CN110045695A - A kind of technological parameter on-line early warning method based on variance analysis - Google Patents
A kind of technological parameter on-line early warning method based on variance analysis Download PDFInfo
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- CN110045695A CN110045695A CN201910236443.3A CN201910236443A CN110045695A CN 110045695 A CN110045695 A CN 110045695A CN 201910236443 A CN201910236443 A CN 201910236443A CN 110045695 A CN110045695 A CN 110045695A
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- early warning
- technological parameter
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a kind of technological parameter on-line early warning method and computer readable storage medium based on variance analysis cannot find the constant technical problem of manufacturing parameter for solving in time in production process.This method includes that the monitoring data sample of technological parameter is obtained from production system real-time data base；The variance yields of the monitoring data sample is calculated, to characterize the fluctuation situation of the technological parameter；The warning information of the technological parameter is generated based on the preset early warning configuration of the technological parameter and the variance yields；The warning information is pushed according to preset early warning classification push-mechanism.The present invention can be carried out on-line continuous analysis and diagnosis to various technological parameters, be scented a hidden danger based on real time data, predict risk, using grading forewarning system and push-mechanism, warning information and alarm mode have been refined, stronger support is provided to Production scheduling management.
Description
Technical field
The invention belongs to industrial production monitoring technology field more particularly to a kind of technological parameter based on variance analysis are online
Method for early warning and computer readable storage medium.
Background technique
Technological parameter is the important indicator for reflecting enterprise safety operation state.It is looked forward in the industry such as oil extraction, oil refining, chemical industry
Industry, the production and operation are typically characterised by continuous production round the clock, and production process mostly uses the equipment such as pipeline, container, and material is flowing
Dynamic process completes physics and chiral process, and wherein the technological parameter needs in production process and flow process are monitored,
It especially needs to make pre-adjusting according to variation tendency, to avoid halt production accident or safety accident occurs.
In actual production, occur that data are constant, this is very common because of sensor fault or transmission fault
Instrument phenomenon.The constant overlong time of data, then will lead to operator can not observe the variation having occurred and that, thus may
Production efficiency is caused to reduce or cause production accident.
For example, the inlet amount of the reactor of the catalytic unit normally produced changes constantly, inlet amount can seriously shadow
Ring reaction temperature and reaction pressure.Once the feed flow meter of reactor breaks down, the data for inlet amount occur are constant
When situation, since system can not obtain correct response parameter in time, the temperature and pressure of reactor is possible to occur
Variation by a relatively large margin.And the follow up device as reactor, the temperature and pressure of regenerator can also fluctuate therewith, this meeting so that
Technological operation difficulty increases severely, and then influences product quality, or even cause the accident.
Summary of the invention
In view of the above-mentioned problems, the present invention proposes a kind of technological parameter on-line early warning method based on variance analysis, for pair
Production scheduling adjustment provides auxiliary and supports, to avoid halt production accident or safety accident occurs.
According to an embodiment of the invention, a kind of technological parameter on-line early warning method based on variance analysis, including walk as follows
Suddenly,
The monitoring data sample of technological parameter is obtained from production system real-time data base；The monitoring data sample be
The set of the numerical value of the technological parameter in the sample window phase including current time；
The variance yields for calculating the monitoring data sample, for characterizing the fluctuation situation of the technological parameter；
The warning information of the technological parameter is generated based on the preset early warning configuration of the technological parameter and the variance yields；
The warning information is pushed according to preset early warning classification push-mechanism.
Preferably, the early warning configuration includes threshold value of warning section, and the warning information includes the event class of early warning；Its
In, generate and push the warning information specifically:
The variance yields is compared with each endpoint value in the threshold value of warning section, is judged locating for the variance yields
Threshold value of warning section, with the event class of the determination early warning；
According to the event class of the early warning, the warning information is pushed according to preset early warning classification push-mechanism.
Preferably, the event class of the early warning is determined specifically:
When the variance yields is greater than first end point value less than the second endpoint value, the variance yields falls into high report threshold zone
Between, the event class of the early warning is high alert event；
When the variance yields is less than or equal to first end point value, the variance yields falls into superelevation report threshold interval, described pre-
Alert event class is superelevation alert event,
Wherein, second endpoint value is greater than the first end point value.
Preferably, the first end point value is 0.001, and second endpoint value is 0.01.
Preferably, according to the event class of the early warning, the early warning is pushed according to preset early warning classification push-mechanism
Information specifically:
When the event class of the early warning is superelevation alert event, by the warning information to push of company level；
When the event class of the early warning is high alert event, by the warning information to responsible department and/or level of factory
Push.
Preferably, the event class different using different color identifiers.
Preferably, the monitoring data sample of technological parameter is obtained from production system real-time data base specifically:
According to the position parameter of the technological parameter from the data at real-time data base acquisition current time and current
The historical data of specified number before the data at moment, by the historical data of the data comprising current time and the specified number
Set as the monitoring data sample.
Preferably, the specified number is more than or equal to 10 and is less than or equal to 60.
In addition, the embodiments of the present invention also provide a kind of computer readable storage mediums, wherein it is stored with program, it is described
Program realizes the technological parameter on-line early warning method described in any of the above-described scheme based on variance analysis when being executed by processor.
Compared with prior art, one or more embodiments in above scheme can have following advantage or beneficial to effect
Fruit:
1) the present invention is based on the technological parameter early warning of variance analysis be built upon real time data basis in line computation
And analysis system, various technological parameters can continuously be analyzed and diagnosed, scent a hidden danger, predict risk ahead of time, so as to and
When provide science instruction；
2) present invention has refined warning information and alarm mode, to production scheduling using grading forewarning system and the mechanism of push
Management provides more strong support.
Other advantages, target and feature of the invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.Target and other advantages of the invention can be wanted by following specification, right
Specifically noted structure is sought in book and attached drawing to be achieved and obtained.
Detailed description of the invention
Attached drawing is used to provide to further understand technical solution of the present invention or the prior art, and constitutes specification
A part.Wherein, the attached drawing of embodiment of the present invention technical side for explaining the present invention together with embodiments of the present invention is expressed
Case, but do not constitute the limitation to technical solution of the present invention.
Fig. 1 is the flow diagram of technological parameter on-line early warning method according to an embodiment of the present invention；
Fig. 2 is the schematic diagram of three kinds of typicalness of variance analysis according to an embodiment of the invention；
Fig. 3 is flow diagram of the method for early warning according to an embodiment of the invention in continuous monitoring；
Fig. 4 is the warning information display interface of technological parameter early warning system according to an embodiment of the invention；
Fig. 5 is the warning information configuration interface of technological parameter early warning system according to an embodiment of the invention；
Fig. 6 be include that the disposal method of dispatching management information system of a technological parameter early warning system embodiment of the invention pushes away
Send interface.
Specific embodiment
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and examples, how to apply to the present invention whereby
Technological means solves technical problem, and the realization process for reaching relevant art effect can fully understand and implement.This hair
Each feature in bright embodiment and embodiment, can be combined with each other under the premise of not colliding, be formed by technical solution
It is within the scope of the present invention.
Fig. 1 is the flow chart of the technological parameter on-line early warning method of the invention based on variance analysis.
Save the monitoring data of each sensor technological parameter collected in production system real-time data base, and according to
Certain rule stores these monitoring data.For example, being that major key is stored with position number.When needing to be divided using monitoring data
It, can be by way of accessing the position number of production system real-time data base, to obtain corresponding sensor work collected when analysis
The monitoring data of skill parameter, and then on the basis of these monitoring data, analyze the operation conditions of production system.The present invention proposes
Method using having the characteristics of Real-time Monitoring Data in production system real-time data base, pass through access production system real time data
Library obtains a certain amount of monitoring data, to form monitoring data sample.
In the present embodiment, due to the above-mentioned storage feature of production system real-time data base, variance analysis is being carried out
Before, it is necessary first to obtain the position number of sensor corresponding to technological parameter to be analyzed, then be from production according to the position number
The monitoring data of technological parameter corresponding to this number are read in system real-time data base.Since variance analysis is to be directed to have pass by
Monitoring data in a period of time are analyzed, therefore the detection data at the current time of the technological parameter in addition to obtaining this number
Except, also to obtain the Historical Monitoring data of the specified number before current time.
In order to can either analysis process parameter amplitude of variation, and can ensure the timeliness of early warning and alarming, history detection
The specified number of data should not also should not be generally advisable very little with 10~60 too much.On the other hand, in order to guarantee analysis and
The stability of when property and analysis, the sample frequency for obtaining monitoring data also should not be too low or too high.Optionally, one is adopted within every 15 seconds
Secondary sample, i.e., the variance analysis of progress in every 15 seconds.The monitoring data of number are specified to extract this, as monitoring data sample
This, for subsequent variance analysis.
Particularly, as shown in figure 3, if not monitoring data sample is obtained for the first time, then only needing to lose existing prison
The monitoring data of history at most in measured data sample, and acquire current time newest monitoring data, that is, constitute one it is new
Monitoring data sample.
After obtaining monitoring data sample, variance calculating is carried out to the monitoring data sample, the variance of acquisition being capable of table
Levy the fluctuation situation of this technological parameter.
Variance be each data respectively and itself and average difference square and average, usually use letter D table
Show.In probability theory and mathematical statistics, variance is used to measure the deviation between stochastic variable and its mathematic expectaion (i.e. average)
Degree.
In actually calculating, we calculate variance with following formula.
Wherein, x indicates that the average of data sample, n indicate the number of the data in monitoring data sample, xiIndicate each
Monitoring data value, S2Indicate variance yields.
It is each when the data distribution in monitoring data sample more dispersed (i.e. data fluctuate larger near average)
The quadratic sum of data and the difference of average is larger, and variance is with regard to larger；When data distribution compares concentration, each data and average
Difference quadratic sum it is smaller, variance is just smaller.Therefore variance is bigger, indicates that the fluctuation of data is bigger；Variance is smaller, indicates data
Fluctuation with regard to smaller.Variance reflects the fluctuation situation of data, can judge technological parameter by real time data variance yields
Whether value no longer changes suddenly.
Fig. 2 shows a kind of constant Early-warning Model, horizontal axis indicates the time, and the longitudinal axis indicates monitoring data value.
On the left of the vertical line that " constant " is identified, monitoring data value is changed constantly, if monitoring data sample all takes
The vertical line left area identified from " constant ", since monitoring data are fluctuating always, the variance of monitoring data sample is just
It is bigger；If monitoring data sample a part is derived from the vertical line left area that " constant " is identified, and another part is derived from " no
The right area for the vertical line that change " is identified, since the fluctuating range of monitoring data is smaller and smaller, the side of monitoring data sample
Difference is just smaller；If monitoring data sample is derived from the vertical line left area that " constant " is identified, due to monitoring data almost without
Fluctuation, therefore the variance of monitoring data sample is minimum, close to zero.
In normal productive process, most of numerical value moment is not in variance close or equal to zero all in fluctuation
State.When the variance of monitoring data sample is close to zero, mean that monitoring data for some time without variation,
This typicallys represent sensor fault or signal transmission path failure, and then is likely to cause production efficiency reduction, stops production even
Safety accident.Therefore, it can use state of this variance close to zero to carry out judgement and early warning.
Data in monitoring data sample are substituted into above-mentioned variance formula, that is, are produced corresponding under current time
Monitoring data sample variance yields, such as 0.005 or 0.0005.
Then, variance yields is compared with each endpoint value in preset threshold value of warning section, is judged locating for variance yields
Threshold value of warning section, to determine the event class of early warning.
For example, when first end point value is 0.001, when the second endpoint value is 0.01,
If variance yields is 0.005 at this time, due to 0.001 <, 0.005 < 0.01, variance yields falls into high report threshold zone
Between, the event class of early warning is high alert event；At this point it is possible to identified with orange color, and by warning information to responsible department
And/or level of factory push；
If variance yields is 0.0005 at this time, due to 0.0005 <, 0.001 < 0.01, variance yields falls into superelevation report threshold value
Section, the event class of early warning are superelevation alert event；At this point it is possible to be identified with red color, illustrate that its urgency level is higher,
It need to be pushed to wide range, such as to push of company level；
If variance yields is zero at this time, the constant situation of monitoring data is had occurred in expression.Monitoring data at this time do not have make
With value, other calculating based on this monitoring data do not have directive significance yet, it may be possible to which failure occurs in instrument.Work as variance
When value is zero, other than above-mentioned early warning, notice needs the related personnel using monitoring data.
In addition, embodiments herein additionally provides a kind of technological parameter early warning system based on variance analysis.Shown in Fig. 4
Interface is the warning information display interface of the system, and interface shown in Fig. 5 is the warning information configuration interface of the system.Have at one
In the embodiment of body, above-mentioned early warning system can be a kind of subsystem of dispatching management information system.As shown in fig. 6, in the scheduling pipe
In reason system, the disposal method for the warning information is provided when pushing a warning information, while for dispatcher, dispatches people
Member clicks " disposition " i.e. release matching scheme, operator's " use " optional for the scheme of system automatic push " do not use+
It is newly-built " a certain item in " use+modification ", practical disposal method is generated, subsequently into instruction flow.Early warning event handling terminates
Afterwards, determine it is to need no precipitating new departure according to the processing of this early warning event record, if desired, then in early warning event handling side
Increase or update the new plan template of the early warning event in case allocation list newly.
The above content is merely preferred embodiments of the present invention, but scope of protection of the present invention is not limited thereto,
Within the technical scope disclosed by the invention, any changes or substitutions that can be easily thought of by any those skilled in the art, should all contain
Lid is within protection scope of the present invention.Therefore, protection scope of the present invention should be subject to the protection scope in claims.
Claims (9)
1. a kind of technological parameter on-line early warning method based on variance analysis, includes the following steps,
The monitoring data sample of technological parameter is obtained from production system real-time data base；The monitoring data sample be including
The set of the numerical value of the technological parameter in the sample window phase at current time；
The variance yields for calculating the monitoring data sample, for characterizing the fluctuation situation of the technological parameter；
The warning information of the technological parameter is generated based on the preset early warning configuration of the technological parameter and the variance yields；
The warning information is pushed according to preset early warning classification push-mechanism.
2. technological parameter on-line early warning method according to claim 1, which is characterized in that the early warning configuration includes early warning
Threshold interval, the warning information include the event class of early warning；Wherein, generate and push the warning information specifically:
The variance yields is compared with each endpoint value in the threshold value of warning section, judges early warning locating for the variance yields
Threshold interval, with the event class of the determination early warning；
According to the event class of the early warning, the warning information is pushed according to preset early warning classification push-mechanism.
3. technological parameter on-line early warning method according to claim 2, which is characterized in that determine the event etc. of the early warning
Grade specifically:
When the variance yields is greater than first end point value less than the second endpoint value, the variance yields falls into high report threshold interval, institute
The event class for stating early warning is high alert event；
When the variance yields is less than or equal to first end point value, the variance yields falls into superelevation report threshold interval, the early warning
Event class is superelevation alert event,
Wherein, second endpoint value is greater than the first end point value.
4. technological parameter on-line early warning method according to claim 3, which is characterized in that the first end point value is
0.001, second endpoint value is 0.01.
5. technological parameter on-line early warning method according to claim 3, which is characterized in that according to the event etc. of the early warning
Grade pushes the warning information according to preset early warning classification push-mechanism specifically:
When the event class of the early warning is superelevation alert event, by the warning information to push of company level；
When the event class of the early warning is high alert event, the warning information is pushed to responsible department and/or level of factory.
6. technological parameter on-line early warning method according to claim 5, which is characterized in that
Utilize the different event class of different color identifiers.
7. technological parameter on-line early warning method according to claim 1, which is characterized in that from production system real-time data base
The middle monitoring data sample for obtaining technological parameter specifically:
According to the position parameter of the technological parameter from the data at real-time data base acquisition current time and at current time
Data before specified number historical data, by the collection of the data comprising current time and the historical data of the specified number
Cooperation is the monitoring data sample.
8. technological parameter method for early warning according to claim 7, which is characterized in that it is small that the specified number is more than or equal to 10
In equal to 60.
9. a kind of computer readable storage medium, wherein being stored with program, described program is realized when being executed by processor as weighed
Benefit require any one of 1 to 8 described in technological parameter method for early warning.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110956390A (en) * | 2019-11-28 | 2020-04-03 | 珠海格力电器股份有限公司 | Method and device for prompting early warning risk |
| CN111850892A (en) * | 2020-07-10 | 2020-10-30 | 绍兴数智科技有限公司 | Method and device for realizing abnormal alarm in dyeing process of overflow dyeing machine |
| CN115496424A (en) * | 2022-11-09 | 2022-12-20 | 广东能创科技有限公司 | Safety management method and system for methanol hydrogen production process |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1069636A2 (en) * | 1999-07-06 | 2001-01-17 | General Motors Corporation | Fuel cell stack monitoring and system control |
| CN104102668A (en) * | 2013-04-12 | 2014-10-15 | 袁晓东 | Automatic database performance analysis and early warning system |
| CN106227640A (en) * | 2016-07-27 | 2016-12-14 | 北京市环境保护监测中心 | A kind of abnormal deviation data examination method based on automatic monitor and system |
| CN106290763A (en) * | 2016-07-29 | 2017-01-04 | 湖南永清水务有限公司 | A kind of sewage disposal operational factor trend analysis and system |
| CN107316444A (en) * | 2016-06-12 | 2017-11-03 | 中国石油化工股份有限公司 | Chemicals production device actual time safety method for early warning |
| CN108663995A (en) * | 2017-03-30 | 2018-10-16 | 郑州大学 | A kind of industrial process variable trend anomaly detection method and device |
-
2019
- 2019-03-26 CN CN201910236443.3A patent/CN110045695A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1069636A2 (en) * | 1999-07-06 | 2001-01-17 | General Motors Corporation | Fuel cell stack monitoring and system control |
| CN104102668A (en) * | 2013-04-12 | 2014-10-15 | 袁晓东 | Automatic database performance analysis and early warning system |
| CN107316444A (en) * | 2016-06-12 | 2017-11-03 | 中国石油化工股份有限公司 | Chemicals production device actual time safety method for early warning |
| CN106227640A (en) * | 2016-07-27 | 2016-12-14 | 北京市环境保护监测中心 | A kind of abnormal deviation data examination method based on automatic monitor and system |
| CN106290763A (en) * | 2016-07-29 | 2017-01-04 | 湖南永清水务有限公司 | A kind of sewage disposal operational factor trend analysis and system |
| CN108663995A (en) * | 2017-03-30 | 2018-10-16 | 郑州大学 | A kind of industrial process variable trend anomaly detection method and device |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110956390A (en) * | 2019-11-28 | 2020-04-03 | 珠海格力电器股份有限公司 | Method and device for prompting early warning risk |
| CN110956390B (en) * | 2019-11-28 | 2022-04-22 | 珠海格力电器股份有限公司 | Method and device for prompting early warning risk |
| CN111850892A (en) * | 2020-07-10 | 2020-10-30 | 绍兴数智科技有限公司 | Method and device for realizing abnormal alarm in dyeing process of overflow dyeing machine |
| CN111850892B (en) * | 2020-07-10 | 2023-08-11 | 绍兴数智科技有限公司 | Method and device for realizing abnormal alarm in dyeing process of overflow dyeing machine |
| CN115496424A (en) * | 2022-11-09 | 2022-12-20 | 广东能创科技有限公司 | Safety management method and system for methanol hydrogen production process |
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