CN118504232A - An artificial intelligence-based network equipment operation risk assessment system - Google Patents

Abstract

The present invention belongs to the field of network equipment and relates to data analysis technology. It is used to solve the problem that the network equipment operation risk assessment system in the prior art cannot optimize the load according to the aging analysis results of the network equipment. Specifically, it is a network equipment operation risk assessment system based on artificial intelligence, including a risk assessment platform. The risk assessment platform is communicatively connected with an aging analysis module, a task allocation module, an architecture management module and a storage module; the aging analysis module is used to detect and analyze the aging status of the network equipment; the task allocation module is used to allocate and analyze the data processing tasks of the network equipment; the architecture management module is used to manage and analyze the network architecture; the present invention can detect and analyze the aging status of the network equipment, evaluate the aging status of the network equipment according to the aging coefficient, replace the analysis object that does not meet the use requirements, and improve the operation stability of the network architecture.

Description

An artificial intelligence-based network equipment operation risk assessment system

Technical Field

The present invention belongs to the field of network equipment and relates to data analysis technology, and specifically is a network equipment operation risk assessment system based on artificial intelligence.

Background Art

With the rapid development of information technology, network equipment has become an indispensable infrastructure for enterprise operations. However, network equipment operations face a variety of risks. For example, equipment performance stability is the basis of network equipment operations. If equipment performance is unstable, it may lead to network interruptions, data transmission delays and other problems, seriously affecting the business operations of enterprises.

The network equipment operation risk assessment system in the prior art can only monitor the operational stability of the network equipment, but cannot optimize the load according to the aging analysis results of the network equipment, so that the load of the network equipment cannot be dynamically balanced, resulting in poor operational stability of the network equipment.

In view of the above technical problems, this application proposes a solution.

Summary of the invention

The purpose of the present invention is to provide a network equipment operation risk assessment system based on artificial intelligence, which is used to solve the problem that the network equipment operation risk assessment system in the prior art cannot perform load optimization according to the aging analysis results of the network equipment;

The technical problem to be solved by the present invention is: how to provide an artificial intelligence-based network equipment operation risk assessment system that can perform load optimization according to the aging analysis results of the network equipment.

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

An artificial intelligence-based network equipment operation risk assessment system includes a risk assessment platform, wherein the risk assessment platform is communicatively connected to an aging analysis module, a task allocation module, an architecture management module, and a storage module;

The aging analysis module is used to detect and analyze the aging status of network devices: mark the network devices covered by the network architecture as analysis objects, generate a detection cycle, obtain the life data SM, interruption data ZD and delay data YC of the analysis object within the detection cycle; obtain the aging coefficient LH of the analysis object within the detection cycle by numerically calculating the life data SM, interruption data ZD and delay data YC; determine whether the aging status of the analysis object meets the requirements by the aging coefficient LH;

The task allocation module is used to allocate and analyze the data processing tasks of the network devices: when the network node receives the data processing task, the network device under the network node is marked as the allocation object, the data packet memory value of the unfinished data processing task of the allocation object is marked as the basic value of the allocation object, the allocation objects are arranged in the order of the aging coefficient LH value from small to large to obtain the allocation sequence, the allocation sequence is analyzed for decision making to obtain the processing object, and the data processing task is sent to the processing object;

The architecture management module is used to manage and analyze the network architecture.

As a preferred embodiment of the present invention, the process of obtaining the life data SM includes: marking the difference between the current system time and the factory time of the analysis object as the operating time, and marking the ratio of the operating time to the rated life of the analysis object as the life data SM; the process of obtaining the interruption data ZD includes: marking the number of times the analysis object performs a data calculation task within the detection period as the processing value of the analysis object, marking the number of times the analysis object causes network interruption within the detection period as the interruption value, and marking the ratio of the interruption value to the processing value as the interruption data ZD; the process of obtaining the delay data YC includes: marking the number of times the analysis object performs a data transmission task within the detection period as the transmission value, marking the number of times the analysis object has a data transmission delay within the detection period as the delay value, and marking the ratio of the delay value to the transmission value as the delay data YC.

As a preferred embodiment of the present invention, the specific process of determining whether the aging status of the analysis object meets the requirements includes: obtaining the aging threshold LHmax through the storage module, and comparing the aging coefficient LH with the aging threshold LHmax: if the aging coefficient LH is less than the aging threshold LHmax, then it is determined that the aging status of the analysis object meets the requirements; if the aging coefficient LH is greater than or equal to the aging threshold LHmax, then it is determined that the aging status of the analysis object does not meet the requirements, generating a device update signal and sending the device update signal to the risk assessment platform, and after receiving the device update signal, the risk assessment platform sends the device update signal to the mobile phone terminal of the administrator.

As a preferred embodiment of the present invention, the specific process of performing decision analysis on the allocation sequence includes: selecting the first to nth allocation objects in the allocation sequence as candidates, marking the sum of the basic values of all candidates as the decision value, marking the sum of the basic values of all allocation objects as the basic performance value, marking the absolute value of the difference between the decision value and the basic performance value as the decision coefficient, obtaining the decision threshold through the storage module, and comparing the decision coefficient with the decision threshold: if the decision coefficient is less than the decision threshold, the candidate object with the smallest serial number is marked as the processing object, and the data processing task is sent to the processing object; if the decision coefficient is greater than or equal to the decision threshold, the second to n+1th allocation objects in the allocation sequence are selected as candidates, and the decision coefficient of the candidate object is recalculated, and so on, until the decision coefficient is less than the decision threshold and the processing object is marked.

As a preferred embodiment of the present invention, the specific process of the architecture management module managing and analyzing the network architecture includes: arranging all analysis objects in order of the aging coefficient LH value from small to large to obtain the aging sequence of the network architecture, obtaining the sum of the memory values of the data packets of the analysis objects performing all data processing tasks in the detection period and marking them as the load value of the analysis objects, arranging the analysis objects in order of the load values from large to small to obtain the load sequence of the network architecture, marking the absolute value of the difference between the sequence number of the analysis object in the aging sequence and the sequence number in the load sequence as the reasonable value of the analysis object, summing and averaging the reasonable values of all analysis objects to obtain the reasonable coefficient of the network architecture, and judging whether the rationality of the network architecture layout meets the requirements through the reasonable coefficient.

As a preferred embodiment of the present invention, the specific process of determining whether the rationality of the network architecture layout meets the requirements includes: obtaining a reasonable threshold through a storage module, and comparing a reasonable coefficient with the reasonable threshold: if the reasonable coefficient is less than the reasonable threshold, then the rationality of the network architecture layout is determined to meet the requirements; if the reasonable coefficient is greater than or equal to the reasonable threshold, then the rationality of the network architecture layout is determined to not meet the requirements, and an architecture optimization signal is generated and sent to a risk assessment platform. After receiving the architecture optimization signal, the risk assessment platform sends the architecture optimization signal to the mobile phone terminal of the administrator.

As a preferred embodiment of the present invention, the working method of the network equipment operation risk assessment system based on artificial intelligence includes the following steps:

Step 1: Detect and analyze the aging status of network devices: mark the network devices covered by the network architecture as analysis objects, generate a detection cycle, obtain the life data SM, interruption data ZD and delay data YC of the analysis object within the detection cycle, and perform numerical calculations to obtain the aging coefficient LH. The aging coefficient LH is used to determine whether the aging status of the analysis object meets the requirements;

Step 2: Perform allocation analysis on the data processing tasks of the network devices: When a network node receives a data processing task, the network devices under the network node are marked as allocation objects, and the allocation objects are arranged in order from small to large according to the aging coefficient LH values to obtain an allocation sequence, and a decision analysis is performed on the allocation sequence to obtain the processing object;

Step 3: Manage and analyze the network architecture: obtain the aging sequence and load sequence of the network architecture, perform numerical calculations on the sequence numbers of the analysis objects in the aging sequence and load sequence to obtain the reasonable coefficient of the network architecture, and use the reasonable coefficient to determine whether the rationality of the network architecture layout meets the requirements.

The present invention has the following beneficial effects:

1. The aging analysis module can detect and analyze the aging status of network equipment, and obtain the aging coefficient by statistically analyzing the various operating parameters of the analysis object within the detection period. The aging status of the network equipment can be evaluated according to the aging coefficient, and the analysis object that does not meet the use requirements can be replaced to improve the operation stability of the network architecture;

2. The task allocation module can be used to allocate and analyze the data processing tasks of network devices. The allocation sequence is obtained by sorting the allocation objects based on the aging coefficient of the allocation objects. The decision analysis is performed based on the aging status of the allocation objects and the data packet memory value of the current unfinished data processing tasks. The allocation objects with the highest comprehensive priority are selected for task processing, thereby improving data processing efficiency and reducing the risk of failures as a whole.

3. The architecture management module can be used to manage and analyze the network architecture. On the basis of dynamic task allocation, the aging status and task processing volume of all assigned objects are comprehensively analyzed and calculated to obtain a reasonable coefficient. Under the premise of ensuring the rationality of task allocation, when the aging status of the assigned objects and the task processing volume are poorly matched, the network architecture is optimized and the network layout is adjusted from a macro perspective.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a system block diagram of Embodiment 1 of the present invention;

FIG. 2 is a flow chart of a method according to a second embodiment of the present invention.

DETAILED DESCRIPTION

The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Embodiment 1

As shown in FIG1 , an artificial intelligence-based network equipment operation risk assessment system includes a risk assessment platform, which is communicatively connected to an aging analysis module, a task allocation module, an architecture management module, and a storage module.

The aging analysis module is used to detect and analyze the aging status of network equipment: mark the network equipment covered by the network architecture as the analysis object, generate a detection cycle, and obtain the life data SM, interruption data ZD and delay data YC of the analysis object within the detection cycle. The process of obtaining the life data SM includes: marking the difference between the current system time and the factory time of the analysis object as the operating time, and marking the ratio of the operating time to the rated life of the analysis object as the life data SM; the process of obtaining the interruption data ZD includes: marking the number of times the analysis object performs data calculation tasks within the detection cycle as the processing value of the analysis object, marking the number of times the analysis object causes network interruption within the detection cycle as the interruption value, and marking the ratio of the interruption value to the processing value as the interruption data ZD; the process of obtaining the delay data YC includes: marking the number of times the analysis object performs data transmission tasks within the detection cycle as the transmission value, marking the number of times the analysis object has data transmission delay within the detection cycle as the delay value, and marking the ratio of the delay value to the transmission value as the delay data YC; through the formula L H＝k1*SM+k2*ZD+k3*YC to obtain the aging coefficient LH of the analysis object within the detection period, wherein k1, k2 and k3 are all proportional coefficients, and k1＞k2＞k3＞1; the aging threshold LHmax is obtained through the storage module, and the aging coefficient LH is compared with the aging threshold LHmax: if the aging coefficient LH is less than the aging threshold LHmax, it is determined that the aging state of the analysis object meets the requirements; if the aging coefficient LH is greater than or equal to the aging threshold LHmax, it is determined that the aging state of the analysis object does not meet the requirements, and a device update signal is generated and sent to the risk assessment platform. After receiving the device update signal, the risk assessment platform sends the device update signal to the mobile phone terminal of the administrator; the aging state of the network equipment is detected and analyzed, and the various operating parameters of the analysis object within the detection period are statistically analyzed to obtain the aging coefficient, so as to evaluate the aging state of the network equipment according to the aging coefficient, replace the analysis object that does not meet the use requirements, and improve the operation stability of the network architecture.

The task allocation module is used to allocate and analyze the data processing tasks of network devices: when a network node receives a data processing task, the network device under the network node is marked as an allocation object, the data packet memory value of the unfinished data processing task of the allocation object is marked as the basic value of the allocation object, and the allocation objects are arranged in order from small to large according to the aging coefficient LH value to obtain an allocation sequence, and a decision analysis is performed on the allocation sequence: the first to nth allocation objects in the allocation sequence are selected as candidates, the sum of the basic values of all candidates is marked as the decision value, the sum of the basic values of all allocation objects is marked as the basic performance value, the absolute value of the difference between the decision value and the basic performance value is marked as the decision coefficient, the decision threshold is obtained through the storage module, and the decision coefficient is compared with the decision threshold Compare: if the decision coefficient is less than the decision threshold, the candidate object with the smallest serial number is marked as the processing object; if the decision coefficient is greater than or equal to the decision threshold, the second to n+1th allocation objects in the allocation sequence are selected as the candidate objects, and the decision coefficients of the candidate objects are recalculated, and so on, until the decision coefficient is less than the decision threshold and the processing object is marked, and the data processing task is sent to the processing object; the data processing tasks of the network equipment are allocated and analyzed, and the allocation sequence is obtained by sorting the allocation objects based on the aging coefficient of the allocation objects. The decision analysis is performed based on the aging status of the allocation objects and the data packet memory value of the current unfinished data processing tasks, and the allocation object with the highest comprehensive priority is selected for task processing, thereby improving data processing efficiency and reducing failure risks as a whole.

The architecture management module is used to manage and analyze the network architecture: all analysis objects are arranged in the order of aging coefficient LH values from small to large to obtain the aging sequence of the network architecture, the sum of the memory values of the data packets of the analysis object performing all data processing tasks in the detection period is obtained and marked as the load value of the analysis object, the analysis objects are arranged in the order of load values from large to small to obtain the load sequence of the network architecture, the absolute value of the difference between the sequence number of the analysis object in the aging sequence and the sequence number in the load sequence is marked as the reasonable value of the analysis object, the reasonable values of all analysis objects are summed and averaged to obtain the reasonable coefficient of the network architecture, the reasonable threshold is obtained through the storage module, and the reasonable coefficient is compared with the reasonable threshold: if the reasonable coefficient is If the number is less than the reasonable threshold, the rationality of the network architecture layout is determined to meet the requirements; if the reasonable coefficient is greater than or equal to the reasonable threshold, the rationality of the network architecture layout is determined to not meet the requirements, and an architecture optimization signal is generated and sent to the risk assessment platform. After receiving the architecture optimization signal, the risk assessment platform sends the architecture optimization signal to the mobile terminal of the manager; the network architecture is managed and analyzed, and on the basis of dynamic task allocation, the aging status and task processing volume of all allocated objects are comprehensively analyzed and calculated to obtain a reasonable coefficient. Under the premise of ensuring the rationality of task allocation, when the aging status of the allocated objects and the task processing volume are poorly matched, the network architecture is optimized, and the network layout is adjusted from a macro perspective.

Embodiment 2

As shown in FIG2 , a network equipment operation risk assessment method based on artificial intelligence includes the following steps:

Step 1: Detect and analyze the aging status of network devices: mark the network devices covered by the network architecture as analysis objects, generate a detection cycle, obtain the life data SM, interruption data ZD and delay data YC of the analysis object within the detection cycle, and perform numerical calculations to obtain the aging coefficient LH. The aging coefficient LH is used to determine whether the aging status of the analysis object meets the requirements;

Step 2: Perform allocation analysis on the data processing tasks of the network devices: When a network node receives a data processing task, the network devices under the network node are marked as allocation objects, and the allocation objects are arranged in order from small to large according to the aging coefficient LH values to obtain an allocation sequence, and a decision analysis is performed on the allocation sequence to obtain the processing object;

Step 3: Manage and analyze the network architecture: obtain the aging sequence and load sequence of the network architecture, perform numerical calculations on the sequence numbers of the analysis objects in the aging sequence and load sequence to obtain the reasonable coefficient of the network architecture, and use the reasonable coefficient to determine whether the rationality of the network architecture layout meets the requirements.

A network equipment operation risk assessment system based on artificial intelligence, when working, marks the network equipment covered by the network architecture as an analysis object, generates a detection cycle, obtains the life data SM, interruption data ZD and delay data YC of the analysis object in the detection cycle, and performs numerical calculation to obtain an aging coefficient LH, and judges whether the aging state of the analysis object meets the requirements through the aging coefficient LH; when a network node receives a data processing task, marks the network equipment under the network node as an allocation object, arranges the allocation objects in order from small to large according to the values of the aging coefficient LH, obtains an allocation sequence, performs decision analysis on the allocation sequence and obtains a processing object; obtains the aging sequence and load sequence of the network architecture, performs numerical calculation on the sequence numbers of the analysis objects in the aging sequence and the load sequence to obtain a reasonable coefficient of the network architecture, and judges whether the rationality of the network architecture layout meets the requirements through the reasonable coefficient.

The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; for example: formula LH = k1*SM + k2*ZD + k3*YC; technicians in this field collect multiple groups of sample data and set corresponding aging coefficients for each group of sample data; substitute the set aging coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation group, screen the calculated coefficients and take the average, and obtain the values of k1, k2 and k3 as 2.63, 2.01 and 1.85 respectively;

The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding aging coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the aging coefficient is proportional to the value of the life data.

In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims (7)

1. The network equipment operation risk assessment system based on the artificial intelligence is characterized by comprising a risk assessment platform, wherein the risk assessment platform is in communication connection with an aging analysis module, a task allocation module, a framework management module and a storage module;

The aging analysis module is used for detecting and analyzing the aging state of the network equipment: marking network equipment covered by a network architecture as an analysis object, generating a detection period, and acquiring life data SM, interrupt data ZD and delay data YC of the analysis object in the detection period; obtaining an aging coefficient LH of an analysis object in a detection period by carrying out numerical calculation on life data SM, interrupt data ZD and delay data YC; judging whether the aging state of the analysis object meets the requirement or not through an aging coefficient LH;

The task allocation module is used for carrying out allocation analysis on the data processing tasks of the network equipment: when a network node receives a data processing task, marking network equipment under the network node as an allocation object, marking a data packet memory value of the data processing task which is not completed by the allocation object as a base value of the allocation object, arranging the allocation object according to the sequence of the aging coefficient LH from small to large to obtain an allocation sequence, carrying out decision analysis on the allocation sequence to obtain a processing object, and sending the data processing task to the processing object;

The architecture management module is used for performing management analysis on the network architecture.

2. The network device operation risk assessment system based on artificial intelligence according to claim 1, wherein the process of obtaining life data SM comprises: marking a difference value between the current system time and the delivery time of the analysis object as an operation time length, and marking a ratio of the operation time length to the rated life of the analysis object as life data SM; the process of obtaining the interrupt data ZD comprises: marking the times of executing the data calculation task by the analysis object in the detection period as the processing value of the analysis object, marking the times of causing network interruption by the analysis object in the detection period as an interruption value, and marking the ratio of the interruption value to the processing value as interruption data ZD; the acquisition process of the delay data YC includes: the number of times that the analysis object performs the data transmission task in the detection period is marked as a transmission value, the number of times that the analysis object has data transmission delay in the detection period is marked as a delay value, and the ratio of the delay value to the transmission value is marked as delay data YC.

3. The artificial intelligence based network equipment operation risk assessment system according to claim 2, wherein the specific process of determining whether the aging state of the analysis object meets the requirement comprises: the aging threshold LHmax is obtained by the storage module, and the aging coefficient LH is compared with the aging threshold LHmax: if the aging coefficient LH is smaller than the aging threshold LHmax, judging that the aging state of the analysis object meets the requirement; if the aging coefficient LH is greater than or equal to the aging threshold LHmax, determining that the aging state of the analysis object does not meet the requirement, generating an equipment update signal, sending the equipment update signal to a risk assessment platform, and sending the equipment update signal to a mobile phone terminal of a manager after the risk assessment platform receives the equipment update signal.

4. A network device operation risk assessment system based on artificial intelligence according to claim 3, wherein the specific process of decision analysis of the allocation sequence comprises: selecting first to nth allocation objects in the allocation sequence as objects to be selected, marking the sum of basic values of all the objects to be selected as decision values, marking the sum of basic values of all the allocation objects as basic representation values, marking the absolute value of the difference value between the decision values and the basic representation values as decision coefficients, acquiring decision thresholds through a storage module, and comparing the decision coefficients with the decision thresholds: if the decision coefficient is smaller than the decision threshold, marking the object to be selected with the minimum sequence number as a processing object, and sending the data processing task to the processing object; if the decision coefficient is greater than or equal to the decision threshold, selecting the second to n+1th allocation objects in the allocation sequence as objects to be selected, recalculating the decision coefficient of the objects to be selected, and the like until the decision coefficient is smaller than the decision threshold and marking the processing objects.

5. The system for risk assessment of network equipment operation according to claim 4, wherein the specific process of the architecture management module for management analysis of the network architecture comprises: arranging all analysis objects according to the sequence of the aging coefficient LH values from small to large to obtain an aging sequence of the network architecture, obtaining the sum value of the memory values of the data packets of the analysis objects for executing all data processing tasks in a detection period, marking the sum value as the load value of the analysis objects, arranging the analysis objects according to the sequence of the load values from large to small to obtain a load sequence of the network architecture, marking the absolute value of the difference value between the serial numbers of the analysis objects in the aging sequence and the serial numbers of the load sequence as the reasonable value of the analysis objects, summing the reasonable values of all the analysis objects, averaging to obtain the reasonable coefficient of the network architecture, and judging whether the rationality of the network architecture layout meets the requirement or not through the reasonable coefficient.

6. The artificial intelligence based network equipment operation risk assessment system according to claim 5, wherein the specific process of determining whether the network architecture layout rationality meets the requirement comprises: the reasonable threshold value is obtained through the storage module, and the reasonable coefficient is compared with the reasonable threshold value: if the reasonable coefficient is smaller than the reasonable threshold, judging that the rationality of the network architecture layout meets the requirement; if the reasonable coefficient is greater than or equal to the reasonable threshold, judging that the rationality of the network architecture layout does not meet the requirement, generating an architecture optimization signal, sending the architecture optimization signal to a risk assessment platform, and sending the architecture optimization signal to a mobile phone terminal of a manager after the risk assessment platform receives the architecture optimization signal.

7. An artificial intelligence based network equipment operation risk assessment system according to any of claims 1-6, characterized in that the working method of the artificial intelligence based network equipment operation risk assessment system comprises the following steps:

Step one: detecting and analyzing the aging state of the network equipment: marking network equipment covered by a network architecture as an analysis object, generating a detection period, acquiring life data SM, interrupt data ZD and delay data YC of the analysis object in the detection period, performing numerical value calculation to obtain an aging coefficient LH, and judging whether the aging state of the analysis object meets the requirement or not through the aging coefficient LH;

Step two: and carrying out distribution analysis on the data processing tasks of the network equipment: when a network node receives a data processing task, marking network equipment under the network node as an allocation object, arranging the allocation object according to the sequence of the aging coefficient LH from small to large to obtain an allocation sequence, and carrying out decision analysis on the allocation sequence to obtain a processing object;

Step three: management analysis is carried out on the network architecture: and (3) acquiring an aging sequence and a load sequence of the network architecture, carrying out numerical calculation on sequence numbers of the analysis objects in the aging sequence and the load sequence to obtain reasonable coefficients of the network architecture, and judging whether the rationality of the network architecture layout meets the requirement or not through the reasonable coefficients.