System aging state detection method, device and equipment and readable storage medium

Abstract

The embodiment of the application discloses a system aging state detection method, device, equipment and readable storage medium, the method comprises: obtaining at least one running state of the system to be detected and at least one state transition model corresponding to the running state; obtain the target parameter corresponding to the state transition model, and obtain the first weight of the target parameter by hierarchical analysis; determine the anti-decay index corresponding to each running state based on the target parameter and the first weight corresponding to the target parameter, and determine the aging state information of the system to be detected according to the anti-decay index. By analyzing the target parameter to determine the weight corresponding to the target parameter, the association between the target parameter and the system aging state is strengthened, so that the anti-decay index determined by the target parameter and the weight corresponding to the target parameter is more objective and accurate, thereby improving the effectiveness and accuracy of the aging state detection of the system to be detected when determining the aging state information of the system to be detected based on the anti-decay index.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a system aging state detection method, apparatus, device, and computer-readable storage medium. Background Technology

[0002] Over extended periods of operation, software systems inevitably experience aging phenomena such as performance degradation and abnormal states. These aging issues plague developers and users, posing a significant challenge to subsequent security and maintenance. For large-scale software systems (such as cloud service systems), the requirements for security and stability are typically even higher. Therefore, anti-aging measures are particularly crucial, and the core of anti-aging lies in the accurate and effective assessment and prediction of the software system's aging state.

[0003] In related technologies, one approach is to predict system aging based on fixed time intervals. This involves using an iterative model to determine the system maintenance cycle at fixed intervals. However, this method uses a model with fixed parameters, and the model's parameters need to be adjusted according to system fluctuations to predict the system's aging state. Therefore, it lacks real-time adaptability to system operating conditions, reducing the accuracy of system aging state detection. Another approach is to predict system aging based on real-time detection of key parameters. This method predicts whether the system has entered an aging state based on core hardware parameters. However, there may be a problem that changes in core hardware parameters are not entirely correlated with the system's aging state, making it difficult to guarantee the effectiveness and accuracy of system aging state detection. Summary of the Invention

[0004] To address the aforementioned technical problems, this application aims to provide a system aging state detection method that can improve the effectiveness and accuracy of system aging state detection.

[0005] The technical solution of the application embodiment is implemented as follows:

[0006] This application provides a system aging state detection method, including:

[0007] Obtain at least one operating state of the system to be tested, and the state transition model corresponding to the at least one operating state;

[0008] Obtain the target parameters corresponding to the state transition model when the system under test is in each operating state, perform hierarchical analysis on the target parameters, and obtain the first weight corresponding to the target parameters;

[0009] Based on the target parameters and the first weights corresponding to the target parameters, the anti-aging index corresponding to each of the at least one operating state is determined;

[0010] Based on the anti-aging index, the current aging status information of the system under test is determined.

[0011] This application provides a system aging state detection device, including:

[0012] The first acquisition module is used to acquire at least one operating state of the system to be detected, and the state transition model corresponding to the at least one operating state.

[0013] The second acquisition module is used to acquire the target parameters corresponding to the state transition model when the system under test is in various operating states, perform hierarchical analysis on the target parameters, and obtain the first weight corresponding to the target parameters.

[0014] The first determining module is used to determine the anti-aging index corresponding to each of the at least one operating state based on the target parameter and the first weight corresponding to the target parameter;

[0015] The second determining module is used to determine the current aging state information of the system under test based on the anti-aging index.

[0016] This application provides a system aging status detection device, including:

[0017] Memory, used to store executable system aging state detection instructions;

[0018] The processor, when executing executable system aging state detection instructions stored in the memory, implements the system aging state detection method provided in the embodiments of this application.

[0019] This application provides a computer-readable storage medium storing computer-executable system aging state detection instructions, which are configured to execute the system aging state detection method provided in this application.

[0020] This application provides a method, apparatus, device, and computer-readable storage medium for detecting the aging state of a system. Using this technical solution, firstly, at least one operating state of the system to be tested and the corresponding state transition model for each operating state are obtained. Then, target parameters corresponding to the state transition model for each operating state of the system to be tested are obtained, and hierarchical analysis is performed on the target parameters to obtain the first weights corresponding to the target parameters. Finally, based on the target parameters and their corresponding first weights, an anti-aging index corresponding to each operating state is determined, and the current aging state information of the system to be tested is determined based on the obtained anti-aging index. Thus, by performing hierarchical analysis on the target parameters to determine their corresponding weights, the correlation between the target parameters and the system aging state is strengthened, making the anti-aging index determined by the target parameters and their corresponding weights more objective and accurate. This improves the effectiveness and accuracy of detecting the aging state of the system to be tested when determining its aging state information based on the anti-aging index. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of a system aging state detection method provided in an embodiment of this application;

[0022] Figure 2 A schematic diagram of the operational state transition process of a system under test provided in the application embodiment;

[0023] Figure 3 A schematic diagram of a hierarchical analysis structure provided in an embodiment of this application;

[0024] Figure 4 This application provides a schematic flowchart of a method for determining aging status information of a software system.

[0025] Figure 5 This is a schematic diagram of the composition structure of a system aging status detection device provided in an embodiment of this application;

[0026] Figure 6 This is a schematic diagram of the composition structure of a system aging status detection device provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] In the following description, references to “some embodiments” or “other embodiments” describe a subset of all possible embodiments. However, it is understood that “some embodiments” or “other embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0030] In the following description, the terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0031] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this application is for the purpose of describing embodiments of this application only and is not intended to be limiting of this application.

[0032] With the increasing trend of large-scale software systems moving to the cloud, the secure and stable operation of cloud-based software systems has become a core technical indicator for cloud-based software services. Unlike terminal software systems, cloud-based software systems have higher reliability requirements. However, during long-term operation, cloud-based software systems may experience performance degradation, abnormal states, instability, or even sudden failures due to untimely process and thread termination and accumulated data errors, severely impacting the secure and stable operation of cloud systems.

[0033] In response to the aging phenomenon of software systems, Bell Labs first proposed Software System Rejuvenation (SSR) technology. This technology is a proactive maintenance technique for software system aging. It actively identifies the aging state of the software and, once it determines that the software system has entered an aging state, it clears the internal error states and parameters of the software system by restarting the core modules in a step-by-step manner, thereby improving the overall performance of the software system. The key to realizing SSR is to identify or predict the aging state of the software system in real time, accurately and effectively.

[0034] Among related technologies, software system aging prediction methods mainly include: software system aging prediction methods based on fixed time intervals and software system aging prediction methods based on real-time detection of important parameters.

[0035] Software system aging prediction methods based on fixed time intervals determine the software system maintenance cycle at fixed intervals using an iterative model. During the maintenance period, anti-aging operations are performed on the software system without affecting the main maintenance. Common methods for determining the time intervals include using maintenance experience or simulating a state transition model with fixed parameters using data. The drawback of this method is its heavy reliance on past maintenance experience and its reliance on a purely theoretical framework. After establishing the simulation model, it lacks real-time adaptability to the operational status of cloud-based software systems. When the software system experiences significant operational fluctuations, it lacks flexible judgment capabilities, requiring modifications to the model's parameters and structure to continue predicting and sensing the aging state of the cloud-based software system. This can significantly impact the stability of the cloud-based software system.

[0036] A software system aging prediction method based on real-time detection of key parameters uses basic detection techniques to collect core hardware parameters involved in the operation of cloud-based software systems. After predicting that the software system is about to enter an aging state, anti-aging operations are performed in real time. However, this method faces the challenge that parameter changes are not always entirely correlated with the aging state of the software system, and it lacks the ability to quantify and understand other factors affecting the operation of the software system, making it difficult to guarantee the effectiveness and accuracy of the software system aging prediction.

[0037] In view of the above-mentioned problems in related technologies, this application provides a system aging state detection method, which can improve the effectiveness and accuracy of system aging state detection.

[0038] The system aging state detection method provided in the embodiments of this application will be described below, such as... Figure 1 The diagram shown is a flowchart of a system aging state detection method provided in an embodiment of this application. The method includes the following steps:

[0039] S101. Obtain at least one operating state of the system to be detected, and the state transition model corresponding to at least one operating state.

[0040] It should be noted that the operating state can refer to the state of the system under test during operation, such as task processing state, task completion state, anti-aging state, etc. There can be one or more operating states, and multiple operating states can correspond to different moments or times. During the operation of the system under test, as time progresses, the system can transition from one operating state to another. In practice, multiple operating states can be determined based on the different speeds at which the system processes tasks.

[0041] In some embodiments, multiple operating states of the system under test correspond to a state transition model, which can simulate the state transition process of each operating state. For example... Figure 2 The diagram shown is a schematic representation of the operational state transition process of a system under test according to an embodiment of this application. Figure 2 It can be seen that the system under test includes j operating states, S1-Sj. The state transitions from S1 to Sj follow a two-parameter Weiber distribution, with state transitions occurring sequentially from S1 to Sj. The transition relationship is irreversible. Here, j is the preset maximum number of operating states of the system under test, and the maximum number of states can be determined according to different operating environments and maintenance cycles. S0 is the initial state of the system under test; Sf is the completed state of the cloud system; Sr is the anti-aging state of the cloud system; and D1-Dj are the time costs of performing anti-aging in states S1-Sj. In practice, any operating state of the system under test may transition to the completed state Sf or the anti-aging state Sr, and any operating state may be detected or determined to be an aging state.

[0042] S102. Obtain the target parameters corresponding to the state transition model when the system under test is in each operating state, perform hierarchical analysis on the target parameters, and obtain the first weight corresponding to the target parameters.

[0043] It should be noted that the target parameters may include the model parameters corresponding to the state transition model and the core hardware parameters. The model parameters are associated with the state transition model corresponding to each operating state. The model parameters may be the maximum number of anti-aging cycles and the probability of completing the task when the system under test is in different operating states. The core hardware parameters may represent the performance indicators of the system under test during operation, such as the utilization rate of the central processing unit (CPU) and the disk read / write speed.

[0044] In some embodiments, the target parameters obtained may differ when the system under test is in different operating states. Multiple target parameters may be obtained under the same operating state. After obtaining the target parameters corresponding to each operating state, the hierarchical analysis method can be used to analyze the target parameters and obtain the first weight corresponding to each target parameter. In implementation, the system's decision objective can be obtained, a hierarchical analysis structure can be established based on the system's decision objective, and the importance value corresponding to the target parameter in the hierarchical analysis structure can be determined. The corresponding judgment matrix can be determined based on the importance value of the target parameter, and the first weight corresponding to the target parameter can be determined based on the eigenvalues ​​of the judgment matrix. In practice, the first weights corresponding to different target parameters may be different. Target parameters with larger first weight values ​​have a greater influence on the detection of the aging state of the system under test than target parameters with smaller first weight values.

[0045] S103. Based on the target parameters and the first weights corresponding to the target parameters, determine the anti-aging index corresponding to at least one operating state.

[0046] In some embodiments, the anti-aging index can represent the aging state of the system under test. Once the target parameters and the first weights corresponding to the target parameters are obtained, the anti-aging index corresponding to each operating state can be determined based on the target parameters and the first weights of the target parameters. Based on the magnitude of the anti-aging index, the degree of aging or aging level of the system under test in each operating state can be determined.

[0047] S104. Based on the anti-aging index, determine the current aging status information of the system to be tested.

[0048] In some embodiments, after obtaining the anti-aging index for each operating state, the current operating state of the system to be tested can be predicted based on the obtained multiple anti-aging indices to determine the current aging state information of the system to be tested. The aging state information may include information such as aging degree and aging level. The aging degree may include low aging degree, medium aging degree, high aging degree, and high aging degree. The aging level information may include level 1, level 2, level 3, level 4, etc. In some embodiments, the aging degree may gradually increase with the increase of the level; in other embodiments, the aging degree may gradually increase with the decrease of the level.

[0049] In the system aging state detection method provided in this application embodiment, firstly, at least one operating state of the system to be tested and the corresponding state transition model for the at least one operating state are obtained. Then, the target parameters corresponding to the state transition model when the system to be tested is in each operating state are obtained, and hierarchical analysis is performed on the target parameters to obtain the first weights corresponding to the target parameters. Finally, based on the target parameters and the first weights corresponding to the target parameters, the anti-aging index corresponding to each operating state is determined, and the current aging state information of the system to be tested is determined according to the obtained anti-aging index. In this way, by performing hierarchical analysis on the target parameters to determine the weights corresponding to the target parameters, the correlation between the target parameters and the system aging state is strengthened, making the anti-aging index determined by the target parameters and the weights corresponding to the target parameters more objective and accurate. Thus, when determining the aging state information of the system to be tested based on the anti-aging index, the effectiveness and accuracy of aging state detection of the system to be tested are improved.

[0050] In some embodiments of this application, the target parameters may include the first model parameters and the second model parameters. Based on this, the "obtaining the target parameters corresponding to the state transition model when the system under test is in each operating state" in step S102 can also be achieved by the following steps S201 to S205. Each step will be described in detail below.

[0051] S201. Determine the first task processing state parameters, second task processing state parameters, third task processing state parameters, first anti-aging state parameters, and second anti-aging state parameters corresponding to the system under test when the system under test is in each operating state, and determine the distribution parameters corresponding to the state transition model.

[0052] In some embodiments, the first task processing state parameter may include the preset remaining task completion time of the system under test; the second task processing state parameter may include the actual time required for the system under test to complete the remaining workload; the third task processing state parameter may include the total completion time corresponding to the completion of the task by the system under test; the first anti-aging state parameter may include the unit time cost of the anti-aging operation; the second anti-aging state parameter may include the total anti-aging cost; the distribution parameters corresponding to the state transition model may be the proportional parameter and shape parameter in the Weiber distribution function. The Weiber distribution function simulates the state transition process of at least one operating state of the system under test. From each operating state, the failure time and probability of failure of the system to complete the task can be obtained. The Weiber distribution function is shown in formula (1), where t represents the failure time of the system to complete the task, R(t) represents the probability of failure to complete the task, and exp represents an exponential function with the natural constant e as the base. After obtaining t and R(t), the proportional parameter δ and the shape parameter θ can be obtained through quantitative analysis, which will not be elaborated here.

[0053]

[0054] S202. Determine the absolute value of the difference between the first task processing status parameter and the second task processing status parameter.

[0055] After obtaining the first and second task processing status parameters corresponding to each operating state, the difference or absolute value of the difference between the first and second task processing status parameters can be obtained through subtraction. For example, when the first task processing status parameter is a preset remaining task completion time for the system under test, and the second task processing status parameter is the actual time required for the system under test to complete the remaining workload, this difference or absolute value can represent the time difference between the actual time required for the system under test to complete the remaining workload and the preset remaining task completion time for the system under test.

[0056] S203. The ratio between the absolute value of the difference and the first anti-aging state parameter is determined as the first model parameter.

[0057] In some embodiments, after obtaining the absolute value of the difference between the first task processing status parameter and the second task processing status parameter, and the first anti-aging status parameter, the first model parameter can be determined based on the ratio of the absolute value of the difference to the first anti-aging status parameter. In practice, when the first model parameter is the maximum number of anti-aging operations, the absolute value of the difference can be the time difference between the actual time required for the system under test to complete the remaining workload and the preset remaining task completion time of the system under test, and the first anti-aging status parameter can be the unit time cost of the anti-aging operation, where the unit time cost of the anti-aging operation represents the number of times the anti-aging operation is performed per unit time.

[0058] S204. Determine the difference between the third task processing status parameters and the second anti-aging status parameters.

[0059] In some embodiments, after obtaining the third task processing status parameter and the second anti-aging status parameter, the difference between the third task processing status parameter and the second anti-aging status parameter can be further determined.

[0060] S205. Determine the parameters of the second model based on the difference and distribution parameters.

[0061] In some embodiments, when the second model parameter is the probability of completing the task, the third task processing state parameter and the second anti-aging state parameter are the total completion time and the total anti-aging cost, respectively. The probability F of completing the task can be calculated using formula (2), where T represents the total completion time and C... R Let represent the total cost of anti-aging, and m and a be the proportional and shape parameters of the Weiber distribution function, respectively.

[0062] F = exp(-(TC) R ) / m) a (2);

[0063] In some embodiments of this application, the target parameter includes at least one, and the step S102 of “performing hierarchical analysis on the target parameter to obtain the first weight corresponding to the target parameter” can also be achieved through the following steps S301 to S305.

[0064] S301. Obtain the decision target of the system to be tested, and determine the importance value of each target parameter according to the decision target.

[0065] In some embodiments, the decision objective of the system under test can be the purpose of performing state inspection on the system under test. For example, the decision objective could be to make anti-aging decisions for the system under test. After obtaining the decision objective of the system under test, the following can be determined: Figure 3The hierarchical analysis structure shown contains three layers: the objective layer 301, the criterion layer 302, and the alternative layer 303. The objective layer 301 is the decision objective, i.e. Figure 3 Anti-aging decisions within the context of [the process]. To determine whether the decision objective has been achieved, one can [consider...]. Figure 3 The analysis focuses on three aspects: real-time anti-aging status, system reliability, and core operating parameters, as shown in the criterion layer 302. Furthermore, the real-time anti-aging status can be determined by the maximum number of anti-aging cycles in the current state as shown in the scheme layer 303; system reliability can be determined by the probability of completing the task in the current state as shown in the scheme layer 303; and core operating parameters can be determined by the real-time monitoring data in the scheme layer 303. In other words, the various target parameters related to the system's decision objectives can be determined through these objectives, and these target parameters may include, for example... Figure 3 The maximum number of anti-aging cycles, the probability of completing the task, and the real-time monitoring data shown can be used to determine the importance value of each target parameter after the target parameters are determined.

[0066] In some embodiments, the importance of each target parameter can be represented by an importance value. Target parameters with larger importance values ​​correspond to higher importance, and target parameters with smaller importance values ​​correspond to lower importance. In practical applications, the correspondence between importance and importance value can be as follows: when importance is equal, the importance value is determined to be 1; when it is slightly important, the importance value is determined to be 3; when it is important, the importance value is determined to be 5; when it is very important, the importance value is determined to be 7; and when it is extremely important, the importance value is determined to be 9.

[0067] S302. Based on the importance values ​​of each target parameter, determine the judgment matrix corresponding to the target parameter.

[0068] In some embodiments, after obtaining the importance values ​​corresponding to each target parameter, a judgment matrix corresponding to the target parameter can be determined based on the importance values ​​corresponding to each target parameter. For example, if the target parameters include a1, a2, and a3, and the importance values ​​corresponding to a1, a2, and a3 are 3, 9, and 1 respectively, then the determined judgment matrix can be [1 1 / 3 9; 3 15; 1 / 3 1 / 9 1].

[0069] S303. Perform a consistency check on the judgment matrix and obtain the check results.

[0070] In some embodiments, after determining the judgment matrix, a consistency check needs to be performed on the judgment matrix to determine that the difference between the judgment matrix and the consistency matrix is ​​within a certain range. In practice, the consistency check of the judgment matrix can be achieved through the following steps A to C:

[0071] Step A: Calculate the largest eigenvalue λ corresponding to the judgment matrix. max The consistency index CI can be calculated using formula (3), where n is the dimension of the judgment matrix and the largest eigenvalue of the consistency matrix is ​​n. When the largest eigenvalue of the judgment matrix is ​​n, the judgment matrix is ​​a consistency matrix.

[0072]

[0073] Step B: Based on the size of the dimension n of the judgment matrix, determine the average random consistency index RI corresponding to n from the average random consistency index table shown in Table 1 below. As can be seen from Table 1, when the dimension n of the judgment matrix is ​​3, the corresponding average random consistency index is 0.52.

[0074] Table 1 Random Consistency Indicators

[0075] RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41

[0076] Step C: Calculate the consistency ratio CR according to formula (4).

[0077]

[0078] S304. When the test result is that the test passes, obtain the eigenvalues ​​corresponding to the judgment matrix.

[0079] In some embodiments, after determining the consistency ratio (CR), the consistency test result of the judgment matrix can be determined based on the CR value. In practice, if CR < 0.1, the consistency test result of the judgment matrix can be considered to have passed; otherwise, the judgment matrix needs to be corrected. The correction method can be to adjust the values ​​in the judgment matrix to reduce the difference between the judgment matrix and the consistency matrix, so that the consistency test result of the corrected judgment matrix passes. If the test result of the judgment matrix passes, the corresponding eigenvalues ​​of the judgment matrix can be directly obtained.

[0080] S305. Determine the eigenvalues ​​corresponding to the judgment matrix as the first weights corresponding to each target parameter.

[0081] In some embodiments, once the consistency check of the judgment matrix passes and the eigenvalues ​​of the judgment matrix are obtained, the eigenvalues ​​corresponding to the judgment matrix can be determined as the first weights corresponding to each target parameter. In some embodiments, after obtaining the eigenvalues ​​corresponding to the judgment matrix, the eigenvalues ​​can be arranged from largest to smallest, and the first weights corresponding to each target parameter can be determined according to the importance of each target parameter. For example, if the obtained eigenvalues ​​are 0.6, 0.3, and 0.1, and the importance of the target parameters from highest to lowest is a2, a3, and a1, then the first weights corresponding to target parameters a1, a2, and a3 are 0.1, 0.6, and 0.3, respectively.

[0082] It is understood that, in the embodiments of this application, the importance of each target parameter can be determined based on the correlation between the target parameter and the decision objective of the system, and then, combined with the hierarchical analysis method, the corresponding first weight of each target parameter can be reasonably determined.

[0083] In some embodiments of this application, the target parameter also includes system operating parameters, which include at least one. Based on this, after obtaining the first weight corresponding to the target parameter, the following steps S401 to S403 can also be performed. Each step is described in detail below.

[0084] S401. Determine the information entropy value corresponding to each system operating parameter.

[0085] It should be noted that system operating parameters can be core hardware parameters, which can be obtained through real-time monitoring data. For example, core hardware parameters can be one or more of the following: CPU utilization, disk read rate, disk read operation rate, disk write rate, disk write operation rate, out-of-band network inflow rate, and out-of-band network outflow rate. In practice, the corresponding core hardware parameters may be different for different systems, and the core hardware parameters depend on the specific system.

[0086] In some embodiments, when determining the information entropy value corresponding to each system parameter, it can be obtained according to the calculation formula corresponding to the information entropy. The calculation method of the information entropy value will not be described in detail here.

[0087] S402. Determine the second weight corresponding to each system operating parameter based on the information entropy value.

[0088] In some embodiments, after determining the information entropy value corresponding to each system parameter, the information entropy redundancy value corresponding to each system parameter can be further determined. It should be noted that the information entropy value can measure the effective value of a system parameter. When the information entropy value is 1, it indicates that the uncertainty of the system parameter is large, and the corresponding effective value is zero. Therefore, the effective value corresponding to each system parameter can be determined by calculating the information entropy redundancy value. For example, if the information entropy value of the i-th (i is a positive integer greater than 0) system parameter is h... i Then its corresponding information entropy redundancy value r i r i =1-h i After obtaining the information entropy redundancy value corresponding to each system parameter, the second weight corresponding to each system parameter can be determined based on the information entropy redundancy value.

[0089] For example, after obtaining the information entropy redundancy value corresponding to the system parameter, the second weight ω corresponding to the i-th system parameter... i It can be calculated according to the following formula (5), where N represents the number of system parameters, r i This is the information entropy redundancy value corresponding to the i-th system parameter.

[0090]

[0091] S403. Obtain the first weight corresponding to each system operating parameter, and determine the target weight corresponding to each system operating parameter based on the first weight and the second weight corresponding to each system operating parameter.

[0092] It should be noted that, as mentioned earlier, the first weights corresponding to the operating parameters of each system are determined based on the analytic hierarchy process (AHP). The first weights corresponding to the operating parameters of each system are the same, while the second weights corresponding to the operating parameters of each system may be different.

[0093] In some embodiments, the target weight corresponding to each system operating parameter can be determined by multiplying the first weight and the second weight, and using the result as the target weight. For example, if the first weight and the second weight corresponding to the system operating parameter are 0.6 and 0.3 respectively, then the target weight corresponding to the system operating parameter is determined to be 0.18.

[0094] In some embodiments of this application, the anti-aging index corresponding to at least one operating state is determined based on the target parameter and the first weight corresponding to the target parameter, i.e., step S103. This can also be achieved through the following steps S1031 to S1032. Each step is described in detail below.

[0095] S1031. Perform a weighted summation on the model parameters, the first weights corresponding to the model parameters, the operating parameters of each system, and the target weights corresponding to the operating parameters of each system to obtain the weighted summation result.

[0096] In some embodiments, the model parameters include first model parameters and second model parameters. After obtaining the first weights corresponding to the model parameters and the target weights corresponding to the system parameters, the model parameters, the first weights corresponding to the model parameters, the system operating parameters, and the target weights corresponding to the system operating parameters can be weighted and summed.

[0097] For example, if the target parameters include two model parameters m1 and m2 and three system operation parameters n1, n2, and n3, and the first weights corresponding to the two model parameters m1 and m2 are k1 and k2 respectively, and the target weights corresponding to the three system operation parameters n1, n2, and n3 are k3, k4, and k5 respectively, then the weighted summation result S is: S = k1*m1 + k2*m2 + k3*n1 + k4*n2 + k5*n3.

[0098] S1032. The weighted summation result is determined as the anti-aging index corresponding to at least one operating state.

[0099] In some embodiments, after obtaining the model parameters, the first weights corresponding to the model parameters, the system operating parameters, and the target weights corresponding to the system operating parameters in any operating state, the weighted summation result can be determined as the anti-aging index corresponding to that operating state.

[0100] It is understood that in the embodiments of this application, the anti-aging index is jointly determined by model parameters, system operating parameters, and the weights corresponding to the model parameters and the system operating parameters. The analytic hierarchy process is combined to determine the weights of the model parameters and the system operating parameters, and the entropy analysis method is further used to determine the entropy weights of each system operating parameter. This solves the problem of lack of correlation between hardware parameters and software aging phenomena caused by the use of a single method in the prior art, and improves the objectivity, rationality and accuracy of system aging state detection.

[0101] In some embodiments of this application, the current aging state information of the system to be tested is determined based on the anti-aging index, i.e., step S104. This can also be achieved through the following steps S1041 to S1042. Each step is described in detail below.

[0102] S1041. Obtain the pre-established system state prediction model and the anti-aging index corresponding to each operating state.

[0103] In some embodiments, the pre-established system state prediction model can be an autoregressive (AR) model, a moving average (MA) model, an autoregressive moving average (ARMA) model, or an autoregressive integrated moving average (ARIMA) model. There are multiple anti-aging indices. Since each operating state of the system corresponds to a different time point or period, the multiple anti-aging indices corresponding to multiple operating states constitute an anti-aging index sequence.

[0104] In some embodiments, after obtaining the anti-aging index sequence, the autocorrelation coefficient function (ACF) and partial autocorrelation coefficient function (PACF) corresponding to the anti-aging index sequence can be further obtained. Based on the distribution of ACF and PACF, the system state prediction model and the model order of the system state prediction model can be determined.

[0105] For example, the order of the preset system state prediction model can be determined based on the correspondence between the ACF, PACF, and the preset system state model. For instance, if the ACF gradually decays and eventually approaches zero, while the PACF is truncated after order p, then the system state prediction model is determined to be an AR model with order p. If the ACF is truncated after order q, while the PACF gradually decays and eventually approaches zero, then the system state prediction model is determined to be an MA model with order q. If both the ACF and PACF are truncated after order p, then the system state prediction model is determined to be an ARMA model, which combines the AR and MA models, where the AR and MA models correspond to order p and q, respectively.

[0106] S1042. The system state prediction model is used to predict the aging index sequence to obtain the current aging state prediction result of the system under test.

[0107] In some embodiments, after obtaining the anti-aging index sequence, a system state model can be used to predict the anti-aging index sequence, thereby obtaining a prediction result of the current or future aging state of the system. For example, if the determined system state prediction model is an ARIMA model, before using ARIMA to predict the anti-aging index sequence, the anti-aging index sequence can first be differencing (i.e., stabilizing the anti-aging index sequence) to obtain a stable anti-aging index sequence; then, the ARIMA model is used to predict the stable anti-aging index sequence to obtain a prediction result, which is the prediction result of the current aging state of the system to be detected.

[0108] S1043. Based on the aging state prediction results, determine the current aging state information of the system to be tested.

[0109] In some embodiments, the aging state prediction result includes an anti-aging index, which can be used to further determine the current aging state information of the system under test.

[0110] It is understood that, in the embodiments of this application, by using the system state prediction model to predict the aging index sequence, the current aging state information of the system to be detected can be detected, ensuring the real-time nature of the system aging state detection.

[0111] In other embodiments, after obtaining the anti-aging index corresponding to the current state of the system, a new anti-aging index sequence can be formed by the anti-aging index sequence and the anti-aging index corresponding to the current state of the system under test. The updated anti-aging index sequence can be predicted using a system state prediction model to obtain the aging state prediction result of the system under test at future times. This allows the aging state of the system under test to be determined in advance, so that anti-aging operations can be performed before the system under test ages, ensuring the stability of the system under test.

[0112] In some embodiments of this application, the aging state prediction result is the current aging index of the system under test, and the aging state information includes the aging level. Based on this, the aging state prediction result determines the current aging state information of the system under test. That is, step S1043 can also be implemented by the following steps S501 to S503.

[0113] S501. Obtain the correspondence between the preset aging index range and the preset aging level.

[0114] In some embodiments, both the preset aging index range and the preset aging level can be preset. For example, the preset aging index range may include: [0,0.2), [0.2,0.4), [0.4,0.6), [0.6,0.8), [0.8,1]. The preset aging levels corresponding to each preset aging index range are: Level 1, Level 2, Level 3, Level 4, and Level 5. Level 1 can represent a low degree of aging, Level 2 can represent a low degree of aging, Level 3 can represent a medium degree of aging, Level 4 can represent a high degree of aging, and Level 5 can represent a high degree of aging.

[0115] S502. Determine the target aging index range that corresponds to the current aging index of the system to be tested within the preset aging index range.

[0116] In some embodiments, after obtaining the current aging index of the system under test, a preset aging index range corresponding to the aging index can be determined. For example, if the current aging index of the system under test is 0.65, then the target aging index range for the current aging index of the system under test can be determined from the preset aging index range as [0.6, 0.8].

[0117] S503. Based on the correspondence and the target aging index range, determine the current aging level of the system to be tested.

[0118] In one embodiment, after determining the target aging index range corresponding to the current aging index of the system under test, the aging level corresponding to the target aging index range can be determined according to the correspondence between the preset aging index range and the preset aging level. For example, the preset aging level corresponding to the preset aging index range [0.6, 0.8) is level four. When the target aging index range is determined to be [0.6, 0.8), the current aging level of the system under test can be further determined to be level four, that is, the current aging degree of the system under test is relatively high.

[0119] In this embodiment, at least one operating state of the system under test and a state transition model corresponding to the at least one operating state are obtained. Then, the target parameters corresponding to the state transition model when the system under test is in each operating state are obtained, and hierarchical analysis is performed on the target parameters to obtain the first weights corresponding to the target parameters. Finally, based on the target parameters and the first weights corresponding to the target parameters, the anti-aging index corresponding to each operating state is determined, and the current aging state information of the system under test is determined according to the obtained anti-aging index. In this way, by performing hierarchical analysis on the target parameters to determine the weights corresponding to the target parameters, the correlation between the target parameters and the aging state of the system is strengthened, making the anti-aging index determined by the target parameters and the weights corresponding to the target parameters more objective and accurate. Thus, when determining the aging state information of the system under test based on the anti-aging index, the effectiveness and accuracy of aging state detection of the system under test are improved.

[0120] The implementation process of the embodiments of this application in a practical application scenario will be described below.

[0121] In some embodiments, such as Figure 4 The diagram shown is a flowchart illustrating a method for determining the aging state information of a software system according to an embodiment of this application. The method for determining the aging state of a software system provided in this embodiment can be implemented through the following steps S601 to S605, which are described in detail below.

[0122] S601. Create a software system aging state transition model (state transition model).

[0123] In some embodiments, multiple running states of the software system can be acquired in real time, each corresponding to a different time or moment. In implementation, different running states can be determined based on the task processing speed of the software system, where the task processing speed can be the number of tasks processed per second. After obtaining the multiple running states of the software system, an aging state transition model for the software system can be established based on the Weiber distribution, for example, as... Figure 2 The aging state transition model shown includes various operating states, completion states, and anti-aging states, as well as the transition process between each state and the anti-aging time cost corresponding to the execution of anti-aging operations in each operating state.

[0124] Understandably, the aging process of cloud software systems can be simulated based on the Weiber distribution. The aging state transition model established based on the Weiber distribution provides the theoretical basis for determining the aging state of the entire software system, and provides the prerequisite for subsequently determining the aging state of the software system.

[0125] S602. Based on the software system aging state transition model, determine the model parameters and core operating parameters (target parameters).

[0126] It should be noted that both model parameters and core operating parameters (system operating parameters) can include at least one. Model parameters can include the maximum number of anti-aging events corresponding to the current state of the software system (first model parameter) and the probability of completing the task (second model parameter). Core operating parameters can include real-time monitoring data such as CPU utilization and disk read rate. Real-time monitoring data can be collected in real time during the operation of the software system. Furthermore, based on the degree of influence of real-time monitoring data on the stability of the software system, the monitoring data with the highest degree of influence in the real-time monitoring data is used as the core operating parameters.

[0127] In some embodiments, state parameters corresponding to each running state can be obtained from the software system aging state transition model, such as anti-aging time cost, remaining workload, and the system's initial state task completion speed. Based on the obtained state parameters, the maximum number of anti-aging events and the probability of completing the task corresponding to each running state can be determined. For example, the probability of completing the task corresponding to the running state can be calculated according to formula (2), and the maximum number of anti-aging events K can be calculated according to formula (6), where T r W represents the remaining time to complete the task. r η represents the remaining workload, η0 represents the initial state of the system completing the task, and ψ represents the unit time cost of the anti-aging operation.

[0128]

[0129] S603. Use the analytic hierarchy process (AHP) to determine the first weights corresponding to the model parameters and core operating parameters, and use the entropy analysis method to determine the sub-weights (second weights) corresponding to the core operating parameters.

[0130] In some embodiments, after determining the model parameters and core operating parameters, the analytic hierarchy process (AHP) can be used to further determine the first weights corresponding to the model parameters. In implementation, if the model parameters include X1 and X2, and the core operating parameter is X3, then it is first necessary to determine the decision objective of the software system and establish, for example... Figure 3The hierarchical analysis structure is shown. Then, the judgment matrices of each layer in the hierarchical analysis structure are determined. The values ​​in the judgment matrices are set according to the current goal of the software system. For example, if the current software system is a real-time system and requires reliability, the priority of the first-layer judgment matrix from high to low is system reliability, anti-aging cost, and resource scheduling. The corresponding judgment matrix can be [1 1 / 3 3; 3 1 5; 1 / 3 1 / 5 1]. After that, the consistency of the judgment matrix is ​​checked and the check result is obtained. If the check result passes, the eigenvalues ​​corresponding to the judgment matrix can be determined as the first weights K1, K2, and K3 corresponding to the model parameters X1, X2 and the core operating parameter X3. Finally, the sub-weights U corresponding to each core operating parameter are determined using the entropy analysis method.

[0131] S604. Based on the first weights corresponding to the model parameters and the core operating parameters, and the sub-weights corresponding to the core operating parameters, determine the anti-aging index sequence.

[0132] In some embodiments, after obtaining the first weights and sub-weights corresponding to the core operating parameters, the target weights corresponding to each core operating parameter can be further determined (based on the first weights and second weights corresponding to each system operating parameter, the target weights corresponding to each system operating parameter are determined). For example, if there are 5 core operating parameters, the sub-weight corresponding to the m-th core operating parameter is Um, and the final target weight corresponding to the m-th core operating parameter is K3*Um. Then, the model parameters, the first weights corresponding to the model parameters, the core operating parameters, and the target weights corresponding to the core operating parameters can be weighted and summed to obtain the weighted summation result, which is used as the anti-aging index. Furthermore, since each operating state corresponds to a different time, the obtained model parameters and system operating parameters are different. Therefore, based on different model parameters and system operating parameters, multiple anti-aging indices can be obtained, forming an anti-aging index sequence.

[0133] S605. Use the preset system state prediction model to predict the aging index sequence, obtain the prediction results, and determine the aging state information of the software system based on the prediction results.

[0134] In some embodiments, the preset system state prediction model can be an ARIMA model, and the aging state information of the software system can include the aging level corresponding to the software system. When using the ARIMA model to predict the anti-aging index sequence, the anti-aging index sequence can first be stabilized to obtain a stable anti-aging index sequence; then, the ARIMA model is used to predict the stabilized anti-aging index sequence to obtain the prediction result, which includes the current anti-aging index of the software system or the anti-aging index corresponding to the operating state at a future time; subsequently, based on the relationship between the preset anti-aging index interval and the preset aging level, the target anti-aging index interval corresponding to the anti-aging index in the prediction result and the aging level corresponding to the target anti-aging index interval can be determined.

[0135] In other embodiments, after obtaining the prediction processing result, the obtained prediction processing result can also be verified. For example, the difference between the anti-aging index included in the prediction processing result and the actual anti-aging index can be determined. When the difference is less than a preset threshold, the prediction result is considered accurate. The preset threshold can be set to 0.1% of the total number of operation tasks when the system processes tasks.

[0136] It is understood that in this embodiment, the Weiber distribution is used to simulate the state transition process of software system aging. Then, real-time system operating parameters and calculated model parameters are combined to optimize the weighted analytic hierarchy process (AHP) coefficients. Finally, a system state prediction model is used for the final software system aging state detection operation. The method proposed in this application solves the problem of system flexibility and adaptability caused by using a single model method, ensuring system stability. Simultaneously, it balances unpredictable factors in software system aging during the use of AHP and entropy analysis methods, thereby addressing to some extent the problem of lack of correlation between system operating parameters and software system aging phenomena caused by using a single method, and improving the accuracy and effectiveness of software system aging state detection.

[0137] This application also provides a system aging status detection device. Figure 5 This is a schematic diagram of the composition structure of a system aging state detection device provided in an embodiment of this application, as shown below. Figure 5 As shown, the system aging status detection device 700 includes:

[0138] The first acquisition module 701 is used to acquire at least one operating state of the system to be detected, and the state transition model corresponding to the at least one operating state.

[0139] The second acquisition module 702 is used to acquire the target parameters corresponding to the state transition model when the system under test is in various operating states, perform hierarchical analysis on the target parameters, and obtain the first weight corresponding to the target parameters.

[0140] The first determining module 703 is used to determine the anti-aging index corresponding to each of the at least one operating state based on the target parameter and the first weight corresponding to the target parameter;

[0141] The second determining module 704 is used to determine the current aging state information of the system to be tested based on the anti-aging index.

[0142] In some embodiments, the target parameters include a first model parameter and a second model parameter. The second acquisition module 702 is further configured to determine the first task processing state parameter, the second task processing state parameter, the third task processing state parameter, the first anti-aging state parameter, and the second anti-aging state parameter corresponding to the system under test when the system under test is in various operating states, and to determine the distribution parameter corresponding to the state transition model; determine the absolute value of the difference between the first task processing state parameter and the second task processing state parameter; determine the ratio between the absolute value of the difference and the first anti-aging state parameter as the first model parameter; determine the difference between the third task processing state parameter and the second anti-aging state parameter; and determine the second model parameter based on the difference and the distribution parameter.

[0143] In some embodiments, the second acquisition module 702 is further configured to acquire the decision target of the system to be detected, and determine the importance value corresponding to each target parameter according to the decision target; determine the judgment matrix corresponding to the target parameter based on the importance value corresponding to each target parameter; perform a consistency check on the judgment matrix to obtain the check result; when the check result is a pass, acquire the feature value corresponding to the judgment matrix; and determine the feature value corresponding to the judgment matrix as the first weight corresponding to each target parameter.

[0144] In some embodiments, the first determining module 703 is further configured to perform a weighted summation process on the model parameters, the first weights corresponding to the model parameters, the various system operating parameters, and the target weights corresponding to the various system operating parameters to obtain a weighted summation result, wherein the model parameters include the first model parameters and the second model parameters; and to determine the weighted summation result as the anti-aging index corresponding to each of the at least one operating state.

[0145] In some embodiments, the second determining module 704 is further configured to acquire a pre-established system state prediction model and anti-aging indices corresponding to each operating state, wherein the anti-aging indices include multiple indices, and the multiple anti-aging indices constitute an anti-aging index sequence; use the system state prediction model to perform prediction processing on the anti-aging index sequence to obtain the current aging state prediction result of the system under test; and determine the current aging state information of the system under test based on the aging state prediction result.

[0146] In some embodiments, the aging state prediction result is the current aging index of the system under test, and the aging state information includes the aging level; the second determining module 704 is further configured to obtain the correspondence between a preset aging index range and a preset aging level; determine the target aging index range in the preset aging index range that corresponds to the current aging index of the system under test; and determine the current aging level of the system under test based on the correspondence and the target aging index range.

[0147] In some embodiments, the target parameters further include system operating parameters, and the system aging state detection device 700 further includes a third determining module 705, which is used to determine the information entropy value corresponding to each system operating parameter; determine the second weight corresponding to each system operating parameter based on the information entropy value; obtain the first weight corresponding to each system operating parameter; and determine the target weight corresponding to each system operating parameter based on the first weight and the second weight corresponding to each system operating parameter.

[0148] It should be noted that the description of the system aging state detection device in this application embodiment is similar to the description of the method embodiment above, and has similar beneficial effects as the method embodiment; therefore, it will not be repeated. For technical details not disclosed in this device embodiment, please refer to the description of the method embodiment of this application for understanding.

[0149] It should be noted that, in the embodiments of this application, if the above-described control method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0150] Accordingly, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the system aging state detection method provided in the above embodiments.

[0151] This application also provides a system aging status detection device. Figure 6 This is a schematic diagram of the composition structure of a system aging state detection device provided in an embodiment of this application, as shown below. Figure 6 As shown, the system aging state detection device 800 includes: a memory 801, a processor 802, a communication interface 803, and a communication bus 804. The memory 801 stores executable system aging state detection instructions; the processor 802 executes the executable system aging state detection instructions stored in the memory to implement the system aging state detection method provided in the above embodiment.

[0152] The descriptions of the system aging condition detection device and storage medium embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the system aging condition detection device and storage medium embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0153] It should be understood that the phrases "some embodiments" or "one embodiment" throughout the specification mean that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, "some embodiments" or "one embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0154] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0156] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0157] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0158] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0159] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a product to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0160] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting the aging state of a system, characterized in that, include: Obtain at least one operating state of the system to be tested, and the state transition model corresponding to the at least one operating state; Obtain the target parameters corresponding to the state transition model when the system under test is in each operating state, perform hierarchical analysis on the target parameters, and obtain the first weight corresponding to the target parameters; Based on the target parameters and the first weights corresponding to the target parameters, the anti-aging index corresponding to each of the at least one operating state is determined; Based on the anti-aging index, the current aging status information of the system under test is determined; The target parameters include system operating parameters, and the system operating parameters include at least one. The method further includes: Determine the information entropy value corresponding to each system operating parameter; The second weight corresponding to each system operating parameter is determined based on the information entropy value; Obtain the first weight corresponding to each system operating parameter, and determine the target weight corresponding to each system operating parameter based on the first weight and the second weight corresponding to each system operating parameter.

2. The method according to claim 1, characterized in that, The target parameters also include first model parameters and second model parameters. Obtaining the target parameters corresponding to the state transition model when the system under test is in various operating states includes: When the system under test is in each operating state, the first task processing state parameter, the second task processing state parameter, the third task processing state parameter, the first anti-aging state parameter, and the second anti-aging state parameter corresponding to the system under test are determined, and the distribution parameters corresponding to the state transition model are determined. Determine the absolute value of the difference between the first task processing status parameter and the second task processing status parameter; The ratio between the absolute value of the difference and the first anti-aging state parameter is determined as the first model parameter; Determine the difference between the third task processing status parameter and the second anti-aging status parameter; The second model parameters are determined based on the difference and the distribution parameters.

3. The method according to claim 1, characterized in that, The target parameter includes at least one, and the hierarchical analysis of the target parameter to obtain the first weight corresponding to the target parameter includes: Obtain the decision objective of the system to be detected, and determine the importance value of each objective parameter based on the decision objective; Based on the importance values ​​corresponding to each of the target parameters, determine the judgment matrix corresponding to each target parameter; Perform a consistency check on the judgment matrix to obtain the check result; If the test result is a pass, the eigenvalues ​​corresponding to the judgment matrix are obtained; The eigenvalues ​​corresponding to the judgment matrix are determined as the first weights corresponding to each target parameter.

4. The method according to claim 2, characterized in that, The step of determining the anti-aging index corresponding to each of the at least one operating state based on the target parameter and the first weight corresponding to the target parameter includes: The model parameters, the first weights corresponding to the model parameters, the operating parameters of each system, and the target weights corresponding to the operating parameters of each system are subjected to weighted summation to obtain a weighted summation result. The model parameters include the first model parameters and the second model parameters. The weighted summation result is determined as the anti-aging index corresponding to each of the at least one operating state.

5. The method according to any one of claims 1-4, characterized in that, The process of determining the current aging status information of the system under test based on the anti-aging index includes: Obtain a pre-established system state prediction model and anti-aging indices corresponding to each operating state. The anti-aging indices include multiple indices, and the multiple anti-aging indices constitute an anti-aging index sequence. The anti-aging index sequence is predicted using the system state prediction model to obtain the current aging state prediction result of the system under test; Based on the aging state prediction results, the current aging state information of the system under test is determined.

6. The method according to claim 5, characterized in that, The aging state prediction result is the current aging index of the system under test, and the aging state information includes the aging level; determining the current aging state information of the system under test based on the aging state prediction result includes: Obtain the correspondence between preset aging index ranges and preset aging levels; Determine the target aging index range within the preset aging index range that corresponds to the current aging index of the system to be tested; Based on the correspondence and the target aging index range, the current aging level of the system under test is determined.

7. A system aging status detection device, characterized in that, include: The first acquisition module is used to acquire at least one operating state of the system to be detected, and the state transition model corresponding to the at least one operating state. The second acquisition module is used to acquire the target parameters corresponding to the state transition model when the system under test is in various operating states, perform hierarchical analysis on the target parameters, and obtain the first weight corresponding to the target parameters. The first determining module is used to determine the anti-aging index corresponding to each of the at least one operating state based on the target parameter and the first weight corresponding to the target parameter; The second determining module is used to determine the current aging state information of the system under test based on the anti-aging index; The target parameters include system operating parameters, and the system operating parameters include at least one third determining module, which is used to determine the information entropy value corresponding to each system operating parameter. The second weight corresponding to each system operating parameter is determined based on the information entropy value; the first weight corresponding to each system operating parameter is obtained, and the target weight corresponding to each system operating parameter is determined based on the first weight corresponding to each system operating parameter and the second weight corresponding to each system operating parameter.

8. A system aging status detection device, characterized in that, include: Memory, used to store executable system aging state detection instructions; A processor, when executing executable system aging state detection stored in the memory, implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The system stores system aging state detection instructions, which, when executed by the processor, implement the method as described in any one of claims 1 to 6.

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