodiment of the present application is described below with reference to the network analysis apparatus shown in fig. 1. In which, the terms and the like related to the embodiments of the present application may refer to each other without limitation. In the embodiment of the present application, the name of the message or the name of the parameter in the message, etc. interacted between the devices are only an example, and other names may also be adopted in the specific implementation, and are not limited. The actions involved in the embodiments of the present application are just an example, and other names may be used in specific implementations, for example: the "included" of the embodiments of the present application may also be replaced by "carried on" or the like.

In order to solve the problems in the prior art, the embodiment of the application provides a network analysis method for intelligently processing the network performance problem of the device and optimizing the network. As shown in fig. 5, the method includes:

s501, the network analysis device acquires monitoring data of the equipment.

The monitoring data comprise network performance data and network operation conditions.

In one possible implementation manner, the network analysis device acquires network performance data and network operation conditions of the devices in the network in real time through the data acquisition module, so as to ensure that the network analysis device can monitor the network performance of the devices in real time. In the process of data acquisition, monitoring equipment and monitoring data are required to be safely encrypted so as to protect sensitive information in the monitoring data.

S502, the network analysis device determines network performance parameters and network condition parameters of the equipment based on the monitoring data.

In one possible implementation manner, after the data acquisition module in the network analysis device acquires the monitoring data of the equipment, the data acquisition module can send the monitoring data to the data analysis module through the data transmission module, the data analysis module receives the monitoring data and analyzes and processes the monitoring data, and in the transmission process of the monitoring data, the monitoring data also needs to be encrypted, so that the safety of the data is ensured.

In one possible implementation, the network analysis device analyzes and processes the network performance data in the monitored data through statistics or a machine learning algorithm to obtain network performance parameters, and the network performance parameters can describe the performance status of the network. The network analysis device analyzes and processes the network operation condition in the monitored data through statistics or a machine learning algorithm to obtain network condition parameters, wherein the network operation condition represents other parameters which can influence decision logic besides the network performance data.

Optionally, the network performance data includes, but is not limited to: delay, bandwidth, packet loss rate. Network performance parameters include, but are not limited to: average delay, bandwidth utilization, packet loss rate, connection success rate, service availability, throughput, network response time, error rate. Network operating conditions include, but are not limited to: device behavior, network load. The device behavior may include at least one of: request mode of device, connection duration, service usage frequency. The network load may include at least one of: traffic and pattern, number of device connections, network topology. Network performance parameters include, but are not limited to: average service usage frequency, average device connection duration.

It should be noted that, both the device behavior and the network load are factors that affect the network performance in addition to the network performance data. The request modes of the devices refer to different request behaviors of different devices, frequent large-scale data downloading, real-time video flow and the like, and the different request modes can influence the distribution and the load of network resources. The connection duration of different devices varies, some devices remain connected for a longer period of time, and some devices remain briefly connected, the connection duration affecting connection management and resource allocation in the network. For service usage frequencies, different services or applications may be used differently, and higher service frequencies may place greater loads on the network.

For traffic and patterns, peak time and traffic bursty conditions, as well as different types of data traffic, can have an impact on network load. The number of devices connected to the network may vary at different points in time, and may peak at a point in time, and the number of devices connected may also affect the capacity of the network and the allocation of resources. Network topology is the manner in which devices in a network are physically or logically connected, as well as the organization of the network, and may also affect network performance.

S503, the network analysis device predicts the target network performance of the equipment based on the network performance parameter and the network condition parameter.

In one possible embodiment, the implementation procedure of the network analysis apparatus for predicting the target network performance of the device based on the network performance parameter and the network condition parameter may be: the network analysis device determines predicted first network performance based on the network performance data and the network performance parameters; the network analysis device determines predicted second network performance based on the network operation condition and the network condition parameter; the network analysis means predicts a target network performance of the device based on the first network performance and the second network performance.

In one possible implementation, the predicted first network performance satisfies the following formula:

W＝f(X|θ)；

wherein W is predicted first network performance, f is at least one of a preset model or algorithm for processing network performance data, X is network performance data, and θ is a network performance parameter.

Alternatively, the model or algorithm f may be a linear regression model, a neural network, or the like.

In one possible implementation, the network analysis device processes the network performance data through statistical analysis or machine learning analysis to obtain a network performance parameter, then extracts any one network performance data from the plurality of network performance data of the device, and then inputs any one network performance data and the network performance parameter into a trained model or a preset formula to determine the predicted first network performance.

The network analysis device analyzes the network performance data to obtain bandwidth utilization, wherein the network performance data of the device comprises delay, bandwidth and packet loss rate, the network analysis device extracts the bandwidth data from the network performance data, and then analyzes and processes the bandwidth data and the bandwidth utilization through a formula or a model to determine the predicted first network performance.

In one possible implementation, the predicted second network performance satisfies the following formula:

V＝α·g(Z|φ)；

wherein V is predicted second network performance, alpha is a weight coefficient, g is at least one of a model or an algorithm for processing network operation conditions, Z is network operation conditions, and phi is a network condition parameter.

Alternatively, the model or algorithm g may be a linear regression model, a neural network, or the like.

In one possible implementation, in order to ensure the accuracy of the predicted network performance, network operation condition data that affects the network performance also needs to be processed. The network analysis device analyzes the network operation condition to determine the network operation parameter, then determines the weight coefficient of the network operation condition, wherein the weight coefficient is used for representing the influence degree of the network operation condition on the predicted target network performance, and finally processes the network operation condition, the network operation parameter and the weight coefficient through a preset model or algorithm to determine the second network performance affecting the predicted target network performance.

In one possible implementation, the network analysis means may predict the target network performance of the device based on the first network performance and the second network performance, i.e. predict the target network performance according to the following formula:

Y＝f(X|θ)+α·g(Z|φ)；

and Y in the formula represents the target network performance of the predicted equipment, the network analysis device can analyze and process the network performance data through the formula, obtain the first network performance based on the network performance data and the network performance parameters, process the network operation condition, obtain the second network performance based on the network operation condition, the network operation parameters and the weight coefficient, and finally predict the target network performance of the equipment by combining the first network performance and the second network performance.

It can be understood that the network performance data can be analyzed to obtain the network performance parameter and the predicted first network performance, and then the network operation condition is analyzed to obtain the network condition parameter and the predicted second network performance, wherein the network operation condition is other factors which can influence the target network performance of the device outside the network performance data, and the accuracy of the predicted target network performance can be improved by analyzing multiple factors.

S504, the network analysis device adjusts network parameters of the equipment based on the target network performance.

In one possible embodiment, the implementation procedure of the network analysis apparatus to adjust the network parameters of the device based on the target network performance may be: inputting the target network performance into a preset decision model to obtain corresponding decision logic; based on the decision logic, network parameters of the device are adjusted.

In one implementation manner, a data acquisition module in the network analysis device acquires network performance data and network operation conditions of the equipment, inputs the network performance data and the network operation conditions into a preset model, processes the network performance data and the network operation conditions to obtain predicted target network performance of the equipment, inputs the target network performance of the equipment into a preset decision model, and further automatically outputs a corresponding decision for the target network performance.

Illustratively, the network analysis device inputs bandwidth data, device behavior data, network load, security log, device information into a preset model, predicts future bandwidth utilization, and makes a plan for adjusting network parameters based on the predicted bandwidth utilization.

It can be understood that the method and the device can input the target network performance into the preset decision model, automatically adjust the network parameters of the device based on the decision logic generated by the preset decision model, improve the network performance, realize automatic management, reduce manual intervention, save labor cost, realize accurate and rapid management and control of the network performance, and rapidly optimize the network.

According to the network analysis method, the monitoring data of the equipment can be obtained, the monitoring data are processed to obtain the network performance parameters and the network condition parameters, the target network performance of the equipment is predicted according to the monitoring data and the network performance parameters and the network condition parameters, the network parameters of the equipment are automatically adjusted, the network performance of the equipment is automatically managed through adjustment of the network parameters of the equipment, and further the performance problem of the network can be timely and accurately processed, and the network is optimized.

In a possible embodiment, after adjusting the network parameters of the device based on the network performance, this embodiment provides a possible implementation on the basis of the method embodiment shown in fig. 5, and in connection with fig. 5, as shown in fig. 6, the method may be determined by the following S601 to S603.

S601, the network analysis device acquires current network performance data of the equipment.

Wherein the current network performance data is network performance data when the device is operating based on the current network parameters.

It should be appreciated that after the network analysis means adjusts the network parameters of the device, the network analysis means may continue to obtain the results or performance of the device after the decision is performed so that the network analysis means may continue to optimize the network performance based on the results or performance of the device.

S602, the network analysis device inputs the current network performance data into a preset feedback function to obtain feedback adjustment logic.

In one implementation, the network analysis device may derive the feedback adjustment logic using the following formula:

Q＝F(R)；

wherein R is current network performance data, F is a preset feedback function, and Q is the obtained valve deficiency adjustment logic. And inputting the current network performance data into a preset feedback function to obtain feedback adjustment logic.

S603, the network analysis device adjusts the current network parameters of the equipment based on the feedback adjustment logic.

In one manner that may be implemented, the network analysis device may make an automated decision using the following algorithm:

D＝M(I)·L(P)+F(R)；

wherein D is an automated decision made according to the monitoring data, I represents the monitoring data, M represents a preset model or algorithm, the monitoring data I is input into M, P is the output or result of M, L is a decision logic function, a specific action or decision path can be defined according to the output of M, F is a feedback function, network parameters can be continuously adjusted according to the actual performance of the decision, and R is the performance after the decision is executed.

In one possible implementation manner, the network analysis device obtains the predicted target network performance based on the monitoring data, then inputs the target network performance into the decision logic function to obtain the corresponding decision logic, further adjusts the network parameters, and the network analysis device obtains the network performance data of the device again, namely the current network performance data of the device, and continuously adjusts the network parameters of the device based on the current network performance data of the device.

The network monitoring device obtains the predicted bandwidth utilization rate based on the monitoring data, inputs the predicted bandwidth utilization rate into the decision logic function, adjusts the network parameters, increases the bandwidth in the peak period, and obtains the actual bandwidth utilization rate after the bandwidth adjustment to continuously adjust the network parameters so as to adapt to the change of the network flow.

It can be understood that after the network parameters of the device are adjusted based on the target network performance, the performance of the device after the network parameters are adjusted can be obtained, the current network performance data of the device is input into a preset feedback function, and the device is adjusted and optimized again through the feedback function.

It is understood that the above network analysis method may be implemented by a network analysis device. In order to realize the functions, the network analysis device comprises a hardware structure and/or a software module corresponding to each function. Those of skill in the art will readily appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware 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 disclosed embodiments.

The embodiment of the disclosure may divide the functional modules according to the network analysis device generated by the method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment disclosed in the present application, the division of the modules is merely a logic function division, and other division manners may be implemented in actual practice.

Fig. 7 is a schematic structural diagram of a network analysis device according to an embodiment of the present invention. As shown in fig. 7, the network analysis device 70 may be used to perform the network analysis methods shown in fig. 5-6. The network analysis device 70 includes: a communication unit 701 and a processing unit 702.

A communication unit 701, configured to acquire monitoring data of a device; the monitoring data comprises network performance data and network running conditions; network performance data includes, but is not limited to: delay, bandwidth, packet loss rate; network operating conditions include, but are not limited to: device behavior, network load;

a processing unit 702 for determining network performance parameters and network condition parameters of the device based on the monitoring data; network performance parameters include, but are not limited to: average delay, bandwidth utilization, packet loss; network operating conditions include, but are not limited to: device behavior, network load;

a processing unit 702, configured to predict a target network performance of the device based on the network performance parameter and the network condition parameter;

the processing unit 702 is further configured to adjust a network parameter of the device based on the target network performance.

In one possible implementation, the processing unit 702 is specifically configured to: determining a predicted first network performance based on the network performance data and the network performance parameter; determining a predicted second network performance based on the network operating condition and the network condition parameter; the target network performance of the device is predicted based on the first network performance and the second network performance.

In one possible implementation, the predicted first network performance satisfies the following formula: w=f (x|θ); wherein W is predicted first network performance, f is at least one of a preset model or algorithm for processing network performance data, X is network performance data, and θ is a network performance parameter.

In one possible implementation, the predicted second network performance satisfies the following formula: v=α·g (z|Φ); wherein V is predicted second network performance, alpha is a weight coefficient, g is at least one of a model or an algorithm for processing network operation conditions, Z is network operation conditions, and phi is a network condition parameter.

In one possible implementation, the processing unit 702 is specifically configured to: inputting the target network performance into a preset decision logic function to obtain corresponding decision logic; based on the decision logic, network parameters of the device are adjusted.

In one possible implementation, after adjusting the network parameters of the device based on the decision logic, the processing unit 702 is further configured to: acquiring current network performance data of equipment; the current network performance data is the network performance data when the equipment operates based on the current network parameters; inputting the current network performance data into a preset feedback function to obtain feedback adjustment logic; based on the feedback adjustment logic, current network parameters of the device are adjusted.

From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.

The present disclosure also provides a computer-readable storage medium having instructions stored thereon that, when executed by a processor of an electronic device, enable the electronic device to perform the network analysis method provided by the embodiments of the present disclosure described above.

The disclosed embodiments also provide a computer program product containing instructions that, when run on an electronic device, cause the electronic device to perform the network analysis method provided by the disclosed embodiments described above.

The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (Random Access Memory, RAM), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a register, a hard disk, an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuit, ASIC). In the context of the present application, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The foregoing is merely a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A method of network analysis, comprising:

acquiring monitoring data of equipment; the monitoring data comprises network performance data and network running conditions; the network performance data includes, but is not limited to: delay, bandwidth, packet loss rate; the network operating conditions include, but are not limited to: device behavior, network load;

determining network performance parameters and network condition parameters of the device based on the monitoring data; the network performance parameters include, but are not limited to: average delay, bandwidth utilization, packet loss; the network performance parameters include, but are not limited to: average service usage frequency, average device connection duration;

predicting a target network performance of the device based on the network performance parameter and the network condition parameter;

based on the target network performance, network parameters of the device are adjusted.

2. The method of claim 1, wherein predicting the target network performance of the device based on the network performance parameter and the network condition parameter comprises:

determining a predicted first network performance based on the network performance data and the network performance parameter;

determining a predicted second network performance based on the network operating condition and the network condition parameter;

predicting a target network performance of the device based on the first network performance and the second network performance.

3. The method of claim 2, wherein the predicted first network performance satisfies the following equation:

W＝f(X|θ)；

wherein W is predicted first network performance, f is at least one of a preset model or algorithm for processing network performance data, X is network performance data, and θ is a network performance parameter.

4. The method of claim 2, wherein the predicted second network performance satisfies the following equation:

V＝α·g(Z|φ)；

wherein V is predicted second network performance, alpha is a weight coefficient, g is at least one of a model or an algorithm for processing network operation conditions, Z is network operation conditions, and phi is a network condition parameter.

5. The method of claim 1, wherein said adjusting network parameters of the device based on the target network performance comprises:

inputting the target network performance into a preset decision logic function to obtain a corresponding decision logic;

based on the decision logic, network parameters of the device are adjusted.

6. The method of claim 5, further comprising, after said adjusting network parameters of said device based on said decision logic:

acquiring current network performance data of the equipment; the current network performance data is the network performance data of the equipment when running based on the current network parameters;

inputting the current network performance data into a preset feedback function to obtain feedback adjustment logic;

based on the feedback adjustment logic, current network parameters of the device are adjusted.

7. A network analysis device, the device comprising: a communication unit and a processing unit;

the communication unit is used for acquiring monitoring data of the equipment; the monitoring data comprises network performance data and network running conditions; the network performance data includes, but is not limited to: delay, bandwidth, packet loss rate; the network operating conditions include, but are not limited to: device behavior, network load;

The processing unit is used for determining network performance parameters and network condition parameters of the equipment based on the monitoring data; the network performance parameters include, but are not limited to: average delay, bandwidth utilization, packet loss; the network performance parameters include, but are not limited to: average service usage frequency, average device connection duration;

the processing unit is further configured to predict a target network performance of the device based on the network performance parameter and the network condition parameter;

the processing unit is further configured to adjust a network parameter of the device based on the target network performance.

8. A network analysis device, characterized in that the processing unit is specifically configured to:

determining a predicted first network performance based on the network performance data and the network performance parameter;

determining a predicted second network performance based on the network operating condition and the network condition parameter;

predicting a target network performance of the device based on the first network performance and the second network performance.

9. A network analysis device, comprising: a processor and a communication interface; the communication interface is coupled to the processor for running a computer program or instructions to implement the network analysis method as claimed in any one of claims 1-6.

10. A computer readable storage medium having instructions stored therein, characterized in that when executed by a computer, the computer performs the network analysis method as claimed in any one of the preceding claims 1-6.