CN106845690A - A kind of boiler water level method for early warning based on decision tree system - Google Patents

A kind of boiler water level method for early warning based on decision tree system Download PDF

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Publication number
CN106845690A
CN106845690A CN201611238786.6A CN201611238786A CN106845690A CN 106845690 A CN106845690 A CN 106845690A CN 201611238786 A CN201611238786 A CN 201611238786A CN 106845690 A CN106845690 A CN 106845690A
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water level
decision tree
boiler
tree system
boiler water
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刘海涛
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Hunan Kun Yu Network Technology Co Ltd
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Hunan Kun Yu Network Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
    • G08B25/002—Generating a prealarm to the central station

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Emergency Management (AREA)
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  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
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  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • Development Economics (AREA)
  • Alarm Systems (AREA)

Abstract

The invention discloses a kind of boiler water level method for early warning based on decision tree system, comprise the following steps：Step (1), acquisition boiler room environment and boiler operating parameter data A, then boiler water level critical value B is obtained, set up water level error rate table T；Step (2)：Decision tree system is set up, contradistinction system is set up, decision tree system and contradistinction system are carried out into logic matches；Step (3)：Electronic sensor obtains real-time boiler water level height Value Data and transmits to decision tree system, obtains water level low value probability P high；Step (4)：If P is more than 0.8, boiler water level, console provides alarm；If P is less than 0.8, boiler normally runs；Step (5)：Boiler staff is confirmed after obtaining the alarm that console sends, if it is confirmed that rear boiler water level then illustrates decision tree system misjudgment, amendment decision tree system less than water level critical value.The present invention realizes the automatization judgement of boiler water level height, accuracy of judgement.

Description

A kind of boiler water level method for early warning based on decision tree system
Technical field
The invention belongs to early warning technology field, the pre- police of more particularly to a kind of boiler water level based on decision tree system Method.
Background technology
Current domestic each water level early warning system is provided with electronic sensor prompt system.Traditional electronic sensor principle It is, by the low value high of water level, to be perceived by electronic sensor and the numerical value of each section is timely fed back into central control system.Work Make to point out to learn the low value high of boiler water level by the picture and text of central control system.But high temperature, the corrosion of stove water due to generator tube Property, a certain degree of influence is caused on electronic sensor so that wrong estimate is caused in water level value of feedback, or occur false Value, causes major accident occur with the judgement for causing boiler staff generation mistake.And sensitivity electronic sensor high It is expensive, replacing is difficult, and is replaced as frequently as so that producing family's very headache.So current domestic water level early warning system without Method accurately reacts the water level low value high of boiler.It is free during most electronic sensor uses electrochemical principle to water before this Metal ion produces electrification, by the transmission of electric signal come the low value high of prompting water level.But furnace temperature is too high be underwater gold belong to from Son motion is active to cause certain interference to result.
The content of the invention
The purpose of the present invention is that and overcomes the deficiencies in the prior art, there is provided a kind of boiler water based on decision tree system Position method for early warning, can immediate correction electronic sensor under circumstances data error, remind boiler staff's water level high Low situation so that staff obtains an accurate water level height situation to ensure the operation of boiler normal table, to prolong In the life-span for using of electronic sensor long, the maintenance cost of boiler is reduced, realize the automatization judgement of boiler water level height, judged Accurately, artificial judgment is no longer needed, mitigates the labour intensity of staff.
To achieve these goals, the invention provides a kind of boiler water level method for early warning based on decision tree system, bag Include following steps：
Step (1), acquisition boiler room environment and boiler operating parameter data A, then boiler water level critical value B is obtained, according to Mutual pace of learning in data A and critical value BCorrespondence go out error rate table t, by the numerical quantization in error rate table t be 0-1 it Between decimal after set up water level error rate table T；
Step (2)：Water level error rate table T in step (1) sets up decision tree system as decision tree system skeleton System, at the same obtain staff's artificial judgment boiler water level height historical data set up contradistinction system, by decision tree system with Contradistinction system carries out logic matching；
Step (3)：Real-time boiler water level data are obtained by electronic sensor, and is transmitted to decision tree system, decision tree Water level low value probability P high is obtained after system repeatedly training；
Step (4)：Decision tree system judges the size of water level low value probability P high, if P is more than 0.8, illustrates boiler water Position is less than water level critical value, and result is transferred to console by decision tree system, and console provides alarm；If P is less than 0.8, then illustrate that boiler water level is normal, console will not provide alarm；
Step (5)：After boiler staff obtains the alarm that console sends, boiler actual water level situation is carried out Confirm, if it is confirmed that rear boiler water level normally then illustrates decision tree system misjudgment, now boiler staff will correctly tie Fruit inputs to contradistinction system, and decision tree system is corrected after now contradistinction system is matched with decision tree system logic again；If Boiler water level then illustrates decision tree system correct judgment less than water level critical value after confirmation；
Step (6)：Repeat step (3)-(5), so constantly circulation constantly corrects decision tree system until decision tree system Accuracy of judgement, no longer needs staff's artificial judgment boiler water level height situation.
Further, the formula of decision tree system meets in step (2)：
Wherein：XSIt is feedback score, XBHIt is convolution constant, KXIt is the converse feedback number of plies, SOIt is vector convolution constant, KOHIt is fixed Adopted vector constant collection, fpIt is subset probability, bHIt is counts, KhFor error in judgement is counted.
Beneficial effects of the present invention：The present invention can immediate correction electronic sensor under circumstances data error, carry The situation of awake boiler staff's water level height so that staff family obtains an accurate water level height situation to ensure The operation of boiler normal table, to extend the life-span for using of electronic sensor, reduces the maintenance cost of boiler, realizes boiler water The automatization judgement of position height, accuracy of judgement no longer needs artificial judgment, mitigates the labour intensity of staff.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the flow chart of the embodiment of the present invention.
Specific embodiment
Invention is further illustrated below in conjunction with the accompanying drawings, but is not limited to the scope of the present invention.
Embodiment
As shown in figure 1, a kind of boiler water level method for early warning based on decision tree system that the present invention is provided, including following step Suddenly：
Step (1), acquisition boiler room environment and boiler operating parameter data A, then boiler water level critical value B is obtained, according to Mutual pace of learning in data A and critical value BCorrespondence go out error rate table t, by the numerical quantization in error rate table t be 0-1 it Between decimal after set up water level error rate table T；
Boiler room environmental data includes：Boiler room size, there is a several usable boilers, the species of boiler, uses Time, energy supply type etc..Boiler operating parameter data include：Furnace temperature, cigarette temperature, hydraulic pressure, vapour pressure, water inlet pump discharge, burning Machine temperature, air channel data etc..
Step (2)：Water level error rate table T in step (1) sets up decision tree system as decision tree system skeleton System, at the same obtain staff's artificial judgment boiler water level height historical data set up contradistinction system, by decision tree system with Contradistinction system carries out logic matching；
Step (3)：Real-time boiler water level data are obtained by electronic sensor, and is transmitted to decision tree system, decision tree Water level low value probability P high is obtained after system repeatedly training；
Step (4)：Decision tree system judges the size of water level low value probability P high, if P is more than 0.8, illustrates boiler water Position is less than water level critical value, and result is transferred to console by decision tree system, and console provides alarm；If P is less than 0.8, then illustrate that boiler water level is normal, console will not provide alarm；
Step (5)：After boiler staff obtains the alarm that console sends, boiler actual water level situation is carried out Confirm, if it is confirmed that rear boiler water level normally then illustrates decision tree system misjudgment, now boiler staff will correctly tie Fruit inputs to contradistinction system, and decision tree system is corrected after now contradistinction system is matched with decision tree system logic again；If Boiler water level then illustrates decision tree system correct judgment less than water level critical value after confirmation；
Step (6)：Repeat step (3)-(5), so constantly circulation constantly corrects decision tree system until decision tree system Accuracy of judgement, no longer needs staff's artificial judgment boiler water level height situation.
The formula of decision tree system meets in step (2)：
Wherein：XSIt is feedback score, XBHIt is convolution constant, KXIt is the converse feedback number of plies, SOIt is vector convolution constant, KOHIt is fixed Adopted vector constant collection, fpIt is subset probability, bHIt is counts, KhFor error in judgement is counted.
The present invention can immediate correction electronic sensor under circumstances data error, remind boiler staff's water level The situation of height so that staff family obtains an accurate water level height situation to ensure the operation of boiler normal table, To extend the life-span for using of electronic sensor, the maintenance cost of boiler is reduced, realizes the automatization judgement of boiler water level height, Accuracy of judgement, no longer needs artificial judgment, mitigates the labour intensity of staff.
General principle of the invention, principal character and advantages of the present invention has been shown and described above.The technology of the industry Personnel it should be appreciated that the present invention is not limited to the above embodiments, simply explanation described in above-described embodiment and specification this The principle of invention, various changes and modifications of the present invention are possible without departing from the spirit and scope of the present invention, these changes Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appending claims and its Equivalent is defined.

Claims (2)

1. a kind of boiler water level method for early warning based on decision tree system, it is characterised in that comprise the following steps：
Step (1), acquisition boiler room environment and boiler operating parameter data A, then boiler water level critical value B is obtained, according to data A With the mutual pace of learning in critical value BCorrespondence goes out error rate table t, by the numerical quantization in error rate table t between 0-1 Water level error rate table T is set up after decimal；
Step (2)：Water level error rate table T in step (1) sets up decision tree system, together as decision tree system skeleton When obtain staff's artificial judgment boiler water level low value high historical data set up contradistinction system, by decision tree system with compare System carries out logic matching；
Step (3)：Real-time boiler water level data are obtained by electronic sensor, and is transmitted to decision tree system, decision tree system Water level low value probability P high is obtained after repetition training；
Step (4)：Decision tree system judges the size of water level low value probability P high, if P is more than 0.8, illustrates that boiler water level is low In water level critical value, result is transferred to console by decision tree system, and console provides alarm；If P is less than 0.8, Illustrate that boiler water level is normal, console will not provide alarm；
Step (5)：After boiler staff obtains the alarm that console sends, boiler actual water level situation is carried out really Recognize, if it is confirmed that rear boiler water level normally then illustrates decision tree system misjudgment, now boiler staff is by correct result Contradistinction system is inputed to, decision tree system is corrected after now contradistinction system is matched with decision tree system logic again；If really Recognize rear boiler water level and then illustrate decision tree system correct judgment less than water level critical value；
Step (6)：Repeat step (3)-(5), so constantly circulation constantly corrects decision tree system until decision tree system judges Accurately, staff's artificial judgment boiler water level height situation is no longer needed.
2. a kind of boiler water level method for early warning based on decision tree system according to claim 1, it is characterised in that in step Suddenly the formula of decision tree system meets in (2)：
dX S d t = ( 1 - f p ) b H X B H - k h ( X S X B H K X + X S X B H ) ( S O K O H + S O ) X B H ;
Wherein：XSIt is feedback score, XBHIt is convolution constant, KXIt is the converse feedback number of plies, SOIt is vector convolution constant, KOHFor define to Amount constant collection, fpIt is subset probability, bHIt is counts, KhFor error in judgement is counted.
CN201611238786.6A 2016-12-28 2016-12-28 A kind of boiler water level method for early warning based on decision tree system Pending CN106845690A (en)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1841422A (en) * 2005-02-08 2006-10-04 神马科技公司 Method and apparatus for optimizing operation of a power generating plant using artificial intelligence techniques
US20090125155A1 (en) * 2007-11-08 2009-05-14 Thomas Hill Method and System for Optimizing Industrial Furnaces (Boilers) through the Application of Recursive Partitioning (Decision Tree) and Similar Algorithms Applied to Historical Operational and Performance Data
CN101752866A (en) * 2008-12-10 2010-06-23 上海申瑞电力科技股份有限公司 Automatic heavy-load equipment early warning implementation method based on decision tree
CN105573329A (en) * 2015-12-16 2016-05-11 上海卫星工程研究所 Attitude and orbit control data analysis method based on decision tree
CN106054104A (en) * 2016-05-20 2016-10-26 国网新疆电力公司电力科学研究院 Intelligent ammeter fault real time prediction method based on decision-making tree

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1841422A (en) * 2005-02-08 2006-10-04 神马科技公司 Method and apparatus for optimizing operation of a power generating plant using artificial intelligence techniques
US20090125155A1 (en) * 2007-11-08 2009-05-14 Thomas Hill Method and System for Optimizing Industrial Furnaces (Boilers) through the Application of Recursive Partitioning (Decision Tree) and Similar Algorithms Applied to Historical Operational and Performance Data
CN101752866A (en) * 2008-12-10 2010-06-23 上海申瑞电力科技股份有限公司 Automatic heavy-load equipment early warning implementation method based on decision tree
CN105573329A (en) * 2015-12-16 2016-05-11 上海卫星工程研究所 Attitude and orbit control data analysis method based on decision tree
CN106054104A (en) * 2016-05-20 2016-10-26 国网新疆电力公司电力科学研究院 Intelligent ammeter fault real time prediction method based on decision-making tree

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