CN110765486A - Asset fault identification method - Google Patents
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Abstract
The invention discloses an asset fault identification method which is used for carrying out fault detection on information infrastructure assets. The method comprises the steps of installing a baseboard management controller on the managed asset, constructing an operation and maintenance data network and an out-of-band management system, obtaining the collected managed asset operation information data on the baseboard management controller, labeling and preprocessing the managed asset operation information data, inputting the preprocessed managed asset operation information data into a constructed classification model based on cost sensitive learning, outputting the prediction probability that the asset is a device fault, and comparing the prediction probability with a set threshold value to judge whether the asset is a faulty device. The invention solves the problem that in the prior art, the staff cannot quickly and accurately judge the fault equipment in the asset equipment and the fault sample is insufficient, realizes the information management of the asset, and improves the working efficiency.
Description
Technical Field
The invention relates to the technical field of asset management, in particular to an asset fault identification method.
Background
Information infrastructure assets include general storage devices, network and communication devices, and the like. At present, with the development of information technology, information infrastructure is applied to services to provide support and assistance for the services. The normal operation of the information infrastructure is an important guarantee for ensuring the sustainable operation of the organization business.
In the conventional information infrastructure management, it is necessary for a worker to periodically check the information infrastructure based on the work experience, confirm the operation state of the facility by performing on-site inspection on the information infrastructure, determine the type of failure of the information infrastructure, and repair or replace the failed facility. Due to the large number of infrastructures, the distribution is wide. In the prior art, a worker cannot quickly and accurately judge the fault equipment and the fault type, so that the working efficiency is greatly reduced.
Disclosure of Invention
The asset fault identification method solves the problem that in the prior art, a worker cannot quickly and accurately judge fault equipment and fault samples in asset equipment.
The asset fault identification method provided by the application comprises the following steps:
step S1: the baseboard management controller monitors and collects managed asset operation information data;
step S2: the out-of-band management system acquires managed asset operation information data from the baseboard management controller through an operation and maintenance data network;
step S3: the out-of-band management system labels the acquired managed asset operation information data to form label data;
step S4: performing data preprocessing operation on the label data, and taking the label data subjected to the preprocessing operation as training data;
step S5: constructing a classification model based on cost sensitive learning, inputting training data into the classification model for training to obtain a trained classification model based on cost sensitive learning, and inputting preprocessed real-time data into the trained classification model based on cost sensitive learning to obtain the prediction probability that the managed assets are fault equipment;
step S6: comparing the prediction probability with a preset threshold value, and judging whether the managed asset is fault equipment or normal equipment according to the comparison result;
step S7: and the out-of-band management system outputs a final judgment result.
Preferably, an out-of-band management interface technology is adopted to construct an operation and maintenance data network, and a B/S framework is adopted to construct an out-of-band management system.
Preferably, the out-of-band management system receives data of the baseboard management controller and stores the collected data in a system event log by using an instruction specified in an intelligent platform management interface specification.
Preferably, in step S2, the managed asset operation information data includes temperature, voltage, fan speed, power status, CPU, memory utilization, and failure asset operation information data, and the failure asset operation information data includes actual failure asset operation information data and artificial simulation failure asset operation information data.
Preferably, in step S3, the asset operation information data is manually labeled, and the actual faulty asset operation information data and the manually simulated faulty asset operation information data are both labeled as faulty devices, and the other asset operation information data are labeled as normal devices.
Preferably, in step S4, the training data is preprocessed by data cleaning, data transformation, and data normalization; the data cleaning is used for eliminating abnormal data; the data transformation makes the long tail distribution data accord with normal distribution by carrying out logarithmic conversion on the long tail distribution data, and meets the assumed condition of a linear model; the data standardization reduces the size difference between characteristic values by carrying out dimensionless processing on numerical characteristics, and avoids the interference of data with larger order of magnitude on data with smaller order of magnitude.
Preferably, in the data transformation process, through data exploration and analysis, the long-tail distribution characteristics are subjected to logarithmic transformation, and the specific calculation process is as follows:
A'＝log(A)；
wherein A is the long tail distribution characteristic, and A' is the result of the long tail distribution characteristic A after logarithmic transformation;
the formula of the calculation for normalizing the data is:
wherein X is the value of a certain column of characteristics, mu represents the mean value of the characteristics X, and sigma represents the standard deviation of the characteristics X;
after the eigenvalue X is centered according to the mean value mu and then scaled according to the standard deviation sigma, the eigenvalue X will follow the normal distribution with the mean value of 0 and the variance of 1.
Preferably, in step S5, the objective function of the classification model based on cost-sensitive learning is:
wherein, C0And C1Punishment factors of the misclassification of normal equipment and fault equipment respectively; l (y)i，fθ(xi) Is a loss function; y isiA real tag class representing an ith device; f. ofθ(xi) A prediction function that is a classification model based on cost sensitive learning; theta denotes the prediction function fθ(xi) The parameters of (1); x is the number ofiRepresenting the characteristic data of the ith device, wherein lambda R (theta) is a regularization term and represents the penalty to the complex model; lambda is not less than 0Regularization coefficients to trade-off empirical risk and model complexity; r (theta) is the model complexity.
The calculation formula for calculating the prediction probability that the managed asset is the failure device is as follows:
p(xi)＝fθ(xi)；
preferably, in step S5, the process of preprocessing the real-time data includes two steps of data transformation and data normalization.
Preferably, in step S6, if the prediction probability is greater than the threshold value, the managed asset is determined as a faulty device, and if the prediction probability is less than the threshold value, the managed asset is determined as a normal device; the specific formula for comparing the predicted probability that the managed asset is a failed device to the threshold is as follows:
wherein F represents equipment failure, T represents equipment normality, y (x)i) The prediction result of the ith equipment is shown, and only two results of fault equipment and normal equipment are taken; p (x)i) Representing the predicted probability for the ith device, α is a threshold.
According to the technical scheme, the method has the following advantages:
the method comprises the steps of installing a baseboard management controller on a managed asset, obtaining collected managed asset operation information data on the baseboard management controller through constructing an operation and maintenance data network and an out-of-band management system, marking and preprocessing the managed asset operation information data, inputting the preprocessed managed asset operation information data into a constructed classification model based on cost sensitive learning, outputting the prediction probability that the asset is a device fault, and comparing the prediction probability with a set threshold value to judge whether the asset is a faulty device.
According to the technical scheme, the managed assets are automatically detected based on the collected data by collecting the managed asset operation information data, whether the assets are fault assets is judged, the defect that manual detection is needed in the prior art is overcome, time cost and labor cost are greatly saved, and working efficiency is greatly improved;
another technical scheme in the above technical scheme has the following advantages: the asset informatization management is realized by acquiring the running state information data of the information infrastructure installed on the managed assets by using the out-of-band management interface technology, the asset management efficiency is improved, and in the process of distinguishing the assets, a classification model based on cost sensitive learning is used, so that the problem of class imbalance can be effectively solved, and the fault equipment can be accurately identified.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a flow diagram of one embodiment of a method for asset failure identification provided by the present invention;
fig. 2 is a structural framework diagram of an embodiment of an asset failure identification method provided by the present invention.
Detailed Description
Embodiments of the present invention provide an asset fault identification method, which is used to solve the defect that in the prior art, in the management process of an information infrastructure, an information infrastructure needs to be periodically checked manually, and in order to make the objects, features, and advantages of the present invention more obvious and understandable, a technical solution in an embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 and fig. 2, fig. 1 is a flowchart of an asset fault identification method according to an embodiment of the present invention. Fig. 2 is a structural framework diagram of an asset fault identification method according to an embodiment of the present invention.
The asset failure identification method provided by the embodiment, as shown in fig. 1, includes the following steps:
step S1: as shown in fig. 2, a Baseboard Management Controller (BMC) is installed in the managed assets, and the BMC is responsible for monitoring and collecting the managed asset operation information data;
step S2: adopting an out-of-band management interface (IPMI) technology to construct an operation and maintenance data network and an out-of-band management system; the out-of-band management system accesses a baseboard management controller of the managed asset through an operation and maintenance data network and collects the managed asset operation information data from the baseboard management controller; the out-of-band management system performs centralized management on asset equipment through a special operation and maintenance data network channel independent of service data, realizes connection between the asset equipment and the out-of-band management system through a substrate management controller, transmits control information and data information through building an operation and maintenance data network separated from the service data network, performs data acquisition on various physical indexes of the managed asset, transmits the data to the out-of-band management system, and executes various control instructions from the out-of-band management system to realize automatic discovery and running state monitoring of the managed asset equipment.
Step S3: the out-of-band management system labels the collected managed asset operation information data to form label data;
step S4: performing data preprocessing operation on the label data, and taking the preprocessed label data as training data;
step S5: constructing a classification model based on cost-sensitive learning, inputting training data into the classification model based on cost-sensitive learning for training to obtain a trained classification model based on cost-sensitive learning, and then inputting the preprocessed real-time data into the trained classification model based on cost-sensitive learning to obtain the probability that the managed assets are faulty equipment;
step S6: setting a threshold value in an out-of-band management system, comparing the prediction probability of the managed asset as fault equipment with the threshold value, if the prediction probability is greater than the threshold value, judging the managed asset as fault equipment, and if the prediction probability is less than the threshold value, judging the managed asset as normal equipment;
step S7: and the out-of-band management system outputs a final judgment result.
As a preferred embodiment, in step S2, an out-of-band management interface technology is used to construct an operation and maintenance data network, a B/S framework is used to construct an out-of-band management system, and the out-of-band management system realizes asset automatic discovery and operation state monitoring by remotely accessing a baseboard management controller of a managed asset;
in a preferred embodiment, the out-of-band management system receives baseboard management controller data and saves the collected data in a system event log by using instructions specified in the intelligent platform management interface specification.
As a preferred embodiment, in step S2, the managed asset operation information data includes temperature, voltage, fan speed, power status, CPU, memory utilization, and failure asset operation information data, and the failure asset operation information data includes actual failure asset operation information data and artificial simulation failure asset operation information data.
As a preferred embodiment, in step S3, the asset operation information data is labeled manually, the actual faulty asset operation information data and the manually simulated faulty asset operation information data are both labeled as faulty devices, and the other asset operation information data are labeled as normal devices; in actual conditions, the number of the fault devices is small, and the number of the normal devices is large, so that the problem of unbalanced category in actual conditions is solved.
As a preferred embodiment, in step S4, the data preprocessing process includes data cleaning, data transformation, and data normalization; the data cleaning eliminates the data with the characteristic value not conforming to the common knowledge of the business and eliminates the characteristics containing a large number of null values, thereby reducing the influence of the data such as noise, missing values and the like on the model; the data transformation makes the long tail distribution data accord with normal distribution by carrying out logarithmic conversion on the long tail distribution data, and meets the assumed condition of a linear model; data standardization reduces the size difference between characteristic values by carrying out dimensionless processing on numerical characteristics, and avoids the interference of data with larger order of magnitude on data with smaller order of magnitude.
As a preferred embodiment, in the data transformation process, through data exploration and analysis, the long-tail distribution characteristics are logarithmically transformed, and the specific calculation process is as follows:
A'＝log(A)；
wherein A is the long tail distribution characteristic, and A' is the result of the long tail distribution characteristic A after logarithmic transformation;
the formula of the calculation for normalizing the data is:
wherein X is the value of a certain column of characteristics, mu represents the mean value of the characteristics X, and sigma represents the standard deviation of the characteristics X;
after the eigenvalue X is centered according to the mean value mu and then scaled according to the standard deviation sigma, the eigenvalue X will follow the normal distribution with the mean value of 0 and the variance of 1.
As a preferred embodiment, in step S5, the objective function of the classification model based on cost-sensitive learning is:
wherein, C0And C1Punishment factors of the misclassification of normal equipment and fault equipment respectively; l (y)i，fθ(xi) Is a Loss function, such as a Logarithmic Loss function (Logorithmic Loss Fun)Section) or Hinge loss function (Hinge LossFunction); y isiA real tag class representing an ith device; f. ofθ(xi) A prediction function that is a classification model based on cost sensitive learning; theta denotes the prediction function fθ(xi) The parameters of (1); x is the number ofiRepresenting the characteristic data of the ith device, wherein lambda R (theta) is a regularization term and represents the penalty to the complex model; lambda is more than or equal to 0 and is a regularization coefficient used for balancing experience risk and model complexity; r (θ) is the model complexity, e.g. L1Norm or L2And (4) norm.
As a preferred embodiment, in step S5, a predicted probability p (x) that the managed asset is a failed device is calculatedi) The calculation formula of (2) is as follows:
p(xi)＝fθ(xi)；
as a preferred embodiment, in step S6, the specific formula for comparing the predicted probability that the managed asset is a faulty device with the threshold value is as follows:
wherein F represents equipment failure, T represents equipment normality, y (x)i) The prediction result of the ith equipment is shown, and only two results of fault equipment and normal equipment are taken; p (x)i) Representing the predicted probability for the ith device, α is a threshold.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An asset failure identification method, comprising the steps of:
step S1: the baseboard management controller monitors and collects managed asset operation information data;
step S2: the out-of-band management system acquires managed asset operation information data from the baseboard management controller through an operation and maintenance data network;
step S3: the out-of-band management system labels the acquired managed asset operation information data to form label data;
step S4: performing data preprocessing operation on the label data, and taking the label data subjected to the preprocessing operation as training data;
step S5: constructing a classification model based on cost sensitive learning, inputting training data into the classification model for training to obtain a trained classification model based on cost sensitive learning, and inputting preprocessed real-time data into the trained classification model based on cost sensitive learning to obtain the prediction probability that the managed assets are fault equipment;
step S6: comparing the prediction probability with a preset threshold value, and judging whether the managed asset is fault equipment or normal equipment according to the comparison result;
step S7: and the out-of-band management system outputs a final judgment result.
2. The asset fault identification method according to claim 1, wherein in step S2, an out-of-band management interface technology is used to construct the operation and maintenance data network, and a B/S framework is used to construct the out-of-band management system.
3. The asset failure recognition method of claim 2, wherein the out-of-band management system receives baseboard management controller data and saves the collected data in a system event log by using instructions specified in the intelligent platform management interface specification.
4. The asset failure recognition method according to claim 3, wherein in step S2, the managed asset operation information data includes temperature, voltage, fan speed, power status, CPU, memory utilization, and failed asset operation information data, and the failed asset operation information data includes actual failed asset operation information data and artificial simulated failed asset operation information data.
5. The asset failure recognition method according to claim 4, wherein in step S3, the asset operation information data is manually labeled, and the actual failed asset operation information data and the manually simulated failed asset operation information data are both labeled as failed devices, and the other asset operation information data are labeled as normal devices.
6. The asset failure recognition method according to claim 5, wherein in step S4, the training data is preprocessed by data cleaning, data transformation and data normalization; the data cleaning is used for eliminating abnormal data; the data transformation makes the long tail distribution data accord with normal distribution by carrying out logarithmic conversion on the long tail distribution data, and meets the assumed condition of a linear model; the data standardization reduces the size difference between characteristic values by carrying out dimensionless processing on numerical characteristics, and avoids the interference of data with larger order of magnitude on data with smaller order of magnitude.
7. The asset fault identification method according to claim 6, wherein in the data transformation process, the long tail distribution characteristics are logarithmically transformed through data exploration and analysis, and the specific calculation process is as follows:
A'＝log(A)；
wherein A is the long tail distribution characteristic, and A' is the result of the long tail distribution characteristic A after logarithmic transformation;
the formula of the calculation for normalizing the data is:
wherein X is the value of a certain column of characteristics, mu represents the mean value of the characteristics X, and sigma represents the standard deviation of the characteristics X;
after the eigenvalue X is centered according to the mean value mu and then scaled according to the standard deviation sigma, the eigenvalue X will follow the normal distribution with the mean value of 0 and the variance of 1.
8. The asset failure identification method according to claim 7, wherein in step S5, the objective function of the classification model based on the cost-sensitive learning is:
wherein, C0And C1Punishment factors of the misclassification of normal equipment and fault equipment respectively; l (y)i，fθ(xi) Is a loss function; y isiA real tag class representing an ith device; f. ofθ(xi) A prediction function that is a classification model based on cost sensitive learning; theta denotes the prediction function fθ(xi) The parameters of (1); x is the number ofiRepresenting the characteristic data of the ith device, wherein lambda R (theta) is a regularization term and represents the penalty to the complex model; lambda is more than or equal to 0 and is a regularization coefficient used for balancing experience risk and model complexity; r (theta) is the model complexity;
computing a predicted probability p (x) of a managed asset being a failed devicei) The calculation formula of (2) is as follows:
p(xi)＝fθ(xi)。
9. the asset failure recognition method according to claim 8, wherein the step S5 of preprocessing the real-time data comprises two steps of data transformation and data normalization.
10. The asset failure recognition method according to claim 9, wherein in step S6, if the prediction probability is greater than the threshold, the managed asset is determined as a failed device, and if the prediction probability is less than the threshold, the managed asset is determined as a normal device; the specific formula for comparing the predicted probability that the managed asset is a failed device to the threshold is as follows:
wherein F represents equipment failure, T represents equipment normality, y (x)i) The prediction result of the ith equipment is shown, and only two results of fault equipment and normal equipment are taken; p (x)i) Representing the predicted probability for the ith device, α is a threshold.
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