CN111831526A - Method, system and electronic equipment for representing robustness degree of monitoring system - Google Patents

Method, system and electronic equipment for representing robustness degree of monitoring system Download PDF

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CN111831526A
CN111831526A CN202010681104.9A CN202010681104A CN111831526A CN 111831526 A CN111831526 A CN 111831526A CN 202010681104 A CN202010681104 A CN 202010681104A CN 111831526 A CN111831526 A CN 111831526A
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preset
mrt
monitoring system
index
samples
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冯今亮
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Beijing Si Tech Information Technology Co Ltd
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Beijing Si Tech Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3466—Performance evaluation by tracing or monitoring
    • G06F11/3476—Data logging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3055—Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data

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  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
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  • General Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Computing Systems (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

The invention relates to a method, a system and electronic equipment for representing the robustness of a monitoring system, wherein all samples are classified according to the response time and the preset response time of each sample acquired by the monitoring system in a preset time period, an MRT index for representing the robustness of the monitoring system is obtained according to the number of samples in preset categories and the number of all samples, and the MRT index is provided to a user.

Description

Method, system and electronic equipment for representing robustness degree of monitoring system
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, a system, and an electronic device for characterizing the robustness of a monitoring system.
Background
Various hardware devices, software applications and the like exist in any service scene, and are divided according to logical levels, so that a complex service system is formed, along with the vigorous development of technologies such as cloud computing, big data and the like, the service system is more complex, and the monitoring system plays a crucial role in ensuring the stable operation of the service system.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and provides a method, a system and electronic equipment for representing the robustness of a monitoring system.
The technical scheme of the method for representing the robustness of the monitoring system is as follows:
classifying all samples according to the response time and the preset response time of each sample collected by a monitoring system in a preset time period;
and obtaining an MRT index for representing the robustness degree of the monitoring system according to the number of the samples of the preset category and the number of all the samples, and providing the MRT index to a user.
The method for representing the robustness of the monitoring system has the following beneficial effects:
on one hand, the user can conveniently know the robustness of the monitoring system through the MRT index, on the other hand, when the monitoring system breaks down, the MRT index changes, the user can conveniently know that the monitoring system breaks down in time through the change of the MRT index, and the fault tolerance rate of the monitoring system is reduced.
On the basis of the above scheme, the method for characterizing the robustness degree of the monitoring system can be further improved as follows.
Further, classifying all samples according to the response time and the preset response time of each sample collected by the monitoring system in the preset time period includes:
when the response time of any sample collected by the monitoring system in a preset time period is not more than the preset response time, classifying the sample into a robust class, when the response time of the sample is more than the preset response time and not more than a preset multiple of the preset response time, classifying the sample into a tolerable class, and when the response time of the sample is more than the preset multiple of the preset response time, classifying the sample into a dysphoric class until all samples are classified.
Further, the preset category includes the robust category and the tolerant category, the MRT index is a first MRT index, and the obtaining of the 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 includes:
obtaining the first MRT index according to a first formula, wherein the first formula is as follows:
P1＝(A+B)/D，
wherein, P1Representing the first MRT index, A representing the number of samples of the robust class, B representing the number of samples of the tolerable class, and D representing the number of all samples acquired by the monitoring system within a preset time period.
Further, the obtaining an MRT index used for characterizing the robustness degree of the monitoring system according to the number of samples of the preset category and the number of all samples includes:
obtaining the second MRT index according to a second formula, wherein the second formula is as follows:
P2＝C/D，
wherein, P2Representing the second MRT index, C representing the number of samples of the category of the fidget period, D representing the number of all samples collected by the monitoring system during a preset time period.
The technical scheme of the system for representing the robustness of the monitoring system is as follows:
comprises a classification module and a calculation module;
the classification module is used for classifying all samples according to the response time of each sample acquired by the monitoring system in a preset time period and the preset response time;
the calculation module is used for obtaining an MRT index for representing the robustness degree of the monitoring system according to the number of the samples of the preset category and the number of all the samples, and providing the MRT index to a user.
The system for representing the robustness of the monitoring system has the following beneficial effects:
on one hand, the user can conveniently know the robustness of the monitoring system through the MRT index, on the other hand, when the monitoring system breaks down, the MRT index changes, the user can conveniently know that the monitoring system breaks down in time through the change of the MRT index, and the fault tolerance rate of the monitoring system is reduced.
On the basis of the above scheme, the system for characterizing the robustness degree of the monitoring system can be further improved as follows.
Further, the classification module is specifically configured to:
when the response time of any sample collected by the monitoring system in a preset time period is not more than the preset response time, classifying the sample into a robust class, when the response time of the sample is more than the preset response time and not more than a preset multiple of the preset response time, classifying the sample into a tolerable class, and when the response time of the sample is more than the preset multiple of the preset response time, classifying the sample into a dysphoric class until all samples are classified.
Further, 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 configured to:
obtaining the first MRT index according to a first formula, wherein the first formula is as follows:
P1＝(A+B)/D，
wherein, P1Representing the first MRT index, A representing the number of samples of the robust class, B representing the number of samples of the tolerable class, and D representing the number of all samples acquired by the monitoring system within a preset time period.
Further, the preset category is a category of the dysphoric period, the MRT index is a second MRT index, and the calculation module is specifically configured to:
obtaining the second MRT index according to a second formula, wherein the second formula is as follows:
P2＝C/D，
wherein, P2Representing the second MRT index, C representing the number of samples of the category of the fidget period, D representing the number of all samples collected by the monitoring system during a preset time period.
The technical scheme of the electronic equipment is as follows:
comprising a memory, a processor and a program stored on said memory and running on said processor, characterized in that said processor, when executing said program, carries out the steps of a method of characterizing the robustness of a monitoring system as claimed in any one of the preceding claims.
The electronic equipment has the following beneficial effects:
on one hand, the user can conveniently know the robustness of the monitoring system through the MRT index, on the other hand, when the monitoring system breaks down, the MRT index changes, the user can conveniently know that the monitoring system breaks down in time through the change of the MRT index, and the fault tolerance rate of the monitoring system is reduced.
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FIG. 1 is a flow chart illustrating a method for characterizing the health of a monitoring system in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system for characterizing the health of a monitoring system, in accordance with an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
Detailed Description
As shown in FIG. 1, a method for characterizing the robustness of a monitoring system according to an embodiment of the present invention includes the following steps:
s1, classifying all samples according to the response time and preset response time of each sample collected by the monitoring system in a preset time period;
and S2, obtaining an MRT index for representing the robustness of the monitoring system according to the number of the samples of the preset category and the number of all the samples, and providing the MRT index to a user.
On one hand, the user can conveniently know the robustness of the monitoring system through the MRT index, on the other hand, when the monitoring system breaks down, the MRT index changes, the user can conveniently know that the monitoring system breaks down in time through the change of the MRT index, and the fault tolerance rate of the monitoring system is reduced.
The fault tolerance rate of the monitoring system is reduced by the following specific steps:
the existing monitoring system cannot find the problems of the monitoring system in time, and whether the monitoring system breaks down or not can be found in time through the application, so that the fault tolerance of the monitoring system is reduced.
Wherein, MRT in the MRT index is an abbreviation of Monitoring Response Time, and the sample represents the operation state or information of each component in the service system, such as CPU utilization, load of the service system, connection state of the port, bandwidth flow, website access state, and the like;
the response time of the sample is explained by taking the CPU utilization rate as the sample:
when the monitoring system sends an instruction for acquiring the CPU utilization rate to a server of the service system, timing is started, and when the monitoring system acquires the CPU utilization rate, timing is stopped, wherein the timing time is the response time of the sample;
the user can set the preset response time according to the actual situation, and it can be understood that the optimal threshold, that is, the optimal preset response time, can be obtained after the historical data, that is, the response time of a large number of samples, is analyzed; for samples of different objects, the optimal preset response time is also different, for example, the CPU utilization is generally defined in the monitoring system to acquire the CPU utilization once in 5 minutes, and 5 minutes is the optimal preset response time of the sample as the CPU utilization.
The preset time period may be set according to an actual situation, for example, the preset time period is an integral multiple of an optimal preset response time, so as to ensure that the response of the sample can be completed within the preset time period, and it can be understood that each sample in the response time of each sample acquired by the monitoring system within the preset time period is the same.
The MRT index can be provided to the user through a pop-up box, a short message, an email or other modes, and the user can conveniently check the MRT index.
In S1, all samples in the preset time period can be classified into two categories, specifically:
when the response time of any sample collected by the monitoring system in a preset time is not more than the preset response time, classifying the sample into a first category;
when the response time of any sample collected by the monitoring system in a preset time is longer than the preset response time, classifying the sample into a second category;
until all samples are classified, then:
1) when the preset category is a first category, obtaining an MRT index for characterizing the robustness of the monitoring system through a third formula, and recording the MRT index as a third MRT index, wherein the third formula is as follows:
P3＝E/D，
wherein, P3Representing the third MRT index, E representing the number of samples of the first category, and D representing the number of all samples collected by the monitoring system in a preset time period;
it can be understood that when the response time of any sample is not greater than the preset response time, which indicates that the monitoring system is in a normal state for monitoring the sample, assuming that E is 100 and D is 100, the third MRT index P is obtained3When the health degree of the monitoring system is optimal, 1 indicates that E is 0 and D is 100, the third MRT index P is obtained30 means that the monitoring system is least robust, i.e. passes the third MRT index P3Reflecting the robustness of the monitoring system in the interval [0,1]In this case, the user can pass through the third MRT index P3The robustness of the monitoring system can be intuitively understood, for example, the third MRT index P in two consecutive preset time periods30.9 and 0.6, respectively, two third MRT index P3The third deviation is 0.3, and if the third deviation is large, the monitoring system can be determined to have a fault, and at the moment, the monitoring system is subjected to manual troubleshooting to ensure that the monitoring system can stably operate.
The third deviation threshold may be preset according to actual conditions, and when the third deviation is greater than the third deviation threshold, the third deviation is considered to be large, for example, when the requirement on the monitoring performance of the monitoring system is high, the preset third deviation threshold may be set to 0.1 or 0.05, and when the requirement on the monitoring performance of the monitoring system is low, the preset third deviation threshold may be set to 0.2 or 0.25, and the like.
It can be understood that whether to maintain the monitoring system may be determined according to a preset third MRT index threshold, for example, the preset third MRT index threshold is 0.7, and the third MRT index P obtained in a certain preset time period is3If the value is 0.65, maintaining the monitoring system;
2) when the preset category is a second category, obtaining an MRT index for characterizing the robustness of the monitoring system through a fourth formula, and recording the MRT index as a fourth MRT index, wherein the fourth formula is as follows:
P4＝F/D，
wherein, P4Representing the fourth MRT index, F representing the number of samples of the second category, D representing the number of all samples collected by the monitoring system within a preset time period.
It will be appreciated that the fourth MRT index P4And the third MRT index P3On the contrary, when the fourth MRT index P4When the value is 1, the robustness of the monitoring system is the worst, and when the fourth MRT index P is used4When the value is 0, the robustness of the monitoring system is optimal;
that is, by the fourth MRT index P4Reflecting the robustness of the monitoring system in the interval [0,1]In this case, the user can pass through the fourth MRT index P4The robustness of the monitoring system can be intuitively understood, for example, the fourth MRT index P in two consecutive preset time periods40.2 and 0.5, respectively, two fourth MRT indices P3BetweenThe fourth deviation is 0.3, and if the deviation is large, the monitoring system can be determined to have a fault, and at the moment, the monitoring system is subjected to manual troubleshooting to ensure that the monitoring system can stably operate.
A fourth deviation threshold may be preset according to actual conditions, and when the fourth deviation is greater than the fourth deviation threshold, the fourth deviation is considered to be large, for example, when the requirement on the monitoring performance of the monitoring system is high, the preset fourth deviation threshold may be set to 0.1 or 0.05, and when the requirement on the monitoring performance of the monitoring system is low, the preset fourth deviation threshold may be set to 0.2 or 0.25, and the like;
it can be understood that whether to maintain the monitoring system may be determined according to a preset fourth MRT index threshold, for example, the preset fourth MRT index threshold is 0.2, and the fourth MRT index P obtained in a certain preset time period is4And 0.25, maintaining the monitoring system.
Preferably, in the above technical solution, S1 includes:
s10, when the response time of any sample collected by the monitoring system in a preset time period is not more than the preset response time, classifying the sample into a robust class, when the response time of the sample is more than the preset response time and not more than a preset multiple of the preset response time, classifying the sample into a tolerable class, and when the response time of the sample is more than the preset multiple of the preset response time, classifying the sample into a dysphoric class until all samples are classified.
The preset multiple may be set to 2, 3, 4, or 5, or different preset multiples may be set according to different requirements for monitoring performance of the monitoring system, generally speaking, the preset multiple is 4, the CPU utilization is used as a sample, the preset multiple is 4, and the preset response time is 5s for example:
1) the samples in the Robust (Robust) category are: samples with response times of no more than 5 s;
2) samples in the tolerable (Tolerating) class are: samples with response times greater than 5s and not greater than 4 × 5 s-20 s;
3) samples in the restlessness phase (frosted) category are: samples with response times greater than 4 × 5 s-20 s.
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 obtaining, according to the number of samples of the preset category and the number of all samples, the MRT index used for characterizing the robustness of the monitoring system includes:
s20, obtaining the first MRT index according to a first formula, wherein the first formula is as follows:
P1＝(A+B)/D，
wherein, P1Representing the first MRT index, A representing the number of samples of the robust class, B representing the number of samples of the tolerable class, and D representing the number of all samples acquired by the monitoring system within a preset time period.
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 monitoring the sample; when the response time of any sample is longer than the preset response time and is not longer than the preset response time of the preset multiple, the sample still can normally respond within a preset time period although the response time of the sample is longer than the response time of the sample in the robust category, and the monitoring of the monitoring system on the sample can be considered to be in a normal state;
assuming that a is 100, B is 100, and D is 200, the first MRT index P is obtained1When the health degree of the monitoring system is optimal, 1 indicates that a is 0, B is 0, and D is 200, the first MRT index P is obtained10, the robustness of the monitoring system is the worst;
that is, by the first MRT index P1Reflecting the robustness of the monitoring system in the interval [0,1]In the method, the user can pass through the first MRT index P1The robustness of the monitoring system can be intuitively understood, for example, the first MRT index P in two consecutive preset time periods10.9 and 0.6, respectively, two first MRT indices P1A first deviation of 0.3 therebetween, the deviationAnd if the fault is large, the monitoring system can be determined to have a fault, and at the moment, the monitoring system is subjected to manual troubleshooting so as to ensure that the monitoring system can stably operate.
A first deviation threshold may be preset according to actual conditions, and when the first deviation is greater than the first deviation threshold, the first deviation is considered to be large, for example, when the requirement on the monitoring performance of the monitoring system is high, the preset first deviation threshold may be set to 0.1 or 0.05, and when the requirement on the monitoring performance of the monitoring system is low, the preset first deviation threshold may be set to 0.2 or 0.25, and the like;
it is understood that whether to maintain the monitoring system may be determined according to a preset first MRT index threshold, for example, the preset first MRT index threshold is 0.7, and the first MRT index P obtained in a certain preset time period is1If the value is 0.65, maintaining the monitoring system;
preferably, in the above technical solution, the preset category is the category of the fidget period, the MRT index is a second MRT index, and the obtaining the 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 includes:
s200, obtaining the second MRT index according to a second formula, wherein the second formula is as follows:
P2＝C/D，
wherein, P2Representing the second MRT index, C representing the number of samples of the category of the fidget period, D representing the number of all samples collected by the monitoring system during a preset time period.
It will be appreciated that the second MRT index P2And the first MRT index P1In contrast, when the second MRT index P2When the value is 1, the robustness of the monitoring system is the worst, and when the second MRT index P is used2When the value is 0, the robustness of the monitoring system is optimal;
that is, by the second MRT index P2Reflecting the robustness of the monitoring system in the interval [0,1]In this case, the subscriber can pass through the second MRT index P2Can intuitively know the robustness of the monitoring systemTo a degree, e.g. a second MRT index P for two consecutive predetermined time periods20.2 and 0.5, respectively, two second MRT indices P2The second deviation between the two is 0.3, and if the deviation is large, the monitoring system can be determined to have a fault, and at the moment, the monitoring system is subjected to manual troubleshooting to ensure that the monitoring system can stably operate.
A second deviation threshold may be preset according to actual conditions, and when the second deviation is greater than the second deviation threshold, the second deviation is considered to be large, for example, when the requirement on the monitoring performance of the monitoring system is high, the preset second deviation threshold may be set to 0.1 or 0.05, and when the requirement on the monitoring performance of the monitoring system is low, the preset second deviation threshold may be set to 0.2 or 0.25, and the like;
it can be understood that whether to maintain the monitoring system may be determined according to a preset second MRT index threshold, for example, the preset second MRT index threshold is 0.2, and the second MRT index P obtained in a certain preset time period is2And 0.25, maintaining the monitoring system.
Wherein A, B, C, D, E and F are both greater than or equal to 0 and are both integers.
In the above embodiments, although the steps are numbered as S1, S2, etc., but only the specific embodiments are given in the present application, and those skilled in the art can adjust the execution sequence of S1, S2, etc. according to the actual situation, which is also within the protection scope of the present invention.
As shown in FIG. 2, a system 200 for characterizing the health of a monitoring system according to an embodiment of the present invention includes a classification module 210 and a calculation module 220;
the classification module 210 is configured to classify all samples according to the response time of each sample acquired by the monitoring system within a preset time period and a preset response time;
the calculating module 220 is configured to obtain an MRT index used for characterizing the robustness of the monitoring system according to the number of samples in the preset category and the number of all samples, and provide the MRT index to a user.
On one hand, the user can conveniently know the robustness of the monitoring system through the MRT index, on the other hand, when the monitoring system breaks down, the MRT index changes, the user can conveniently know that the monitoring system breaks down in time through the change of the MRT index, and the fault tolerance rate of the monitoring system is reduced.
Preferably, in the above technical solution, the classification module 210 is specifically configured to:
when the response time of any sample collected by the monitoring system in a preset time period is not more than the preset response time, classifying the sample into a robust class, when the response time of the sample is more than the preset response time and not more than a preset multiple of the preset response time, classifying the sample into a tolerable class, and when the response time of the sample is more than the preset multiple of the preset response time, classifying the sample into a dysphoric class until all samples are classified.
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 calculating module 220 is specifically configured to:
obtaining the first MRT index according to a first formula, wherein the first formula is as follows:
P1＝(A+B)/D，
wherein, P1Representing the first MRT index, A representing the number of samples of the robust class, B representing the number of samples of the tolerable class, and D representing the number of all samples acquired by the monitoring system within a preset time period.
Preferably, in the above technical solution, the preset category is a category of the dysphoric period, the MRT index is a second MRT index, and the calculating module 220 is specifically configured to:
obtaining the second MRT index according to a second formula, wherein the second formula is as follows:
P2＝C/D，
wherein, P2Representing the second MRT index, C representing the number of samples of the category of the fidget period, D representing the number of samples acquired by the monitoring system during a preset time periodThere is a number of samples.
The above steps for realizing the corresponding functions of each parameter and each unit module in the system 200 for representing the degree of health of the monitoring system according to the present invention may refer to each parameter and step in the above embodiment of the method for representing the degree of health of the monitoring system, which are not described herein again.
As shown in fig. 3, an electronic device 300 according to 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, wherein when the processor 320 executes the program 330, the steps of a method for characterizing the robustness of a monitoring system implemented by any of the above embodiments are implemented.
On one hand, the user can conveniently know the robustness of the monitoring system through the MRT index, on the other hand, when the monitoring system breaks down, the MRT index changes, the user can conveniently know that the monitoring system breaks down in time through the change of the MRT index, and the fault tolerance rate of the monitoring system is reduced.
The electronic device 300 may be a computer, a mobile phone, or the like, and correspondingly, the program 330 is computer software or a mobile phone APP, and for each parameter and step in the electronic device 300 of the present invention, reference may be made to each parameter and step in the above embodiment of the method for representing the robustness of the monitoring system, which is not described herein again.
In the present invention, the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (9)

1. A method of characterizing the robustness of a monitoring system, comprising:
classifying all samples according to the response time and the preset response time of each sample collected by a monitoring system in a preset time period;
and obtaining an MRT index for representing the robustness degree of the monitoring system according to the number of the samples of the preset category and the number of all the samples, and providing the MRT index to a user.
2. The method of claim 1, wherein the classifying all the samples according to the response time of each sample collected by the monitoring system in the preset time period and the preset response time comprises:
when the response time of any sample collected by the monitoring system in a preset time period is not more than the preset response time, classifying the sample into a robust class, when the response time of the sample is more than the preset response time and not more than a preset multiple of the preset response time, classifying the sample into a tolerable class, and when the response time of the sample is more than the preset multiple of the preset response time, classifying the sample into a dysphoric class until all samples are classified.
3. The method as claimed in claim 2, wherein the preset category includes the robust category and the tolerant category, the MRT index is a first MRT index, and the obtaining of the 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 includes:
obtaining the first MRT index according to a first formula, wherein the first formula is as follows:
P1＝(A+B)/D，
wherein, P1Representing the first MRT index, A representing the number of samples of the robust class, B representing the number of samples of the tolerable class, and D representing the number of all samples acquired by the monitoring system within a preset time period.
4. The method as claimed in claim 2, wherein the predetermined category is the category of the fidget period, the MRT index is a second MRT index, and the obtaining of the MRT index for characterizing the health of the monitoring system according to the number of samples of the predetermined category and the number of all samples comprises:
obtaining the second MRT index according to a second formula, wherein the second formula is as follows:
P2＝C/D，
wherein, P2Representing the second MRT index, C representing the number of samples of the category of the fidget period, D representing the number of all samples collected by the monitoring system during a preset time period.
5. A system for characterizing the degree of health of a monitoring system, comprising a classification module and a calculation module;
the classification module is used for classifying all samples according to the response time of each sample acquired by the monitoring system in a preset time period and the preset response time;
the calculation module is used for obtaining an MRT index for representing the robustness degree of the monitoring system according to the number of the samples of the preset category and the number of all the samples, and providing the MRT index to a user.
6. The system for characterizing the health of a monitoring system as claimed in claim 5, wherein the classification module is specifically configured to:
when the response time of any sample collected by the monitoring system in a preset time period is not more than the preset response time, classifying the sample into a robust class, when the response time of the sample is more than the preset response time and not more than a preset multiple of the preset response time, classifying the sample into a tolerable class, and when the response time of the sample is more than the preset multiple of the preset response time, classifying the sample into a dysphoric class until all samples are classified.
7. The system for characterizing the robustness of a monitoring system according to claim 6, wherein 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 configured to:
obtaining the first MRT index according to a first formula, wherein the first formula is as follows:
P1＝(A+B)/D，
wherein, P1Representing the first MRT index, A representing the number of samples of the robust class, B representing the number of samples of the tolerable class, and D representing the number of all samples acquired by the monitoring system within a preset time period.
8. The system for characterizing the robustness of a monitoring system according to claim 6, wherein the preset category is the category of the fidget period, the MRT index is a second MRT index, and the calculating module is specifically configured to:
obtaining the second MRT index according to a second formula, wherein the second formula is as follows:
P2＝C/D，
wherein, P2Representing the second MRT index, C representing the number of samples of the category of the fidget period, D representing the number of all samples collected by the monitoring system during a preset time period.
9. An electronic device comprising a memory, a processor and a program stored on the memory and running on the processor, characterized in that the steps of a method of characterizing the robustness of a monitoring system as claimed in any one of claims 1 to 4 are carried out by the processor when executing the program.
CN202010681104.9A 2020-07-15 2020-07-15 Method, system and electronic equipment for representing robustness degree of monitoring system Pending CN111831526A (en)

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