CN106202443A - Method for building up based on visual prolog dredging knowledge base - Google Patents
Method for building up based on visual prolog dredging knowledge base Download PDFInfo
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- CN106202443A CN106202443A CN201610552900.6A CN201610552900A CN106202443A CN 106202443 A CN106202443 A CN 106202443A CN 201610552900 A CN201610552900 A CN 201610552900A CN 106202443 A CN106202443 A CN 106202443A
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Abstract
The invention discloses a kind of method for building up based on visual prolog dredging knowledge base, it is characterized in that, comprise the following steps: 1) determine the task that knowledge base is to be completed, and the function that knowledge base is to be realized, carry out knowledge acquisition pointedly；2) from dredging related data, the knowledge required for knowledge base is extracted；3) relevant knowledge is expressed with the form that prolog is true；4) knowledge base correlation module is set up based on visual prolog.The beneficial effect that the present invention is reached: this method is based on dredging plant acquired numerous parameters regulation and control data in construction operation, therefrom extract and dredge relevant knowledge, utilize the artificial intelligence language of prolog, dredging knowledge is carried out knowledge representation, on the premise of high yield low energy consumption, setting up dredging knowledge base, provide Professional knowledge for dredging operation aid decision, theoretical basis is laid in the raising for the level of dredging automatization.
Description
Technical field
The present invention relates to a kind of method for building up based on visual prolog dredging knowledge base, belong to Dredging Technology
Field.
Background technology
Dredging is as underwater performance, and process control parameter is numerous, and this certainly will add the complexity of problem analysis.In view of dredging
Dredging comprehensive, the complexity of dredging operation process and the restriction of experiment condition relating to multidisciplinary theoretical knowledge, dredging technique is certainly
The fundamental research of plan logic lags far behind the needs of reality；Change for a long time because dredging process knowledge lacks with theory
The weary present situation causing dredging operation difficult, inefficient, instructs dredging operation parameter to regulate and control, and reduces operation easier, based on
The foundation that method is dredging technique decision-making mechanism of visual prolog dredging knowledge base lays the foundation, and dredges knowledge base energy
There is provided necessary DECISION KNOWLEDGE for decision-making mechanism, thus improve dredging automatization level.
Summary of the invention
For solving the deficiencies in the prior art, it is an object of the invention to provide a kind of dredging based on visual prolog and know
Knowing the method for building up in storehouse, the raising for the foundation of dredging intelligent decision mechanism provides reliable knowledge.
In order to realize above-mentioned target, the present invention adopts the following technical scheme that:
A kind of method for building up based on visual prolog dredging knowledge base, is characterized in that, comprise the following steps:
Step 1) determine the task that knowledge base is to be completed, and the function that knowledge base is to be realized, know pointedly
Know and collect；
Step 2) from dredging related data, extract the knowledge required for knowledge base:
21) according to a large amount of initial datas of dredging site operation real-time collecting, using the ratio of yield and energy consumption as evaluation
Standard；
22) draw out the productivity ratio curve chart of correspondence, take the productivity ratio part higher than meansigma methods according to curve chart, according to product
The interval that energy ratio is the highest respectively regulates and controls the threshold value of parameter when determining high yield low energy consumption, by relative with threshold value for each regulation and control parameter
Should, it is regulated parameter threshold value table；
Step 3) relevant knowledge is expressed with the form that prolog is true；
Step 4) set up knowledge base correlation module based on visual prolog.
Further, described step 1) in knowledge base function to be realized include: knowledge is preserved with form one by one
In knowledge base；Knowledge is expressed with true form, and provides relevant knowledge for dredging intelligent decision mechanism.
Further, described step 2) in evaluation criterion be: productivity ratio the highest explanation dredging efficiency is the highest, takes productivity ratio
The threshold value of parameter is respectively regulated and controled, by each regulation and control parameter and corresponding threshold list pair when high interval determines high yield low energy consumption
Should.
The beneficial effect that the present invention is reached: this method is based on dredging plant acquired numerous parameters in construction operation
Regulation and control data, therefrom extract and dredge relevant knowledge, utilizing the artificial intelligence language of prolog, and dredging knowledge is carried out knowledge
Express, on the premise of high yield low energy consumption, set up dredging knowledge base, provide Professional knowledge for dredging operation aid decision, for
Theoretical basis is laid in the raising of the level of dredging automatization, and the automation mechanized operation to improving dredger is significant.
Accompanying drawing explanation
Fig. 1 is the flow chart of the present invention；
Fig. 2 is productivity ratio data and curves.
Detailed description of the invention
The invention will be further described below in conjunction with the accompanying drawings.Following example are only used for clearly illustrating the present invention
Technical scheme, and can not limit the scope of the invention with this.
With reference to shown in Fig. 1, present invention method for building up based on visual prolog dredging knowledge base, comprise the following steps:
(1) function that the task that clear and definite knowledge base is to be completed, and knowledge base is to be realized:
The function of database is to provide the Professional knowledge in terms of dredging, therefore knowledge base institute for dredging intelligent decision mechanism
Function to be realized mainly includes the following aspects: knowledge be saved in knowledge base with form one by one；By knowledge with thing
Real form is expressed, and provides relevant knowledge for dredging intelligent decision mechanism.
(2) from dredging related data, the knowledge required for knowledge base is extracted:
21) for each regulation and control parameter, according to the data of site operation real-time collecting, initial data includes reamer rotating speed, strand
The value of the parameters such as cutter transverse moving speed, with the ratio i.e. productivity ratio of yield and energy consumption as evaluation criterion:
According to initial data, for reducing the error between phase dot interlace, take a numerical value every 60s, altogether 3092 groups of data
In choose 50 groups and be analyzed.Wherein cumulative production can directly read on dredger；Accumulative energy consumption then needs note
Each power of motor on record dredger, calculates corresponding energy consumption (unit kw/h) according to the changing value of power, is finally added institute
?.
Calculate gained cumulative production and corresponding accumulative energy consumption, both are divided by, and draw its ratio doubling line chart, i.e.
Gem-pure can see yield and energy consumption ratio trend.Data for yield Yu total energy consumption ratio as shown in table 1 below.
Table 1 productivity ratio data
2) draw out the productivity ratio curve chart of correspondence, take productivity ratio according to curve chart and be higher than meansigma methods, and according to productivity ratio
The threshold value of parameter is respectively regulated and controled when the interval that value is the highest determines high yield low energy consumption, by corresponding with threshold value for each regulation and control parameter,
To regulation and control parameter threshold value table:
Cumulative production is from zero moment, the summation of all yield；Accumulative energy consumption is to start to consume from zero moment
Gross energy.Changing over time, cumulative production is also continually changing with accumulative energy consumption simultaneously.But the increase of accumulative energy consumption is to accumulative
Whether the increase of yield meets yield optimization；As long as whether cumulative production increase is exactly best-case, the most so, according to table
1 productivity ratio tables of data can draw cumulative production and accumulative energy consumption variation tendency.It is illustrated in fig. 2 shown below.So be conducive to finding height
The parameter ranges that effect is corresponding when dredging.
Can significantly find in this image, cutter suction dredger cumulative production and the ratio of total energy consumption are opening down
Parabolical, the ratio point more than 0.0005 accounts for 1/5, and remaining some distribution is the most steady, focuses mostly in 0.0002～0.0005.
If the corresponding parameter of dredger can be arranged, adjust the ratio of this section of interval dredger yield and energy consumption to more than 0.0006,
And the persistent period is longer, so the most considerable to the benefit of dredger raising yield, reduction energy consumption.
In Fig. 2 (unit of the physical quantity that transverse and longitudinal axle represents is identical with table 1), the productivity ratio point more than 0.0006, analyze
Reamer rotating speed in time period corresponding thereto, reamer depth, reamer cut mud thickness, flow rate of pipelines, exit velocity, pipeline put down
Equal concentration, transverse moving speed are the most as shown in table 2 below.
Table 2 number of principal components evidence
The interval that can draw reamer rotating speed from upper table is 26.02～28.01；Reamer depth 16.07～16.17；Reamer
Cut mud thickness 17.67～17.77；Flow rate of pipelines is 4.68～5.26；Exit velocity is 11.98～13.46；Pipeline mean concentration
It is 23.59～53.06；Transverse moving speed is 7.2～10.45.
(3) relevant knowledge is expressed with true form:
The linguistic structure of Prolog is mainly: true (knowledge representation), rule, target.The fact is known as genuine conclusion,
The most each regulation and control parameter and related data are just known as genuine conclusion, and knowledge representation is equivalent to the foundation of knowledge base, and applies
It is the basis building dredging decision model that prolog carries out knowledge representation, i.e. prolog emphasis is knowledge representation just, knows
Knowledge is expressed as next step perfect rule and lays the first stone, thus builds dredging decision model.
(4) knowledge base correlation module is set up based on visual prolog.
In the present embodiment, dredging plant is cutter suction dredger, and dredging soil property type is sand, affects cutter suction dredger
The main technique parameter of dredging operation yield includes: reamer rotating speed, reamer transverse moving speed, and mud thickness cut by reamer, reamer advance away from
From etc., these experimental parameters are decision-making parameter.
For each regulation and control parameter, according to the data of site operation real-time collecting, threshold value when determining high yield low energy consumption and
The threshold value of each regulation and control parameter, and collect related data, specific as follows:
According to each regulation and control parameter, with the ratio i.e. productivity ratio of yield and energy consumption as evaluation criterion, the highest explanation of productivity ratio is dredged
Dredge efficiency the highest, when taking the highest interval of productivity ratio to determine high yield low energy consumption, respectively regulate and control the threshold value of parameter, by each regulation and control ginseng
Measure and corresponding threshold list 1:
| Regulation and control parameter | Domain | Threshold value |
| Reamer rotating speed (r/min) | float | 26.02～28.01 |
| Reamer depth (m) | float | 16.07～16.17 |
| Mud thickness (m) cut by reamer | float | 17.67～17.77 |
| Transverse moving speed (m/s) | float | 7.2～10.45 |
| Concentration of hydraulic mixture (%) | float | 23.59～53.06 |
| Flow rate of pipelines (m/s) | float | 4.68～5.26 |
| Exit velocity | float | 11.98～13.46 |
Table 3 regulates and controls parameter threshold value table
Each regulation and control parameter and related data application prolog are carried out knowledge representation as follows:
Dredging soil property type is sand, can carry out following knowledge representation according to table 1:
range("cutter_head_rotation_speed","sand",26.02,28.01,"rotations_per_
minute"),range("cutter_head_swinging_speed","sand",7.2,10.45,"metres_per_
minute"),
…………………
range("slurry_density","sand",23.59,53.06,"percentage"),
range("slurry_speed","sand",11.98,13.46,"metres_per_second"),
Based on above true, set up dredging knowledge base by visual prolog, knowledge is added to knowledge base, and by
One lists.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For Yuan, on the premise of without departing from the technology of the present invention principle, it is also possible to make some improvement and deformation, these improve and deformation
Also should be regarded as protection scope of the present invention.
Claims (3)
1. a method for building up based on visual prolog dredging knowledge base, is characterized in that, comprise the following steps:
Step 1) determine the task that knowledge base is to be completed, and the function that knowledge base is to be realized, carry out knowledge pointedly and receive
Collection；
Step 2) from dredging related data, extract the knowledge required for knowledge base:
21) according to a large amount of initial datas of dredging site operation real-time collecting, using the ratio of yield and energy consumption as evaluation criterion；
22) draw out the productivity ratio curve chart of correspondence, take the productivity ratio part higher than meansigma methods according to curve chart, according to productivity ratio
The threshold value of parameter is respectively regulated and controled when the interval that value is the highest determines high yield low energy consumption, by corresponding with threshold value for each regulation and control parameter,
To regulation and control parameter threshold value table；
Step 3) relevant knowledge is expressed with the form that prolog is true；
Step 4) set up knowledge base correlation module based on visual prolog.
A kind of method for building up based on visual prolog dredging knowledge base the most according to claim 1, is characterized in that,
Described step 1) in knowledge base function to be realized include: knowledge is saved in knowledge base with form one by one；By knowledge
Express with true form, and provide relevant knowledge for dredging intelligent decision mechanism.
A kind of method for building up based on visual prolog dredging knowledge base the most according to claim 1, is characterized in that,
Described step 2) in evaluation criterion be: productivity ratio the highest explanation dredging efficiency is the highest, takes the highest interval of productivity ratio to determine height
Yield poorly and can respectively regulate and control the threshold value of parameter, by corresponding with corresponding threshold list for each regulation and control parameter.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110297819A (en) * | 2019-06-27 | 2019-10-01 | 河海大学常州校区 | Method for building up based on MySQL dredging knowledge base |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104573869A (en) * | 2015-01-16 | 2015-04-29 | 河海大学常州校区 | Optimization method and system for achieving dredging operations based on BP neural network and NSGA-II |
| CN105045091A (en) * | 2015-07-14 | 2015-11-11 | 河海大学常州校区 | Dredging process intelligent decision analysis method based on fuzzy neural control system |
| CN105894204A (en) * | 2016-04-01 | 2016-08-24 | 河海大学常州校区 | Prolog-based dredging decision-making analysis method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104573869A (en) * | 2015-01-16 | 2015-04-29 | 河海大学常州校区 | Optimization method and system for achieving dredging operations based on BP neural network and NSGA-II |
| CN105045091A (en) * | 2015-07-14 | 2015-11-11 | 河海大学常州校区 | Dredging process intelligent decision analysis method based on fuzzy neural control system |
| CN105894204A (en) * | 2016-04-01 | 2016-08-24 | 河海大学常州校区 | Prolog-based dredging decision-making analysis method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110297819A (en) * | 2019-06-27 | 2019-10-01 | 河海大学常州校区 | Method for building up based on MySQL dredging knowledge base |
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