A management method for building a multi-probe collaborative monitoring system based on an operation and maintenance system

Abstract

The present invention discloses a management method for building a multi-probe collaborative monitoring system based on an operation and maintenance system, which relates to the field of computer Internet technology. A distance association model and an information association model between any probe collection position and each IoT device in the system are constructed to obtain a data security reference value of the probe collection position. Data is collected in the system through a plurality of probes. According to the difference between the collected data and the data security threshold, a first evaluation value of the operating status of the IoT device in the IoT system is calculated. According to the distribution density of the plurality of probes in the IoT system, the confidence of a sampling group composed of the plurality of probes is calculated to obtain an average value of the first evaluation value of the IoT device. The difference between the first evaluation value of each IoT device and the average value is quantified through the confidence, and the quantified result of the first evaluation value is recorded as a second evaluation value.

Description

Technical Field

[0001] The present invention relates to the field of computer Internet technology, and in particular to a construction and management method of a multi-probe collaborative monitoring system based on an operation and maintenance system. Background Art

[0002] In the computer field, a probe is a tool used to monitor and collect system data. It can be a software program, hardware device, or code snippet that monitors and records various system indicators and information in real time. These indicators and information include system performance, network traffic, application status, security vulnerabilities, etc. Through the data collected by the probe, relevant technicians can better understand the operating status of the system.

[0003] With the development of computer technology, the scale of computer systems continues to expand. In order to better understand the operating status of computer systems, the number of probes used by relevant technicians also needs to increase. The traditional probe management method lacks collaborative management between multiple probes, so the operation and maintenance data obtained through multiple probes is only the lack of effective integration of the data detected by a single probe, so the feedback results are relatively one-sided and easily form data islands. At the same time, when multiple probes lack collaboration, it makes it difficult for relevant technicians to troubleshoot and locate faults, because different probes may only focus on a certain aspect or level of the system and lack global consideration of the entire system. Summary of the invention

[0004] The purpose of the present invention is to provide a management method for constructing a multi-probe collaborative monitoring system based on an operation and maintenance system to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solution: a method for building and managing a multi-probe collaborative monitoring system based on an operation and maintenance system, the method comprising:

[0006] Step S100: In an IoT system composed of a number of IoT devices, a number of probe collection locations are set, and a distance association model and an information association model between any probe collection location and each IoT device in the system are constructed;

[0007] Step S200: obtaining the historical collection data of the probe collection position to obtain the data security reference value of each probe collection position;

[0008] Step S300: collecting data in the system through a plurality of probes, and calculating a first evaluation value of the operating state of the IoT device in the IoT system according to the difference between the collected data and the data security threshold;

[0009] Step S400: Calculating the confidence of a sampling group consisting of a number of probes according to the distribution density of the probes in the Internet of Things system;

[0010] Step S500: Obtain an average value of the first evaluation values ​​of the IoT devices, quantify the difference between the first evaluation value of each IoT device and the average value by confidence, obtain a second evaluation value, and feed back the second evaluation value to relevant managers.

[0011] Furthermore, step S100 includes:

[0012] Step S101: Obtain a probe collection location h from a number of probe collection locations, obtain a certain IoT device D along the network connection direction of the data at h in the IoT system, and obtain the shortest path L between the probe collection location h and the IoT device D hD ;

[0013] Step S102: Obtain path L hD The number of IoT devices n between the upper probe collection position h and the IoT device D hD , n hD It is recorded as the logical distance between the probe collection position h and the IoT device D;

[0014] Step S103: Obtain the logical distance between the probe collection position h and any IoT device in the IoT, record the distance relationship sequence of the probe collection position h, collect the distance relationship sequences of all probe collection positions, and obtain a distance association model.

[0015] Furthermore, step S100 also includes:

[0016] Step S104: setting a unit time period with a time length of T0, the probe collects data A flowing through the position h in a certain unit time period, and after a number of unit time periods, obtains data B flowing out of the IoT device D;

[0017] Step S105: Calculate the data correlation γ between data A and data B, obtain the data correlation between the probe collection position h and any IoT device in the IoT, and record it in the correlation relationship sequence of the probe collection position h;

[0018] Step S106: Gather the association relationship sequences of all probe collection positions to obtain an information association model.

[0019] Further, step S105 includes:

[0020] Step S11: vectorize data A and data B respectively to obtain the data vector α corresponding to data A A The data vector α corresponding to data B B ;

[0021] Step S12: Calculate the data vector α A and data vector α BThe cosine value ai of the vector angle is taken as the data correlation degree γ1 between data A and data B.

[0022] Furthermore, step S105 further includes:

[0023] Step S21: Obtain the information entropy H(A) of data A and the information entropy H(B) of data B, and calculate the conditional entropy H(B|A) of data B under the condition that data A is known;

[0024] Step S22: Calculate the mutual information I between data A and data B BA , I BA =H(B)-H(B|A), the mutual information I BA As the data correlation degree γ2 between data A and data B;

[0025] Mutual Information (MI) is a metric used to measure the correlation or dependency between two random variables. Mutual information measures the amount of shared information between two random variables, that is, the amount of information one variable contains about another variable. If the mutual information of two variables is high, the correlation between them is strong, and if the mutual information of two variables is low, the correlation between them is weak.

[0026] Furthermore, step S300 includes:

[0027] Step S301: Acquire the collected data corresponding to a probe p from a plurality of probes, wherein the probe collection position corresponding to the probe p is recorded as h p , get h p The data collection value ga p , calculate h p The safety difference value r p , r p =β p -ga p ;

[0028] Step S302: Obtain a certain IoT device D2 in the IoT system and obtain h p The logical distance c from D2 p2 And data correlation γ p2 , calculate the evaluation value g of a probe p on the IoT device p ,

[0029] in,

[0030] The coefficient of the safety difference value is formed by the logical distance and the correlation. The first part of the coefficient is the inverse of the logical distance. When the logical distance between the probe collection position and the device is farther, the feedback degree of the actual operation status of the device at the probe collection position is lower. The second part of the coefficient is the data correlation between the two positions. When the data correlation between the probe collection position and the device is higher, it means that the relationship between this probe collection position and the device is closer.

[0031] From the perspectives of system structure and data transmission, the association between the probe collection location and the device is measured, which includes the degree of association between the probe collection location and the IoT device on the surface layer, and also reflects the degree of association of the data transmitted in the inner layer, which improves the accuracy of the association judgment process and also reflects the impact of the sampling results on the system evaluation under different sampling methods.

[0032] Step S303: Obtain the number of probes N of several probes, sum up the evaluation values ​​of all probes on the IoT device, and obtain the first evaluation value G of the IoT device D2 21 .

[0033] Furthermore, step S400 includes:

[0034] Step S401: Record several probes into a sampling group, and select any two probes p from the sampling group. m1 and p m2 , get probe p m1 and p m2 The probe distance q;

[0035] Step S402: among all the probes, the distances between any two probes are traversed, and the average value Q of the probe distances among the probes is calculated, and 1 / Q is used as the confidence of the sampling group;

[0036] The confidence of the sampling group indicates the credibility of the sampling method corresponding to the sampling group. Different probe setting methods, such as the number of probes in the IoT system and the density of probes in the IoT system, can measure the number and density of probes at the same time by calculating the average distance of probes in the sampling group under different sampling methods. When the number of probes decreases, the value of Q increases and the confidence 1 / Q decreases. When the two sampling schemes use the same number of probes, in the scheme where the probes are more evenly distributed in the system, the average value Q of the distance between the probes is smaller and the confidence 1 / Q is larger. In the scheme where the probes are more unevenly distributed in the system, the average value Q of the distance between the probes is larger and the confidence 1 / Q is smaller.

[0037] Furthermore, step S401 includes:

[0038] The probe p m1 and p m2The logical distance is taken as the probe distance;

[0039] Alternatively, obtain the probe p m1 The corresponding probe collection position h m1 and probe p m2 The corresponding probe collection position h m2 , h m1 With h m2 The actual distance is taken as the probe distance;

[0040] Or, obtain the device topology diagram of the IoT system and convert h m1 With h m2 The distance on the device topology map is used as the probe distance.

[0041] Furthermore, step S500 includes:

[0042] Step S501: obtaining a first evaluation value of each IoT device in the IoT system, and calculating an average value of the first evaluation values ​​of all IoT devices, which is recorded as w;

[0043] Step S502: Calculate the evaluation difference k between the first evaluation value of the IoT device D2 and w, k=G 21 -w;

[0044] Step S503: Calculate the second evaluation value G of the IoT device D2 22 , G 22 =(1 / Q)×k;

[0045] The evaluation difference represents the difference distribution between the IoT device and the safe operation status value. This application aims to quantify and characterize different difference distributions. On the one hand, it evaluates the overall operation status of the system. On the other hand, it reflects the relevant technicians in setting up the probe collection plan. The same difference method is presented through different data, which provides a certain reference for the technicians to determine whether they need to adjust the sampling position.

[0046] Step S504: collect the second evaluation values ​​of each IoT device in the IoT system, draw a chart of the second evaluation values, and feed it back to relevant managers.

[0047] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention collects and analyzes the system data collected by the probes, and through the association of system results and the connection of data transmission, analyzes the information collected by the probes from multiple angles, thereby improving the accuracy of the evaluation of the equipment when multiple probes are collaboratively sampled. The distribution of multiple probes in the system is further measured, and in the process of multi-probe sampling, the association between different sampling methods and the system as a whole is evaluated, which improves the angle of information analysis, overcomes the one-by-one analysis method in traditional solutions, expands the field of view of data analysis, and provides analysis results of the system from a global perspective. This solution adopts a collaborative analysis method for multiple probes to improve the accuracy of the overall evaluation of the system in the probe sampling detection method. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 It is a flow chart of a method for building and managing a multi-probe collaborative monitoring system based on an operation and maintenance system according to the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figure 1 , the present invention provides a technical solution:

[0052] Step S100: In an IoT system composed of a number of IoT devices, a number of probe collection locations are set, and a distance association model and an information association model between any probe collection location and each IoT device in the system are constructed;

[0053] Wherein, step S100 includes:

[0054] Step S101: Obtain a probe collection location h from a number of probe collection locations, obtain a certain IoT device D along the network connection direction of the data at h in the IoT system, and obtain the shortest path L between the probe collection location h and the IoT device D hD ;

[0055] Step S102: Obtain path L hD The number of IoT devices n between the upper probe collection position h and the IoT device DhD , n hD It is recorded as the logical distance between the probe collection position h and the IoT device D;

[0056] Step S103: obtaining the logical distance between the probe collection position h and any IoT device in the IoT, recording the distance relationship sequence of the probe collection position h, and collecting the distance relationship sequences of all probe collection positions to obtain a distance association model;

[0057] Step S104: setting a unit time period with a time length of T0, the probe collects data A flowing through the position h in a certain unit time period, and after a number of unit time periods, obtains data B flowing out of the IoT device D;

[0058] Step S105: Calculate the data correlation γ between data A and data B, obtain the data correlation between the probe collection position h and any IoT device in the IoT, and record it in the correlation relationship sequence of the probe collection position h;

[0059] Wherein, step S105 includes:

[0060] Step S11: vectorize data A and data B respectively to obtain the data vector α corresponding to data A A The data vector α corresponding to data B B ;

[0061] Choose different data vectorization methods according to the different types of data flowing through the system. For example, when the data includes text information, use the bag-of-words model for vectorization.

[0062] When the data includes audio information, extract the time domain information or frequency domain information of the audio information for vectorization, the time domain information is such as the amplitude of the audio information, the frequency domain information is such as the intensity of different frequency components, or the audio information is vectorized by using Mel frequency cepstral coefficients (MFCC), which describes the logarithm of the short-time power spectrum of the audio signal on the Mel scale;

[0063] When the data includes image information, deep learning models such as neural convolutional networks can be used to extract image features and convert these features into vectors;

[0064] Step S12: Calculate the data vector α A and data vector α B The cosine value ai of the vector angle is taken as the data correlation degree γ1 between data A and data B;

[0065] Wherein, step S105 further includes:

[0066] Step S21: Obtain the information entropy H(A) of data A and the information entropy H(B) of data B, and calculate the conditional entropy H(B|A) of data B under the condition that data A is known;

[0067] Step S22: Calculate the mutual information I between data A and data B BA , I BA =H(B)-H(B|A), the mutual information I BA As the data correlation degree γ2 between data A and data B;

[0068] Step S106: Gather the association relationship sequences of all probe collection positions to obtain an information association model.

[0069] Step S200: obtaining the historical collection data of the probe collection position to obtain the data security reference value of each probe collection position;

[0070] Step S300: collecting data in the system through a plurality of probes, and calculating a first evaluation value of the operating state of the IoT device in the IoT system according to the difference between the collected data and the data security threshold;

[0071] Wherein, step S300 includes:

[0072] Step S301: Acquire the collected data corresponding to a probe p from a plurality of probes, wherein the probe collection position corresponding to the probe p is recorded as h p , get h p The data collection value ga p , calculate h p The safety difference value r p , r p =β p -ga p ;

[0073] Step S302: Obtain a certain IoT device D2 in the IoT system and obtain h p The logical distance c from D2 p2 And data correlation γ p2 , calculate the evaluation value g of a probe p on the IoT device p ,

[0074] in,

[0075] For example, for IoT device D2, there are 4 probes for data collection;

[0076] h1 represents the first probe position, the logical distance c12 between h1 and D2 is 4, the data association is 0.4, and the safety difference value is -1.8;

[0077] h2 represents the second probe position, the logical distance c22 between h2 and D2 is 3, the data association is 0.35, and the safety difference value is 0.4;

[0078] h3 represents the third probe position, the logical distance c32 between h3 and D2 is 4, the data association is 0.5, and the safety difference value is 1.2;

[0079] h4 represents the fourth probe position, the logical distance c12 between h4 and D2 is 6, the data association is 0.4, and the safety difference value is -0.8;

[0080] g1=(1 / 4+0.4)×(-1.8)=-2.45, g2=(1 / 3+0.35)×0.4=0.27;

[0081] g3=(1 / 4+0.5)×1.2=0.9, g4=(1 / 6+0.4)×(-0.8)=-0.45;

[0082] Step S303: Obtain the number of probes N of several probes, sum up the evaluation values ​​of all probes on the IoT device, and obtain the first evaluation value G of the IoT device D2 21；

[0083] Preferably, the weighted average is used as the calculation method of the average, and the data collection values ​​corresponding to the N probes are obtained respectively, and the data collection values ​​are classified according to the collection values, wherein the i-th data collection value c1 i The number is n i ;

[0084] Calculate the i-th data collection value cl i The weight ni / N;

[0085] Calculate the i-th data collection value cl i The weighted value η i , η i =cl i ×n i / N;

[0086] Calculate the weighted average E of the data collection values ​​corresponding to N probes,

[0087] Step S400: Calculating the confidence of a sampling group consisting of a number of probes according to the distribution density of the probes in the Internet of Things system;

[0088] Wherein, step S400 includes:

[0089] Step S401: Record several probes into a sampling group, and select any two probes p from the sampling group. m1 and p m2 , get probe pm1 and p m2 The probe distance q;

[0090] The probe distance is obtained by:

[0091] The probe p m1 and p m2 The logical distance is taken as the probe distance;

[0092] Alternatively, obtain the probe p m1 The corresponding probe collection position h m1 and probe p m2 The corresponding probe collection position h m2 , h m1 With h m2 The actual distance is taken as the probe distance;

[0093] Or, obtain the device topology diagram of the IoT system and convert h m1 With h m2 The distance on the device topology map is used as the probe distance;

[0094] Step S402: Among all the probes, the distances between any two probes are traversed, and the average value Q of the probe distances among several probes is calculated, and 1 / Q is used as the confidence of the sampling group.

[0095] Step S500: obtaining an average value of the first evaluation values ​​of the IoT devices, quantifying the difference between the first evaluation value of each IoT device and the average value by confidence, obtaining a second evaluation value, and feeding back the second evaluation value to relevant managers;

[0096] Wherein, step S500 includes:

[0097] Step S501: obtaining a first evaluation value of each IoT device in the IoT system, and calculating an average value of the first evaluation values ​​of all IoT devices, which is recorded as w;

[0098] Step S502: Calculate the evaluation difference k between the first evaluation value of the IoT device D2 and w, k=G 21 -w;

[0099] Step S503: Calculate the second evaluation value G of the IoT device D2 22 , G 22 =(1 / Q)×k;

[0100] Step S504: collect the second evaluation values ​​of each IoT device in the IoT system, draw a chart of the second evaluation values, and feed it back to relevant managers;

[0101] Preferably, a plane diagram of the Internet of Things system topology diagram is obtained as the bottom surface of the three-dimensional coordinate system, and a coordinate axis perpendicular to the plane diagram is set as the z-axis, and the z-axis is used to record the evaluation value of each Internet of Things device in the Internet of Things system;

[0102] Drawing a reference plane in a three-dimensional coordinate system according to the first evaluation value;

[0103] The second evaluation value of each IoT device is marked in a three-dimensional coordinate system, and the degree of difference between each IoT device and the safety reference value is presented through a visualization solution.

[0104] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0105] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A management method for building a multi-probe collaborative monitoring system based on an operation and maintenance system, characterized in that: The method comprises the following steps: Step S100: In an IoT system composed of a plurality of IoT devices, a plurality of probe collection locations are set, and a distance association model and an information association model between any probe collection location and each IoT device in the system are constructed; Step S100 includes: Step S101: Obtain a probe collection position h from the plurality of probe collection positions, obtain a certain IoT device D along the network connection direction of the data at h in the IoT system, and obtain the shortest path L between the probe collection position h and the IoT device D hD ; Step S102: Obtain path L hD The number of IoT devices n between the upper probe collection position h and the IoT device D hD , n hD It is recorded as the logical distance between the probe collection position h and the IoT device D; Step S103: obtaining the logical distance between the probe collection position h and any IoT device in the IoT, recording the distance relationship sequence of the probe collection position h, and collecting the distance relationship sequences of all probe collection positions to obtain a distance association model; Step S104: setting a unit time period with a time length of T0, the probe collects data A flowing through the position h in a certain unit time period, and after a number of unit time periods, obtains data B flowing out of the IoT device D; Step S105: Calculate the data correlation γ between data A and data B, obtain the data correlation between the probe collection position h and any IoT device in the IoT, and record the correlation relationship sequence of the probe collection position h; Step S106: Collect the association relationship sequences of all probe collection positions to obtain an information association model; Step S200: obtaining the historical collection data of the probe collection position to obtain the data security reference value of each probe collection position; Step S300: collecting data in the system through a plurality of probes, and calculating a first evaluation value of the operating state of the IoT device in the IoT system according to the difference between the collected data and the data security threshold; Step S300 includes: Step S301: Acquire the collected data corresponding to a certain probe p from the plurality of probes, wherein the probe collection position corresponding to the certain probe p is recorded as h p , get h p The data collection value ga p , calculate h p The safety difference value r p , r p =β p -ga p ; Step S302: Obtain a certain IoT device D2 in the IoT system and obtain h p The logical distance c from D2 p2 And data correlation γ p2 , calculate the evaluation value g of the probe p on the IoT device p , in, Step S303: Obtain the number of probes N of the plurality of probes, sum the evaluation values ​​of all probes on the IoT device, and obtain the first evaluation value G of the IoT device D2. 21 ; Step S400: Calculating the confidence of the sampling group composed of the plurality of probes according to the distribution density of the plurality of probes in the Internet of Things system; Step S500: obtaining an average value of the first evaluation values ​​of the IoT devices, quantifying the difference between the first evaluation value of each IoT device and the average value by confidence, obtaining a second evaluation value, and feeding back the second evaluation value to relevant managers.

2. According to claim 1, a method for building and managing a multi-probe collaborative monitoring system based on an operation and maintenance system is characterized by: The calculation method of the data association degree in step S105 includes: Step S11: vectorize data A and data B respectively to obtain the data vector α corresponding to data A A The data vector α corresponding to data B B ; Step S12: Calculate data vector α A and data vector α B The cosine value ai of the vector angle is taken as the data association degree γ1 between data A and data B.

3. According to claim 2, a method for building and managing a multi-probe collaborative monitoring system based on an operation and maintenance system is characterized by: The calculation method of the data association degree in step S105 includes: Step S21: Obtain the information entropy H(A) of data A and the information entropy H(B) of data B, and calculate the conditional entropy H(B|A) of data B under the condition that data A is known; Step S22: Calculate the mutual information I between data A and data B BA , I BA =H(B)-H(B|A), the mutual information I BA As the data association degree γ2 between data A and data B.

4. A method for building and managing a multi-probe collaborative monitoring system based on an operation and maintenance system according to claim 3, characterized in that: Step S400 includes: Step S401: Record the probes into a sampling group, and select any two probes p from the sampling group. m1 and p m2 , get probe p m1 and p m2 The probe distance q; Step S402: Among all the probes, the distances between any two probes are traversed, and the average value Q of the probe distances among the plurality of probes is calculated, and 1 / Q is used as the confidence of the sampling group.

5. A method for building and managing a multi-probe collaborative monitoring system based on an operation and maintenance system according to claim 4, characterized in that: The probe distance can be obtained by: The probe p m1 and p m2 The logical distance is taken as the probe distance; Alternatively, obtain the probe p m1 The corresponding probe collection position h m1 and probe p m2 The corresponding probe collection position h m2 , h m1 With h m2 The actual distance is taken as the probe distance; Alternatively, obtain the device topology diagram of the Internet of Things system and convert h m1 With h m2 The distance on the device topology map is used as the probe distance.

6. A method for building and managing a multi-probe collaborative monitoring system based on an operation and maintenance system according to claim 5, characterized in that: Step S500 includes: Step S501: obtaining a first evaluation value of each IoT device in the IoT system, and calculating an average value of the first evaluation values ​​of all IoT devices, which is recorded as w; Step S502: Calculate the evaluation difference k between the first evaluation value of the IoT device D2 and w, k=G 21 -w; Step S503: Calculate the second evaluation value G of the IoT device D2 22 , G 22 =(1 / Q)×k; Step S504: collect the second evaluation values ​​of each IoT device in the IoT system, draw a chart of the second evaluation values, and feed it back to relevant managers.

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