CN109639500A - The Real-time Alarm generation method of internet of things oriented Application in Sensing - Google Patents
The Real-time Alarm generation method of internet of things oriented Application in Sensing Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0604—Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time
- H04L41/0609—Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time based on severity or priority
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
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Abstract
A kind of Real-time Alarm generation method of internet of things oriented Application in Sensing can be compared according to the multiple groups threshold value and raw sensory data that user specifies, to generate or eliminate in real time the alarm with a variety of severity levels.There are two types of Real-time Alarms, they be it is parallel, without any dependence.One kind is threshold alarm, introduces two threshold values of height respectively to every kind of alarm severity grade, when raw sensory data are higher than high threshold, generate the alarm of this severity level, when lower than Low threshold, then eliminates the alarm of this severity level.Another is trend alarm, in analytical calculation specific time length, the average gradient of a certain sensor reported data curve, when it is more than the threshold value of alarm severity grade, just the trend alarm of corresponding severity level is generated, specific time length, the threshold value of alarm severity grade are set by the user.
Description
Technical field
The invention belongs to the technical fields of Internet of Things, generate more particularly to the Real-time Alarm of internet of things oriented Application in Sensing
Method.
Background technique
In Internet of Things field of sensing technologies, front end sensors are during continuous reported data, how from a large amount of
In various data, quickly tell abnormal data in time, user then notified in the form of alarm, to cause user note that from
And take control measure early, to exclude possible security risk, this be very important and Internet of Things Application in Sensing very
Valuable place.
Summary of the invention
Technology of the invention solves the problems, such as: overcoming the deficiencies of the prior art and provide a kind of internet of things oriented Application in Sensing
Real-time Alarm generation method, can be compared according to the multiple groups threshold value and raw sensory data that user specifies, thus in real time
Generate or eliminate the alarm with a variety of severity levels.
The technical solution of the invention is as follows: the Real-time Alarm generation method of this internet of things oriented Application in Sensing is
Threshold alarm, to every kind of alarm severity grade introduce respectively height two threshold values, when raw sensory data more than high threshold
Gao Shi generates the alarm of this severity level, when lower than Low threshold, then eliminates the alarm of this severity level.
Threshold alarm of the invention, it is serious to every kind of alarm under conditions of continuous generation alarm is eliminated and alerted again at once
Property grade introduce respectively height two threshold values, when raw sensory data are higher than high threshold, generate alarm, more than Low threshold
When low, then alarm is eliminated, therefore can be according to the comparison for the multiple groups threshold value and raw sensory data that user specifies, so as to real-time
Generate or eliminate the alarm with a variety of severity levels.
A kind of Real-time Alarm generation method of internet of things oriented Application in Sensing is additionally provided, is alerted for trend, analysis meter
It calculates in specific time length, the average gradient of a certain sensor reported data curve, when it is more than the threshold of alarm severity grade
When value, the trend alarm of corresponding severity level is just generated, specific time length, the threshold value of alarm severity grade are by user
Setting.
By analyzing a certain monitored amount (raw sensory data) changed trend over time in real time,
When this trend is accumulated over certain threshold value, trend alarm can be generated, equally, the threshold value of trend alarm is also can be by user
Specified multiple groups threshold value can generate the trend alarm of a variety of severity levels in this way.
Detailed description of the invention
Fig. 1 shows the flow chart of a preferred embodiment of threshold alarm according to the present invention.
Fig. 2 shows the flow charts of a preferred embodiment of trend according to the present invention alarm.
Fig. 3 illustrates how to calculate the average gradient of sensing data curve.
Specific embodiment
The Real-time Alarm generation method of this internet of things oriented Application in Sensing, is threshold alarm, serious to every kind of alarm
Property grade introduce respectively height two threshold values, when raw sensory data are higher than high threshold, generate this severity level announcement
It is alert, when lower than Low threshold, then eliminate the alarm of this severity level.
Threshold alarm of the invention, it is serious to every kind of alarm under conditions of continuous generation alarm is eliminated and alerted again at once
Property grade introduce respectively height two threshold values, when raw sensory data are higher than high threshold, generate alarm, more than Low threshold
When low, then alarm is eliminated, therefore can be compared according to the multiple groups threshold value and raw sensory data that user specifies, thus
Generate or eliminate in real time the alarm with a variety of severity levels.
Preferably, the alarm includes: slight alarm, minor alarm, significant alarm, high severity alarm, and slight alarm has
Slight alarm high threshold and slight alarm Low threshold, minor alarm have minor alarm high threshold and minor alarm Low threshold, weight
It alerts with significant alarm high threshold and significant alarm Low threshold, high severity alarm is with high severity alarm high threshold and high severity alarm
Low threshold.
Preferably, as shown in Figure 1, the Real-time Alarm generation method of this internet of things oriented Application in Sensing includes following step
It is rapid:
(1) start；
(2) judge that raw sensory data are to hold whether between slight alarm high threshold and minor alarm high threshold
Row step (3), it is no to then follow the steps (4)；
(3) slight alarm is generated；
(4) raw sensory data are judged whether between minor alarm high threshold and significant alarm high threshold, are to hold
Row step (5), it is no to then follow the steps (7)；
(5) slight alarm is generated；
(6) minor alarm is generated；
(7) raw sensory data are judged whether between significant alarm high threshold and high severity alarm high threshold, are to hold
Row step (8), it is no to then follow the steps (11)；
(8) slight alarm is generated；
(9) minor alarm is generated；
(10) significant alarm is generated；
(11) judge whether raw sensory data are more than high severity alarm high threshold, are to then follow the steps (12), otherwise execute
Step (16)；
(12) slight alarm is generated；
(13) minor alarm is generated；
(14) significant alarm is generated；
(15) high severity alarm is generated；
(16) judge whether raw sensory data are lower than high severity alarm Low threshold, be to then follow the steps (17), otherwise execute
Step (18)；
(17) high severity alarm is eliminated；
(18) judge whether raw sensory data are lower than significant alarm Low threshold, be to then follow the steps (19), otherwise execute
Step (20)；
(19) significant alarm is eliminated；
(20) judge whether raw sensory data are lower than minor alarm Low threshold, be to then follow the steps (21), otherwise execute
Step (22)；
(21) minor alarm is eliminated；
(22) judge whether raw sensory data are lower than slight alarm Low threshold, be to then follow the steps (23), otherwise execute
Step (24)；
(23) slight alarm is eliminated；
(24) terminate.
Preferably, when the new alarm for having high menace level on a certain sensor generates, if not having on this sensor
When having the alarm of movable low menace level, then sensor generates all low menace level alarms thus；Have when on a certain sensor
When the alarm clearance of low menace level, if having the alarm of movable high menace level on this sensor, this sensor is removed
Generate all high menace level alarms.
A kind of Real-time Alarm generation method of internet of things oriented Application in Sensing is additionally provided, is alerted for trend, analysis meter
It calculates in specific time length, the average gradient of a certain sensor reported data curve, when it is more than the threshold of alarm severity grade
When value, the trend alarm of corresponding severity level is just generated, specific time length, the threshold value of alarm severity grade are by user
Setting.
By analyzing a certain monitored amount (raw sensory data) changed trend over time in real time,
When this trend is accumulated over certain threshold value, trend alarm can be generated, equally, the threshold value of trend alarm is also can be by user
Specified multiple groups threshold value can generate the trend alarm of a variety of severity levels in this way.
Preferably, the alarm includes: slight tendency alarm, general trend alarm, important trend alarm, serious trend announcement
Alert, slight tendency alarm has slight tendency alarm high threshold and slight tendency alerts Low threshold, and general trend alarm has one
As trend alarm high threshold and general trend alert Low threshold, important trend alarm has important trend alarm high threshold and important
Trend alerts Low threshold, and there is serious trend alarm high threshold and serious trend to alert Low threshold for serious trend alarm.
Preferably, as shown in figure 3, calculating average gradient average_gradient according to drag,
Assuming that a certain sensor:
A. n times data are acquired altogether in the time of a length of Duration in acquisition
B. the data and curves generated in the time of a length of Duration in acquisition are (C1, C2, C3 ..., Cn-1, Cn)
The average gradient of that data and curves (C1, C2, C3 ..., Cn-1, Cn) is calculated by following formula:
Average_gradient=area of curve (C1, C2, C3 ..., Cn-1, Cn)
*2/(n-1)
Wherein:
The area of curve (C1, C2, C3 ..., Cn-1, Cn) refers to by curve (C1, C2, C3 ..., Cn-1, Cn) and line segment
The area in the region that (C1, Hn) and line segment (Cn, Hn) surround
The area of curve (C1, C2, C3 ..., Cn-1, Cn) can be approximately equal under curve itself covering small triangle (C1,
C2, H2), (C2, C3, H3) ..., (Cn-1, Cn, Hn) and small rectangle (C2, V3, H3, H2) ..., (Cn-1, Vn, Hn, Hn-1)
The sum of area
The geometric meaning of curve (C1, C2, C3 ..., Cn-1, Cn) is the length of line segment (G, Hn).
Preferably, this internet of things oriented Application in Sensing Real-time Alarm generation method the following steps are included:
(1) start；
(2) judge whether average gradient alerts between high threshold and general trend alarm high threshold between slight tendency, be
It thens follow the steps (3), it is no to then follow the steps (4)；
(3) slight tendency alarm is generated；
(4) judge whether average gradient alerts between high threshold and important trend alarm high threshold between general trend, be
It thens follow the steps (5), it is no to then follow the steps (7)；
(5) slight tendency alarm is generated；
(6) general trend alarm is generated；
(7) judge raw sensory data whether between important trend alert high threshold and serious trend alarm high threshold it
Between, it is to then follow the steps (8), it is no to then follow the steps (11)；
(8) slight tendency alarm is generated；
(9) general trend alarm is generated；
(10) important trend alarm is generated；
(11) judge whether raw sensory data are more than serious trend alarm high threshold, are to then follow the steps (12), otherwise
It executes step (16)；
(12) slight tendency alarm is generated；
(13) general trend alarm is generated；
(14) important trend alarm is generated；
(15) serious trend alarm is generated；
(16) judge whether raw sensory data are lower than serious trend alarm Low threshold, be to then follow the steps (17), otherwise
It executes step (18)；
(17) serious trend alarm is eliminated；
(18) judge whether raw sensory data are lower than important trend alarm Low threshold, be to then follow the steps (19), otherwise
It executes step (20)；
(19) important trend alarm is eliminated；
(20) judge whether raw sensory data are lower than general trend alarm Low threshold, be to then follow the steps (21), otherwise
It executes step (22)；
(21) general trend alarm is eliminated；
(22) judge whether raw sensory data are lower than slight tendency alarm Low threshold, be to then follow the steps (23), otherwise
It executes step (24)；
(23) slight tendency alarm is eliminated；
(24) terminate.
There are two types of Real-time Alarms, they be it is parallel, without any dependence.
The above is only presently preferred embodiments of the present invention, is not intended to limit the present invention in any form, it is all according to
According to technical spirit any simple modification, equivalent change and modification to the above embodiments of the invention, still belong to the present invention
The protection scope of technical solution.
Claims (8)
1. the Real-time Alarm generation method of internet of things oriented Application in Sensing, it is characterised in that: it is threshold alarm, is alerted to every kind
Severity level introduces two threshold values of height respectively and generates this seriousness etc. when raw sensory data are higher than high threshold
Grade alarm, when lower than Low threshold, then eliminates the alarm of this severity level.
2. the Real-time Alarm generation method of internet of things oriented Application in Sensing according to claim 1, it is characterised in that: described
Alarm includes: slight alarm, minor alarm, significant alarm, high severity alarm, and slight alarm has slight alarm high threshold and slightly
Low threshold is alerted, minor alarm has minor alarm high threshold and minor alarm Low threshold, and significant alarm has significant alarm high
Threshold value and significant alarm Low threshold, high severity alarm have high severity alarm high threshold and high severity alarm Low threshold.
3. the Real-time Alarm generation method of internet of things oriented Application in Sensing according to claim 2, it is characterised in that: it is wrapped
Include following steps:
(1) start；
(2) judge that raw sensory data are to execute step whether between slight alarm high threshold and minor alarm high threshold
Suddenly (3), it is no to then follow the steps (4)；
(3) slight alarm is generated；
(4) raw sensory data are judged whether between minor alarm high threshold and significant alarm high threshold, are to execute step
Suddenly (5), it is no to then follow the steps (7)；
(5) slight alarm is generated；
(6) minor alarm is generated；
(7) raw sensory data are judged whether between significant alarm high threshold and high severity alarm high threshold, are to execute step
Suddenly (8), it is no to then follow the steps (11)；
(8) slight alarm is generated；
(9) minor alarm is generated；
(10) significant alarm is generated；
(11) judge whether raw sensory data are more than high severity alarm high threshold, are to then follow the steps (12), it is no to then follow the steps
(16)；
(12) slight alarm is generated；
(13) minor alarm is generated；
(14) significant alarm is generated；
(15) high severity alarm is generated；
(16) judge whether raw sensory data are lower than high severity alarm Low threshold, be to then follow the steps (17), it is no to then follow the steps
(18)；
(17) high severity alarm is eliminated；
(18) judge whether raw sensory data are lower than significant alarm Low threshold, be to then follow the steps (19), it is no to then follow the steps
(20)；
(19) significant alarm is eliminated；
(20) judge whether raw sensory data are lower than minor alarm Low threshold, be to then follow the steps (21), it is no to then follow the steps
(22)；
(21) minor alarm is eliminated；
(22) judge whether raw sensory data are lower than slight alarm Low threshold, be to then follow the steps (23), it is no to then follow the steps
(24)；
(23) slight alarm is eliminated；
(24) terminate.
4. the Real-time Alarm generation method of internet of things oriented Application in Sensing according to claim 3, it is characterised in that: when certain
When thering is the new alarm of high menace level to generate on one sensor, if there is no movable low menace level to accuse on this sensor
When alert, then sensor generates all low menace level alarms thus；When the alarm clearance for having low menace level on a certain sensor
When, if having the alarm of movable high menace level on this sensor, removes this sensor and generate all high menace levels announcements
It is alert.
5. the Real-time Alarm generation method of internet of things oriented Application in Sensing, it is characterised in that: it is trend alarm, and analytical calculation is special
It fixes time in length, the average gradient of a certain sensor reported data curve, when it is more than the threshold value of alarm severity grade,
Just the trend alarm of corresponding severity level is generated, specific time length, the threshold value of alarm severity grade are set by the user.
6. the Real-time Alarm generation method of internet of things oriented Application in Sensing according to claim 5, it is characterised in that: described
Alarm includes: slight tendency alarm, general trend alarm, important trend alarm, the alarm of serious trend, and slight tendency alarm has
Slight tendency alerts high threshold and slight tendency alerts Low threshold, and there is general trend to alert high threshold and one for general trend alarm
As trend alert Low threshold, important trend alarm have important trend alarm high threshold and important trend alert Low threshold, seriously
There is serious trend alarm high threshold and serious trend to alert Low threshold for trend alarm.
7. the Real-time Alarm generation method of internet of things oriented Application in Sensing according to claim 6, it is characterised in that: according to
Average gradient average_gradient is calculated with drag,
Assuming that a certain sensor:
A. n times data are acquired altogether in the time of a length of Duration in acquisition
B. the data and curves generated in the time of a length of Duration in acquisition are (C1, C2, C3 ..., Cn-1, Cn)
The average gradient of that data and curves (C1, C2, C3 ..., Cn-1, Cn) is calculated by following formula:
Average_gradient=area of curve (C1, C2, C3 ..., Cn-1, Cn) * 2/ (n-1)
Wherein:
The area of curve (C1, C2, C3 ..., Cn-1, Cn) refer to by curve (C1, C2, C3 ..., Cn-1, Cn) and line segment (C1,
) and the area in region that surrounds of line segment (Cn, Hn) Hn
The area of curve (C1, C2, C3 ..., Cn-1, Cn) can be approximately equal under curve itself covering small triangle (C1, C2,
), H2 (C2, C3, H3) ..., (Cn-1, Cn, Hn) and small rectangle (C2, V3, H3, H2) ..., (Cn-1, Vn, Hn, Hn-1's)
The sum of area
The geometric meaning of curve (C1, C2, C3 ..., Cn-1, Cn) is the length of line segment (G, Hn).
8. the Real-time Alarm generation method of internet of things oriented Application in Sensing according to claim 7, it is characterised in that: it is wrapped
Include following steps:
(1) start；
(2) judge whether average gradient alerts between high threshold and general trend alarm high threshold between slight tendency, be to hold
Row step (3), it is no to then follow the steps (4)；
(3) slight tendency alarm is generated；
(4) judge whether average gradient alerts between high threshold and important trend alarm high threshold between general trend, be to hold
Row step (5), it is no to then follow the steps (7)；
(5) slight tendency alarm is generated；
(6) general trend alarm is generated；
(7) judge whether raw sensory data alert between high threshold and serious trend alarm high threshold between important trend, be
It thens follow the steps (8), it is no to then follow the steps (11)；
(8) slight tendency alarm is generated；
(9) general trend alarm is generated；
(10) important trend alarm is generated；
(11) judge whether raw sensory data are more than serious trend alarm high threshold, are to then follow the steps (12), otherwise execute
Step (16)；
(12) slight tendency alarm is generated；
(13) general trend alarm is generated；
(14) important trend alarm is generated；
(15) serious trend alarm is generated；
(16) judge whether raw sensory data are lower than serious trend alarm Low threshold, be to then follow the steps (17), otherwise execute
Step (18)；
(17) serious trend alarm is eliminated；
(18) judge whether raw sensory data are lower than important trend alarm Low threshold, be to then follow the steps (19), otherwise execute
Step (20)；
(19) important trend alarm is eliminated；
(20) judge whether raw sensory data are lower than general trend alarm Low threshold, be to then follow the steps (21), otherwise execute
Step (22)；
(21) general trend alarm is eliminated；
(22) judge whether raw sensory data are lower than slight tendency alarm Low threshold, be to then follow the steps (23), otherwise execute
Step (24)；
(23) slight tendency alarm is eliminated；
(24) terminate.
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