CN106538437A - Penaeus vannamei leukoderma monitoring early-warning system and method based on rough set theory - Google Patents
Penaeus vannamei leukoderma monitoring early-warning system and method based on rough set theory Download PDFInfo
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Abstract
The invention provides a kind of Penaeus vannamei leukoderma monitoring early-warning system and method based on rough set theory, the system includes：Alert source information acquisition module, for obtaining the water monitoring data of Penaeus vannamei；Early-warning Model builds module, for building leukoderma Early-warning Model based on rough set theory；Warning module, the water monitoring data and the Early-warning Model for being obtained according to the alert source information acquisition module build the leukoderma Early-warning Model that module is set up, and determine the leukodermal advanced warning grade of Penaeus vannamei.The present invention can realize early warning analysis leukodermal to Penaeus vannamei, be conducive to scientific and reasonable planning to cultivate, and reduce cultivation loss, increase culture benefit.
Description
Technical field
The present invention relates to technical field of aquaculture, and in particular to a kind of Penaeus vannamei white macula based on rough set theory
Sick monitoring early-warning system and method.
Background technology
The structural reform of aquatic products quantity is often absorbed in China's fishery for a long time, to meet people to aquatic products and its product
Increasing need, impact of the factor of curing the disease in ignoring aquatic products to yield.Simultaneously because the complexity of cause of disease and various
Property, it is difficult to the effective control means of illness outbreak are found, so that it cannot efficiently reduce or avoid the generation of similar event.
Penaeus vannamei (Penaeusvannamei Boone) is one of three big excellent cultivation shrimp species in the world, is China
Main prawn culturing kind.Penaeus vannamei have time-to-live length, idiophase it is long, it is adaptable the features such as, Er Qienan
Penaeus vannamei Boone is delicious in taste, and body fat meat is thick, and with high protein, abundant vitamin, flavour is delicious, with a relatively high
Nutritive value.But the behind developed rapidly in culture of Penaeus vannamei industry, disease harm be restrict its develop it is important because
Element, raiser have certain understanding to disease, but lack effective technological means and Professional knowledge to its early warning and control.
The content of the invention
For defect of the prior art, the present invention provides a kind of Penaeus vannamei leukoderma based on rough set theory and supervises
Control early warning system and method, the present invention can be realized early warning analysis leukodermal to Penaeus vannamei, be conducive to scientific and reasonable
Planning cultivation, reduces cultivation loss, increases culture benefit.
To solve above-mentioned technical problem, the present invention provides technical scheme below：
In a first aspect, a kind of the invention provides Penaeus vannamei leukoderma monitoring and early warning system based on rough set theory
System, including：
Alert source information acquisition module, for obtaining the water monitoring data of Penaeus vannamei；
Early-warning Model builds module, for building leukoderma Early-warning Model based on rough set theory；
Warning module, for water monitoring data and the early warning mould according to the alert source information acquisition module acquisition
Type builds the leukoderma Early-warning Model that module is set up, and determines the leukodermal advanced warning grade of Penaeus vannamei.
Further, the alert source information acquisition module, specifically for：
The water monitoring data of Penaeus vannamei is obtained, wherein, water monitoring data includes：Water temperature, dissolved oxygen, salinity,
PH value, NH3Concentration and turbidity.
Further, the Early-warning Model builds module, specifically for：
Decision table is set up, wherein decision table is described using following four-tuple：
DT=(U, C ∪ D, V, f)；
Wherein, U is domain, and U={ R1, R2 ..., Rn }, n are the regular number in decision table, and C is conditional attribute collection, and D is for certainly
Plan property set, V are the codomains of information function f, and f is the information function of decision table；
Using water monitoring data as conditional attribute collection C, C={ water temperature, dissolved oxygen, PH, NH3, turbidity, salinity }；
Using advanced warning grade as decision kind set D, D={ without police, low police, middle police, is warned } again.
Further, the warning module, specifically for：
Using the water monitoring data for obtaining, according to the decision table set up, using the method for forward reasoning to decision ruless
Make inferences, obtain the leukodermal advanced warning grade of Penaeus vannamei.
Further, the system also includes：Update module；The update module, for carrying out about to the decision table
Letter is processed.
Second aspect, present invention also offers a kind of Penaeus vannamei leukoderma monitoring and early warning side based on rough set theory
Method, including：
Obtain the water monitoring data of Penaeus vannamei；
Leukoderma Early-warning Model is built based on rough set theory；
According to the leukoderma Early-warning Model of the water monitoring data and structure for obtaining, determine that Penaeus vannamei is leukodermal
Advanced warning grade.
Further, the water monitoring data for obtaining Penaeus vannamei, including：
The water monitoring data of Penaeus vannamei is obtained, wherein, water monitoring data includes：Water temperature, dissolved oxygen, salinity,
PH value, NH3Concentration and turbidity.
Further, it is described that leukoderma Early-warning Model is built based on rough set theory, including：
Decision table is set up, wherein decision table is described using following four-tuple：
DT=(U, C ∪ D, V, f)；
Wherein, U is domain, and U={ R1, R2 ..., Rn }, n are the regular number in decision table, and C is conditional attribute collection, and D is for certainly
Plan property set, V are the codomains of information function f, and f is the information function of decision table；
Using water monitoring data as conditional attribute collection C, C={ water temperature, dissolved oxygen, PH, NH3, turbidity, salinity }；
Using advanced warning grade as decision kind set D, D={ without police, low police, middle police, is warned } again.
Further, the leukoderma Early-warning Model according to the water monitoring data and structure for obtaining, determines that South America is right in vain
The leukodermal advanced warning grade of shrimp, including：
Using the water monitoring data for obtaining, according to the decision table set up, using the method for forward reasoning to decision ruless
Make inferences, obtain the leukodermal advanced warning grade of Penaeus vannamei.
Further, methods described also includes：Yojan process is carried out to the decision table.
As shown from the above technical solution, the Penaeus vannamei leukoderma monitoring based on rough set theory that the present invention is provided is pre-
Alarm system, is primarily based on rough set theory and builds leukoderma Early-warning Model, then according to the water monitoring data and structure for obtaining
The leukoderma Early-warning Model built, determines the leukodermal advanced warning grade of Penaeus vannamei.The present invention provide based on rough set theory
Penaeus vannamei leukoderma monitoring early-warning system, early warning analysis leukodermal to Penaeus vannamei can be realized, be conducive to section
Rational planning cultivation is learned, cultivation loss is reduced, is increased culture benefit.
Description of the drawings
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
Accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are the present invention
Some embodiments, for those of ordinary skill in the art, on the premise of not paying creative work, can be with basis
These accompanying drawings obtain other accompanying drawings.
Fig. 1 is the Penaeus vannamei leukoderma monitoring early-warning system based on rough set theory that the embodiment of the present invention one is provided
Structural representation；
Fig. 2 is the Penaeus vannamei leukoderma monitoring and early warning method based on rough set theory that the embodiment of the present invention two is provided
Flow chart；
The Penaeus vannamei leukoderma monitoring and early warning method based on rough set theory that Fig. 3 embodiment of the present invention two is provided
Actual process schematic diagram.
Specific embodiment
To make purpose, technical scheme and the advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, clear, complete description is carried out to the technical scheme in the embodiment of the present invention, it is clear that described embodiment is
The a part of embodiment of the present invention, rather than the embodiment of whole.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.
The embodiment of the present invention one provides a kind of Penaeus vannamei leukoderma monitoring early-warning system based on rough set theory,
Referring to Fig. 1, the monitoring early-warning system includes：Alert source information acquisition module 11, Early-warning Model builds module 12 and warning module 13,
Wherein：
Alert source information acquisition module 11, for obtaining the water monitoring data of Penaeus vannamei；
Early-warning Model builds module 12, for building leukoderma Early-warning Model based on rough set theory；
Warning module 13, for the water monitoring data that obtained according to the alert source information acquisition module 11 and described pre-
The leukoderma Early-warning Model that alert model construction module 12 is set up, determines the leukodermal advanced warning grade of Penaeus vannamei.
Penaeus vannamei leukoderma monitoring early-warning system based on rough set theory provided in an embodiment of the present invention, first base
Leukoderma Early-warning Model is built in rough set theory, then the leukoderma early warning according to the water monitoring data and structure for obtaining
Model, determines the leukodermal advanced warning grade of Penaeus vannamei.South America based on rough set theory provided in an embodiment of the present invention is white
Prawn white spot disease monitoring early-warning system, can realize early warning analysis leukodermal to Penaeus vannamei, be conducive to scientific and reasonable
Planning cultivation, reduces cultivation loss, increases culture benefit.
Preferably, the alert source information acquisition module 11, specifically for：
The water monitoring data of Penaeus vannamei is obtained, wherein, water monitoring data includes：Water temperature, dissolved oxygen, salinity,
PH value, NH3Concentration and turbidity.
The leukodermal pathogenic factorss of Penaeus vannamei are complex, the leukodermal outburst of Penaeus vannamei often it is various because
The result that element interacts.These factors mainly include raw factor in shrimp body (mainly including body weight, trophophase, Immunity etc.)
In terms of external factor (mainly including water quality factors, meteorological factor, source of disease factor etc.) two.The Alert promise index related to leukoderma
For shrimp food ration die-off, empty stomach, do not ingest, significantly white or faint yellow speckle on swim powerless, delay of response, carapace,
Body colour is dark or faint yellow etc..As leukoderma has rapid onset, the features such as mortality rate is high, it is desirable to which early warning system should have real-time
Property, accuracy and automaticity, therefore, the embodiment of the present invention have chosen can be using the water quality factors of direct access as Penaeus vannamei
The early warning factor of leukoderma disease, chooses 6 water quality factors, including：Water temperature, dissolved oxygen, salinity, pH value, NH3Concentration and turbid
Degree.
Further, the source of disease Monitoring Data of Penaeus vannamei, for example, vibrio can also be obtained.Source of disease Monitoring Data is (such as
Vibrio) the early warning shape of current cultivation environment can directly be determined according to the size of source of disease Monitoring Data as Alert promise index
Condition, to remind user to carry out respective handling.For example, with reference to Fig. 3, when vibrio concentration is more than preset value, it is possible to directly send
Warning message, to remind user that current cultivation environment occurs in that undesirable condition, needs to check in time and process.
Preferably, the Early-warning Model builds module 12, specifically for：
Decision table is set up, wherein decision table is described using following four-tuple：
DT=(U, C ∪ D, V, f)；
Wherein, U is domain, and U={ R1, R2 ..., Rn }, n are the regular number in decision table, and C is conditional attribute collection, and D is for certainly
Plan property set, V are the codomains of information function f, and f is the information function of decision table；
Using water monitoring data as conditional attribute collection C, C={ water temperature, dissolved oxygen, PH, NH3, turbidity, salinity }；
Using advanced warning grade as decision kind set D, D={ without police, low police, middle police, is warned } again.
In the present embodiment, can build original decision table mode is inserted according to the lookup of document and expert.For example, according to
The data that table 1 below shows build original decision table.
Table 1
For example, when water temperature is 31～35 DEG C or 15～19 DEG C, advanced warning grade is low police；When water temperature is 36～40 DEG C or 10
It is middle police when～14 DEG C；Attach most importance to when water temperature is 41～45 DEG C or when 5～9 DEG C police.For other specification can be similar to setting, this
In no longer illustrate one by one.
Wherein, the decision table that rough set (Rough Set, abbreviation RS) directly can be constituted with data makes inferences, and is not required to
Want any additional or extra condition.Decision table is described with a four-tuple：
DT=(U, C ∪ D, V, f)；
Wherein U is domain, and C is conditional attribute collection, and D is decision kind set, and V is the codomain of information function f, and f is decision table
Information function.By to monitoring, class set and advanced warning grade collection carry out the foundation of equivalence class and equivalent partition introduces four-tuple DT,
U={ R1, R2 ..., Rn } (n is the regular number in decision table), C={ water temperature, dissolved oxygen, PH, NH can be drawn3, turbidity, salt
Degree }, D={ without police, low police, middle police, is warned } again, creates the rough set of Penaeus vannamei leukoderma early warning system.
Preferably, the warning module 13, specifically for：
Using the water monitoring data for obtaining, according to the decision table set up, using the method for forward reasoning to decision ruless
Make inferences, obtain the leukodermal advanced warning grade of Penaeus vannamei.
Here, build in research in the Penaeus vannamei leukoderma Early-warning Model based on rough set theory, the early warning mould
Block 13 carries out forward reasoning, verification algorithm specifically for the water monitoring data input Early-warning Model by Penaeus vannamei.Algorithm
Forward Reasoning is described as follows：The water monitoring data of input culture of Penaeus vannamei water environment carries out Fuzzy processing life
Into alert source data；Carry out early warning computing and whether operation result is had comprising description phase with the rule base multilevel iudge in data base
The rule of water quality situation is answered, if including, analytic operation, early warning result is provided；Next step is performed otherwise.According to conflict resolution
Strategy, selects a rule from currently available Knowledge Set and makes inferences, and the new rule released is added rule in data base
In storehouse.Source of disease data need manual detection, if any data input, can improve early warning precision, help out.User passes through the prediction
Method can carry out early warning to Penaeus vannamei leukoderma, be beneficial to scientific and reasonable planning cultivation, reduce cultivation loss, increase
Culture benefit.
Preferably, the system also includes：Update module 14；The update module 14, for carrying out to the decision table
Yojan is processed.
Wherein, Penaeus vannamei leukoderma early warning system can make rule according to the time length for coming into operation, the difference of region
Then there is redundancy in Ji Ku, declines system computing capacity.In order to optimize system process performance, the present invention carries out yojan to decision table
Process.Consistency check, attribute reduction and attribute are carried out respectively by the conditional attribute to strictly all rules using rough set theory
New decision table is drawn after value yojan.
On the basis of dividing to original decision table, draw each parameters of conditional attribute collection C with regard to each in decision kind set D
The importance degree of parameter.
WhereinWith the NH in conditional attribute3For height, the rule comprising this conditional attribute in domain U is pressed into pre-
Alert grade carries out division π (U)1, π (U)2, π (U)3, π (U)4.Substitute into formula to obtain a result.Meanwhile, occur rule set with
Monitoring Data without matching it is regular when, so that it may in the hope of Monitoring Data each property value and advanced warning grade importance degree sum, according to
As a result the advanced warning grade high to choose importance degree, generates new rule and enters rule set.
The decision table that rough set (Rough Set, abbreviation RS) directly can be constituted with data makes inferences, and needs any attached
Plus or extra condition.Decision table is described with a four-tuple.
DT=(U, C ∪ D, V, f)；
Wherein U is domain, and C is conditional attribute collection, and D is decision kind set, and V is the codomain of information function f, and f is decision table
Information function.By to monitoring, class set and advanced warning grade collection carry out the foundation of equivalence class and equivalent partition introduces four-tuple DT,
U={ R1, R2 ..., Rn } (n is the regular number in decision table), C={ water temperature, dissolved oxygen, PH, NH3, turbidity, salt can be drawn
Degree }, D={ without police, low police, middle police, is warned } again, creates the rough set of Penaeus vannamei leukoderma early warning system.
Penaeus vannamei leukoderma early warning system can make rule set according to the time length for coming into operation, the difference of region
There is redundancy in storehouse, declines system computing capacity.In order to optimize system process performance, yojan process is carried out to decision table.Utilize
Rough set theory carries out consistency check, attribute reduction and Value reduction respectively by the conditional attribute to strictly all rules in table
New decision table is drawn afterwards.Brief algorithm steps are as follows：
(1) difference is set up according to the definition of decision table (DT) differential matrix for setting up Penaeus vannamei leukoderma early warning system
Matrix Mn×n(DT)=(cij)n×nLower triangular matrix, wherein i, j=1,2 ..., n (n is the regular number in decision table)
(2) all elements of differential matrix are searched for, if not havingStep (3) is then gone to, is otherwise exited.
(3) all single property element in searching matrix, is assigned to COREC(D) export
(4) all possible combinations of attributes comprising relative D cores is obtained, judges whether to meet following rule：
①WhenWhen, if havingDo not considerFeelings
Shape；
2. whether B is independent.
If meeting above-mentioned rule, RED is assigned it toC(D) all combinations of attributes comprising relative D cores are traveled through,；
(5) export REDC(D), algorithm terminates to draw final decision table.
On the basis of dividing to original decision table, draw each parameters of conditional attribute collection C with regard to each in decision kind set D
The importance degree of parameter.
WhereinWith the NH in conditional attribute3For height, the rule comprising this conditional attribute in domain U is pressed into pre-
Alert grade carries out division π (U)1, π (U)2, π (U)3, π (U)4.Substitute into formula to obtain a result.Meanwhile, occur rule set with
Monitoring Data without matching it is regular when, so that it may in the hope of Monitoring Data each property value and advanced warning grade importance degree sum, according to
As a result the advanced warning grade high to choose importance degree, generates new rule and enters rule set.
Finally, the actually detected data entry system model of Penaeus vannamei water quality is carried out into forward reasoning, verification algorithm.
Algorithm Forward Reasoning is described as follows：
(1) water monitoring data for being input into culture of Penaeus vannamei water environment carries out Fuzzy processing and generates alert source number
According to；
(2) carry out early warning computing and whether operation result is had comprising description phase with the rule base multilevel iudge in data base
Answer the rule of water quality situation, if comprising if analytic operation, provide early warning result；Next step is performed otherwise.
(3) according to Strategy of Conflict Resolution, a rule is selected from currently available Knowledge Set and is made inferences, and by release
In new rule addition data base in rule base, then turn (2).
(4) source of disease data need manual detection, if any data input, can improve early warning precision, help out.
Thus, you can obtain early warning output result.Current cultivation situation comprehensively can be analyzed, be drawn South America
The warning level of white shrimp, and then foundation is provided for cultivation operation.
In summary, the Penaeus vannamei leukoderma monitoring and early warning system based on rough set theory provided in an embodiment of the present invention
System is with following advantage：
1st, Alert source index, Alert promise index and Alert feeling index are analyzed, build Penaeus vannamei leukoderma warning index
System so that the result of early warning more science.
2nd, by the attribute reduction process based on rough set theory, and the increase of new decision-making, realize the renewal to knowledge base.
3rd, the reasoning flow process of algorithm is realized using the method for forward reasoning, it is ensured that early warning result it is scientific and reasonable.
The embodiment of the present invention two provides a kind of Penaeus vannamei leukoderma monitoring and early warning method based on rough set theory,
Referring to Fig. 2, the method comprises the steps：
Step 101：Obtain the water monitoring data of Penaeus vannamei.
Step 102：Leukoderma Early-warning Model is built based on rough set theory.
Step 103：According to the leukoderma Early-warning Model of the water monitoring data and structure for obtaining, Penaeus vannamei is determined
Leukodermal advanced warning grade.
Preferably, the step 101 is specifically included：
The water monitoring data of Penaeus vannamei is obtained, wherein, water monitoring data includes：Water temperature, dissolved oxygen, salinity,
PH value, NH3Concentration and turbidity.
Preferably, the step 102 is specifically included：
Decision table is set up, wherein decision table is described using following four-tuple：
DT=(U, C ∪ D, V, f)；
Wherein, U is domain, and U={ R1, R2 ..., Rn }, n are the regular number in decision table, and C is conditional attribute collection, and D is for certainly
Plan property set, V are the codomains of information function f, and f is the information function of decision table；
Using water monitoring data as conditional attribute collection C, C={ water temperature, dissolved oxygen, PH, NH3, turbidity, salinity }；
Using advanced warning grade as decision kind set D, D={ without police, low police, middle police, is warned } again.
Preferably, the step 103 is specifically included：
Using the water monitoring data for obtaining, according to the decision table set up, using the method for forward reasoning to decision ruless
Make inferences, obtain the leukodermal advanced warning grade of Penaeus vannamei.
Preferably, methods described also includes：
Step 104：Yojan process is carried out to the decision table.
Build in research in the Penaeus vannamei leukoderma Early-warning Model based on rough set theory, affiliated rough set theory point
Analysis module.Penaeus vannamei leukoderma early warning system can make rule set storehouse according to the time length for coming into operation, the difference of region
There is redundancy, decline system computing capacity.In order to optimize system process performance, the present invention carries out yojan process to decision table.
Consistency check, attribute reduction and Value reduction are carried out respectively by the conditional attribute to strictly all rules using rough set theory
New decision table is drawn afterwards.Wherein specific reduction steps can be found in the introduction of above-described embodiment, no longer describe in detail herein.
On the basis of dividing to original decision table, draw each parameters of conditional attribute collection C with regard to each in decision kind set D
The importance degree of parameter.
WhereinWith the NH in conditional attribute3For height, the rule comprising this conditional attribute in domain U is pressed into pre-
Alert grade carries out division π (U)1, π (U)2, π (U)3, π (U)4.Substitute into formula to obtain a result.Meanwhile, occur rule set with
Monitoring Data without matching it is regular when, so that it may in the hope of Monitoring Data each property value and advanced warning grade importance degree sum, according to
As a result the advanced warning grade high to choose importance degree, generates new rule and enters rule set.
Method provided in an embodiment of the present invention, can adopt above-described embodiment described in system performed, its principle and
Technique effect is similar to, and no longer describes in detail herein.
It should be noted that herein, such as first and second or the like relational terms are used merely to a reality
Body or operation are made a distinction with another entity or operation, and are not necessarily required or implied these entities or deposit between operating
In any this actual relation or order.And, term " including ", "comprising" or its any other variant are intended to
Nonexcludability is included, so that a series of process, method, article or equipment including key elements not only will including those
Element, but also including other key elements being not expressly set out, or also include for this process, method, article or equipment
Intrinsic key element.In the absence of more restrictions, the key element for being limited by sentence "including a ...", it is not excluded that
Also there is other identical element in process, method, article or equipment including the key element.
Above example is merely to illustrate technical scheme, rather than a limitation；Although with reference to the foregoing embodiments
The present invention has been described in detail, it will be understood by those within the art that：Which still can be to aforementioned each enforcement
Technical scheme described in example is modified, or carries out equivalent to which part technical characteristic；And these are changed or replace
Change, do not make the essence of appropriate technical solution depart from the spirit and scope of various embodiments of the present invention technical scheme.
Claims (10)
1. a kind of Penaeus vannamei leukoderma monitoring early-warning system based on rough set theory, it is characterised in that include：
Alert source information acquisition module, for obtaining the water monitoring data of Penaeus vannamei；
Early-warning Model builds module, for building leukoderma Early-warning Model based on rough set theory；
Warning module, for water monitoring data and the Early-warning Model structure according to the alert source information acquisition module acquisition
The leukoderma Early-warning Model that modeling block is set up, determines the leukodermal advanced warning grade of Penaeus vannamei.
2. system according to claim 1, it is characterised in that the alert source information acquisition module, specifically for：
The water monitoring data of Penaeus vannamei is obtained, wherein, water monitoring data includes：Water temperature, dissolved oxygen, salinity, pH value,
NH3Concentration and turbidity.
3. system according to claim 2, it is characterised in that the Early-warning Model builds module, specifically for：
Decision table is set up, wherein decision table is described using following four-tuple：
DT=(U, C ∪ D, V, f)；
Wherein, U is domain, and U={ R1, R2 ..., Rn }, n are the regular number in decision table, and C is conditional attribute collection, and D is that decision-making belongs to
Property collection, V is the codomain of information function f, and f is the information function of decision table；
Using water monitoring data as conditional attribute collection C, C={ water temperature, dissolved oxygen, PH, NH3, turbidity, salinity }；
Using advanced warning grade as decision kind set D, D={ without police, low police, middle police, is warned } again.
4. system according to claim 3, it is characterised in that the warning module, specifically for：
Using the water monitoring data for obtaining, according to the decision table set up, decision ruless are carried out using the method for forward reasoning
Reasoning, obtains the leukodermal advanced warning grade of Penaeus vannamei.
5. the system according to claim 3 or 4, it is characterised in that the system also includes：Update module；The renewal
Module, for carrying out yojan process to the decision table.
6. a kind of Penaeus vannamei leukoderma monitoring and early warning method based on rough set theory, it is characterised in that include：
Obtain the water monitoring data of Penaeus vannamei；
Leukoderma Early-warning Model is built based on rough set theory；
According to the leukoderma Early-warning Model of the water monitoring data and structure for obtaining, the leukodermal early warning of Penaeus vannamei is determined
Grade.
7. method according to claim 6, it is characterised in that the water monitoring data of the acquisition Penaeus vannamei, bag
Include：
The water monitoring data of Penaeus vannamei is obtained, wherein, water monitoring data includes：Water temperature, dissolved oxygen, salinity, pH value,
NH3Concentration and turbidity.
8. method according to claim 7, it is characterised in that described that leukoderma early warning mould is built based on rough set theory
Type, including：
Decision table is set up, wherein decision table is described using following four-tuple：
DT=(U, C ∪ D, V, f)；
Wherein, U is domain, and U={ R1, R2 ..., Rn }, n are the regular number in decision table, and C is conditional attribute collection, and D is that decision-making belongs to
Property collection, V is the codomain of information function f, and f is the information function of decision table；
Using water monitoring data as conditional attribute collection C, C={ water temperature, dissolved oxygen, PH, NH3, turbidity, salinity }；
Using advanced warning grade as decision kind set D, D={ without police, low police, middle police, is warned } again.
9. method according to claim 8, it is characterised in that according to the white macula of the water monitoring data and structure for obtaining
Sick Early-warning Model, determines the leukodermal advanced warning grade of Penaeus vannamei, including：
Using the water monitoring data for obtaining, according to the decision table set up, decision ruless are carried out using the method for forward reasoning
Reasoning, obtains the leukodermal advanced warning grade of Penaeus vannamei.
10. method according to claim 8 or claim 9, it is characterised in that methods described also includes：The decision table is carried out
Yojan is processed.
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