CN106886481B - Static analysis and prediction method and device for system health degree - Google Patents
Static analysis and prediction method and device for system health degree Download PDFInfo
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Abstract
The invention provides a static analysis and prediction method and device for system health degree. The static analysis and prediction method for the system health degree comprises the following steps: acquiring an index value of at least one system index input by a user; inputting the index value into a preset health degree prediction model, and analyzing and predicting the health degree of the system corresponding to the index value through the health degree prediction model; and outputting the analysis prediction result to the user. The method and the device can analyze and predict the health degree of the system corresponding to the index value according to the index value of at least one system index input by the user, can enable operation and maintenance personnel to judge the health degree of the system in the future according to the index value, can help the operation and maintenance personnel to analyze and predict possible problems in time and give some suggestions to the operation and maintenance personnel, further carry out system maintenance in time, can avoid the problems of system breakdown, delay and the like under a certain index value, and can improve the experience of the user on the system.
Description
Technical Field
The invention relates to the technical field of system health degree prediction, in particular to a static analysis and prediction method and device for system health degree.
Background
At present, a company having a large number of services and customers may have a plurality of systems at the same time, and the number of users on different systems is large, and when the systems are intensively applied in a large number, many emergency situations may occur, for example, the system may not be opened or operated after entering due to the large number of users, and thus, the operation and maintenance personnel may be required to continuously maintain the system.
For example, a national integrated securities company has a large number of business systems and customers. Besides a plurality of systems such as a centralized transaction system, a financing and financing system, and an online transaction system related to core transaction, other peripheral systems (the data volume is about 50) exist. These systems are in turn deployed on different servers, making manual maintenance difficult. With the continuous explosion of recent stock market trading quotations, the deal amount and the deal stroke number of the stock market are also continuously new and high, which often causes the system of the exchange to explode and makes manual maintenance more difficult.
At present, no intelligent operation and maintenance auxiliary system exists, the system cannot be monitored in real time, the operation state of the system at a certain future moment cannot be predicted, operation and maintenance personnel cannot maintain the system in time, and the system is easy to crash, delay and the like, so that the use of the system by a user is influenced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the static analysis and prediction method and device for the system health degree, which can help operation and maintenance personnel to analyze and predict the future health degree of the system, so that the system is maintained in time, and the user experience is improved.
In a first aspect, the present invention provides a static analysis and prediction method for system health, including:
acquiring an index value of at least one system index input by a user;
inputting the index value into a preset health degree prediction model, and analyzing and predicting the health degree of the system corresponding to the index value through the health degree prediction model;
and outputting the analysis prediction result to the user.
Optionally, before the step of inputting the index value into a preset health degree prediction model and analyzing and predicting the health degree of the system corresponding to the index value by using the health degree prediction model, the method further includes:
acquiring historical data and historical health degree of a system;
and training a health degree prediction model by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree.
Optionally, the training of the health degree prediction model by using a multiple regression model and/or a neural network model according to the historical data and the historical health degree includes:
analyzing key indexes influencing the health degree of the system by adopting one or more combined modes of principal component analysis, factor analysis and cluster analysis according to the historical data and the historical health degree of the system;
and training a health degree prediction model by adopting a multiple regression model and/or a neural network model according to the key indexes.
Optionally, the training of the health degree prediction model by using a multiple regression model and/or a neural network model according to the historical data and the historical health degree includes:
according to the historical data and the historical health degree, analyzing and calculating threshold values of various indexes of the system by adopting a multivariate regression model and/or a neural network model, wherein the threshold values are used for representing the health condition of the system;
and establishing a health degree prediction model by using the threshold value.
Optionally, the training of the health degree prediction model by using a multiple regression model and/or a neural network model according to the historical data and the historical health degree includes:
analyzing a correlation curve of index values of all indexes of the system and the health degree of the system by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree;
and establishing a health degree prediction model for predicting the health degree of the system according to the correlation curve.
In a second aspect, the present invention provides a system health static analysis and prediction device, including:
the index value acquisition module is used for acquiring the index value of at least one system index input by a user;
the analysis and prediction module is used for inputting the index value into a preset health degree prediction model and analyzing and predicting the health degree of the system corresponding to the index value through the health degree prediction model;
and the output module is used for outputting the analysis prediction result to the user.
Optionally, the apparatus further includes:
the historical data acquisition module is used for acquiring the historical data and the historical health degree of the system;
and the model establishing module is used for training a health degree prediction model by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree.
Optionally, the model building module includes:
the key index analysis unit is used for analyzing key indexes influencing the health degree of the system in one or more combined modes of principal component analysis, factor analysis and cluster analysis according to the historical data and the historical health degree of the system;
and the key index model establishing unit is used for training the health degree prediction model by adopting a multiple regression model and/or a neural network model according to the key indexes.
Optionally, the model building module includes:
the threshold value analysis unit is used for analyzing and calculating threshold values of various indexes of the system by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree, and the threshold values are used for representing the health condition of the system;
and the first model establishing unit is used for establishing a health degree prediction model by utilizing the threshold value.
Optionally, the model building module includes:
the association curve analysis unit is used for analyzing an association curve between index values of various indexes of the system and the health degree of the system by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree;
and the second model establishing unit is used for establishing a health degree prediction model for predicting the health degree of the system according to the association curve.
According to the technical scheme, the invention provides a static analysis and prediction method for the system health degree, which comprises the following steps: acquiring an index value of at least one system index input by a user; inputting the index value into a preset health degree prediction model, and analyzing and predicting the health degree of the system corresponding to the index value through the health degree prediction model; and outputting the analysis prediction result to the user. The method and the device can analyze and predict the health degree of the system corresponding to the index value according to the index value of at least one system index input by the user, can enable operation and maintenance personnel to judge the health degree of the system in the future according to the index value, can help the operation and maintenance personnel to analyze and predict possible problems in time and give some suggestions to the operation and maintenance personnel, further carry out system maintenance in time, can avoid the problems of system breakdown, delay and the like under a certain index value, and can improve the experience of the user on the system.
The invention provides a system health degree static analysis and prediction device, which has the same beneficial effects as the system health degree static analysis and prediction method based on the same inventive concept.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart illustrating a method for static analysis and prediction of system health according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of a system health static analysis and prediction device according to a second embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
The invention provides a static analysis and prediction method and device for system health degree. Embodiments of the present invention will be described below with reference to the drawings.
Fig. 1 shows a flowchart of a system health static analysis prediction method according to a first embodiment of the present invention. As shown in fig. 1, a static analysis and prediction method for system health according to a first embodiment of the present invention includes the following steps:
step S101: and acquiring an index value of at least one system index input by a user.
In this step, the system may be each network transaction system, business system, etc. implemented based on the server, or may be the whole system implemented by an enterprise based on the server. A network transaction system or an overall system may include multiple core systems and multiple peripheral systems. The multiple systems may be established on multiple servers or on the same server. A system may also employ multiple servers.
The system index may be an index required to perform operation and maintenance on the system. The system index may include: and one or more of multiple indexes such as basic monitoring indexes, application process indexes, log record indexes and the like. The basic monitoring index can include: one or more of various indexes such as CPU utilization rate, memory utilization rate, network flow, disk utilization rate and the like; the application process index may include: one or more of port application data, process state, application internal indicators, and the like; the log record index may include: one or more of an index of a service log record, an index of an application log record, an index of an operating system log record, and the like.
Step S102: and inputting the index value into a preset health degree prediction model, and analyzing and predicting the health degree of the system corresponding to the index value through the health degree prediction model.
In this step, the health degree of the system can exist in a plurality of states, and the optimal health degree classification can be three types: a healthy state, a hidden trouble state, an unhealthy state. These states may be segmented according to the experience of the operation and maintenance personnel. For example, a health degree of more than 90 is a healthy state, a health degree of not more than 90 and not more than 60 is a hidden trouble state, and a health degree of less than 60 is an unhealthy state.
Before this step, may also include: acquiring historical data and historical health degree of a system; and training a health degree prediction model by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree.
In obtaining the historical data and the historical health of the system, in the first step, the source of the data needs to be determined, and the source of the database and/or the source of the log, such as the basic monitoring data, the data recorded by the log, etc., needs to be determined. And secondly, selecting data of a proper time period from the historical time period covered by the database and/or the log. And thirdly, performing quality inspection on the selected data to ensure the reliability of the data quality, thereby improving the reliability of the health degree prediction model. And fourthly, converting the type, the format and the like of the data. For example, converting a character type to a numeric type, a time format, a percentage format, etc. The transformation of the data may also include normalizing the data units or variables by certain statistical principles.
The historical data may include: all data before the current time or data within a period of time before the current time. The data may include all or part of data corresponding to part of the index, and may be all or part of data corresponding to all the index. The data may include: one or more of base monitoring data, application process data, logged data, and the like. The basic monitoring data may include: one or more of CPU utilization rate, memory utilization rate, network flow rate, disk utilization rate and the like; the application process data may include: one or more of port application data, process state data, application internal index data, and the like; the logged data may include: one or more of system traffic logged data, application logged data, operating system logged data, and the like.
In a specific embodiment of the present invention, the training a health degree prediction model using a multiple regression model and/or a neural network model according to the historical data and the historical health degree may include: analyzing key indexes influencing the health degree of the system by adopting one or more combined modes of principal component analysis, factor analysis and cluster analysis according to the historical data and the historical health degree of the system; and training a health degree prediction model by adopting a multiple regression model and/or a neural network model according to the key indexes.
Firstly, after the historical data and the historical health degree of the system are obtained, the relationship between the historical data and the historical health degree can be analyzed in a mode of one or more of principal component analysis, factor analysis and cluster analysis, and the relationship between the historical data with different indexes can be analyzed. The health degree of a system can have a plurality of variables, the variables can have certain correlation, and the information contained in the variables can be highly overlapped, so that the relationship among the variables can be analyzed by adopting one or more combinations of principal component analysis, factor analysis and cluster analysis, at least one main variable is determined, and the main variable is used as a key index influencing the health degree of the system. The key criteria chosen may be as low as possible in relation to the other criteria. The key indexes can be automatically determined by a statistical program according to certain statistical indexes, and can also be subjectively determined by business analysis personnel according to actual needs. Therefore, resource waste caused by analyzing all indexes one by one can be avoided.
When analyzing key indexes affecting the system health degree, the historical data and the health degree can also be described and summarized, and common technical methods comprise: frequency, mean, median, variance, maximum, minimum, 1/4 intervals, and so forth. By utilizing the technologies, distribution characteristics such as the concentration and dispersion of data can be obtained, the relationship among the data can be preliminarily explored, and key indexes influencing the health degree of the system can be analyzed.
Then, only the data of the key indexes can be selected, and a multiple regression model and/or a neural network model can be adopted to train a health degree prediction model. By using the method to establish the health degree prediction model, the computing resources can be saved. When the health degree prediction model is trained, the multiple regression model and/or the neural network model can be adopted to train the health degree prediction model according to the historical data and the historical health degree of all indexes. The method does not need to analyze key indexes influencing the system, and directly trains the health degree prediction model by using the historical data and the historical health degree of all the indexes, which is within the protection scope of the invention. Compared with the method for training the health degree prediction model by using the key indexes, the method for establishing the health degree prediction model has high accuracy, but has large calculation amount and higher requirement on a machine for training the health degree prediction model.
In a specific embodiment of the present invention, the training a health degree prediction model using a multiple regression model and/or a neural network model according to the historical data and the historical health degree may include: according to the historical data and the historical health degree, analyzing and calculating threshold values of various indexes of the system by adopting a multivariate regression model and/or a neural network model, wherein the threshold values are used for representing the health condition of the system; and establishing a health degree prediction model by using the threshold value.
Firstly, according to the historical data and the historical health degree, a multivariate regression model and/or a neural network model are adopted to analyze and calculate the threshold value of each index of the system, or the threshold value of at least one key index of the system can be analyzed and calculated. Such as thresholds for transaction amount, user response time, number of user concurrencies, etc. When analyzing and calculating the threshold values of various indexes of the system, the relationship between the threshold values and the health condition and the health degree of the system is also needed to be analyzed. The threshold value is used to characterize the health of the system. The threshold value for one index may be determined according to the classification of the system health degree, and may be one or more.
If the health of the system is classified into two categories: healthy and unhealthy, there may be one threshold for one index. If the index value is larger than the threshold value, the system is in an unhealthy state; and if the index value is smaller than the threshold value, the system is in a healthy state. Here, the relationship between the threshold value and the health state is not necessarily the above case, and the opposite case may be included. If the index value is larger than the threshold value, the system is in a healthy state; and if the index value is smaller than the threshold value, the system is in an unhealthy state. For example, the threshold value of the network traffic index of the system is 100M/s, and when the network traffic of the system is greater than 100M/s, the system is in a healthy state; when the network flow of the system is less than 100M/s, the system is in an unhealthy state. Therefore, the health of the system is related differently to the threshold value of the indicator for different indicators. The threshold may also be used to determine a range of system health, i.e., the health of the system.
And then, establishing a health degree prediction model according to the threshold value. When the health degree prediction model is established, a health degree prediction model which can independently judge the health degree of the system according to the corresponding index can be established according to the threshold value of each index, that is, the health degree of the system corresponding to the corresponding index can also be judged according to the index value of the index. When the health degree prediction model is established, a multivariate regression model and/or a neural network model can be adopted to establish a relational algorithm between the threshold values of the multiple indexes and the health degree of the system, and then the health degree prediction model is established, namely the health degree of the system can be judged according to the index values of the multiple indexes. Compared with the method for judging the health degree according to one index, the method for judging the health degree of the system according to a plurality of indexes can improve the accuracy and the qualification of prediction. The health degree prediction model can help operation and maintenance personnel to analyze and predict possible problems and give some suggestions to the operation and maintenance personnel.
For example, the operation and maintenance personnel predict the index value of one or more indexes at a certain time in the future and want to predict the health degree of the system at that time, so that the operation and maintenance personnel can input the index value of one or more indexes into the health degree prediction model to predict the health degree of the system at that time, and thus, the operation and maintenance personnel can be helped to analyze and predict problems which may exist at that time.
The health prediction model established according to the threshold value can be used for a period of time. During the use process, the health degree prediction model can be checked within a certain time period, and high prediction accuracy of the health degree prediction model is guaranteed. When the health degree prediction model is tested, the health degree prediction model can be tested in a sampling mode. Data at a moment or within a small time period can be selected from historical data and historical health degree, the data is substituted into the health degree prediction model to check whether the health degree prediction model is reliable, and if the health degree prediction model is not reliable, the health degree prediction model needs to be reestablished. And if the reliability is high, continuing to use the health degree prediction model. In the process of using the health degree prediction model, the threshold value of the health degree prediction model is not changed, and the health degree of the static prediction system can be regarded as the health degree.
In a specific embodiment provided by the present invention, the training a health degree prediction model by using a multiple regression model and/or a neural network model according to the historical data and the historical health degree includes: analyzing a correlation curve of index values of all indexes of the system and the health degree of the system by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree; and establishing a health degree prediction model for predicting the health degree of the system according to the correlation curve.
When analyzing the correlation curve between the index values of the various indexes of the system and the health degree of the system, only the correlation curve between the index values of the key indexes of the system and the health degree of the system may be analyzed. And then establishing a corresponding algorithm according to the correlation curve to form a health degree prediction model. For example, a correlation curve of the memory usage rate and the system health degree may be simulated, an algorithm of the memory usage rate and the system health degree may be established, and when a user inputs a value of the memory usage rate, the corresponding system health degree may be obtained according to the correlation curve or the algorithm. When the correlation curve or algorithm between the health degree and the index value is established, the correlation curve or algorithm between the health degree and one index value may be established, or the correlation curve or algorithm between the health degree and a plurality of index values may be established. All within the scope of the invention.
In the invention, the health degree prediction model established by using the threshold value and the health degree prediction model established by using the association curve can be used independently or together, and the method and the device are within the protection scope of the invention.
The method for establishing the health degree prediction model is suitable for the whole system, and is also suitable for a core system, a peripheral system and the like contained in the system. This is within the scope of the invention. If the whole system comprises a plurality of small systems, health degree prediction models of the small systems can be established according to needs, so that the small systems can be conveniently checked. When the health degree prediction model of the whole system is established, the health degree prediction model can be established according to all historical data and historical health degrees, can also be established according to the historical health degrees of each core system and/or peripheral system, and can also be established according to the hardware utilization rate and/or the fault utilization rate, which are all in the protection scope of the invention.
When the health degree prediction model is established, a hardware utilization rate prediction model and/or a fault rate prediction model can be established according to the method. The hardware usage calculation model can be used for predicting the usage rate of certain hardware of the system at a certain moment or a small time period, and the fault rate prediction model can be used for predicting the fault rate of the system under a certain condition.
In this step, the method may further include: comparing the future health degree of the analysis prediction with a preset alarm threshold value, wherein the alarm threshold value is the health degree when the system is just in an unhealthy state; if the future health degree is greater than the alarm threshold value, not sending an alarm; and if the future health degree is smaller than the alarm threshold value, an alarm is given.
Example 1, the system is an entire system of a stock company, the system including: three core transaction systems. The core transaction system includes: a centralized transaction system, a financing and financing ticket transaction system and an online transaction system. When the health degree prediction model is established, a multiple regression model and/or a neural network model can be adopted to establish a health degree prediction model of a centralized transaction system, a health degree prediction model of a financing and financing transaction system and a health degree prediction model of an online transaction system. Taking the health degree prediction model of the centralized transaction system as an example, the health degree prediction model of the centralized transaction system can be established by determining the association curve between the historical entrusted number of times, the number of operations of stock per second, the number of operations of log-in per second and the current health degree according to the historical entrusted number of times, the number of operations of log-in per second and the current health degree.
When the health degree prediction model is established, a multivariate regression model and/or a neural network model can be adopted to determine the correlation curves between the health degrees of the three core systems and the hardware utilization rate according to the health degrees of the three core systems, and the hardware utilization rate prediction model is established.
When the health degree prediction model is established, a multivariate regression model and/or a neural network model can be adopted to determine the correlation curves between the CPU utilization rate, the memory utilization rate, the disk utilization rate, the network flow rate and the like of the whole system and the health degree of the whole system, establish a regression curve, establish a corresponding algorithm and form the health degree prediction model.
In one embodiment of the present invention, when the health degree prediction model is established, an index value prediction model may be established. The establishment method of the index value prediction model is the same as the establishment method of the health degree prediction model. The metric value prediction model can also be used to predict the metric value of one or more metrics at a future time or over a short period of time.
In one embodiment provided by the present invention, in establishing the health prediction model, a multivariate regression model combined with machine learning, i.e., a dynamic baseline algorithm, may be used. The weight or the parameter of each index can be determined by machine learning, and then the relation between each index and the health degree is analyzed by a multiple regression analysis method, a regression curve is established, an algorithm is established, and a health degree prediction model is formed.
The dynamic baseline algorithm comprises: single index baseline algorithm and multi-index baseline algorithm.
Single index baseline algorithm:
establishing a base line: the single index baseline can be established by a curve fitting algorithm, wherein fitting refers to that a plurality of discrete function values { f1, f2, …, fn } of a certain function are known, and a plurality of coefficients f (lambda 1, lambda 2, …, lambda n) to be determined in the function are adjusted to enable the difference (least squares meaning) between the function and a known point set to be minimum. If the function to be determined is linear, it is called linear fitting, otherwise it is called non-linear fitting. The expression may also be a piecewise function, in this case called spline fitting.
Baseline analysis: if the day is taken as an observation scope, the day is divided into a plurality of time intervals, the average value of the indexes in the same time interval every day is calculated, and a base line record is formed according to the historical records of the indexes. For example, a 24 hour per day CPU average utilization baseline map may be generated, or a minute per day CPU average utilization baseline may be generated. According to the value of the historical baseline, if the current observation value is larger than the value corresponding to the historical record, alarm information can be sent, and the index value of the future time period can be predicted according to the current index value and the value of the historical baseline.
Alarming at a base line: because the index value has a peak and a trough, and each alarm state is a region range according to a general alarm classification system, the index value is suitable for corresponding classification as long as the index value is in the region. Therefore, whether the index is abnormal is judged by simply using the dynamic baseline value, a certain error is generated, and more false alarms of abnormal information are caused, so that a dynamic critical area can be formulated to perform index grading alarm, and the method specifically comprises the following steps:
assuming that the historical index values y1, y2, y3, … … and yt of the previous t days and the same time to be tested are samples, and b is a baseline value, the deviation degree of the to-be-tested index from the baseline can be expressed as:
suppose next timeThe actual index value is yt+1Then there are:
when yt+1-b|≤stAnd when the index value condition is normal, the deviation of the index value relative to the dynamic baseline at the next moment is in an allowable range.
When s ist≤|yt+1-b|≤2stAnd generating prompt information, wherein the index value at the next moment has smaller deviation relative to the dynamic baseline.
When 2st≤|yt+1B | time, an alarm is generated, and the index value has larger deviation relative to the dynamic baseline at the next moment.
The multi-index baseline algorithm: the relationship between the dependent variable index and the independent variable index can be determined through specific function analysis. Such as the regression relationship between the memory utilization rate and the number of transaction strokes and the number of system deployments. And determining the baseline of the multiple indexes according to the calculated result. The establishment, analysis and alarm of the base line are similar to a single-index base line algorithm.
In this step, the health degree at any time or in a short period of time in the future can be predicted according to the health degree prediction model. The health degree of the system can be predicted by using a health degree prediction model according to the index value input by the user. Or predicting an index value of one or more indexes of the system in the future according to the health degree prediction model, and then predicting the health degree of the system in the future according to the index value.
In this step, if the system includes a core system and/or a peripheral system, the health degree of the system can be directly analyzed and predicted according to a health degree prediction model of the whole system; or analyzing and predicting the health degree of the core system and/or the peripheral system according to a core system health degree prediction model and/or a peripheral system health degree prediction model which are established in advance, and then analyzing and predicting the health degree of the whole system according to the health degree of the core system and/or the peripheral system.
Example 2, the system refers to an entire system of one stock company, the system including: three core transaction systems. The core transaction system includes: a centralized transaction system, a financing and financing ticket transaction system and an online transaction system. When the health degree is predicted, the health degree of the corresponding system can be predicted by using a health degree prediction model of the centralized transaction system, a health degree prediction model of the financing and financing transaction system and a health degree prediction model of the online transaction system. Then, the hardware utilization rate and/or the failure rate of the system are predicted according to the health degrees of the three core systems. And finally, predicting the health degree of the whole system according to the hardware utilization rate and/or the failure rate.
In this step, the method may include: after the health degree at any time after the current time or within a short period of time is predicted according to the health degree prediction model, the health trend of the system within a short period of time in the future can be formed according to the health degree, and operation and maintenance personnel can visually see the health degree change trend of the system. When the health degree is predicted, key factors influencing the health degree of the system can be predicted according to the health degree prediction model, so that operation and maintenance personnel can quickly analyze main reasons of the unhealthy system, further, preparation in relevant aspects is made in advance, and the system is better maintained.
Step S103: and outputting the analysis prediction result to the user.
In this step, the method may include: and outputting the analysis prediction result to the user. The prediction result may include: health degree, health degree trend, key factors affecting health, and the like. The health degree can be expressed by percentage or Chinese characters. The health-affecting key factors output to the user may be a result of ranking according to importance. In the output result, the method may further include: and (5) advices to operation and maintenance personnel. For example, if the memory usage rate of the system at a certain time or for a certain period of time is found to be large when the health degree is predicted, the operation and maintenance personnel may be reminded to maintain the memory heavily.
In the first embodiment, a method for static analysis and prediction of system health is provided, and correspondingly, a device for static analysis and prediction of system health is also provided. Please refer to fig. 2, which is a schematic diagram of a system health static analysis and prediction apparatus according to a second embodiment of the present invention. Since the apparatus embodiments are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
A system health static analysis and prediction apparatus according to a second embodiment of the present invention includes:
an index value obtaining module 101, configured to obtain an index value of at least one system index input by a user;
the analysis and prediction module 102 is configured to input the index value into a preset health degree prediction model, and analyze and predict the health degree of the system corresponding to the index value through the health degree prediction model;
and the output module 103 is used for outputting the analysis prediction result to the user.
In a specific embodiment provided by the present invention, the apparatus further includes:
the historical data acquisition module is used for acquiring the historical data and the historical health degree of the system;
and the model establishing module is used for training a health degree prediction model by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree.
In a specific embodiment provided by the present invention, the model building module includes:
the key index analysis unit is used for analyzing key indexes influencing the health degree of the system in one or more combined modes of principal component analysis, factor analysis and cluster analysis according to the historical data and the historical health degree of the system;
and the key index model establishing unit is used for training the health degree prediction model by adopting a multiple regression model and/or a neural network model according to the key indexes.
In a specific embodiment provided by the present invention, the model building module includes:
the threshold value analysis unit is used for analyzing and calculating threshold values of various indexes of the system by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree, and the threshold values are used for representing the health condition of the system;
and the first model establishing unit is used for establishing a health degree prediction model by utilizing the threshold value.
In a specific embodiment provided by the present invention, the model building module includes:
the association curve analysis unit is used for analyzing an association curve between index values of various indexes of the system and the health degree of the system by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree;
and the second model establishing unit is used for establishing a health degree prediction model for predicting the health degree of the system according to the association curve.
The above is a description of an embodiment of a system health static analysis and prediction apparatus according to a second embodiment of the present invention.
The system health degree static analysis and prediction device provided by the invention and the system health degree static analysis and prediction method have the same beneficial effects on the basis of the same inventive concept, and are not repeated herein.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an 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. It is to be noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer (which may be a personal computer, a server, or a network machine) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (8)
1. A static analysis and prediction method for system health is characterized by comprising the following steps:
acquiring an index value of at least one system index input by a user;
inputting the index value into a preset health degree prediction model, and analyzing and predicting the health degree of the system corresponding to the index value through the health degree prediction model;
outputting the analysis prediction result to the user;
before the step of inputting the index value into a preset health degree prediction model and analyzing and predicting the health degree of the system corresponding to the index value through the health degree prediction model, the method further comprises the following steps:
acquiring historical data and historical health degree of a system;
according to the historical data and the historical health degree, a multiple regression model and/or a neural network model are adopted to train a health degree prediction model;
the system indexes comprise one or more of basic monitoring indexes, application process indexes and log record indexes;
when the health degree prediction model is established, an index value prediction model is also established.
2. The method of static analysis and prediction of system health as claimed in claim 1 wherein the training of the health prediction model using multivariate regression models and/or neural network models based on the historical data and the historical health comprises:
analyzing key indexes influencing the health degree of the system by adopting one or more combined modes of principal component analysis, factor analysis and cluster analysis according to the historical data and the historical health degree of the system;
and training a health degree prediction model by adopting a multiple regression model and/or a neural network model according to the key indexes.
3. The method of static analysis and prediction of system health as claimed in claim 1 wherein the training of the health prediction model using multivariate regression models and/or neural network models based on the historical data and the historical health comprises:
according to the historical data and the historical health degree, analyzing and calculating threshold values of various indexes of the system by adopting a multivariate regression model and/or a neural network model, wherein the threshold values are used for representing the health condition of the system;
and establishing a health degree prediction model by using the threshold value.
4. The method of static analysis and prediction of system health as claimed in claim 1 wherein the training of the health prediction model using multivariate regression models and/or neural network models based on the historical data and the historical health comprises:
analyzing a correlation curve of index values of all indexes of the system and the health degree of the system by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree;
and establishing a health degree prediction model for predicting the health degree of the system according to the correlation curve.
5. A system health static analysis prediction device, comprising:
the index value acquisition module is used for acquiring the index value of at least one system index input by a user;
the analysis and prediction module is used for inputting the index value into a preset health degree prediction model and analyzing and predicting the health degree of the system corresponding to the index value through the health degree prediction model;
the output module is used for outputting the analysis and prediction result to the user;
the device, still include:
the historical data acquisition module is used for acquiring the historical data and the historical health degree of the system;
the model establishing module is used for training a health degree prediction model by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree;
the system index comprises one or more of a basic monitoring index, an application process index and a log record index;
when the health degree prediction model is established, an index value prediction model is also established.
6. The system health static analysis prediction device of claim 5, wherein the model building module comprises:
the key index analysis unit is used for analyzing key indexes influencing the health degree of the system in one or more combined modes of principal component analysis, factor analysis and cluster analysis according to the historical data and the historical health degree of the system;
and the key index model establishing unit is used for training the health degree prediction model by adopting a multiple regression model and/or a neural network model according to the key indexes.
7. The system health static analysis prediction device of claim 5, wherein the model building module comprises:
the threshold value analysis unit is used for analyzing and calculating threshold values of various indexes of the system by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree, and the threshold values are used for representing the health condition of the system;
and the first model establishing unit is used for establishing a health degree prediction model by utilizing the threshold value.
8. The system health static analysis prediction device of claim 5, wherein the model building module comprises:
the association curve analysis unit is used for analyzing an association curve between index values of various indexes of the system and the health degree of the system by adopting a multiple regression model and/or a neural network model according to the historical data and the historical health degree;
and the second model establishing unit is used for establishing a health degree prediction model for predicting the health degree of the system according to the association curve.
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| CN110580217B (en) * | 2018-06-08 | 2023-05-05 | 阿里巴巴集团控股有限公司 | Software code health degree detection method, processing method, device and electronic equipment |
| CN109492826B (en) * | 2018-12-06 | 2021-12-14 | 远光软件股份有限公司 | Information system running state risk prediction method based on machine learning |
| CN109800139A (en) * | 2018-12-18 | 2019-05-24 | 东软集团股份有限公司 | Server health degree analysis method, device, storage medium and electronic equipment |
| CN110162445A (en) * | 2019-05-23 | 2019-08-23 | 中国工商银行股份有限公司 | The host health assessment method and device of Intrusion Detection based on host log and performance indicator |
| CN110888788A (en) * | 2019-10-16 | 2020-03-17 | 平安科技（深圳）有限公司 | Anomaly detection method and device, computer equipment and storage medium |
| CN111666978B (en) * | 2020-05-11 | 2023-12-01 | 深圳供电局有限公司 | Intelligent fault early warning system for IT system operation and maintenance big data |
| CN111950912A (en) * | 2020-08-14 | 2020-11-17 | 中国民航信息网络股份有限公司 | Method and device for evaluating health degree of equipment, equipment and computer storage medium |
| CN111984513A (en) * | 2020-08-25 | 2020-11-24 | 浙江天垂科技有限公司 | Predictive maintenance method, device, equipment and storage medium |
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