An artificial intelligence-based network equipment operation risk assessment system

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

Technical Field

[0001] 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

[0002] 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.

[0003] 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.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] 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;

[0006] 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.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] 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;

[0009] 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;

[0010] 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;

[0011] The architecture management module is used to manage and analyze the network architecture.

[0012] 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.

[0013] 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.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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:

[0018] 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;

[0019] 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;

[0020] 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.

[0021] The present invention has the following beneficial effects:

[0022] 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;

[0023] 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 currently 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.

[0024] 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

[0025] 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.

[0026] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0027] Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0028] 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.

[0029] Embodiment 1

[0030] like Figure 1 As shown, 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.

[0031] 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 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.

[0032] The task allocation module is used to allocate and analyze the data processing tasks of the network equipment: when the network node receives the data processing task, the network equipment 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, and the allocation objects are arranged in the order of the aging coefficient LH value from small to large to obtain an allocation sequence, and the allocation sequence is analyzed by decision: the first to nth allocation objects in the allocation sequence are selected as the candidates, the sum of the basic values ​​of all the candidates is marked as the decision value, the sum of the basic values ​​of all the 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 Comparison is performed: 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.

[0033] 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 objects 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.

[0034] Embodiment 2

[0035] like Figure 2 As shown, a network equipment operation risk assessment method based on artificial intelligence includes the following steps:

[0036] 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;

[0037] 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;

[0038] 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.

[0039] 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 the aging coefficient LH, and judges whether the aging state of the analysis object meets the requirements through the aging coefficient LH; when the network node receives a data processing task, marks the network equipment under the network node as an allocation object, arranges the allocation objects in the order of the aging coefficient LH values ​​from small to large to obtain an allocation sequence, performs decision analysis on the allocation sequence and obtains the processing object; obtains the aging sequence and load sequence of the network architecture, performs numerical calculation on the sequence number of the analysis object in the aging sequence and the load sequence to obtain the reasonable coefficient of the network architecture, and judges whether the rationality of the network architecture layout meets the requirements through the reasonable coefficient.

[0040] 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.

[0041] 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 actual conditions; 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;

[0042] 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.

[0043] 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.

[0044] 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

1. A network equipment operation risk assessment system based on artificial intelligence, characterized in that: It includes a risk assessment platform, which 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 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; The specific process of performing decision analysis on the allocation sequence includes: selecting the first to nth allocation objects in the allocation sequence as the objects to be selected, marking the sum of the basic values ​​of all the objects to be selected as the decision value, marking the sum of the basic values ​​of all the objects to be selected 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, marking the object to be selected with the smallest sequence number as the 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 the objects to be selected, recalculating the decision coefficient of the objects to be selected, and so on, until the decision coefficient is less than the decision threshold and the processing object is marked; The specific process of the architecture management module managing and analyzing the network architecture includes: arranging all analysis objects in order of aging coefficient LH values ​​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 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.

2. According to the artificial intelligence-based network equipment operation risk assessment system of claim 1, it is characterized in that: The process of acquiring 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 acquiring the interruption data ZD includes: marking the number of times the analysis object performs data calculation tasks 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 acquiring the delay data YC includes: marking the number of times the analysis object performs data transmission tasks within the detection period as the transmission value, marking the number of times the analysis object has data transmission delays 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.

3. According to claim 2, a network equipment operation risk assessment system based on artificial intelligence is characterized in that: 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, 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 manager.

4. According to claim 3, a network equipment operation risk assessment system based on artificial intelligence is characterized in that: 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 manager.

5. A network equipment operation risk assessment system based on artificial intelligence according to any one of claims 1 to 4, characterized in that: 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.

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