CN118171223A - Meteorological health index anomaly monitoring method, device, equipment and storage medium - Google Patents
Meteorological health index anomaly monitoring method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for monitoring the abnormality of a weather health index, wherein the method analyzes the running state data by acquiring the running state data of a weather service system to obtain the weather health index; acquiring a time sequence of weather health indexes, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm; judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes with abnormal weather health indexes in the weather service system; the system has the advantages that the value of monitoring data can be mined, the automatic troubleshooting capability of faults is improved, the first-line operation and maintenance pressure is effectively reduced, the abnormal monitoring precision of the weather health indexes is improved, a general framework of the system health indexes suitable for weather businesses is formed, the time for processing faults is reduced, alarms of different business systems can be effectively associated, and the abnormal monitoring speed and efficiency of the weather health indexes are improved.
Description
Technical Field
The invention relates to the technical field of intelligent network-connected automatic driving of automobiles, in particular to a method, a device, equipment and a storage medium for monitoring weather health index abnormality.
Background
With the continuous improvement of the complexity of IT operation and maintenance, the manual operation and the automatic operation and maintenance cannot efficiently cope with various challenges in an operation and maintenance scene at low cost, and the intelligent operation and maintenance gradually becomes a new operation and maintenance trend; the log analysis plays an important role in network monitoring, fault removal, root cause analysis, event evidence collection, tracing and other operation and maintenance scenes.
The health examination refers to the medical examination of a subject by medical means and methods, and the diagnosis and treatment actions of knowing the health condition of the subject, early finding disease clues and health hidden dangers; the health check of the business system is a process of detecting whether a series of objects such as storage, a host, application, service and the like are healthy or available by using a technical means; factors affecting service unavailability and slow response of the business system are many, and may be that meteorological data is delayed too high, NAS storage is overloaded, a database CPU load and a disk IO are too high due to an excessive request amount, and a calculation task may not be successfully scheduled.
At present, to ensure high availability of the system, the general means is to eliminate single nodes, then find out problem nodes, and finally reject the problem nodes or switch the business process to other normal nodes; however, the fault location is usually carried out after the alarm is issued, and the problems of multiple manual operation and maintenance fault location links, long time consumption and the like exist; the alarm fault location still needs manual investigation, and because of the problems of more system circulation links, more personnel and the like, the analysis and the processing of the fault root cause are long in time consumption, and effective association between alarms of different service systems cannot be achieved.
Disclosure of Invention
The invention mainly aims to provide a meteorological health index anomaly monitoring method, device, equipment and storage medium, and aims to solve the technical problems that in the prior art, manual operation and maintenance positioning fault links are more and time-consuming, so that analysis and processing of fault roots are longer in time-consuming, and effective association between alarms of different service systems cannot be achieved.
In a first aspect, the present invention provides a weather health indicator anomaly monitoring method, the weather health indicator anomaly monitoring method comprising the steps of:
Acquiring operation state data of a weather service system, and analyzing the operation state data to acquire weather health indexes;
Acquiring a time sequence of the weather health index, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm;
Judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes in the weather service system, wherein the abnormal nodes are abnormal in the weather health index.
Optionally, the acquiring the operation state data of the weather service system, analyzing the operation state data, and obtaining the weather health index includes:
Acquiring operation state data of a weather service system from the acquired log and the comprehensive monitoring information of the weather service;
and analyzing the running state data to obtain weather health indexes reflecting the business capacity and performance state of the running state data.
Optionally, the analyzing the operation state data to obtain weather health indexes reflecting the business capability and performance state of the operation state data includes:
Analyzing the running state data according to basic attributes to obtain classification names, service time, service link flow identifiers, service system names, health index names, maximum service bearing capacity, current service volume, service system performance threshold values, service system performance running values and index statistics periods which reflect the service capacity and performance states of the running state data;
And taking the classification name, the service time, the service link flow identifier, the service system name, the health index name, the maximum service bearing capacity, the current service volume, the service system performance threshold, the service system performance running value and the index statistical period as weather health indexes.
Optionally, the obtaining the time sequence of the weather health indicator, calculating the upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm includes:
when the stable operation of the weather service system is detected, acquiring the sequence fluctuation of the weather health index, and acquiring a time sequence according to the sequence fluctuation and time;
Acquiring the starting time and the ending time of the time sequence, and acquiring a time sequence value from the starting time to the ending time;
Obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value;
And utilizing a 3-sigma algorithm to obtain upper and lower boundaries corresponding to the time sequence average value and the time sequence standard deviation.
Optionally, the obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value includes:
obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value through the following formula:
Wherein, Is the time series average value,/>Is the total sample number of weather health index,/>Is the actual running value of the weather health index of the current time/timeIs the standard deviation of time series,/>Each of the operational data is for a weather health indicator.
Optionally, the using a 3-sigma algorithm according to the upper and lower boundaries corresponding to the time sequence average value and the time sequence standard deviation includes:
and acquiring corresponding upper and lower boundaries by using a 3-sigma algorithm according to the time sequence average value and the time sequence standard deviation through the following formula:
Wherein, As an upper boundary,/>Is the lower boundary,/>Is the time series average value,/>Is the standard deviation of time series,/>3.
Optionally, the determining whether the weather health indicator is normal according to the upper and lower boundaries, and identifying an abnormal node in the weather service system, where the weather health indicator is abnormal, includes:
determining reasonable fluctuation ranges corresponding to different types of weather health indexes according to the upper and lower boundaries;
When the current health index of the weather health index is detected to be in the current reasonable fluctuation range, judging that the current health index is normal;
When the current health index of the weather health index is detected not to be in the current reasonable fluctuation range, judging that the current health index is abnormal, and identifying abnormal nodes corresponding to abnormal data points in the current health index.
In a second aspect, to achieve the above object, the present invention further provides a weather health indicator anomaly monitoring device, the weather health indicator anomaly monitoring device including:
the system comprises an index acquisition module, a weather service system and a weather service system, wherein the index acquisition module is used for acquiring the running state data of the weather service system, analyzing the running state data and acquiring weather health indexes;
the boundary determining module is used for acquiring a time sequence of the weather health index and calculating an upper boundary and a lower boundary corresponding to the time sequence by using a 3-sigma algorithm;
and the abnormal identification module is used for judging whether the weather health index is normal or not according to the upper and lower boundaries and identifying abnormal nodes of the weather health index abnormality in the weather service system.
In a third aspect, to achieve the above object, the present invention further provides a weather-health-index anomaly monitoring device, the weather-health-index anomaly monitoring device including: the system comprises a memory, a processor and a weather health indicator anomaly monitoring program stored on the memory and executable on the processor, wherein the weather health indicator anomaly monitoring program is configured to implement the steps of the weather health indicator anomaly monitoring method as described above.
In a fourth aspect, to achieve the above object, the present invention further provides a storage medium, where a weather health indicator anomaly monitoring program is stored, and the weather health indicator anomaly monitoring program, when executed by a processor, implements the steps of the weather health indicator anomaly monitoring method as described above.
According to the meteorological health index anomaly monitoring method provided by the invention, the operating state data of the meteorological service system is obtained, and the operating state data is analyzed to obtain the meteorological health index; acquiring a time sequence of the weather health index, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm; judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes in the weather service system, wherein the abnormal nodes are abnormal in the weather health index; the value of monitoring data can be mined, the automatic troubleshooting capability of faults is improved, the faults are early-warning and transforming from 'passive' discovery to 'active' in advance, the first-line operation and maintenance pressure is effectively reduced, and the accuracy of monitoring the meteorological health index abnormality is improved; the intelligent operation and maintenance capability of the real-time monitoring system for the weather comprehensive business is improved, an early warning mechanism from passive processing after the occurrence of the past faults to active processing for discovering the abnormality of the system in advance is formed, a general frame of the system health index suitable for the weather business is formed, the time consumption for processing the faults is reduced, alarms of different business systems can be effectively associated, and the speed and the efficiency of monitoring the abnormality of the weather health index are improved.
Drawings
FIG. 1 is a schematic diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a first embodiment of a weather health indicator anomaly monitoring method according to the present invention;
FIG. 3 is a flowchart of a method for monitoring anomalies in weather health indicators according to a second embodiment of the present invention;
FIG. 4 is a flowchart of a third embodiment of a weather health indicator anomaly monitoring method according to the present invention;
FIG. 5 is a flowchart of a fourth embodiment of a weather health indicator anomaly monitoring method according to the present invention;
FIG. 6 is a flowchart of a fifth embodiment of a weather health indicator anomaly monitoring method according to the present invention;
FIG. 7 is a functional block diagram of a first embodiment of an abnormality monitoring device for weather health indicators according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The solution of the embodiment of the invention mainly comprises the following steps: analyzing the operation state data by acquiring the operation state data of a weather service system to obtain weather health indexes; acquiring a time sequence of the weather health index, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm; judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes in the weather service system, wherein the abnormal nodes are abnormal in the weather health index; the value of monitoring data can be mined, the automatic troubleshooting capability of faults is improved, the faults are early-warning and transforming from 'passive' discovery to 'active' in advance, the first-line operation and maintenance pressure is effectively reduced, and the accuracy of monitoring the meteorological health index abnormality is improved; the intelligent operation and maintenance capability of the real-time monitoring system for the weather comprehensive business is improved, an early warning mechanism from passive processing after the occurrence of the past faults to active processing for discovering system anomalies in advance is formed, a general framework of the system health index suitable for the weather business is formed, the time consumption for processing the faults is reduced, alarms of different business systems can be effectively associated, the speed and efficiency of monitoring the weather health index anomalies are improved, and the technical problems that in the prior art, manual operation and maintenance positioning fault links are multiple and long in time consumption exist in the positioning of the weather business faults, the analysis and processing of the fault root cause are long in time consumption, and effective association between alarms of different business systems cannot be achieved are solved.
Referring to fig. 1, fig. 1 is a schematic device structure diagram of a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., wi-Fi interface). The Memory 1005 may be a high-speed RAM Memory or a stable Memory (Non-Volatile Memory), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the apparatus structure shown in fig. 1 is not limiting of the apparatus and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operation device, a network communication module, a user interface module, and a weather health indicator abnormality monitoring program may be included in the memory 1005 as one storage medium.
The device of the present invention invokes the weather health indicator anomaly monitoring program stored in memory 1005 via processor 1001 and performs the following operations:
Acquiring operation state data of a weather service system, and analyzing the operation state data to acquire weather health indexes;
Acquiring a time sequence of the weather health index, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm;
Judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes in the weather service system, wherein the abnormal nodes are abnormal in the weather health index.
The device of the present invention invokes the weather health indicator anomaly monitoring program stored in memory 1005 via processor 1001, and also performs the following operations:
Acquiring operation state data of a weather service system from the acquired log and the comprehensive monitoring information of the weather service;
and analyzing the running state data to obtain weather health indexes reflecting the business capacity and performance state of the running state data.
The device of the present invention invokes the weather health indicator anomaly monitoring program stored in memory 1005 via processor 1001, and also performs the following operations:
Analyzing the running state data according to basic attributes to obtain classification names, service time, service link flow identifiers, service system names, health index names, maximum service bearing capacity, current service volume, service system performance threshold values, service system performance running values and index statistics periods which reflect the service capacity and performance states of the running state data;
And taking the classification name, the service time, the service link flow identifier, the service system name, the health index name, the maximum service bearing capacity, the current service volume, the service system performance threshold, the service system performance running value and the index statistical period as weather health indexes.
The device of the present invention invokes the weather health indicator anomaly monitoring program stored in memory 1005 via processor 1001, and also performs the following operations:
when the stable operation of the weather service system is detected, acquiring the sequence fluctuation of the weather health index, and acquiring a time sequence according to the sequence fluctuation and time;
Acquiring the starting time and the ending time of the time sequence, and acquiring a time sequence value from the starting time to the ending time;
Obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value;
And utilizing a 3-sigma algorithm to obtain upper and lower boundaries corresponding to the time sequence average value and the time sequence standard deviation.
The device of the present invention invokes the weather health indicator anomaly monitoring program stored in memory 1005 via processor 1001, and also performs the following operations:
obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value through the following formula:
Wherein, Is the time series average value,/>Is the total sample number of weather health index,/>Is the actual running value of the weather health index of the current time/timeIs the standard deviation of time series,/>Each of the operational data is for a weather health indicator.
The device of the present invention invokes the weather health indicator anomaly monitoring program stored in memory 1005 via processor 1001, and also performs the following operations:
and acquiring corresponding upper and lower boundaries by using a 3-sigma algorithm according to the time sequence average value and the time sequence standard deviation through the following formula:
Wherein, As an upper boundary,/>Is the lower boundary,/>Is the time series average value,/>Is the standard deviation of time series,/>3.
The device of the present invention invokes the weather health indicator anomaly monitoring program stored in memory 1005 via processor 1001, and also performs the following operations:
determining reasonable fluctuation ranges corresponding to different types of weather health indexes according to the upper and lower boundaries;
When the current health index of the weather health index is detected to be in the current reasonable fluctuation range, judging that the current health index is normal;
When the current health index of the weather health index is detected not to be in the current reasonable fluctuation range, judging that the current health index is abnormal, and identifying abnormal nodes corresponding to abnormal data points in the current health index.
According to the scheme, the operating state data of the weather service system are acquired, and are analyzed to obtain weather health indexes; acquiring a time sequence of the weather health index, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm; judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes in the weather service system, wherein the abnormal nodes are abnormal in the weather health index; the value of monitoring data can be mined, the automatic troubleshooting capability of faults is improved, the faults are early-warning and transforming from 'passive' discovery to 'active' in advance, the first-line operation and maintenance pressure is effectively reduced, and the accuracy of monitoring the meteorological health index abnormality is improved; the intelligent operation and maintenance capability of the real-time monitoring system for the weather comprehensive business is improved, an early warning mechanism from passive processing after the occurrence of the past faults to active processing for discovering the abnormality of the system in advance is formed, a general frame of the system health index suitable for the weather business is formed, the time consumption for processing the faults is reduced, alarms of different business systems can be effectively associated, and the speed and the efficiency of monitoring the abnormality of the weather health index are improved.
Based on the hardware structure, the embodiment of the meteorological health index anomaly monitoring method is provided.
Referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of a weather health indicator anomaly monitoring method according to the present invention.
In a first embodiment, the weather health indicator anomaly monitoring method includes the steps of:
and S10, acquiring the operation state data of the weather service system, and analyzing the operation state data to acquire weather health indexes.
The weather service system is a system covering all services, such as acquisition, transmission, analysis, processing, storage, release, application and the like of weather information, and the operating state data is a system state after the weather service system is operated for a period of time.
And S20, acquiring a time sequence of the weather health index, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm.
It should be appreciated that in time series analysis, the boundaries of data are critical to understanding and interpreting the characteristics, trends and patterns of the data, and relate to the deletion, interpolation, smoothing or adjustment of processing boundary data, the purpose of the boundary processing is to ensure the accuracy and reliability of the analysis, and the complete interpretation of the data, and when the business system is stably running, the weather health indicator generates a stable time series, and the upper and lower boundaries corresponding to the time series are calculated by using a 3-sigma algorithm.
And step S30, judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes in the weather service system, wherein the abnormal nodes are abnormal in the weather health index.
It can be understood that whether the weather health index is normal or not can be judged according to the upper and lower boundaries, and further, abnormal nodes in the weather health index in the weather service system can be identified.
According to the scheme, the operating state data of the weather service system are acquired, and are analyzed to obtain weather health indexes; acquiring a time sequence of the weather health index, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm; judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes in the weather service system, wherein the abnormal nodes are abnormal in the weather health index; the value of monitoring data can be mined, the automatic troubleshooting capability of faults is improved, the faults are early-warning and transforming from 'passive' discovery to 'active' in advance, the first-line operation and maintenance pressure is effectively reduced, and the accuracy of monitoring the meteorological health index abnormality is improved; the intelligent operation and maintenance capability of the real-time monitoring system for the weather comprehensive business is improved, an early warning mechanism from passive processing after the occurrence of the past faults to active processing for discovering the abnormality of the system in advance is formed, a general frame of the system health index suitable for the weather business is formed, the time consumption for processing the faults is reduced, alarms of different business systems can be effectively associated, and the speed and the efficiency of monitoring the abnormality of the weather health index are improved.
Further, fig. 3 is a flow chart of a second embodiment of the method for monitoring abnormal weather health indicators according to the present invention, as shown in fig. 3, the second embodiment of the method for monitoring abnormal weather health indicators according to the present invention is proposed based on the first embodiment, and in this embodiment, the step S10 specifically includes the following steps:
And S11, acquiring the operation state data of the weather service system from the acquired log and the comprehensive monitoring information of the weather service.
It should be noted that, the health index of the weather service system generally refers to a record of the operation state of the service system obtained by using the collection log and pushing the monitoring information means, that is, the operation state data of the weather service system is obtained from the collection log and the comprehensive monitoring information of the weather service.
And step S12, analyzing the running state data to obtain weather health indexes reflecting the business capacity and performance state of the running state data.
It can be understood that, based on the operation state data, after the operation state data is analyzed, the result obtained after the statistical analysis processing can be used to reflect the service capability and performance state of the service system in a period of time, so as to obtain the service capability and performance state reflecting the operation state data.
According to the scheme, the operating state data of the weather service system are obtained from the collected log and the comprehensive monitoring information of the weather service; the operation state data is analyzed to obtain weather health indexes reflecting business capability and performance states of the operation state data, the weather health indexes can be rapidly obtained, the value of monitoring data can be mined, the automatic fault checking capability is improved, and the speed and efficiency of abnormal monitoring of the weather health indexes are improved.
Further, fig. 4 is a flow chart of a third embodiment of the weather-health-index anomaly monitoring method according to the present invention, as shown in fig. 4, based on the second embodiment, the third embodiment of the weather-health-index anomaly monitoring method according to the present invention is provided, and in this embodiment, the step S12 specifically includes the following steps:
And step S121, analyzing the running state data according to basic attributes to obtain a classification name, a service time, a service link flow identifier, a service system name, a health index name, a maximum service bearing capacity, a current service volume, a service system performance threshold, a service system performance running value and an index statistics period which reflect the service capacity and the performance state of the running state data.
It should be noted that, the health index may be classified according to its basic attribute, that is, the running state data may be analyzed according to the basic attribute to obtain a classification name, a service time, a service link flow identifier, a service system name, a health index name, a maximum service bearing capacity, a current service volume, a service system performance threshold, a service system performance running value, and an index statistics period, which reflect the service capability and the performance state of the running state data.
Step S122, using the classification name, the service time, the service link flow identifier, the service system name, the health index name, the maximum service carrying capacity, the current service volume, the service system performance threshold, the service system performance running value and the index statistics period as weather health indexes.
It can be appreciated that different health index fields are used as weather health indexes; the basic attributes of the health index comprise classification names, service time, service link flow identification, service system names, health index names, maximum service bearing capacity, current service volume, service system performance threshold values, service system performance running values and index statistical periods; the health index fields are shown in table 1 below:
TABLE 1 Meteorological service System health index name field
According to the scheme, the running state data are analyzed through basic attributes, and classification names, service time, service link flow identifiers, service system names, health index names, maximum service bearing capacity, current service volume, service system performance threshold values, service system performance running values and index statistics periods reflecting the service capacity and performance states of the running state data are obtained; the classification name, the business time, the business link flow identifier, the business system name, the health index name, the maximum business bearing capacity, the current business volume, the business system performance threshold, the business system performance running value and the index statistics period are used as weather health indexes, weather health indexes can be rapidly obtained, monitoring data value can be mined, fault automatic investigation capability is improved, and the speed and efficiency of monitoring the weather health indexes in an abnormal manner are improved.
Further, fig. 5 is a flow chart of a fourth embodiment of the weather-health-index anomaly monitoring method according to the present invention, as shown in fig. 5, based on the first embodiment, the fourth embodiment of the weather-health-index anomaly monitoring method according to the present invention is provided, and in this embodiment, the step S20 specifically includes the following steps:
and S21, when the stable operation of the weather service system is detected, acquiring the sequence fluctuation of the weather health index, and acquiring a time sequence according to the sequence fluctuation and time.
It will be appreciated that upon detection of stable operation of the weather service system, the health indicator will produce a stable time series that will fluctuate within a reasonable range, and when the series fluctuates beyond a given range, points outside the range will be considered outliers.
Step S22, acquiring the starting time and the ending time of the time sequence, and acquiring the time sequence value from the starting time to the ending time.
The time series includes two columns of time and value, and the start time and the end time of the time series are acquired, for example, the start year and the end year of the time series may be acquired, and then the time series value from the start time to the end time for a period of time may be acquired.
And S23, obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value.
It will be appreciated that the time series mean and the time series standard deviation are calculated from the time series values.
Further, the step S23 specifically includes the following steps:
obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value through the following formula:
Wherein, Is the time series average value,/>Is the total sample number of weather health index,/>Is the actual running value of the weather health index of the current time/timeIs the standard deviation of time series,/>Each of the operational data is for a weather health indicator.
It should be noted that the 3-sigma principle is a concept used in statistics to describe the characteristics and trusted intervals of a normal distribution (also called gaussian distribution); in normal distribution, a set of data can be described by a mean (μ) and a standard deviation (σ); the standard deviation represents the degree of dispersion of the data, and the mean represents the center position of the data.
In a specific implementation, according to the 3-sigma principle, about 68% of the data points are within one standard deviation of the mean plus or minus one standard deviation, about 95% of the data points are within two standard deviation of the mean plus or minus two standard deviation, and about 99.7% of the data points are within three standard deviation of the mean plus or minus three standard deviation; in other words, if the data obeys a normal distribution, about 68% of the data points will fall within one standard deviation plus or minus one standard deviation of the mean, which is referred to as one standard deviation interval or 68% confidence interval; similarly, 95% of the data will fall within the mean plus or minus two standard deviation ranges, referred to as the double standard deviation interval or 95% confidence interval; while 99.7% of the data will fall within the mean plus or minus three standard deviation ranges, referred to as the triple standard deviation interval or the 99.7% confidence interval.
And S24, utilizing a 3-sigma algorithm to obtain upper and lower boundaries corresponding to the time sequence average value and the time sequence standard deviation.
It can be understood that the health index of the historical sequence can be analyzed based on the 3-sigma algorithm, and the threshold range of the health index of the weather service system in normal operation is researched, namely, the upper and lower boundaries corresponding to the time sequence average value and the time sequence standard deviation are used.
Further, the step S24 specifically includes the following steps:
and acquiring corresponding upper and lower boundaries by using a 3-sigma algorithm according to the time sequence average value and the time sequence standard deviation through the following formula:
Wherein, As an upper boundary,/>Is the lower boundary,/>Is the time series average value,/>Is the standard deviation of time series,/>3.
It should be noted that, the corresponding upper and lower boundaries are obtained by the following equation according to the time sequence average value and the time sequence standard deviation by using the 3-sigma algorithm, and the upper and lower boundaries of the index are dynamically adjusted as the number of samples increases.
In a specific implementation, a Model-driven boundary processing (Model-Driven Boundary Handling) method can be used to apply a statistical or mathematical Model to the time series data and explicitly consider the boundary conditions; the model may contain constraints and conditions at the boundaries to more accurately estimate and predict the data; according to the actual service system running condition, when the average value in the sample is smaller, the confidence interval calculated by the 3-sigma algorithm contains a negative value, but when the service system runs, the health index does not have a negative value, taking scheduling delay in a service interface of a weather big data cloud platform and task calculation scheduling time consumption in product processing as examples, the smaller the index proves that the system performance is better, and the condition that no user is used or the process is disconnected at present is indicated when the index is 0, so that judgment processing larger than zero is carried out, and the rationality of the confidence interval is ensured.
According to the scheme, when stable operation of the weather service system is detected, sequence fluctuation of the weather health index is obtained, and a time sequence is obtained according to the sequence fluctuation and time; acquiring the starting time and the ending time of the time sequence, and acquiring a time sequence value from the starting time to the ending time; obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value; the upper and lower boundaries corresponding to the time sequence average value and the time sequence standard deviation can be rapidly and accurately determined by using a 3-sigma algorithm, so that the first-line operation and maintenance pressure is effectively reduced, and the accuracy of monitoring the weather health index abnormality is improved.
Further, fig. 6 is a flowchart of a fifth embodiment of the weather-health-index anomaly monitoring method according to the present invention, as shown in fig. 6, and the fifth embodiment of the weather-health-index anomaly monitoring method according to the present invention is proposed based on the first embodiment, in which the step S30 specifically includes the following steps:
And S31, determining reasonable fluctuation ranges corresponding to different types of weather health indexes according to the upper and lower boundaries.
It should be noted that, to ensure that the health index is displayed correctly, an initial threshold needs to be designed by service experts of each system for actual use conditions and daily operation and maintenance requirements of the system in the initial period of the system, when the service system operates stably, the health index will generate a stable time sequence, and the sequence will fluctuate within a reasonable range, i.e. a reasonable fluctuation range corresponding to different types of weather health indexes can be determined according to the upper and lower boundaries.
And step S32, judging that the current health index is normal when the current health index of the weather health index is detected to be in the current reasonable fluctuation range.
It should be appreciated that upon detecting that the current health indicator of the weather health indicator is within a current reasonable fluctuation range, it may be determined that the current health indicator is normal.
And step S33, judging that the current health index is abnormal when the current health index of the weather health index is detected not to be in the current reasonable fluctuation range, and identifying abnormal nodes corresponding to abnormal data points in the current health index.
It can be appreciated that when it is detected that the current health index of the weather health index is not in the current reasonable fluctuation range, the current health index is determined to be abnormal, and the abnormal node corresponding to the abnormal data point in the current health index is identified.
The anomaly data can be detected by:
abnormal sudden increases (Upper bound, UB) are detected:
abnormal abrupt decreases (Lower bound (LB))aredetected:
It will be appreciated that by performing a 3-sigma analysis on the time series data, it is possible to detect whether abnormal data points are present; under normal distribution assumptions, about 99.7% of the data points should fall within the mean plus three standard deviations; if a data point is outside of this range, it is likely to be an outlier or an outlier event; in the experiment, the mean and standard deviation of the data are calculated first, then the 3-sigma range (the range of the mean plus three times the standard deviation) is determined, and if a certain data point falls outside the range, the data point can be marked as potential abnormality.
According to the scheme, reasonable fluctuation ranges corresponding to different types of weather health indexes are determined through the upper and lower boundaries; when the current health index of the weather health index is detected to be in the current reasonable fluctuation range, judging that the current health index is normal; when the current health index of the weather health index is detected not to be in the current reasonable fluctuation range, judging that the current health index is abnormal, and identifying an abnormal node corresponding to an abnormal data point in the current health index; the value of monitoring data can be mined, the automatic troubleshooting capability of faults is improved, the faults are early-warning and transforming from 'passive' discovery to 'active' in advance, the first-line operation and maintenance pressure is effectively reduced, and the accuracy of monitoring the meteorological health index abnormality is improved; the intelligent operation and maintenance capability of the real-time monitoring system for the weather comprehensive business is improved, an early warning mechanism from passive processing after the occurrence of the past faults to active processing for discovering the abnormality of the system in advance is formed, a general frame of the system health index suitable for the weather business is formed, the time consumption for processing the faults is reduced, alarms of different business systems can be effectively associated, and the speed and the efficiency of monitoring the abnormality of the weather health index are improved.
Correspondingly, the invention further provides a meteorological health index abnormality monitoring device.
Referring to fig. 7, fig. 7 is a functional block diagram of a first embodiment of the weather-health-index anomaly monitoring device according to the present invention.
In a first embodiment of the weather-health-index anomaly monitoring device of the present invention, the weather-health-index anomaly monitoring device includes:
The index acquisition module 10 is configured to acquire operation state data of the weather service system, and analyze the operation state data to obtain weather health indexes.
The boundary determining module 20 is configured to obtain a time sequence of the weather health indicator, and calculate an upper boundary and a lower boundary corresponding to the time sequence by using a 3-sigma algorithm.
The anomaly identification module 30 is configured to determine whether the weather health indicator is normal according to the upper and lower boundaries, and identify an anomaly node in the weather service system where the weather health indicator is anomaly.
The index acquisition module 10 is further configured to acquire operation state data of the weather service system from the collected log and the comprehensive monitoring information of the weather service; and analyzing the running state data to obtain weather health indexes reflecting the business capacity and performance state of the running state data.
The index obtaining module 10 is further configured to analyze the running state data according to a basic attribute to obtain a classification name, a service time, a service link flow identifier, a service system name, a health index name, a maximum service bearing capacity, a current service volume, a service system performance threshold, a service system performance running value and an index statistics period, which reflect the service capability and the performance state of the running state data; and taking the classification name, the service time, the service link flow identifier, the service system name, the health index name, the maximum service bearing capacity, the current service volume, the service system performance threshold, the service system performance running value and the index statistical period as weather health indexes.
The boundary determining module 20 is further configured to obtain a sequence fluctuation of the weather health indicator when the stable operation of the weather service system is detected, and obtain a time sequence according to the sequence fluctuation and time; acquiring the starting time and the ending time of the time sequence, and acquiring a time sequence value from the starting time to the ending time; obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value; and utilizing a 3-sigma algorithm to obtain upper and lower boundaries corresponding to the time sequence average value and the time sequence standard deviation.
The boundary determining module 20 is further configured to obtain a time series average value and a time series standard deviation according to the time series numerical value by:
Wherein, Is the time series average value,/>Is the total sample number of weather health index,/>Is the actual running value of the weather health index of the current time/timeIs the standard deviation of time series,/>Each of the operational data is for a weather health indicator.
The boundary determining module 20 is further configured to obtain, according to the time sequence average value and the time sequence standard deviation, corresponding upper and lower boundaries by using a 3-sigma algorithm:
Wherein, As an upper boundary,/>Is the lower boundary,/>Is the time series average value,/>Is the standard deviation of time series,/>3.
The anomaly identification module 30 is further configured to determine reasonable fluctuation ranges corresponding to different types of weather health indicators according to the upper and lower boundaries; when the current health index of the weather health index is detected to be in the current reasonable fluctuation range, judging that the current health index is normal; when the current health index of the weather health index is detected not to be in the current reasonable fluctuation range, judging that the current health index is abnormal, and identifying abnormal nodes corresponding to abnormal data points in the current health index.
The steps of implementing each functional module of the weather health indicator anomaly monitoring device can refer to each embodiment of the weather health indicator anomaly monitoring method of the present invention, and are not described herein.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium stores a weather health index abnormality monitoring program, and the weather health index abnormality monitoring program realizes the following operations when being executed by a processor:
Acquiring operation state data of a weather service system, and analyzing the operation state data to acquire weather health indexes;
Acquiring a time sequence of the weather health index, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm;
Judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes in the weather service system, wherein the abnormal nodes are abnormal in the weather health index.
Further, the weather health indicator anomaly monitoring program, when executed by the processor, further performs the following operations:
Acquiring operation state data of a weather service system from the acquired log and the comprehensive monitoring information of the weather service;
and analyzing the running state data to obtain weather health indexes reflecting the business capacity and performance state of the running state data.
Further, the weather health indicator anomaly monitoring program, when executed by the processor, further performs the following operations:
Analyzing the running state data according to basic attributes to obtain classification names, service time, service link flow identifiers, service system names, health index names, maximum service bearing capacity, current service volume, service system performance threshold values, service system performance running values and index statistics periods which reflect the service capacity and performance states of the running state data;
And taking the classification name, the service time, the service link flow identifier, the service system name, the health index name, the maximum service bearing capacity, the current service volume, the service system performance threshold, the service system performance running value and the index statistical period as weather health indexes.
Further, the weather health indicator anomaly monitoring program, when executed by the processor, further performs the following operations:
when the stable operation of the weather service system is detected, acquiring the sequence fluctuation of the weather health index, and acquiring a time sequence according to the sequence fluctuation and time;
Acquiring the starting time and the ending time of the time sequence, and acquiring a time sequence value from the starting time to the ending time;
Obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value;
And utilizing a 3-sigma algorithm to obtain upper and lower boundaries corresponding to the time sequence average value and the time sequence standard deviation.
Further, the weather health indicator anomaly monitoring program, when executed by the processor, further performs the following operations:
obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value through the following formula:
Wherein, Is the time series average value,/>Is the total sample number of weather health index,/>Is the actual running value of the weather health index of the current time/timeIs the standard deviation of time series,/>Each of the operational data is for a weather health indicator.
Further, the weather health indicator anomaly monitoring program, when executed by the processor, further performs the following operations:
and acquiring corresponding upper and lower boundaries by using a 3-sigma algorithm according to the time sequence average value and the time sequence standard deviation through the following formula:
Wherein, As an upper boundary,/>Is the lower boundary,/>Is the time series average value,/>Is the standard deviation of time series,/>3.
Further, the weather health indicator anomaly monitoring program, when executed by the processor, further performs the following operations:
determining reasonable fluctuation ranges corresponding to different types of weather health indexes according to the upper and lower boundaries;
When the current health index of the weather health index is detected to be in the current reasonable fluctuation range, judging that the current health index is normal;
When the current health index of the weather health index is detected not to be in the current reasonable fluctuation range, judging that the current health index is abnormal, and identifying abnormal nodes corresponding to abnormal data points in the current health index.
Those skilled in the art will appreciate that all or part of the steps in implementing the methods of the embodiments described above may be implemented by a program stored in a storage medium, including instructions for causing a device (which may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the application; and the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The weather health index anomaly monitoring method is characterized by comprising the following steps of:
Acquiring operation state data of a weather service system, and analyzing the operation state data to acquire weather health indexes;
Acquiring a time sequence of the weather health index, and calculating upper and lower boundaries corresponding to the time sequence by using a 3-sigma algorithm;
Judging whether the weather health index is normal or not according to the upper and lower boundaries, and identifying abnormal nodes in the weather service system, wherein the abnormal nodes are abnormal in the weather health index.
2. The method for monitoring the anomaly of the weather health indicator according to claim 1, wherein the acquiring the operation state data of the weather service system, analyzing the operation state data, and acquiring the weather health indicator comprises:
Acquiring operation state data of a weather service system from the acquired log and the comprehensive monitoring information of the weather service;
and analyzing the running state data to obtain weather health indexes reflecting the business capacity and performance state of the running state data.
3. The method of claim 2, wherein analyzing the operational state data to obtain a weather health indicator reflecting business capabilities and performance states of the operational state data comprises:
Analyzing the running state data according to basic attributes to obtain classification names, service time, service link flow identifiers, service system names, health index names, maximum service bearing capacity, current service volume, service system performance threshold values, service system performance running values and index statistics periods which reflect the service capacity and performance states of the running state data;
And taking the classification name, the service time, the service link flow identifier, the service system name, the health index name, the maximum service bearing capacity, the current service volume, the service system performance threshold, the service system performance running value and the index statistical period as weather health indexes.
4. The method for monitoring the anomaly of the weather health indicator according to claim 1, wherein the step of obtaining the time series of the weather health indicator and calculating the upper and lower boundaries corresponding to the time series by using a 3-sigma algorithm comprises:
when the stable operation of the weather service system is detected, acquiring the sequence fluctuation of the weather health index, and acquiring a time sequence according to the sequence fluctuation and time;
Acquiring the starting time and the ending time of the time sequence, and acquiring a time sequence value from the starting time to the ending time;
Obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value;
And utilizing a 3-sigma algorithm to obtain upper and lower boundaries corresponding to the time sequence average value and the time sequence standard deviation.
5. The method for monitoring the anomaly of the weather health indicator according to claim 4, wherein the obtaining the time series average value and the time series standard deviation from the time series value comprises:
obtaining a time sequence average value and a time sequence standard deviation according to the time sequence numerical value through the following formula:
Wherein, Is the time series average value,/>Is the total sample number of weather health index,/>Is the actual running value of the weather health index of the current time/timeIs the standard deviation of time series,/>Each of the operational data is for a weather health indicator.
6. The method for monitoring the anomaly of the weather health indicator according to claim 4, wherein the using the 3-sigma algorithm comprises:
and acquiring corresponding upper and lower boundaries by using a 3-sigma algorithm according to the time sequence average value and the time sequence standard deviation through the following formula:
Wherein, As an upper boundary,/>Is the lower boundary,/>Is the time series average value,/>Is the standard deviation of time series,/>3.
7. The method for monitoring the anomaly of the weather health indicator according to claim 1, wherein the determining whether the weather health indicator is normal according to the upper and lower boundaries and identifying the anomaly node of the anomaly of the weather health indicator in the weather service system comprises:
determining reasonable fluctuation ranges corresponding to different types of weather health indexes according to the upper and lower boundaries;
When the current health index of the weather health index is detected to be in the current reasonable fluctuation range, judging that the current health index is normal;
When the current health index of the weather health index is detected not to be in the current reasonable fluctuation range, judging that the current health index is abnormal, and identifying abnormal nodes corresponding to abnormal data points in the current health index.
8. The utility model provides a weather health index anomaly monitoring device which characterized in that, weather health index anomaly monitoring device includes:
the system comprises an index acquisition module, a weather service system and a weather service system, wherein the index acquisition module is used for acquiring the running state data of the weather service system, analyzing the running state data and acquiring weather health indexes;
the boundary determining module is used for acquiring a time sequence of the weather health index and calculating an upper boundary and a lower boundary corresponding to the time sequence by using a 3-sigma algorithm;
and the abnormal identification module is used for judging whether the weather health index is normal or not according to the upper and lower boundaries and identifying abnormal nodes of the weather health index abnormality in the weather service system.
9. A weather health indicator anomaly monitoring device, characterized in that the weather health indicator anomaly monitoring device comprises: a memory, a processor and a weather-health-index anomaly monitoring program stored on the memory and executable on the processor, the weather-health-index anomaly monitoring program configured to implement the steps of the weather-health-index anomaly monitoring method of any one of claims 1 to 7.
10. A storage medium having stored thereon a weather-health-index anomaly monitoring program which, when executed by a processor, implements the steps of the weather-health-index anomaly monitoring method of any one of claims 1 to 7.
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