CN104778111A - Alarm method and alarm device - Google Patents
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Abstract
The invention discloses an alarm method and an alarm device, and belongs to the technical field of computers. The method comprises the steps of acquiring a parameter value of a target detection parameter according to a preset cycle, determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value when the acquired parameter value exceeds a preset first value range, and performing alarm if parameter values acquired according to a plurality of continuous cycles exceed the preset first value range and the sum of abnormal values corresponding to the parameter values acquired according to the continuous cycles exceeds a preset second value range. With the adoption of the method and the device, the alarm accuracy can be improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for alarming.
Background
With the rapid development of computer technology and network technology, the internet becomes a very important part in the work and life of people, and various network services are rapidly popularized and developed. In the network side server of each network service, the storage capacity and the processing capacity of data are very large, and correspondingly, the requirement on the stability is also very high, so a certain monitoring and alarming mechanism can be arranged in the server to remind technicians to perform corresponding processing, and the stability of the server is ensured.
The following mechanisms for triggering an alarm are generally used in the prior art: setting a certain threshold (upper limit or lower limit) corresponding to a certain monitoring parameter, acquiring the parameter value of the monitoring parameter according to a certain period, recording one-time abnormity if the acquired parameter value exceeds the set threshold, and triggering alarm if the continuous abnormity reaches the preset continuous times (period number).
In the process of implementing the invention, the inventor finds that the prior art has at least the following problems:
in the mechanism for triggering alarm in the prior art, if the continuous times are set to be too long, rapid alarm cannot be performed on sudden serious faults, and alarm omission occurs, and if the continuous times are set to be too short, false alarm can be performed on non-continuous small abnormal phenomena which do not need alarm, so that the accuracy of alarm by the alarm mechanism is poor.
Disclosure of Invention
In order to solve the problems in the prior art, embodiments of the present invention provide a method and an apparatus for performing an alarm, so as to improve the accuracy of the alarm. The technical scheme is as follows:
in one aspect, a method of alarming is provided, the method comprising:
acquiring parameter values of target detection parameters according to a preset period;
when the acquired parameter value exceeds a preset first numerical value range, determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value;
and if the parameter values acquired in a plurality of continuous periods all exceed the preset first numerical range and the sum of abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range, alarming.
In another aspect, there is provided an apparatus for alarming, the apparatus including:
the acquisition module is used for acquiring parameter values of target detection parameters according to a preset period;
the determining module is used for determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value when the acquired parameter value exceeds a preset first numerical value range;
and the alarm module is used for alarming if the parameter values acquired in the plurality of continuous periods exceed the preset first numerical range and the sum of abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, the parameter values of the target detection parameters are acquired according to the preset period, when the acquired parameter values exceed the preset first numerical range, the abnormal values corresponding to the acquired parameter values are determined according to the acquired parameter values, if the parameter values acquired in a plurality of continuous periods all exceed the preset first numerical range and the sum of the abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range, the alarm is performed moderately, so that the accumulated value of the abnormal values cannot exceed the second numerical range due to the non-continuous small abnormal phenomenon and the false alarm cannot be performed, meanwhile, the accumulated value of the abnormal values can quickly exceed the second numerical range due to sudden serious faults, the alarm is performed, the false alarm cannot occur, and the alarm accuracy can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for alarming according to an embodiment of the present invention;
2a, 2b, 2c, 2d are schematic diagrams of algorithms provided by embodiments of the present invention;
FIG. 3 is a schematic structural diagram of an alarm device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Example one
An embodiment of the present invention provides a method for alarming, as shown in fig. 1, a processing flow of the method may include the following steps:
step 101, obtaining parameter values of target detection parameters according to a preset period.
And 102, when the acquired parameter value exceeds a preset first numerical value range, determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value.
And 103, if the parameter values obtained in the multiple continuous periods all exceed a preset first numerical range, and the sum of abnormal values corresponding to the parameter values obtained in the multiple continuous periods exceeds a preset second numerical range, alarming.
In the embodiment of the invention, the parameter values of the target detection parameters are acquired according to the preset period, when the acquired parameter values exceed the preset first numerical range, the abnormal values corresponding to the acquired parameter values are determined according to the acquired parameter values, if the parameter values acquired in a plurality of continuous periods all exceed the preset first numerical range and the sum of the abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range, the alarm is performed moderately, so that the accumulated value of the abnormal values cannot exceed the second numerical range due to the non-continuous small abnormal phenomenon and the false alarm cannot be performed, meanwhile, the accumulated value of the abnormal values can quickly exceed the second numerical range due to sudden serious faults, the alarm is performed, the false alarm cannot occur, and the alarm accuracy can be improved.
Example two
The embodiment of the invention provides an alarm method, and an execution main body of the method can be any electronic equipment with a processing function, and is preferably a server of network application. The embodiment of the present invention takes a server whose execution subject is a network application as an example to perform the detailed description of the scheme, and the situations of other devices are similar to the above, and will not be described in detail in this embodiment.
The process flow shown in fig. 1 will be described in detail below with reference to specific processing modes, which may be as follows:
step 101, obtaining parameter values of target detection parameters according to a preset period.
The target detection parameter is a parameter that needs to perform state monitoring, and may be any preset parameter, such as a service request amount, a success rate for processing a service request, an timeout rate for processing a service request, and the like. Alternatively, the target detection parameter may be an increased percentage value or a decreased percentage value of the parameter value at the current time point of the parameter relative to the historical average parameter value, for example, the target detection parameter is a request quantity fluctuation rising ratio reflecting an increased percentage value of the parameter value of the request quantity parameter at the current time point relative to the historical average parameter value. The historical average parameter value is an average value of parameter values of a certain parameter detected multiple times in the historical detection process at one moment, for example, the request quantity detected at 12 points in the last three days is 10, 20 and 30 respectively, and then the historical average parameter value of the parameter at 12 points in the request quantity is 20. The preset period may be a time period for acquiring a parameter value of a certain parameter, that is, a period for performing parameter detection, and may be set arbitrarily according to actual requirements, for example, 5 minutes, 10 minutes, and the like.
In implementation, the server may perform periodic detection on one target detection parameter, or may perform periodic detection on different target detection parameters according to respective periods, and for each target detection parameter, alarm determination may be performed according to the processing flow of the embodiment of the present invention.
And 102, when the acquired parameter value exceeds a preset first numerical value range, determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value.
The preset first value range may be a normal value range set by the target detection parameter, and if the value range is exceeded, the parameter value of the target detection parameter is abnormal, and the first value range may be determined by a preset upper limit (or lower limit) and a natural value boundary of the parameter value, for example, for the timeout rate, 10% of the upper limit may be preset, and 0-10% of the first value range corresponding to the timeout rate may be determined together with the natural value boundary 0. The abnormal value may be a value reflecting the degree of abnormality of the parameter value of the target detection parameter, and the abnormal value may be the parameter value itself or a value calculated from the parameter value through a predetermined operation.
In implementation, the obtained parameter value exceeds the preset first value range, for the parameter limiting the upper limit value (for example, the timeout rate, etc.), the obtained parameter value may be greater than the preset first upper limit value, and for the parameter limiting the lower limit value (for example, the success rate, etc.), the obtained parameter value may be smaller than the preset first lower limit value.
Specifically, the processing of determining the abnormal value corresponding to the acquired parameter value is performed according to the acquired parameter value, and based on different actual requirements, there may be a plurality of different processing manners, and several feasible processing manners are given below:
in the first mode, the acquired parameter value is determined as the corresponding abnormal value.
For the parameter with the limited upper limit value (if the parameter value exceeds the upper limit value, an abnormality occurs), when the parameter value is larger than the first upper limit value, the parameter value itself can be used for reflecting the abnormality degree of the parameter value, so the parameter value can be used as a corresponding abnormal value.
And determining the difference value between the acquired parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the acquired parameter value.
The difference may have positive and negative values or may be an absolute value.
For the parameter limiting the upper limit value or the parameter limiting the lower limit value (if the parameter value exceeds the lower limit value, an abnormality occurs), when the parameter value exceeds the first value range, the difference between the parameter value and the historical average parameter value at the current time (for example, the historical average parameter value of 12 points and 10 points) can be used for reflecting the abnormality degree of the parameter value, so that the difference can be used as the corresponding abnormal value.
And determining the difference value between the acquired parameter value and a preset reference value as an abnormal value corresponding to the acquired parameter value.
The difference may have positive and negative values or may be an absolute value. The reference value may be a value used as a reference for determining the degree of abnormality of the parameter value, may be arbitrarily set according to actual needs, is generally set to a value within the first numerical range, and may be set to 0.
For the parameter limiting the upper limit value or the parameter limiting the lower limit value, when the parameter value exceeds the first value range, the difference between the parameter value and the preset reference value can be used to reflect the abnormal degree of the parameter value, so that the difference can be used as the corresponding abnormal value.
And 103, if the parameter values obtained in the multiple continuous periods all exceed a preset first numerical range, and the sum of abnormal values corresponding to the parameter values obtained in the multiple continuous periods exceeds a preset second numerical range, alarming.
In implementation, when the parameter value exceeds the first value range, that is, the parameter value is abnormal, when multiple continuous periods are abnormal, the abnormal values of the abnormal periods can be accumulated, if the accumulated value is too large, the fault is serious, an alarm can be given, a second value range can be set to determine whether the accumulated value is too large, and the second value range can be 0 to a preset second upper limit value.
Specifically, for different target detection parameters, the second value range may have a plurality of different setting manners, and several of them are described below:
in the first case, the above-mentioned first range of values exceeding the preset first upper limit value may be larger than the preset first upper limit value for the parameter (e.g. timeout rate) limiting the upper limit value.
Accordingly, the exceeding of the preset second value range may be greater than a preset second upper limit, where the second upper limit is a product of the width of the first value range and the preset number of cycles, or a product of a difference between the first upper limit and a preset reference value and the preset number of cycles.
The width of the first numerical range may be a difference between two boundary values of the first numerical range, for example, for the timeout rate parameter, the preset upper limit value is 10%, the natural value boundary is 0, and then, the corresponding width of the first numerical range is 10%. The preset number of cycles may be used to adjust the second upper limit value to adjust the sensitivity of the alarm, and may be arbitrarily set according to actual requirements, such as 5, 6, 7, and the like. The reference value here may be the same as or different from the reference value in the third embodiment of step 102, and is preferably smaller than the first upper limit value. The algorithm based on the second upper limit value may be referred to as a threshold area, and accordingly, the sum of the abnormal values may be referred to as an abnormal area.
For example, for the time-out rate parameter, the first upper limit value is 10%, the preset cycle number is 6, and the second upper limit value may be 60% of the product of 10% and 6, or the reference value may be set to be 2%, and the second upper limit value may be 48% of the product of (10% -2%) and 6.
In the second case, for the parameter (e.g. success rate) limiting the lower limit value, the above-mentioned range exceeding the preset first value may be smaller than the preset first lower limit value.
Accordingly, the exceeding of the preset second value range may be greater than a preset second upper limit, where the second upper limit is a product of a difference between a preset reference value and the first lower limit and a preset number of cycles, or a sum of differences between a historical average parameter value at a time corresponding to each of the plurality of consecutive cycles (cycles in which an abnormality occurs) and the first lower limit in a plurality of preset cycles starting from the first cycle of the plurality of consecutive cycles.
The description of the reference value and the preset number of cycles can be referred to as the first condition, and preferably, the reference value is greater than the first lower limit value.
For example, for the success rate parameter, the first lower limit value is 80%, the preset cycle number is 6, the reference value is set to be 95%, the second upper limit value may be 90% of a product of (95% -80%) and 6, or, if the current time is 12 points and 10 minutes, the current cycle is a third continuous abnormal cycle, and the cycle duration is 5 minutes, differences between the historical average parameter values of 12 points and 5 minutes, 12 points and 10 minutes, 12 points and 15 minutes, 12 points and 20 minutes, 12 points and 25 minutes and the first lower limit value may be obtained, assuming that the differences are respectively (99% -80%), (98% -80%), (97% -80%), (96% -80%), (97% -80%), (98% -80%), and then the sum of the differences is calculated to be 105%, which is the second upper limit value.
In addition, in the embodiment of the present invention, it is preferable that if the abnormal value determined in step 102 is out of the preset second value range, an alarm is given.
In practice, the second value range may be 0 to a preset second upper limit value, and the abnormal value exceeding the preset second value range may be larger than the preset second upper limit value. Based on this process, if the degree of abnormality of the parameter value detected at a single time is excessively large, an alarm may be triggered.
The following is a processing procedure of the method for alarming provided by the embodiment of the invention in some practical application scenarios.
Scenario one, the process may be as follows:
step one, acquiring parameter values of target detection parameters according to a preset period.
In this embodiment, as shown in fig. 2a, the target detection parameter may be a timeout rate for processing the service request, and the period may be 5 minutes.
And step two, when the acquired parameter value is larger than a preset first upper limit value, determining the acquired parameter value as a corresponding abnormal value.
In this embodiment, the first upper limit may be 10%. As shown in fig. 2a, if the values of the parameters obtained at 12 o 'clock, 5 o' clock and 12 o 'clock, 10 o' clock are 11%, 15% and 50%, respectively, the corresponding abnormal values are 11%, 15% and 50%, respectively.
And step three, if the parameter values obtained in a plurality of continuous periods are all larger than a preset first upper limit value, and the sum of abnormal values corresponding to the parameter values obtained in the plurality of continuous periods is larger than a preset second upper limit value, alarming.
In this embodiment, the predetermined number of cycles may be 6, and the second upper limit may be 60% of the product of 10% of the first upper limit and 6 of the predetermined number of cycles (indicated by the area of the threshold in fig. 2 a). It can be seen that at 12 o 'clock and 10 o' clock, the sum of the abnormal values at 12 o 'clock, 5 o' clock and 12 o 'clock and 10 o' clock is calculated to be 76% (indicated by abnormal area in fig. 2 a) and greater than the second upper limit value of 60%, and then an alarm is triggered.
Scenario two, the process may be as follows:
step one, acquiring parameter values of target detection parameters according to a preset period.
In this embodiment, as shown in fig. 2b, the target detection parameter may be a success rate of processing the service request, and the period may be 5 minutes.
And step two, when the acquired parameter value is smaller than a preset first lower limit value, determining the difference value between the acquired parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the acquired parameter value.
In this embodiment, the first lower limit value may be 80%. As shown in fig. 2b, the parameter values obtained at 12 o ' clock 5, 12 o ' clock 10 and 12 o ' clock 15 are respectively 50%, 30% and 50%, and for convenience of description, the historical average parameter values at these three times are all 100%, and the corresponding abnormal values are respectively 50%, 70% and 50%.
And step three, if the parameter values obtained in a plurality of continuous periods are all smaller than a preset first lower limit value, and the sum of abnormal values corresponding to the parameter values obtained in the plurality of continuous periods is larger than a preset second upper limit value, alarming.
In this embodiment, the preset number of cycles may be 6, and the difference between the historical average parameter value of 12 points 5 minutes, 12 points 10 minutes, 12 points 15 minutes, 12 points 20 minutes, 12 points 25 minutes, and 12 points 30 minutes and the first lower limit value may be respectively (100% -80%), and then the sum of the differences is calculated to be 120%, which is the second upper limit value (indicated by the threshold area in fig. 2 b). It can be seen that at 12 o 'clock and 15 o' clock, the sum of the abnormal values at 12 o 'clock and 5 o' clock, 12 o 'clock and 10 o' clock and 12 o 'clock and 15 o' clock is 170% (indicated by abnormal area in fig. 2 b) and is greater than the second upper limit value of 120%, and then an alarm is triggered.
In scenario three, the processing procedure may be as follows:
step one, acquiring parameter values of target detection parameters according to a preset period.
In this embodiment, as shown in fig. 2c, the target detection parameter may be a request amount fluctuation rising ratio, which reflects a percentage value of the parameter value of the request amount parameter at the current time point increased relative to the historical average parameter value, and the period may be 5 minutes. Assuming that the request amounts (request amounts in the past 5 minutes) at 12 o ' clock, 12 o ' clock 5 o ' clock, 12 o ' clock 10 o ' clock, 12 o ' clock 15 o ' clock, 12 o ' clock 20 o ' clock and 12 o ' clock 25 o ' clock are 25, 22, 30, 27, 21 and 23 respectively, the historical average parameter values of the request amount parameters at these times are 18, 19, 20, 25, 20 and 21 respectively, and further, the parameter values of the target detection parameters (request amount fluctuation rising rate) at these times can be calculated to be 39%, 16%, 50%, 8%, 5% and 10% respectively.
And step two, when the acquired parameter value is larger than a preset first upper limit value, determining the acquired parameter value as a corresponding abnormal value.
In this embodiment, the first upper limit may be 10%. As shown in fig. 2c, if the values of the parameters obtained at 12 o 'clock, 5 o' clock and 12 o 'clock, 10 o' clock are 39%, 16% and 50%, respectively, the corresponding abnormal values are 39%, 16% and 50%, respectively.
And step three, if the parameter values obtained in a plurality of continuous periods are all larger than a preset first upper limit value, and the sum of abnormal values corresponding to the parameter values obtained in the plurality of continuous periods is larger than a preset second upper limit value, alarming.
In this embodiment, the predetermined number of cycles may be 6, and the second upper limit may be 60% of the product of 10% of the first upper limit and 6 of the predetermined number of cycles (shown as the area of the threshold in fig. 2 c). It can be seen that at 12 o 'clock and 10 o' clock, the sum of the abnormal values at 12 o 'clock, 5 o' clock and 12 o 'clock and 10 o' clock is calculated to be 105% (indicated by abnormal area in fig. 2 c) and greater than the second upper limit value of 60%, and then an alarm is triggered.
In scenario four, the processing procedure may be as follows:
step one, acquiring parameter values of target detection parameters according to a preset period.
In this embodiment, as shown in fig. 2d, the target detection parameter may be a ratio of a decrease in success rate fluctuation, which reflects a percentage value of a decrease of a parameter value of the success rate parameter at a current time point relative to a historical average parameter value, and a period may be 5 minutes. Assuming that the success rates (success rates in the past 5 minutes) at 12 o ' clock, 12 o ' clock 5 o ' clock, 12 o ' clock 10 o ' clock, 12 o ' clock 15 o ' clock, 12 o ' clock 20 o ' clock and 12 o ' clock 25 o ' clock are 90%, 50%, 30%, 50%, 20% and 10% respectively, the historical average parameter values of the success rate parameters at these times are all 100%, and further, the parameter values of the target detection parameters (success rate fluctuation reduction ratios) at these times can be calculated to be 10%, 50%, 70%, 50%, 80% and 90% respectively.
And step two, when the acquired parameter value is larger than a preset first upper limit value, determining the acquired parameter value as a corresponding abnormal value.
In this embodiment, the first upper limit may be 10%. As shown in fig. 2d, the values of the parameters obtained at 12 o 'clock, 5 o' clock, and 12 o 'clock, 10 o' clock are 50% and 70%, respectively, and then the corresponding abnormal values are 50% and 70%, respectively.
And step three, if the parameter values obtained in a plurality of continuous periods are all larger than a preset first upper limit value, and the sum of abnormal values corresponding to the parameter values obtained in the plurality of continuous periods is larger than a preset second upper limit value, alarming.
In this embodiment, the predetermined number of cycles may be 6, and the second upper limit may be 60% of the product of 10% of the first upper limit and 6 of the predetermined number of cycles (shown as the area of the threshold in fig. 2 d). It can be seen that at 12 o 'clock and 10 o' clock, the sum of the abnormal values at 12 o 'clock and 5 o' clock and 12 o 'clock and 10 o' clock is calculated to be 120% (indicated by abnormal area in fig. 2 d), and is greater than the second upper limit value by 60%, and then an alarm is triggered.
In the embodiment of the invention, the parameter values of the target detection parameters are acquired according to the preset period, when the acquired parameter values exceed the preset first numerical range, the abnormal values corresponding to the acquired parameter values are determined according to the acquired parameter values, if the parameter values acquired in a plurality of continuous periods all exceed the preset first numerical range and the sum of the abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range, the alarm is performed moderately, so that the accumulated value of the abnormal values cannot exceed the second numerical range due to the non-continuous small abnormal phenomenon and the false alarm cannot be performed, meanwhile, the accumulated value of the abnormal values can quickly exceed the second numerical range due to sudden serious faults, the alarm is performed, the false alarm cannot occur, and the alarm accuracy can be improved.
EXAMPLE III
Based on the same technical concept, an embodiment of the present invention further provides an alarm device, as shown in fig. 3, the alarm device includes:
an obtaining module 310, configured to obtain a parameter value of a target detection parameter according to a preset period;
the determining module 320 is configured to determine an abnormal value corresponding to the acquired parameter value according to the acquired parameter value when the acquired parameter value exceeds a preset first numerical value range;
the alarm module 330 is configured to alarm if the parameter values obtained in the multiple consecutive periods all exceed the preset first value range, and the sum of abnormal values corresponding to the parameter values obtained in the multiple consecutive periods exceeds the preset second value range.
Preferably, the determining module 320 is configured to:
determining the acquired parameter values as corresponding abnormal values; or,
determining a difference value between the obtained parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the obtained parameter value; or,
and determining the difference value between the acquired parameter value and a preset reference value as an abnormal value corresponding to the acquired parameter value.
Preferably, the exceeding of the preset first numerical range includes: is greater than a preset first upper limit value;
the exceeding of the preset second numerical range includes: and the second upper limit value is larger than a preset second upper limit value, wherein the second upper limit value is the product of the width value of the first numerical range and the preset cycle number, or the product of the difference value between the first upper limit value and a preset reference value and the preset cycle number.
Preferably, the exceeding of the preset first numerical range includes: is smaller than a preset first lower limit value;
the exceeding of the preset second numerical range includes: the second upper limit value is the product of the difference between the preset reference value and the first lower limit value and the preset number of cycles, or the sum of the difference between the historical average parameter value of the time corresponding to each cycle and the first lower limit value in the preset number of cycles beginning with the first cycle in the plurality of continuous cycles.
Preferably, the determining module 320 is configured to: determining the acquired parameter values as corresponding abnormal values;
the exceeding of the preset first numerical range comprises: is greater than a preset first upper limit value;
the exceeding of the preset second numerical range includes: and the second upper limit value is larger than a preset second upper limit value, wherein the second upper limit value is the product of the width value of the first numerical range and a preset cycle number.
Preferably, the determining module 320 is configured to: determining a difference value between the obtained parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the obtained parameter value;
the exceeding of the preset first numerical range comprises: is smaller than a preset first lower limit value;
the exceeding of the preset second numerical range includes: and the second upper limit value is the sum of the difference values between the historical average parameter value and the first lower limit value at the corresponding moment of each period in a plurality of preset periods starting from the first period in the plurality of continuous periods.
Preferably, the alarm module 330 is further configured to:
and if the determined abnormal value exceeds the preset second value range, alarming.
In the embodiment of the invention, the parameter values of the target detection parameters are acquired according to the preset period, when the acquired parameter values exceed the preset first numerical range, the abnormal values corresponding to the acquired parameter values are determined according to the acquired parameter values, if the parameter values acquired in a plurality of continuous periods all exceed the preset first numerical range and the sum of the abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range, the alarm is performed moderately, so that the accumulated value of the abnormal values cannot exceed the second numerical range due to the non-continuous small abnormal phenomenon and the false alarm cannot be performed, meanwhile, the accumulated value of the abnormal values can quickly exceed the second numerical range due to sudden serious faults, the alarm is performed, the false alarm cannot occur, and the alarm accuracy can be improved.
Example four
Fig. 4 is a schematic structural diagram of a server according to an embodiment of the present invention. The server 1900 may vary widely by configuration or performance and may include one or more Central Processing Units (CPUs) 1922 (e.g., one or more processors) and memory 1932, one or more storage media 1930 (e.g., one or more mass storage devices) storing applications 1942 or data 1944. Memory 1932 and storage medium 1930 can be, among other things, transient or persistent storage. The program stored in the storage medium 1930 may include one or more modules (not shown), each of which may include a series of instructions operating on a server. Still further, a central processor 1922 may be provided in communication with the storage medium 1930 to execute a series of instruction operations in the storage medium 1930 on the server 1900.
The server 1900 may also include one or more power supplies 1926, one or more wired or wireless network interfaces 1950, one or more input-output interfaces 1958, one or more keyboards 1956, and/or one or more operating systems 1941, such as Windows Server, Mac OSXTM, UnixTM, LinuxTM, FreeBSDTM, etc.
Server 1900 may include memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors include instructions for:
acquiring parameter values of target detection parameters according to a preset period;
when the acquired parameter value exceeds a preset first numerical value range, determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value;
and if the parameter values acquired in a plurality of continuous periods all exceed the preset first numerical range and the sum of abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range, alarming.
Preferably, the determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value includes:
determining the acquired parameter values as corresponding abnormal values; or,
determining a difference value between the obtained parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the obtained parameter value; or,
and determining the difference value between the acquired parameter value and a preset reference value as an abnormal value corresponding to the acquired parameter value.
Preferably, the exceeding of the preset first numerical range includes: is greater than a preset first upper limit value;
the exceeding of the preset second numerical range includes: and the second upper limit value is larger than a preset second upper limit value, wherein the second upper limit value is the product of the width value of the first numerical range and the preset cycle number, or the product of the difference value between the first upper limit value and a preset reference value and the preset cycle number.
Preferably, the exceeding of the preset first numerical range includes: is smaller than a preset first lower limit value;
the exceeding of the preset second numerical range includes: the second upper limit value is the product of the difference between the preset reference value and the first lower limit value and the preset number of cycles, or the sum of the difference between the historical average parameter value of the time corresponding to each cycle and the first lower limit value in the preset number of cycles beginning with the first cycle in the plurality of continuous cycles.
Preferably, the exceeding of the preset first numerical range includes: is greater than a preset first upper limit value;
determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value, wherein the determining comprises the following steps: determining the acquired parameter values as corresponding abnormal values;
the exceeding of the preset second numerical range includes: and the second upper limit value is larger than a preset second upper limit value, wherein the second upper limit value is the product of the width value of the first numerical range and a preset cycle number.
Preferably, the exceeding of the preset first numerical range includes: is smaller than a preset first lower limit value;
determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value, wherein the determining comprises the following steps: determining a difference value between the obtained parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the obtained parameter value;
the exceeding of the preset second numerical range includes: and the second upper limit value is the sum of the difference values between the historical average parameter value and the first lower limit value at the corresponding moment of each period in a plurality of preset periods starting from the first period in the plurality of continuous periods.
Preferably, the method further comprises the following steps:
and if the determined abnormal value exceeds the preset second value range, alarming.
In the embodiment of the invention, the parameter values of the target detection parameters are acquired according to the preset period, when the acquired parameter values exceed the preset first numerical range, the abnormal values corresponding to the acquired parameter values are determined according to the acquired parameter values, if the parameter values acquired in a plurality of continuous periods all exceed the preset first numerical range and the sum of the abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range, the alarm is performed moderately, so that the accumulated value of the abnormal values cannot exceed the second numerical range due to the non-continuous small abnormal phenomenon and the false alarm cannot be performed, meanwhile, the accumulated value of the abnormal values can quickly exceed the second numerical range due to sudden serious faults, the alarm is performed, the false alarm cannot occur, and the alarm accuracy can be improved.
It should be noted that: in the alarm device provided in the above embodiment, when an alarm is performed, only the division of the functional modules is illustrated, and in practical applications, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the alarm device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the alarm device and the alarm method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments and are not described herein again.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (14)
1. A method of alarming, the method comprising:
acquiring parameter values of target detection parameters according to a preset period;
when the acquired parameter value exceeds a preset first numerical value range, determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value;
and if the parameter values acquired in a plurality of continuous periods all exceed the preset first numerical range and the sum of abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range, alarming.
2. The method of claim 1, wherein determining the corresponding outlier according to the obtained parameter value comprises:
determining the acquired parameter values as corresponding abnormal values; or,
determining a difference value between the obtained parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the obtained parameter value; or,
and determining the difference value between the acquired parameter value and a preset reference value as an abnormal value corresponding to the acquired parameter value.
3. The method according to claim 1 or 2, wherein said exceeding of the preset first range of values comprises: is greater than a preset first upper limit value;
the exceeding of the preset second numerical range includes: and the second upper limit value is larger than a preset second upper limit value, wherein the second upper limit value is the product of the width value of the first numerical range and the preset cycle number, or the product of the difference value between the first upper limit value and a preset reference value and the preset cycle number.
4. The method according to claim 1 or 2, wherein said exceeding of the preset first range of values comprises: is smaller than a preset first lower limit value;
the exceeding of the preset second numerical range includes: the second upper limit value is the product of the difference between the preset reference value and the first lower limit value and the preset number of cycles, or the sum of the difference between the historical average parameter value of the time corresponding to each cycle and the first lower limit value in the preset number of cycles beginning with the first cycle in the plurality of continuous cycles.
5. The method of claim 1, wherein said exceeding of the preset first range of values comprises: is greater than a preset first upper limit value;
determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value, wherein the determining comprises the following steps: determining the acquired parameter values as corresponding abnormal values;
the exceeding of the preset second numerical range includes: and the second upper limit value is larger than a preset second upper limit value, wherein the second upper limit value is the product of the width value of the first numerical range and a preset cycle number.
6. The method of claim 1, wherein said exceeding of the preset first range of values comprises: is smaller than a preset first lower limit value;
determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value, wherein the determining comprises the following steps: determining a difference value between the obtained parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the obtained parameter value;
the exceeding of the preset second numerical range includes: and the second upper limit value is the sum of the difference values between the historical average parameter value and the first lower limit value at the corresponding moment of each period in a plurality of preset periods starting from the first period in the plurality of continuous periods.
7. The method of claim 1, further comprising:
and if the determined abnormal value exceeds the preset second value range, alarming.
8. An apparatus for performing an alarm, the apparatus comprising:
the acquisition module is used for acquiring parameter values of target detection parameters according to a preset period;
the determining module is used for determining an abnormal value corresponding to the acquired parameter value according to the acquired parameter value when the acquired parameter value exceeds a preset first numerical value range;
and the alarm module is used for alarming if the parameter values acquired in the plurality of continuous periods exceed the preset first numerical range and the sum of abnormal values corresponding to the parameter values acquired in the plurality of continuous periods exceeds the preset second numerical range.
9. The apparatus of claim 8, wherein the determining module is configured to:
determining the acquired parameter values as corresponding abnormal values; or,
determining a difference value between the obtained parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the obtained parameter value; or,
and determining the difference value between the acquired parameter value and a preset reference value as an abnormal value corresponding to the acquired parameter value.
10. The apparatus according to claim 8 or 9, wherein said exceeding of the preset first range of values comprises: is greater than a preset first upper limit value;
the exceeding of the preset second numerical range includes: and the second upper limit value is larger than a preset second upper limit value, wherein the second upper limit value is the product of the width value of the first numerical range and the preset cycle number, or the product of the difference value between the first upper limit value and a preset reference value and the preset cycle number.
11. The apparatus according to claim 8 or 9, wherein said exceeding of the preset first range of values comprises: is smaller than a preset first lower limit value;
the exceeding of the preset second numerical range includes: the second upper limit value is the product of the difference between the preset reference value and the first lower limit value and the preset number of cycles, or the sum of the difference between the historical average parameter value of the time corresponding to each cycle and the first lower limit value in the preset number of cycles beginning with the first cycle in the plurality of continuous cycles.
12. The method of claim 8, wherein the determining module is configured to: determining the acquired parameter values as corresponding abnormal values;
the exceeding of the preset first numerical range comprises: is greater than a preset first upper limit value;
the exceeding of the preset second numerical range includes: and the second upper limit value is larger than a preset second upper limit value, wherein the second upper limit value is the product of the width value of the first numerical range and a preset cycle number.
13. The method of claim 8, wherein the determining module is configured to: determining a difference value between the obtained parameter value and the historical average parameter value at the current moment as an abnormal value corresponding to the obtained parameter value;
the exceeding of the preset first numerical range comprises: is smaller than a preset first lower limit value;
the exceeding of the preset second numerical range includes: and the second upper limit value is the sum of the difference values between the historical average parameter value and the first lower limit value at the corresponding moment of each period in a plurality of preset periods starting from the first period in the plurality of continuous periods.
14. The apparatus of claim 8, wherein the alarm module is further configured to:
and if the determined abnormal value exceeds the preset second value range, alarming.
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
RU2105323C1 (en) * | 1996-07-08 | 1998-02-20 | Государственный научный центр РФ Физико-энергетический институт | Radiation monitoring and its methods (options) |
CN101090334A (en) * | 2007-05-23 | 2007-12-19 | 西安交大捷普网络科技有限公司 | Method for solving mass alarm in NIDS |
CN101430817A (en) * | 2007-11-08 | 2009-05-13 | 深圳迈瑞生物医疗电子股份有限公司 | Alarm triggering method and device according to human body rhythmic physiological parameter |
CN102509174A (en) * | 2011-11-01 | 2012-06-20 | 冶金自动化研究设计院 | industrial process data-based method of alarm limit self-learning system based on |
-
2014
- 2014-01-14 CN CN201410015492.1A patent/CN104778111B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
RU2105323C1 (en) * | 1996-07-08 | 1998-02-20 | Государственный научный центр РФ Физико-энергетический институт | Radiation monitoring and its methods (options) |
CN101090334A (en) * | 2007-05-23 | 2007-12-19 | 西安交大捷普网络科技有限公司 | Method for solving mass alarm in NIDS |
CN101430817A (en) * | 2007-11-08 | 2009-05-13 | 深圳迈瑞生物医疗电子股份有限公司 | Alarm triggering method and device according to human body rhythmic physiological parameter |
CN102509174A (en) * | 2011-11-01 | 2012-06-20 | 冶金自动化研究设计院 | industrial process data-based method of alarm limit self-learning system based on |
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