A method, system and electronic device for characterizing the robustness of a monitoring system

Abstract

The present invention relates to a method, system and electronic device for characterizing the robustness of a monitoring system. According to the response time of each sample collected by the monitoring system within a preset time period and the preset response time, all samples are classified, and according to the number of samples of the preset category and the number of all samples, an MRT index for characterizing the robustness of the monitoring system is obtained, and the MRT index is provided to a user. On the one hand, it is convenient for the user to understand the robustness of the monitoring system through the MRT index. On the other hand, when a fault occurs in the monitoring system, the MRT index changes, so that the user can timely understand the fault of the monitoring system through the change of the MRT index, thereby reducing the fault tolerance rate of the monitoring system.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, system and electronic equipment for characterizing the robustness of a monitoring system. Background Art

[0002] In any business scenario, there are various hardware devices, software applications, etc., which can be divided into hardware layer, operation layer and application layer according to the logical hierarchy, thus forming a complex business system. With the vigorous development of technologies such as cloud computing and big data, business systems have become more complex. In order to ensure the stable operation of business systems, monitoring systems play a vital role. When the server, application program, etc. of the business system fails or the monitoring point indicators set in the business system exceed a certain threshold, the monitoring system will generate an alarm message and notify the operation and maintenance personnel in time so that the operation and maintenance personnel can deal with the fault problem in time after receiving the alarm message. However, since the monitoring system cannot detect its own problems in time, serious consequences will occur. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method, system and electronic device for characterizing the robustness of a monitoring system in view of the deficiencies in the prior art.

[0004] The technical solution of a method for characterizing the robustness of a monitoring system of the present invention is as follows:

[0005] Classify all samples according to the response time of each sample collected by the monitoring system within a preset time period and the preset response time;

[0006] According to the number of samples of the preset category and the number of all samples, an MRT index for characterizing the robustness of the monitoring system is obtained, and the MRT index is provided to the user.

[0007] The beneficial effects of a method for characterizing the robustness of a monitoring system of the present invention are as follows:

[0008] On the one hand, it is convenient for users to understand the robustness of the monitoring system through the MRT index. On the other hand, when the monitoring system fails, the MRT index changes, which makes it convenient for users to promptly understand the failure of the monitoring system through the change of the MRT index, thereby reducing the fault tolerance of the monitoring system.

[0009] Based on the above solution, the method for characterizing the robustness of a monitoring system of the present invention can be further improved as follows.

[0010] Further, the method of classifying all samples according to the response time of each sample collected by the monitoring system within a preset time period and the preset response time includes:

[0011] When the response time of any sample collected by the monitoring system within a preset time period is not greater than the preset response time, the sample is classified into a robust category; when the response time of the sample is greater than the preset response time and not greater than a preset multiple of the preset response time, the sample is classified into a tolerable category; when the response time of the sample is greater than a preset multiple of the preset response time, the sample is classified into an irritable period category, until all samples are classified.

[0012] Further, the preset category includes the robust category and the tolerant category, the MRT index is a first MRT index, and the MRT index for characterizing the robustness of the monitoring system is obtained according to the number of samples of the preset category and the number of all samples, including:

[0013] The first MRT index is obtained according to a first formula, wherein the first formula is:

[0014] P1=(A+B) / D,

[0015] Wherein, P1 represents the first MRT index, A represents the number of samples of the robust category, B represents the number of samples of the tolerable category, and D represents the number of all samples collected by the monitoring system within a preset time period.

[0016] Further, the preset category is the category of the irritable period, the MRT index is the second MRT index, and the MRT index for characterizing the robustness of the monitoring system is obtained according to the number of samples of the preset category and the number of all samples, including:

[0017] The second MRT index is obtained according to the second formula, and the second formula is:

[0018] P2=C / D,

[0019] Wherein, P2 represents the second MRT index, C represents the number of samples of the category of the irritability period, and D represents the number of all samples collected by the monitoring system within a preset time period.

[0020] A technical solution of a system for characterizing the robustness of a monitoring system of the present invention is as follows:

[0021] Includes classification module and calculation module;

[0022] The classification module is used to classify all samples according to the response time of each sample collected by the monitoring system within a preset time period and the preset response time;

[0023] The calculation module is used to obtain an MRT index for characterizing the robustness of the monitoring system according to the number of samples of a preset category and the number of all samples, and provide the MRT index to a user.

[0024] The beneficial effects of a system for characterizing the robustness of a monitoring system of the present invention are as follows:

[0025] On the one hand, it is convenient for users to understand the robustness of the monitoring system through the MRT index. On the other hand, when the monitoring system fails, the MRT index changes, which makes it convenient for users to promptly understand the failure of the monitoring system through the change of the MRT index, thereby reducing the fault tolerance of the monitoring system.

[0026] Based on the above solution, the system for characterizing the robustness of a monitoring system of the present invention can also be improved as follows.

[0027] Furthermore, the classification module is specifically used for:

[0028] When the response time of any sample collected by the monitoring system within a preset time period is not greater than the preset response time, the sample is classified into a robust category; when the response time of the sample is greater than the preset response time and not greater than a preset multiple of the preset response time, the sample is classified into a tolerable category; when the response time of the sample is greater than a preset multiple of the preset response time, the sample is classified into an irritable period category, until all samples are classified.

[0029] Further, the preset category includes the robust category and the tolerant category, the MRT index is a first MRT index, and the calculation module is specifically used for:

[0030] The first MRT index is obtained according to a first formula, wherein the first formula is:

[0031] P1=(A+B) / D,

[0032] Wherein, P1 represents the first MRT index, A represents the number of samples of the robust category, B represents the number of samples of the tolerable category, and D represents the number of all samples collected by the monitoring system within a preset time period.

[0033] Further, the preset category is the category of the irritability period, the MRT index is the second MRT index, and the calculation module is specifically used for:

[0034] The second MRT index is obtained according to the second formula, and the second formula is:

[0035] P2=C / D,

[0036] Wherein, P2 represents the second MRT index, C represents the number of samples of the category of the irritability period, and D represents the number of all samples collected by the monitoring system within a preset time period.

[0037] The technical solution of an electronic device of the present invention is as follows:

[0038] The method comprises a memory, a processor and a program stored in the memory and running on the processor, wherein when the processor executes the program, the method implements the steps of a method for characterizing the robustness of a monitoring system as described in any one of the above items.

[0039] The beneficial effects of an electronic device of the present invention are as follows:

[0040] On the one hand, it is convenient for users to understand the robustness of the monitoring system through the MRT index. On the other hand, when the monitoring system fails, the MRT index changes, which makes it convenient for users to promptly understand the failure of the monitoring system through the change of the MRT index, thereby reducing the fault tolerance of the monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of a method for characterizing the robustness of a monitoring system according to an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of the structure of a system for characterizing the robustness of a monitoring system according to an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention; DETAILED DESCRIPTION

[0044] like Figure 1 As shown, a method for characterizing the robustness of a monitoring system according to an embodiment of the present invention includes the following steps:

[0045] S1. Classify all samples according to the response time of each sample collected by the monitoring system within a preset time period and the preset response time;

[0046] S2. Obtain an MRT index for characterizing the robustness of the monitoring system according to the number of samples of the preset category and the number of all samples, and provide the MRT index to the user.

[0047] On the one hand, it is convenient for users to understand the robustness of the monitoring system through the MRT index. On the other hand, when the monitoring system fails, the MRT index changes, which makes it convenient for users to promptly understand the failure of the monitoring system through the change of the MRT index, thereby reducing the fault tolerance of the monitoring system.

[0048] Among them, the reduction of the fault tolerance of the monitoring system is specifically reflected in:

[0049] The current monitoring system cannot detect problems that occur in itself in a timely manner, but through the present application, it can be discovered in a timely manner whether the monitoring system has a fault, thereby reducing the fault tolerance rate of the monitoring system.

[0050] Among them, MRT in the MRT index is the abbreviation of Monitoring Response Time, and the sample represents the operating status or information of each component in the business system, such as CPU utilization, business system load, port connectivity status, bandwidth flow, website access status, etc.;

[0051] Taking CPU utilization as a sample, the response time of the sample is explained:

[0052] When the monitoring system sends a command to obtain the CPU utilization rate to the server of the business system, the timing starts and stops when the monitoring system obtains the CPU utilization rate. The timing time is the response time of the sample.

[0053] The user can set the preset response time according to the actual situation. It is understandable that the optimal threshold, that is, the optimal preset response time, can be obtained by analyzing the historical data, that is, the response time of a large number of samples. The optimal preset response time is also different for samples of different objects. For example, the CPU usage rate is generally defined as being collected every 5 minutes in the monitoring system, and 5 minutes is the optimal preset response time for the sample's CPU usage rate.

[0054] Among them, the preset time period can be set according to actual conditions, for example, the preset time period can be set to an integer multiple of the optimal preset response time to ensure that the sample response can be completed within the preset time period. It can be understood that the response time of each sample collected by the monitoring system within the preset time period is the same for each sample.

[0055] The MRT index may be provided to the user via a pop-up window, text message, email or other means, so that the user can view it easily.

[0056] In S1, all samples in a preset time period can be divided into two categories, specifically:

[0057] When the response time of any sample collected by the monitoring system within a preset time is not greater than the preset response time, the sample is classified into the first category;

[0058] When the response time of any sample collected by the monitoring system within the preset time is greater than the preset response time, the sample is classified into the second category;

[0059] Until all samples are classified, then:

[0060] 1) When the preset category is the first category, the MRT index used to characterize the robustness of the monitoring system can be obtained by the third formula, which is recorded as the third MRT index, wherein the third formula is:

[0061] P3=E / D,

[0062] Wherein, P3 represents the third MRT index, E represents the number of samples of the first category, and D represents the number of all samples collected by the monitoring system within a preset time period;

[0063] It can be understood that when the response time of any sample is not greater than the preset response time, it indicates that the monitoring system is in a normal state for the sample. Assuming E=100, D=100, the third MRT index P3=1 is obtained at this time, indicating that the robustness of the monitoring system is the best. Assuming E=0, D=100, the third MRT index P3=0 is obtained at this time, indicating that the robustness of the monitoring system is the worst. That is to say, the robustness of the monitoring system is reflected in the interval [0,1] through the third MRT index P3. The user can intuitively understand the robustness of the monitoring system through the third MRT index P3. For example, the third MRT indexes P3 in two consecutive preset time periods are 0.9 and 0.6 respectively, and the third deviation between the two third MRT indexes P3 is 0.3. If the third deviation is large, it can be determined that the monitoring system has failed. At this time, the monitoring system is manually troubleshooted to ensure that the monitoring system can operate stably.

[0064] Among them, the third deviation threshold can be preset according to actual conditions. When the third deviation is greater than the third deviation threshold, it is considered that the third deviation is large. For example, when the monitoring performance requirements of the monitoring system are high, the preset third deviation threshold can be set to 0.1 or 0.05, etc. When the monitoring performance requirements of the monitoring system are low, the preset third deviation threshold can be set to 0.2 or 0.25, etc.

[0065] It can be understood that whether to perform maintenance on the monitoring system can be determined according to the preset third MRT index threshold. For example, the preset third MRT index threshold is 0.7. When the third MRT index P3 obtained in a preset time period is 0.65, the monitoring system is maintained.

[0066] 2) When the preset category is the second category, the MRT index used to characterize the robustness of the monitoring system can be obtained by the fourth formula, which is recorded as the fourth MRT index, wherein the fourth formula is:

[0067] P4=F / D,

[0068] Wherein, P4 represents the fourth MRT index, F represents the number of samples of the second category, and D represents the number of all samples collected by the monitoring system within a preset time period.

[0069] It can be understood that the fourth MRT index P4 is exactly opposite to the third MRT index P3. When the fourth MRT index P4=1, it indicates that the robustness of the monitoring system is the worst. When the fourth MRT index P4=0, it indicates that the robustness of the monitoring system is the best.

[0070] That is to say, the robustness of the monitoring system is reflected in the interval [0,1] through the fourth MRT index P4, and the user can intuitively understand the robustness of the monitoring system through the fourth MRT index P4. For example, the fourth MRT index P4 in two consecutive preset time periods is 0.2 and 0.5 respectively, and the fourth deviation between the two fourth MRT indexes P4 is 0.3. If the deviation is large, it can be determined that the monitoring system has failed. At this time, the monitoring system is manually troubleshooted to ensure that the monitoring system can operate stably.

[0071] The fourth deviation threshold may be preset according to actual conditions. When the fourth deviation is greater than the fourth deviation threshold, it is considered that the fourth deviation is large. For example, when the monitoring performance requirements of the monitoring system are high, the preset fourth deviation threshold may be set to 0.1 or 0.05, etc.; when the monitoring performance requirements of the monitoring system are low, the preset fourth deviation threshold may be set to 0.2 or 0.25, etc.;

[0072] It is understandable that whether to perform maintenance on the monitoring system can be determined based on the preset fourth MRT index threshold. For example, the preset fourth MRT index threshold is 0.2. When the fourth MRT index P4 obtained in a preset time period is 0.25, the monitoring system is maintained.

[0073] Preferably, in the above technical solution, S1 includes:

[0074] S10. When the response time of any sample collected by the monitoring system within a preset time period is not greater than the preset response time, the sample is classified into a robust category; when the response time of the sample is greater than the preset response time and not greater than a preset multiple of the preset response time, the sample is classified into a tolerable category; when the response time of the sample is greater than a preset multiple of the preset response time, the sample is classified into an irritable period category, until all samples are classified.

[0075] The preset multiple can be set to 2, 3, 4 or 5, etc. Different preset multiples can also be set according to different requirements for the monitoring performance of the monitoring system. Generally speaking, the preset multiple is 4. Take the CPU utilization as a sample, the preset multiple is 4, and the preset response time is 5s as an example for explanation:

[0076] 1) The samples in the robust category are: samples whose response time is no more than 5 seconds;

[0077] 2) Samples in the Tolerating category are: samples whose response time is greater than 5s and not greater than 4×5s=20s;

[0078] 3) Samples in the Frustrated category are: samples with a response time greater than 4×5s=20s.

[0079] Preferably, in the above technical solution, the preset category includes the robust category and the tolerant category, the MRT index is a first MRT index, and the MRT index for characterizing the robustness of the monitoring system is obtained according to the number of samples of the preset category and the number of all samples, including:

[0080] S20, obtaining the first MRT index according to a first formula, wherein the first formula is:

[0081] P1=(A+B) / D,

[0082] Wherein, P1 represents the first MRT index, A represents the number of samples of the robust category, B represents the number of samples of the tolerable category, and D represents the number of all samples collected by the monitoring system within a preset time period.

[0083] It can be understood that when the response time of any sample is not greater than the preset response time, it indicates that the monitoring system is in a normal state for the sample; when the response time of any sample is greater than the preset response time and not greater than the preset multiple of the preset response time, although the response time of the sample is longer than the response time of the sample in the robust category, it can still respond normally within a preset time period, which can also be considered to indicate that the monitoring system is in a normal state for the sample;

[0084] Assuming A=100, B=100, D=200, the first MRT index P1=1 is obtained, indicating that the robustness of the monitoring system is the best. Assuming A=0, B=0, D=200, the first MRT index P1=0 is obtained, indicating that the robustness of the monitoring system is the worst.

[0085] That is to say, the robustness of the monitoring system is reflected in the interval [0,1] through the first MRT index P1. The user can intuitively understand the robustness of the monitoring system through the first MRT index P1. For example, the first MRT index P1 in two consecutive preset time periods is 0.9 and 0.6 respectively, and the first deviation between the two first MRT indexes P1 is 0.3. If the deviation is large, it can be determined that the monitoring system has failed. At this time, the monitoring system is manually troubleshooted to ensure that the monitoring system can operate stably.

[0086] The first deviation threshold value may be preset according to actual conditions. When the first deviation is greater than the first deviation threshold value, it is considered that the first deviation is large. For example, when the monitoring performance requirement of the monitoring system is high, the preset first deviation threshold value may be set to 0.1 or 0.05, etc. When the monitoring performance requirement of the monitoring system is low, the preset first deviation threshold value may be set to 0.2 or 0.25, etc.;

[0087] It can be understood that whether to perform maintenance on the monitoring system can be determined according to the preset first MRT index threshold. For example, the preset first MRT index threshold is 0.7. When the first MRT index P1 obtained in a preset time period is 0.65, the monitoring system is maintained.

[0088] Preferably, in the above technical solution, the preset category is the category of the irritable period, the MRT index is the second MRT index, and the MRT index for characterizing the robustness of the monitoring system is obtained according to the number of samples of the preset category and the number of all samples, including:

[0089] S200, obtaining the second MRT index according to a second formula, wherein the second formula is:

[0090] P2=C / D,

[0091] Wherein, P2 represents the second MRT index, C represents the number of samples of the category of the irritability period, and D represents the number of all samples collected by the monitoring system within a preset time period.

[0092] It can be understood that the second MRT index P2 is exactly opposite to the first MRT index P1. When the second MRT index P2=1, it indicates that the robustness of the monitoring system is the worst. When the second MRT index P2=0, it indicates that the robustness of the monitoring system is the best.

[0093] That is to say, the robustness of the monitoring system is reflected in the interval [0,1] through the second MRT index P2. The user can intuitively understand the robustness of the monitoring system through the second MRT index P2. For example, the second MRT index P2 in two consecutive preset time periods is 0.2 and 0.5 respectively, and the second deviation between the two second MRT indexes P2 is 0.3. If the deviation is large, it can be determined that the monitoring system has failed. At this time, the monitoring system is manually troubleshooted to ensure that the monitoring system can operate stably.

[0094] The second deviation threshold value may be preset according to actual conditions. When the second deviation is greater than the second deviation threshold value, it is considered that the second deviation is large. For example, when the monitoring performance requirements of the monitoring system are high, the preset second deviation threshold value may be set to 0.1 or 0.05, etc. When the monitoring performance requirements of the monitoring system are low, the preset second deviation threshold value may be set to 0.2 or 0.25, etc.;

[0095] It is understandable that whether to perform maintenance on the monitoring system can be determined based on the preset second MRT index threshold. For example, the preset second MRT index threshold is 0.2. When the second MRT index P2 obtained in a preset time period is 0.25, the monitoring system is maintained.

[0096] Among them, A, B, C, D, E and F are all greater than or equal to 0 and are integers.

[0097] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given in the present application. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the scope of protection of the present invention.

[0098] like Figure 2 As shown, a system 200 for characterizing the robustness of a monitoring system according to an embodiment of the present invention includes a classification module 210 and a calculation module 220;

[0099] The classification module 210 is used to classify all samples according to the response time of each sample collected by the monitoring system within a preset time period and the preset response time;

[0100] The calculation module 220 is used to obtain an MRT index for characterizing the robustness of the monitoring system according to the number of samples of a preset category and the number of all samples, and provide the MRT index to a user.

[0101] On the one hand, it is convenient for users to understand the robustness of the monitoring system through the MRT index. On the other hand, when the monitoring system fails, the MRT index changes, which makes it convenient for users to promptly understand the failure of the monitoring system through the change of the MRT index, thereby reducing the fault tolerance of the monitoring system.

[0102] Preferably, in the above technical solution, the classification module 210 is specifically used for:

[0103] When the response time of any sample collected by the monitoring system within a preset time period is not greater than the preset response time, the sample is classified into a robust category; when the response time of the sample is greater than the preset response time and not greater than a preset multiple of the preset response time, the sample is classified into a tolerable category; when the response time of the sample is greater than a preset multiple of the preset response time, the sample is classified into an irritable period category, until all samples are classified.

[0104] Preferably, in the above technical solution, the preset category includes the robust category and the tolerant category, the MRT index is the first MRT index, and the calculation module 220 is specifically used for:

[0105] The first MRT index is obtained according to a first formula, wherein the first formula is:

[0106] P1=(A+B) / D,

[0107] Wherein, P1 represents the first MRT index, A represents the number of samples of the robust category, B represents the number of samples of the tolerable category, and D represents the number of all samples collected by the monitoring system within a preset time period.

[0108] Preferably, in the above technical solution, the preset category is the category of the irritability period, the MRT index is the second MRT index, and the calculation module 220 is specifically used for:

[0109] The second MRT index is obtained according to the second formula, and the second formula is:

[0110] P2=C / D,

[0111] Wherein, P2 represents the second MRT index, C represents the number of samples of the category of the irritability period, and D represents the number of all samples collected by the monitoring system within a preset time period.

[0112] The above-mentioned parameters in the system 200 of the present invention for characterizing the robustness of a monitoring system and the steps for each unit module to implement corresponding functions can refer to the parameters and steps in the embodiment of the method for characterizing the robustness of a monitoring system mentioned above, and will not be repeated here.

[0113] like Figure 3As shown, an electronic device 300 of an embodiment of the present invention includes a memory 310, a processor 320, and a program 330 stored in the memory 310 and running on the processor 320. When the processor 320 executes the program 330, the steps of a method for characterizing the robustness of a monitoring system implemented in any of the above embodiments are implemented.

[0114] On the one hand, it is convenient for users to understand the robustness of the monitoring system through the MRT index. On the other hand, when the monitoring system fails, the MRT index changes, which makes it convenient for users to promptly understand the failure of the monitoring system through the change of the MRT index, thereby reducing the fault tolerance of the monitoring system.

[0115] Among them, the electronic device 300 can be a computer, a mobile phone, etc., and correspondingly, its program 330 is computer software or a mobile phone APP, etc., and the above-mentioned parameters and steps in an electronic device 300 of the present invention can refer to the parameters and steps in an embodiment of a method for characterizing the robustness of a monitoring system in the above text, and will not be repeated here.

[0116] In the present invention, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0117] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" 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 representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0118] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for characterizing the robustness of a monitoring system, characterized in that: include: Classify all samples according to the response time of each sample collected by the monitoring system within a preset time period and the preset response time; Obtaining an MRT index for characterizing the robustness of the monitoring system according to the number of samples of the preset category and the number of all samples, and providing the MRT index to a user; The method of classifying all samples according to the response time of each sample collected by the monitoring system within a preset time period and the preset response time includes: When the response time of any sample collected by the monitoring system within a preset time period is not greater than the preset response time, the sample is classified into a robust category; when the response time of the sample is greater than the preset response time and not greater than a preset multiple of the preset response time, the sample is classified into a tolerable category; when the response time of the sample is greater than a preset multiple of the preset response time, the sample is classified into an irritable category, until all samples are classified; The preset categories include the robust category and the tolerant category, the MRT index is a first MRT index, and the MRT index for characterizing the robustness of the monitoring system is obtained according to the number of samples of the preset category and the number of all samples, including: The first MRT index is obtained according to a first formula, wherein the first formula is: P1=(A+B) / D, Wherein, P1 represents the first MRT index, A represents the number of samples of the robust category, B represents the number of samples of the tolerable category, and D represents the number of all samples collected by the monitoring system within a preset time period; A first deviation threshold is preset. When the first deviation is greater than the first deviation threshold, it is considered that the first deviation between the first MRT indexes in two consecutive preset time periods is large, and whether to perform maintenance on the monitoring system is determined according to the preset first MRT index threshold; the preset category is the category of the irritability period, the MRT index is the second MRT index, and the MRT index used to characterize the robustness of the monitoring system is obtained according to the number of samples of the preset category and the number of all samples, including: The second MRT index is obtained according to the second formula, and the second formula is: P2=C / D, Wherein, P2 represents the second MRT index, C represents the number of samples of the category of the irritability period, and D represents the number of all samples collected by the monitoring system within a preset time period; A second deviation threshold is preset. When the second deviation is greater than the second deviation threshold, it is considered that the second deviation between the second MRT indexes in two consecutive preset time periods is large, and it is determined whether to perform maintenance on the monitoring system according to the preset second MRT index threshold; When the response time of any sample collected by the monitoring system within the preset time is not greater than the preset response time, the sample is classified into the first category; when the response time of any sample collected by the monitoring system within the preset time is greater than the preset response time, the sample is classified into the second category; until all samples are classified; When the preset category is the first category, the MRT index used to characterize the robustness of the monitoring system is obtained by the third formula, which is recorded as the third MRT index, wherein the third formula is: P3=E / D, wherein P3 represents the third MRT index, E represents the number of samples of the first category, and D represents the number of all samples collected by the monitoring system within the preset time period; When the preset category is the second category, the MRT index used to characterize the robustness of the monitoring system is obtained by the fourth formula, recorded as the fourth MRT index, wherein the fourth formula is: P4=F / D, wherein P4 represents the fourth MRT index, F represents the number of samples of the second category, and D represents the number of all samples collected by the monitoring system within the preset time period.

2. A system for characterizing the robustness of a monitoring system, characterized in that: Includes classification module and calculation module; The classification module is used to classify all samples according to the response time of each sample collected by the monitoring system within a preset time period and the preset response time; The calculation module is used to obtain an MRT index for characterizing the robustness of the monitoring system according to the number of samples of the preset category and the number of all samples, and provide the MRT index to the user; The classification module is specifically used for: When the response time of any sample collected by the monitoring system within a preset time period is not greater than the preset response time, the sample is classified into a robust category; when the response time of the sample is greater than the preset response time and not greater than a preset multiple of the preset response time, the sample is classified into a tolerable category; when the response time of the sample is greater than a preset multiple of the preset response time, the sample is classified into an irritable category, until all samples are classified; The preset categories include the robust category and the tolerant category, the MRT index is a first MRT index, and the calculation module is specifically used for: The first MRT index is obtained according to a first formula, wherein the first formula is: P1=(A+B) / D, Wherein, P1 represents the first MRT index, A represents the number of samples of the robust category, B represents the number of samples of the tolerable category, and D represents the number of all samples collected by the monitoring system within a preset time period; A first deviation threshold is preset. When the first deviation is greater than the first deviation threshold, it is considered that the first deviation between the first MRT indexes in two consecutive preset time periods is large, and it is determined whether to perform maintenance on the monitoring system according to the preset first MRT index threshold; The preset category is the category of the irritability period, the MRT index is the second MRT index, and the calculation module is specifically used for: The second MRT index is obtained according to the second formula, and the second formula is: P2=C / D, Wherein, P2 represents the second MRT index, C represents the number of samples of the category of the irritability period, and D represents the number of all samples collected by the monitoring system within a preset time period; A second deviation threshold is preset. When the second deviation is greater than the second deviation threshold, it is considered that the second deviation between the second MRT indexes in two consecutive preset time periods is large, and it is determined whether to perform maintenance on the monitoring system according to the preset second MRT index threshold; When the response time of any sample collected by the monitoring system within the preset time is not greater than the preset response time, the sample is classified into the first category; when the response time of any sample collected by the monitoring system within the preset time is greater than the preset response time, the sample is classified into the second category; until all samples are classified; When the preset category is the first category, the MRT index used to characterize the robustness of the monitoring system can be obtained by the third formula, which is recorded as the third MRT index, wherein the third formula is: P3=E / D, wherein P3 represents the third MRT index, E represents the number of samples of the first category, and D represents the number of all samples collected by the monitoring system within the preset time period; When the preset category is the second category, the MRT index used to characterize the robustness of the monitoring system can be obtained by the fourth formula, which is recorded as the fourth MRT index. The fourth formula is: P4=F / D, wherein P4 represents the fourth MRT index, F represents the number of samples of the second category, and D represents the number of all samples collected by the monitoring system within the preset time period.

3. An electronic device comprising a memory, a processor, and a program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of the method for characterizing the robustness of a monitoring system as claimed in claim 1 are implemented.

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