CN110928748A - Business system operation monitoring method and device - Google Patents
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Abstract
The invention discloses a method and a device for monitoring the operation of a service system, wherein the method comprises the following steps: calculating the average processing efficiency of each service node according to the historical data of each service node; determining a first data processing capacity predicted value of each service node on the current date by adopting a mathematical fitting method according to the data processing capacity of each service node on the same date with the current date in the historical data every year; calculating date influence factors of all service nodes on the current date according to the annual data processing amount of all the service nodes on the same date as the current date in the historical data and the annual average data processing amount of all the service nodes in the historical data; determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value; and determining the processing time length predicted value of the current date of each service node according to the second data processing capacity predicted value and the average processing efficiency. The invention improves the accuracy of monitoring the working state of the service node.
Description
Technical Field
The invention relates to an operation monitoring method, in particular to a service system operation monitoring method and device.
Background
The banking system needs to process a large amount of data every day, the data processing process is generally several hours, and a large amount of batch processing data is usually performed at night. If an abnormal condition occurs in the process, a response must be made in time to eliminate the abnormal condition. After processing is complete, a daily newspaper is also typically compiled for release and review.
The business system of the bank comprises a plurality of business nodes, each business node is used for processing different business data, and the data volume and the running time of each business node processed each day may be different because the business data volume which the business system may need to process each day is influenced by factors such as holidays, months, sales promotion days of merchants, increase of bank account data and the like. In the prior art, a monitoring system is arranged to monitor the operation state of a service system, and the existing monitoring system synchronously operates when the service system processes data, so as to monitor the operation time of each service node. And simply set the uptime threshold for each service node by manual means. And once the running time of the service node exceeds a normal threshold, judging that the running is abnormal, and then entering a warning or alarming link. Due to the fact that the data volume required to be processed by the service system every day is greatly different along with the change of time, the influence of the change of the data volume on the running time of each service node is not considered in the existing monitoring method, and therefore monitoring is inaccurate.
For example, existing monitoring systems may monitor whether a batch of banking transactions has been processed. And problem handling (alarming if the manual setting is not finished for more than two hours) is performed when a specific preset value is reached. When the amount of the transaction on a certain day is in a peak, the data volume is increased sharply, and the system can give an alarm by mistake when the processing time exceeds 2 hours, but the system is not in a fault and needs maintenance, but only the data volume is increased. This presents the problem of inaccurate monitoring.
Disclosure of Invention
In order to solve at least one of the above problems, the present invention provides a method and an apparatus for monitoring operation of a service system.
In order to achieve the above object, according to an aspect of the present invention, there is provided a service system operation monitoring method, including:
calculating the average processing efficiency of each service node according to the daily data processing amount and the daily processing duration of each service node in the historical data;
determining a first data processing capacity predicted value of each service node on the current date by adopting a mathematical fitting method according to the data processing capacity of each service node on the same date with the current date in the historical data every year;
calculating date influence factors of all service nodes on the current date according to the annual data processing amount of all the service nodes on the same date as the current date in the historical data and the annual average data processing amount of all the service nodes in the historical data;
determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value;
and determining the processing time length predicted value of the current date of each service node according to the second data processing amount predicted value and the average processing efficiency, and monitoring the operation state of each service node on the current date according to the processing time length predicted value of each service node on the current date.
Optionally, the method further includes:
calculating monthly influence factors of the service nodes on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly data processing amount of each service node in the historical data;
the determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value specifically includes:
and determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the monthly influence factor and the first data processing capacity predicted value.
Optionally, the method further includes:
calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day in the historical data every week;
the determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value specifically includes:
and determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the week influence factor and the first data processing capacity predicted value.
Optionally, the method further includes:
calculating monthly influence factors of the service nodes on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly data processing amount of each service node in the historical data;
calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day in the historical data every week;
the determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value specifically includes:
and determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the monthly influence factor, the weekly influence factor and the first data processing capacity predicted value.
Optionally, the determining, by using a mathematical fitting method, a first data throughput prediction value of the current date of each service node according to the data throughput of each service node in the historical data on the same date as the current date includes:
and constructing n times of fitting functions corresponding to the service nodes according to data processing amount of the service nodes in the historical data on the same date as the current date every year, and determining a first data processing amount predicted value of the current date of each service node according to the constructed n times of fitting functions, wherein n is a positive integer.
Optionally, the determining, by using a mathematical fitting method, a first data throughput prediction value of the current date of each service node according to the data throughput of each service node in the historical data on the same date as the current date includes:
respectively constructing a fitting function to m fitting functions for each service node according to the data processing amount of the service node in the historical data every year on the same date as the current date, and respectively calculating the variance of each fitting function, wherein m is a positive integer greater than 1 and less than or equal to 5;
and determining a first data processing capacity predicted value of the current date of the service node according to the fitting function with the minimum variance.
Optionally, the method further includes:
and if the fact that the actual processing time of the current date of a certain service node exceeds the processing time predicted value corresponding to the service node is monitored, sending alarm information to a user.
In order to achieve the above object, according to another aspect of the present invention, there is provided a business system operation monitoring apparatus, including:
the average processing efficiency calculating unit is used for calculating the average processing efficiency of each service node according to the daily data processing amount and the daily processing duration of each service node in the historical data;
the first data processing capacity predicted value determining unit is used for determining the first data processing capacity predicted value of the current date of each service node by adopting a mathematical fitting method according to the data processing capacity of each service node in the historical data on the same date with the current date;
the date influence factor determining unit is used for calculating the date influence factor of each service node on the current date according to the data processing amount of each service node on the same date as the current date in the historical data every year and the annual and annual average data processing amount of each service node in the historical data every year;
the second data processing capacity predicted value determining unit is used for determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value;
and the node running state monitoring unit is used for determining the processing time length predicted value of the current date of each service node according to the second data processing amount predicted value and the average processing efficiency so as to monitor the running state of each service node on the current date according to the processing time length predicted value of each service node on the current date.
Optionally, the apparatus further comprises:
the monthly influence factor determining unit is used for calculating the monthly influence factor of each service node on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly full-month daily average data processing amount of each service node in the historical data;
and the second data processing capacity predicted value determining unit is specifically configured to determine a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the month influence factor and the first data processing capacity predicted value.
Optionally, the apparatus further comprises:
the week influence factor determining unit is used for calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day average in the historical data every week;
the second data processing capacity predicted value determining unit is specifically configured to determine a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the week influence factor, and the first data processing capacity predicted value.
Optionally, the apparatus further comprises:
the monthly influence factor determining unit is used for calculating the monthly influence factor of each service node on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly full-month daily average data processing amount of each service node in the historical data;
the week influence factor determining unit is used for calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day average in the historical data every week;
the second data processing capacity predicted value determining unit is specifically configured to determine a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the month influence factor, the week influence factor, and the first data processing capacity predicted value.
Optionally, the first data throughput prediction value determining unit is specifically configured to construct an n-time fitting function corresponding to each service node according to data throughput of each service node in the historical data every year on the same date as the current date, and determine the first data throughput prediction value of each service node on the current date according to the constructed n-time fitting function, where n is a positive integer.
Optionally, the first data throughput prediction value determining unit includes:
the mathematical fitting module is used for respectively constructing a fitting function from one time to m times for each service node according to the data processing amount of the service node in the historical data every year on the same date as the current date, and respectively calculating the variance of each fitting function, wherein m is a positive integer greater than 1 and less than or equal to 5;
and the prediction module is used for determining a first data processing capacity prediction value of the current date of the service node according to the fitting function with the minimum variance.
Optionally, the apparatus further comprises:
and the alarm unit is used for sending alarm information to a user when the fact that the actual processing time of the current date of a certain service node exceeds the processing time predicted value corresponding to the service node is monitored.
In order to achieve the above object, according to another aspect of the present invention, there is also provided a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps in the business system operation monitoring method when executing the computer program.
In order to achieve the above object, according to another aspect of the present invention, there is also provided a computer-readable storage medium storing a computer program, which when executed in a computer processor implements the steps in the business system operation monitoring method described above.
The invention has the beneficial effects that: according to the embodiment of the invention, the processing capacity predicted value and the processing duration predicted value of the service node at the current time are accurately predicted through the historical data of the service node, and the working state of the service node at the current time is monitored according to the processing capacity predicted value and the processing duration predicted value, so that the monitoring accuracy of the working state of the node is improved.
Drawings
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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a method for monitoring operation of a business system according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for determining a second data throughput prediction value for a current date of each service node according to an embodiment of the present invention;
FIG. 3 is a flowchart of determining a first data throughput prediction value for a current date of each service node according to an embodiment of the present invention;
fig. 4 is a block diagram of a service system operation monitoring apparatus according to an embodiment of the present invention;
fig. 5 is a block diagram showing a configuration of a first data throughput prediction value determination unit according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments 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 described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. The terms "comprises" and "comprising," and any variations thereof, in the description and claims of this invention and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 is a flowchart of a service system operation monitoring method according to an embodiment of the present invention, and as shown in fig. 1, the service system operation monitoring method according to the embodiment includes steps S101 to S105.
Step S101, calculating the average processing efficiency of each service node according to the daily data processing amount and the daily processing time of each service node in the historical data.
In the embodiment of the invention, the historical data of each service node is obtained firstly, and the historical data comprises the data processing amount and the processing time of each service node every day since a period of time. The historical data may be historical data of the previous N years, and preferably N is equal to or greater than 5.
In the embodiment of the invention, the processing time of each service node in the historical data every day is divided by the data processing amount every day to obtain the processing efficiency of each service node in the historical data every day, and the processing efficiency of each service node in the historical data every day is averaged to obtain the average processing efficiency corresponding to each service node.
In another alternative embodiment of the present invention, the average processing efficiency may also be an average of the processing efficiency of the service node every year.
In another optional embodiment of the present invention, the average processing efficiency may also be an average value of quotients of the total data processing duration and the total data processing duration of each year of the service node N years before.
And step S102, determining a first data processing amount predicted value of the current date of each service node by adopting a mathematical fitting method according to the data processing amount of each service node in the historical data on the same date with the current date.
In the embodiment of the invention, the step adopts a mathematical method to estimate the data processing capacity predicted value of the service node on the current date according to the data processing capacity prediction of the service node on the same date with the current date in the historical data every year. For example, the data processing amount of the service node a in 2019 in 11 months and 11 days is estimated, the data processing amounts of the service node a in 2018 in 2017 in 2016 in 2015 in … 11 in 11 months and 11 days are searched from historical data, and the data processing amount predicted value of the service node a in 2019 in 11 months and 11 days is estimated by adopting a mathematical method according to the historical data.
In an embodiment of the present invention, a prior art mathematical fitting method may be employed to calculate the data throughput prediction value from the historical data. When the mathematical fitting method is adopted, a first order function, a second order function, a third order function and the like can be adopted to carry out fitting according to needs.
In other alternative embodiments of the present invention, this step may also use the interpolation method of the prior art to calculate the predicted data throughput value according to the historical data. The interpolation method can adopt the Lagrangian interpolation method in the prior art.
And step S103, calculating the date influence factor of each service node on the current date according to the annual data processing amount of each service node on the same date as the current date in the historical data and the annual daily average data processing amount of each service node in the historical data.
In the embodiment of the present invention, the date influence factor of the current date of the service node may be an average value of the quotient of the data processing amount of the service node on the same date as the current date in the historical data every year and the annual average data processing amount of the service node in the corresponding year. For example, the date influence factor k of 11-month-11-day of 2019 of the service node a is calculated, assuming that the historical data is historical data of the previous 3 years, the data processing amount of the service node a in 11-month-11-day of 2018 is 28000, the data processing amount of the service node a in 11-month-11-day of 2017 is 25000, the data processing amount of the service node a in 11-month-11-day of 2016 is 20000, the average data processing amount of the service node a in the whole year of 2018 is 1050, the average data processing amount of the service node a in the whole year of 2017 is 1020, the average data processing amount of the service node a in the whole year of 2016 is 1000, and then the date influence factor k of 11-month-11-day of 2019 of the service node a is calculatedy11.Comprises the following steps:
ky11.equal to 23.73 means that the daily throughput of 11 day a service node in 11 months in 2019 is 23.73 times the daily average throughput.
The date influence factor is used for reflecting the data processing amount change of different dates, the data processing amount of the business activity days such as every rest day, every final year, every double eleven and the like is greatly increased through experience summary, the date influence factor can well reflect the data processing amount change of different dates, and the accuracy of data processing amount prediction is improved.
And step S104, determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value.
In the embodiment of the invention, the predicted value of the first data processing capacity of the current date of the service node is multiplied by the date influence factor of the current date of the service node to obtain the predicted value of the second data processing capacity of the current date of the service node. Specifically, the following formula can be used:
N＝kyNy
wherein N is a second data processing capacity predicted value of the current date of the service node, NyA first data throughput prediction value, k, for the current date of the service nodeyA date impact factor for the current date of the service node.
And step S105, determining the processing time length predicted value of the current date of each service node according to the second data processing amount predicted value and the average processing efficiency, and monitoring the operation state of each service node on the current date according to the processing time length predicted value of the current date of each service node.
In the embodiment of the invention, the predicted value of the processing duration of the current date of the service node is obtained by multiplying the predicted value of the second data processing capacity of the current date of the service node by the average processing efficiency of the service node. Specifically, the following formula can be used:
T＝N×η
wherein, T is a predicted value of processing duration of the current date of the service node, N is a predicted value of second data processing amount of the current date of the service node, and η is average processing efficiency of the service node.
In the optional embodiment of the invention, the current working state of each service node can be monitored according to the processing time prediction value of the current date of each service node, and early warning is carried out when the current working state of each service node is abnormal. Specifically, if it is monitored that the actual processing time of the current date of a certain service node exceeds the processing time predicted value corresponding to the service node, alarm information is sent to a user.
In other optional embodiments of the present invention, the warning threshold and the alarm threshold may be determined according to the predicted processing time length value of the current date of each service node, if it is monitored that the actual processing time length of the current date of a certain service node exceeds the warning threshold or the alarm threshold corresponding to the service node, a warning message or an alarm message is sent, and when a warning (with a low priority) occurs, an email is sent to the relevant responsible person. When an alarm (high priority, need to be eliminated in time) occurs, the alarm is directly sent to the person on duty, and a mobile phone short message is sent to the related person in charge.
In an optional embodiment of the present invention, the method for monitoring operation of a service system further generates a daily report through data analysis after monitoring each service node every day, and records processing data of the service node on the day.
As can be seen from the above description, in the embodiment of the present invention, the processing amount predicted value and the processing duration predicted value of the service node at the current time are accurately predicted through the historical data of the service node, and then the working state of the service node at the current time is monitored according to the processing amount predicted value and the processing duration predicted value, so that the accuracy of monitoring the working state of the node is improved.
Fig. 2 is a flowchart of determining a second data throughput prediction value of the current date of each service node according to an embodiment of the present invention, and as shown in fig. 2, in an alternative embodiment of the present invention, the flowchart of determining the second data throughput prediction value of the current date of each service node includes steps S201 to S203.
Step S201, calculating monthly influence factors of each service node on the current date according to the data processing amount of each service node on the same order of the current date in the current month in the historical data and the monthly full-month daily average data processing amount of each service node in the historical data.
In the embodiment of the invention, as the payment transaction of the same product is generally a fixed day of the month, the invention provides a monthly influence factor in order to reflect the change of the data processing amount of a certain day of the month.
In an embodiment of the present invention, the monthly influence factor of the current date of the service node may be an average value of the data throughput of the monthly days in the historical data, which are in the same order as the current date in the current month, of the service node, and the data throughput of the service node in the historical data in the whole month and day. For example, a monthly influence factor of 11, month and 1 days of the a service node 2019 is calculated, and 11, month and 1 days are 1 st days of 11 months, so that the data processing amount of the a service node of 1 st day of each month is obtained from historical data, the average data processing amount of the a service node of the whole month and the day of the whole month is obtained according to the historical data, and finally, the average value of the quotient of the data processing amount of the a service node of the 1 st day of each month and the average data processing amount of the whole month and the day of the whole month in the historical data is calculated, so that the monthly influence factor of 11, month and 1 days of the a service node 2019 is obtained. The monthly influence factor represents that the daily processing capacity of the service node is a multiple of the average daily processing capacity of the month.
The monthly influence factor is used for reflecting the data processing amount change of different dates in the current month, and the payment transaction of the same product is generally fixed for a certain day of each month through experience summary, so that the data processing amount of the day is obviously increased.
Step S202, calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data and the data processing amount of each service node on the whole week and day in the historical data.
In the embodiment of the invention, the transaction amount is 0 due to stopping of certain services on the weekend and holiday of the public account number, a large number of Mondays are concentrated, and the transaction amount on the weekend of the private account number is greatly increased.
In the embodiment of the present invention, the week influence factor of the current date of the service node may be an average value of the data throughput of the day (day of the week) corresponding to the week of the current date of the service node in the history data of the service node every week and the average data throughput of the service node in the history data of the whole week and the day. For example, a week influence factor of the service node a in 2019 on day 11/month 1 is calculated, and if the service node a in 2019 on day 11/month 1 is friday, the data processing amount of the service node a in friday per week is obtained from the historical data, the average data processing amount of the service node a in week per day is obtained according to the historical data, and finally, the average value of the quotient of the data processing amount of the service node a in friday per week in the historical data and the average data processing amount of the service node a in week per day per week is calculated, so that the week influence factor of the service node a in 2019 on day 11/month 1 is. The week impact factor represents that the daily (i.e., day of the week) throughput of the service node is a multiple of the daily average throughput.
The week influence factor of the invention is used for reflecting the data processing amount change of different days per week, the transaction amount is 0 caused by stopping certain services of weekends and holidays of a public account number through experience summary, a large number of Mondays are concentrated, and the transaction amount of weekends of a private account number is greatly increased. The week influence factor of the invention can well reflect the change of data processing capacity of different days every week, and improves the accuracy of data processing capacity prediction.
Step S203, determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the month influence factor, the week influence factor and the first data processing capacity predicted value.
In a preferred embodiment of the present invention, the present invention determines a second data processing amount predicted value of the current date of each service node according to the date influence factor, the monthly influence factor, the weekly influence factor and the first data processing amount predicted value. Specifically, the following formula can be used:
N＝ky×km×kw×Ny
wherein N is a second data processing capacity predicted value of the current date of the service node, NyA first data throughput prediction value, k, for the current date of the service nodeyA date impact factor, k, for the current date of the service nodemIs the monthly impact factor, k, of the current date of the service nodewIs the week impact factor of the current date of the service node.
In other optional embodiments of the present invention, the second data throughput prediction value of the current date of each service node may be determined according to the first data throughput prediction value and at least one of the date impact factor, the month impact factor, the week impact factor, and any combination thereof obtained in the above steps.
Fig. 3 is a flowchart of determining a first data throughput prediction value of the current date of each service node according to an embodiment of the present invention, and as shown in fig. 3, the process of determining the first data throughput prediction value of the current date of each service node in step S102 may specifically include step S301 and step S302.
Step S301, a fitting function is respectively constructed to m times for each service node according to the data processing amount of the service node in the historical data every year on the same date as the current date, and the variance of each fitting function is respectively calculated, wherein m is a positive integer larger than 1 and smaller than or equal to 5.
In the embodiment of the present invention, this step uses the existing first-order fitting function and second-order fitting function … to respectively establish the fitting functions according to the data throughput of the service node in the historical data on the same date as the current date. After the fitting functions are established, the variance of each fitting function is calculated respectively, and the variance of the fitting functions can represent the fitting accuracy of the fitting functions.
In an alternative embodiment of the invention, the first-order fit function may be:
wherein N isyA first data throughput prediction value for the current date of the service node, n is the total number of years in the historical data,iyear number, y, of historical data1First year, y representing historical data2Representing the second year … of the historical data,
is the average of the year numbers of the history data, Ni(i is 1,2,3 … … n) is the total amount of data processing in the ith year of the service node,
the data processing amount of each year service node in the historical data is an average value.
The first-order fit function corresponds to a variance of:
wherein N isn(N-1, 2,3 … …) is the actual value for a year, Nyn(n-1, 2,3 … …) is the theoretical value of the year calculated by a first order fit function.
In an alternative embodiment of the invention, the quadratic fit function may be:
wherein N isyA first data throughput prediction value for the current date of the service node, m being the total number of years in the historical data,iyear number, y, of historical data1First year, y representing historical data2…, N second year representing historical dataiAnd (i-1, 2,3 … … n) is the data processing amount of the service node in the ith year.
The quadratic fit corresponding variance is:
wherein N is the total annual number in the historical data, Nn(N-1, 2,3 … …) is the actual value for a year, Nyn(n-1, 2,3 … …) is the theoretical value of the year calculated by a first order fit function.
In an alternative embodiment of the present invention, the function with more than two orders may also be an existing function, and will not be described herein.
Step S302, a first data processing capacity predicted value of the current date of the service node is determined according to the fitting function with the minimum variance.
In the embodiment of the invention, the smaller the variance of the fitting function is, the more accurate the fitting result is, so that the fitting function with the minimum variance is selected as the final prediction function, and the prediction accuracy is improved. The invention calculates N through a high-precision fitting curvey。
In another optional embodiment of the present invention, the method for determining the first data throughput prediction value of the current date of each service node in step S102 may be that an n-times fitting function corresponding to each service node is constructed according to the data throughput of each service node in the historical data every year on the same date as the current date, and the first data throughput prediction value of the current date of each service node is determined according to the constructed n-times fitting function, where n is a positive integer preset according to experience. In a preferred embodiment of the invention n is equal to 1 or 2, i.e. a first order fit function or a second order fit function is used for data throughput prediction.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
Based on the same inventive concept, an embodiment of the present invention further provides a service system operation monitoring apparatus, which can be used to implement the service system operation monitoring method described in the foregoing embodiment, as described in the following embodiments. Because the principle of the service system operation monitoring device for solving the problem is similar to that of the service system operation monitoring method, the embodiment of the service system operation monitoring device can be referred to as the embodiment of the service system operation monitoring method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 4 is a first structural block diagram of a service system operation monitoring device according to an embodiment of the present invention, and as shown in fig. 4, the service system operation monitoring device according to the embodiment of the present invention includes: the system comprises an average processing efficiency calculation unit 1, a first data processing capacity predicted value determination unit 2, a date influence factor determination unit 3, a second data processing capacity predicted value determination unit 4 and a node running state monitoring unit 5.
And the average processing efficiency calculating unit 1 is used for calculating the average processing efficiency of each service node according to the daily data processing amount and the daily processing duration of each service node in the historical data.
And the first data processing capacity predicted value determining unit 2 is used for determining the first data processing capacity predicted value of the current date of each service node by adopting a mathematical fitting method according to the data processing capacity of each service node in the historical data every year on the same date as the current date.
And the date influence factor determining unit 3 is used for calculating the date influence factor of each service node on the current date according to the data processing amount of each service node on the same date as the current date in the historical data every year and the annual and daily average data processing amount of each service node in the historical data every year.
And the second data processing capacity predicted value determining unit 4 is used for determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value.
And the node running state monitoring unit 5 is used for determining the processing time length predicted value of the current date of each service node according to the second data processing amount predicted value and the average processing efficiency, and monitoring the running state of each service node on the current date according to the processing time length predicted value of the current date of each service node.
In an optional embodiment of the present invention, the service system operation monitoring apparatus further includes: and the alarm unit is used for sending alarm information to a user when the fact that the actual processing time of the current date of a certain service node exceeds the processing time predicted value corresponding to the service node is monitored.
In this embodiment of the present invention, the service system operation monitoring apparatus further includes: the monthly influence factor determining unit is used for calculating the monthly influence factor of each service node on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly full-month daily average data processing amount of each service node in the historical data; the second data processing amount predicted value determining unit 4 is specifically configured to determine a second data processing amount predicted value of the current date of each service node according to the date influence factor, the month influence factor, and the first data processing amount predicted value.
In this embodiment of the present invention, the service system operation monitoring apparatus further includes: the week influence factor determining unit is used for calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day average in the historical data every week; the second data processing capacity predicted value determining unit 4 is specifically configured to determine the second data processing capacity predicted value of the current date of each service node according to the date influence factor, the week influence factor, and the first data processing capacity predicted value.
In this embodiment of the present invention, the service system operation monitoring apparatus further includes: the monthly influence factor determining unit is used for calculating the monthly influence factor of each service node on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly full-month daily average data processing amount of each service node in the historical data; the week influence factor determining unit is used for calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day average in the historical data every week; the second data processing capacity predicted value determining unit 4 is specifically configured to determine a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the month influence factor, the week influence factor, and the first data processing capacity predicted value.
In an optional embodiment of the present invention, the first data throughput prediction value determining unit 2 is specifically configured to construct an n-time fitting function corresponding to each service node according to data throughput of each service node in the historical data every year on the same date as the current date, and determine the first data throughput prediction value of each service node on the current date according to the constructed n-time fitting function, where n is a positive integer.
Fig. 5 is a block diagram of a structure of a first predicted data throughput value determining unit according to an embodiment of the present invention, and in another optional embodiment of the present invention, the first predicted data throughput value determining unit 2 includes:
a mathematical fitting module 201, configured to respectively construct a fitting function to m fitting functions for each service node according to data throughput of the service node in the historical data on the same date as the current date every year, and respectively calculate a variance of each fitting function, where m is a positive integer greater than 1 and less than or equal to 5;
and the predicting module 202 is configured to determine a predicted value of the first data processing amount of the service node at the current date according to the fitting function with the minimum variance.
To achieve the above object, according to another aspect of the present application, there is also provided a computer apparatus. As shown in fig. 6, the computer device comprises a memory, a processor, a communication interface and a communication bus, wherein a computer program that can be run on the processor is stored in the memory, and the steps of the method of the above embodiment are realized when the processor executes the computer program.
The processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and units, such as the corresponding program units in the above-described method embodiments of the present invention. The processor executes various functional applications of the processor and the processing of the work data by executing the non-transitory software programs, instructions and modules stored in the memory, that is, the method in the above method embodiment is realized.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and such remote memory may be coupled to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more units are stored in the memory and when executed by the processor perform the method of the above embodiments.
The specific details of the computer device may be understood by referring to the corresponding related descriptions and effects in the above embodiments, and are not described herein again.
In order to achieve the above object, according to another aspect of the present application, there is also provided a computer-readable storage medium storing a computer program, which when executed in a computer processor implements the steps in the business system operation monitoring method described above. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and they may alternatively be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or fabricated separately as individual integrated circuit modules, or fabricated as a single integrated circuit module from multiple modules or steps. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (16)
1. A method for monitoring operation of a service system is characterized by comprising the following steps:
calculating the average processing efficiency of each service node according to the daily data processing amount and the daily processing duration of each service node in the historical data;
determining a first data processing capacity predicted value of each service node on the current date by adopting a mathematical fitting method according to the data processing capacity of each service node on the same date with the current date in the historical data every year;
calculating date influence factors of all service nodes on the current date according to the annual data processing amount of all the service nodes on the same date as the current date in the historical data and the annual average data processing amount of all the service nodes in the historical data;
determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value;
and determining the processing time length predicted value of the current date of each service node according to the second data processing amount predicted value and the average processing efficiency, and monitoring the operation state of each service node on the current date according to the processing time length predicted value of each service node on the current date.
2. The method for monitoring the operation of a business system according to claim 1, further comprising:
calculating monthly influence factors of the service nodes on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly data processing amount of each service node in the historical data;
the determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value specifically includes:
and determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the monthly influence factor and the first data processing capacity predicted value.
3. The method for monitoring the operation of a business system according to claim 1, further comprising:
calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day in the historical data every week;
the determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value specifically includes:
and determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the week influence factor and the first data processing capacity predicted value.
4. The method for monitoring the operation of a business system according to claim 1, further comprising:
calculating monthly influence factors of the service nodes on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly data processing amount of each service node in the historical data;
calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day in the historical data every week;
the determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value specifically includes:
and determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the monthly influence factor, the weekly influence factor and the first data processing capacity predicted value.
5. The method for monitoring operation of a service system according to claim 1, wherein the determining the first predicted data throughput value of the current date of each service node by using a mathematical fitting method according to the data throughput of each service node in the historical data on the same date as the current date comprises:
and constructing n times of fitting functions corresponding to the service nodes according to data processing amount of the service nodes in the historical data on the same date as the current date every year, and determining a first data processing amount predicted value of the current date of each service node according to the constructed n times of fitting functions, wherein n is a positive integer.
6. The method for monitoring operation of a service system according to claim 1, wherein the determining the first predicted data throughput value of the current date of each service node by using a mathematical fitting method according to the data throughput of each service node in the historical data on the same date as the current date comprises:
respectively constructing a fitting function to m fitting functions for each service node according to the data processing amount of the service node in the historical data every year on the same date as the current date, and respectively calculating the variance of each fitting function, wherein m is a positive integer greater than 1 and less than or equal to 5;
and determining a first data processing capacity predicted value of the current date of the service node according to the fitting function with the minimum variance.
7. The method for monitoring the operation of a business system according to claim 1, further comprising:
and if the fact that the actual processing time of the current date of a certain service node exceeds the processing time predicted value corresponding to the service node is monitored, sending alarm information to a user.
8. A service system operation monitoring device, comprising:
the average processing efficiency calculating unit is used for calculating the average processing efficiency of each service node according to the daily data processing amount and the daily processing duration of each service node in the historical data;
the first data processing capacity predicted value determining unit is used for determining the first data processing capacity predicted value of the current date of each service node by adopting a mathematical fitting method according to the data processing capacity of each service node in the historical data on the same date with the current date;
the date influence factor determining unit is used for calculating the date influence factor of each service node on the current date according to the data processing amount of each service node on the same date as the current date in the historical data every year and the annual and annual average data processing amount of each service node in the historical data every year;
the second data processing capacity predicted value determining unit is used for determining a second data processing capacity predicted value of the current date of each service node according to the date influence factor and the first data processing capacity predicted value;
and the node running state monitoring unit is used for determining the processing time length predicted value of the current date of each service node according to the second data processing amount predicted value and the average processing efficiency so as to monitor the running state of each service node on the current date according to the processing time length predicted value of each service node on the current date.
9. The business system operation monitoring device of claim 8, further comprising:
the monthly influence factor determining unit is used for calculating the monthly influence factor of each service node on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly full-month daily average data processing amount of each service node in the historical data;
and the second data processing capacity predicted value determining unit is specifically configured to determine a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the month influence factor and the first data processing capacity predicted value.
10. The business system operation monitoring device of claim 8, further comprising:
the week influence factor determining unit is used for calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day average in the historical data every week;
the second data processing capacity predicted value determining unit is specifically configured to determine a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the week influence factor, and the first data processing capacity predicted value.
11. The business system operation monitoring device of claim 8, further comprising:
the monthly influence factor determining unit is used for calculating the monthly influence factor of each service node on the current date according to the data processing amount of each service node in the historical data on the same order of the current date in the current month and the monthly full-month daily average data processing amount of each service node in the historical data;
the week influence factor determining unit is used for calculating the week influence factor of each service node on the current date according to the data processing amount of each service node on the day corresponding to the week of the current date in the historical data every week and the data processing amount of each service node on the whole week and the day average in the historical data every week;
the second data processing capacity predicted value determining unit is specifically configured to determine a second data processing capacity predicted value of the current date of each service node according to the date influence factor, the month influence factor, the week influence factor, and the first data processing capacity predicted value.
12. The device for monitoring operation of a business system according to claim 8, wherein the first data throughput prediction value determining unit is specifically configured to construct an n-times fitting function corresponding to each business node according to data throughput of each business node in historical data on the same date as the current date every year, and determine the first data throughput prediction value of each business node on the current date according to the constructed n-times fitting function, where n is a positive integer.
13. The business system operation monitoring device according to claim 8, wherein the first data throughput prediction value determining unit includes:
the mathematical fitting module is used for respectively constructing a fitting function from one time to m times for each service node according to the data processing amount of the service node in the historical data every year on the same date as the current date, and respectively calculating the variance of each fitting function, wherein m is a positive integer greater than 1 and less than or equal to 5;
and the prediction module is used for determining a first data processing capacity prediction value of the current date of the service node according to the fitting function with the minimum variance.
14. The business system operation monitoring device of claim 8, further comprising:
and the alarm unit is used for sending alarm information to a user when the fact that the actual processing time of the current date of a certain service node exceeds the processing time predicted value corresponding to the service node is monitored.
15. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the computer program is executed by the processor.
16. A computer-readable storage medium, in which a computer program is stored which, when being executed in a computer processor, carries out the steps of the method according to any one of claims 1 to 7.
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