CN103425513B - Model automatic update method is supported in a kind of forest business decision - Google Patents
Model automatic update method is supported in a kind of forest business decision Download PDFInfo
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Abstract
Model automatic update method is supported in the open a kind of forest business decision of the present invention, comprising: obtain up-to-date sample-plot survey data, is imported in sample ground data sheet, the date when Data Update date in timetable is updated to sample-plot survey data importing; If timetable not having the record on model modification date or the model modification date of record are less than the Data Update date, then according to the current date, the model modification date in timetable is upgraded; Classification according to data in sample-plot survey data, read the model independent variable(s) in sample ground data sheet and model dependent variable, utilize homing method that model prototype in the alternative table of model is carried out model-fitting, obtaining parameter and the relation conefficient of each model, parameter and relation conefficient to model prototype each in the alternative table of model upgrade; The model prototype selecting relation conefficient maximum from the alternative table of model and parameter thereof, according to this model prototype and parametric configuration model expression thereof, and then model expression corresponding in Renewal model table.
Description
Technical field
The present invention relates to field of forestry, specifically, it relates to model automatic update method is supported in a kind of forest business decision.
Background technology
Model in forest reserves management management decision support system along with accumulation modeling data increase need matching again or reselect, to improve the exactness of decision-making. Current technology is when artificially judging whether modeling data changes, if changed, it is then determined that the need of to model again matching or reselect. If it is determined that need model again matching or reselect, modeling data after then needing professional to adopt statistical software instrument to utilize change re-starts matching, whether analytical model changes, once it is determined that model changes, needing professional programmers's compiling of again system source program being modified just can complete the amendment to model, or in manual modification model bank, model and parameter completes the amendment of model. Prior art model modification is delayed, operates loaded down with trivial details, it is necessary to professional carries out, and cost is higher, for these problems, consults relevant data and does not find the research related to the present invention in this field and report, propose the present invention accordingly.
Summary of the invention
The present invention provides a kind of forest business decision to support model automatic update method, in order to overcome in prior art at least one problem existed.
For achieving the above object, the present invention provides a kind of forest business decision and supports model automatic update method, comprises the following steps:
Obtain up-to-date sample-plot survey data, imported in sample ground data sheet, and the date when Data Update date in timetable is updated to sample-plot survey data importing;
If timetable not having the record on model modification date or the model modification date of record are less than the Data Update date, then according to the current date, the model modification date in timetable is upgraded;
Classification according to data in sample-plot survey data, read the model independent variable(s) in sample ground data sheet and model dependent variable, homing method is utilized respectively model prototypes all in the alternative table of model to be carried out model-fitting, obtain parameter and the relation conefficient of each model, and the parameter and relation conefficient to model prototype each in the alternative table of model upgrades respectively, wherein relation conefficientR in formula2Represent relation conefficient, yiRepresent the field actual measured value of model dependent variable,Represent the mean value of the field actual measured value of model dependent variable,Represent that m is natural number by the theoretical value of gained model dependent variable after model independent variable(s) substitution model;
The model prototype selecting relation conefficient maximum from the alternative table of model and parameter thereof, according to this model prototype and parametric configuration model expression thereof, and the model expression by the correspondence in this model expression Renewal model table;
Wherein, the model modification date stored in timetable is date during update method operation; The Data Update date is by date during data importing sample ground, sample ground data sheet; What the model prototype in the alternative table of model stored is the model not having concrete parameter value; What the model expression in model table stored is the model after parameter substitutes into model prototype.
Optionally, if the model modification date of record is not less than the Data Update date in timetable, then the model expression in model table does not need to upgrade.
Optionally, in the alternative table of model, each model prototype and parameter thereof, relation conefficient preserve according to numbering, and the model expression in model table is also preserve according to the numbering of corresponding model prototype.
Optionally, model is that site index curves or growth are gathered in the crops and prediction model.
In the above-described embodiments, by forest business decision is supported that model upgrades automatically, ensure that the on the spot index in the model bank of forest reserves management management decision support system, forest reserves operation control system, growth results and the model moment such as estimate and be in last state, when data change also without the need to reprogramming, drastically increase efficiency; By time controling, sample ground data can be carried out matching by system automatically; And the present invention can realize automatically selecting excellent, it is provided that the most applicable model, it is to increase the accuracy rate of result.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, it is briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.
Model automatic update method schema is supported in the forest business decision that Fig. 1 is one embodiment of the invention;
Model automatic update method program flowchart is supported in the forest business decision that Fig. 2 is a preferred embodiment of the invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is only the present invention's part embodiment, instead of whole embodiments. Based on the embodiment in the present invention, those of ordinary skill in the art, not paying other embodiments all obtained under creative work prerequisite, belong to the scope of protection of the invention.
Model automatic update method schema is supported in the forest business decision that Fig. 1 is one embodiment of the invention. When specific implementation the present embodiment, it is possible to adopting relational database to build model bank in advance, the list structure of model bank comprises:
Timetable, for preservation model update date and Data Update date;
Sample ground data sheet, for numbering with preserving sample, seeds, model independent variable(s), model dependent variable etc.;
The alternative table of model, for the numbering of preservation model, model prototype, parameter 1, parameter 2 ..., parameter n, coefficient R2;
Model table, for the numbering of preservation model, model expression.
Wherein, timetable stores the model modification date, date when namely model update method runs; The Data Update date, by date during data importing sample ground, sample ground data sheet. In the alternative table of model, the numbering of model is identical with the numbering of model in model table. What the model prototype in the alternative table of model stored is the model not having concrete parameter value. What in model table, model expression stored is that parameter substitutes into model after model prototype.
As shown in Figure 1, this forest business decision supports that model automatic update method comprises the following steps:
S110, obtains up-to-date sample-plot survey data, is imported in sample ground data sheet, and the date when Data Update date in timetable is updated to sample-plot survey data importing;
S120, if not having the record on model modification date or the model modification date of record to be less than the Data Update date in timetable, then upgrades the model modification date in timetable according to the current date;
When this step of specific implementation, it is possible to first judge whether timetable has the record on model modification date, if there is no record, then illustrate that this model automatic update method runs first, then using the current date as the model modification date in timetable; If timetable has the record on model modification date, then so judge record the model modification date whether be less than the Data Update date, if the model modification date is less than the Data Update date, then illustrate that modeling data changes, need to be upgraded by model, according to the current date, the model modification date in timetable is upgraded, and then model is upgraded by execution subsequent step; If the model modification date is not less than the Data Update date, then illustrate that modeling data does not change, it is not necessary to upgraded by model.
S130, classification according to data in sample-plot survey data, read the model independent variable(s) in sample ground data sheet and model dependent variable, homing method is utilized respectively model prototypes all in the alternative table of model to be carried out model-fitting, obtain parameter and the relation conefficient of each model, and the parameter and relation conefficient to model prototype each in the alternative table of model upgrades respectively;
Wherein sample-plot survey data preserve according to classification, when this step of specific implementation, it is possible to the type corresponding with model dependent variable according to the model independent variable(s) in sample ground data sheet carries out the reading of data, and then carries out the regression fit of model.
Wherein, relation conefficient for the degree of closeness of the field survey value and theoretical value that represent dependent variable, relation conefficient R2Representing, concrete calculation formula isY in formulaiRepresent the field actual measured value of dependent variable,Represent the mean value of the field actual measured value of dependent variable,Representing the theoretical value that independent variable(s) substitutes into gained dependent variable after model, m is natural number. When dependent variable field survey value and theoretical value is more close shows the more high then R of dependency2More big, on the contrary R2More little. By relation conefficient can decision model fitting degree size, namely this model-fitting degree of the more big expression of relation conefficient is more high, reaches, with this, the foundation whether decision model be applicable to, thus uses this model more can reflect the problem to be studied.
S140, the model prototype selecting relation conefficient maximum from the alternative table of model and parameter thereof, according to this model prototype and parametric configuration model expression thereof, and the model expression by the correspondence in this model expression Renewal model table.
Such as, in above-described embodiment, in the alternative table of model, each model prototype and parameter thereof, relation conefficient preserve according to numbering, and the model expression in model table is also preserve according to the numbering of corresponding model prototype, is convenient to searching fast and mating of data.
Model automatic update method program flowchart is supported in the forest business decision that Fig. 2 is a preferred embodiment of the invention. As shown in Figure 2, this program realizes specifically comprising the following steps:
(1) when user obtains new sample-plot survey data, imported in sample ground data sheet, date when then " the Data Update date " in timetable being revised as sample-plot survey data importing;
(2) first install model automatically more new procedure time, by " the model modification date " in current write time on date table, and matching data can be called and start to carry out renewal rewards theory after program initialize; If not being install and use for the first time, Boot Model more new procedure automatically, in procedure judges timetable, whether the model modification date is greater than the Data Update date. If the model modification date is greater than the Data Update date, program does not carry out model modification, exits this program; If the model modification date is not more than the Data Update date, program carries out model modification;
(3) if the model modification date is less than the Data Update date, program is by " model modification date " attribute in current system date renewal timetable; Read " model independent variable(s) " and " model dependent variable " difference assignment in sample ground data sheet to variable corresponding in the automatic more new procedure of model, call recurrence program and respectively all models in the alternative table of model are carried out model-fitting, obtain parameter and the coefficient R of each model2, parameter and relation conefficient respectively and to model prototype in the alternative table of model upgrade;
(4) record selecting relation conefficient maximum from the alternative table of model, read numbering, model prototype and parameter, utilize model prototype and parametric configuration model expression, then with the model expression of the reference numeral in this model expression substitution model table, complete to upgrade.
The model realized with the development technique of .NET technology Calling MATLAB software automatically more new procedure (model automatically more the statistical software called of new procedure can have multiple, not only only limit MATLAB), adopt above-mentioned " a kind of forest business decision support model automatic update method " contrario Environmental Evaluation Model be updated to example, the model principle that more new procedure realizes automatically is as follows:
(1) after the first installation and operation initialize of model modification program, obtain the current date, (relation schema is Time (UpdateTime to write time table, DataTime), wherein UpdateTime is the model modification date, DataTime is the Data Update date) " model modification date " attribute (UpdateTime), (relation schema is ydsj (ydid to reading database sample ground data sheet, TreeName, age, hight_avg), wherein ydid be sample number, TreeName is seeds, model independent variable(s) age is the standing forest age, model dependent variable hight_avg is that the flat wood of standing forest advantage wood is average high) in matching data, and use SQL (StructuredQueryLanguage, structuralized query language) statement reads standing forest age (age) and standing forest average high of superior tree (hight_avg) data record in matching data. the data that the SQL statement of preservation relation database reads, and give corresponding variable by age and average high of superior tree difference assignment.
(2) equation prototype is carried out matching by Calling MATLAB software, obtains parameter and coefficient R2, and (relation schema is bxmx (ID, mx to the alternative table of model, a, b, c, xgxs), ID representative model is numbered, and mx represents equation prototype, a, b, c representation parameter, xgxs represents relation conefficient) in the parameter (a of equation prototype, b, c) and relation conefficient (xgxs) upgrade.
(3) the record selecting relation conefficient maximum from the alternative table of model, read numbering (ID), model prototype (mx) and parameter (a, b, c), utilizing model prototype and parametric configuration site index curves, then (relation schema is Model (ID to substitution model table, mxbds), ID representative model is numbered, mxbds representative model expression formula) in the site index curves (mxbds) of reference numeral, complete to upgrade.
(4) when model modification program is not install and use for the first time, program starts whether " model modification date " (UpdateTime) in rear differentiation timetable automatically is greater than " Data Update date " (DataTime), if be greater than, directly exit, otherwise read the matching data in reading database sample ground data sheet, start to carry out model modification.
The following is the computer program that on the spot index assessment model upgrades automatically adopts the method for .NET Calling MATLAB algorithm to realize.
1. newly-built some .m files in MATLAB software. A model prototype in the corresponding alternative table of model of each .m file. .m in file, code is as follows, and the 1st row code in this document reads the data in sample ground data sheet, and the 2nd row code is model prototype.
Functionf=curve_fun (x, Adata)
F=x (1) * (1-exp (-x (2) * Adata)) .^x (3);
2. use MATLABDeployTool by step 1. in some .m file generateds some .Net assemblies.
3. in .NET development platform, add quoting of MATLAB assembly.
UsingMathWorks;
UsingMathWorks.MATLAB;
UsingMathWorks.MATLAB.NET.WebFigures;
UsingMathWorks.MATLAB.NET.Arrays;
UsingMathWorks.MATLAB.NET.Utility;
UsingMLApp;
Usingcurve_fun;
4. system operation time is obtained and write into Databasce
Stringdate=DateTime.Now.ToString (" D ");
InsertintoTime(UpdateTime)values(date)
5. the matching data in sample ground database are read.
6. C# data type conversion becomes MWArray type, and the dynamic link storehouse that Calling MATLAB generates carries out model-fitting.
MWNumericArrayAdata=newMWNumericArray (x);
MWNumericArrayHdata=newMWNumericArray (y);
Curve_fun.csA=newcs ();
MWArrayResult=null;
Result=A.cs1 (Adata, Hdata);
7. fitting result is converted to C# data type, and model in the alternative table of model is upgraded
8. the record selecting relation conefficient maximum from the alternative table of model, reads numbering, model prototype and parameter, utilizes model prototype and parametric configuration site index curves, then the site index curves of the reference numeral in substitution model table, completes to upgrade.
Stringsqlmx=" selectID, mx, a, b, cfrombxmxwherexgxs=(selectmax (xgxs) frombxmx) ";
SqlDataAdaptersqlApt=newSqlDataAdapter (sqlmx, con);
SqlApt.Fill (ds, " cs ");
Dt=ds.Tables [" cs "];
Stringid=dt.Rows [0] [0] .ToString ();
Stringa=dt.Rows [0] [1] .ToString ();
Stingb=dt.Rows [0] [2] .ToString ();
Stringc=dt.Rows [0] [3] .ToString ();
Doublea=Convert.ToDouble (a);
Doubleb=Convert.ToDouble (b);
Doublec=Convert.ToDouble (c);
StringASQL=" selectreplace (replace (replace (mx, ' a', ' "+a+ " '); ' b'; ' " + b+ " '), ' c', ' "+c+ " ') frombxmxwherexgxs=(selectmax (xgxs) frombxmx) ";
SqlDataAdapterda1=newSqlDataAdapter (ASQL, con);
Da1.Fill (ds, " mx ");
Dt1=ds.Tables [" mx "];
Stringmx=dt1.Rows [0] [0] .ToString ();
Stringupdatemx=" updateModelsetmxbds=' "+mx+ " ' whereID=' "+id+ " ' ";
9. judge in timetable, whether the model modification date is greater than the Data Update date, if being less than, calling more new procedure and upgrading, otherwise terminate.
In the above-described embodiments, by forest business decision is supported that model upgrades automatically, ensure that the on the spot index in the model bank of forest reserves management management decision support system, forest reserves operation control system, growth results and the model moment such as estimate and be in last state, when data change also without the need to reprogramming, drastically increase efficiency; By time controling, sample ground data can be carried out matching by system automatically; And the present invention can realize automatically selecting excellent, it is provided that the most applicable model, it is to increase the accuracy rate of result.
One of ordinary skill in the art will appreciate that: accompanying drawing is the schematic diagram of an embodiment, module or flow process in accompanying drawing might not be that enforcement the present invention is necessary.
One of ordinary skill in the art will appreciate that: the module in device in embodiment can describe according to embodiment and be distributed in the device of embodiment, it is also possible to carries out respective change and is arranged in the one or more devices being different from the present embodiment. The module of above-described embodiment can merge into a module, it is also possible to splits into multiple submodule block further.
Last it is noted that above embodiment is only in order to illustrate the technical scheme of the present invention, it is not intended to limit; Although with reference to previous embodiment to invention has been detailed description, it will be understood by those within the art that: the technical scheme described in previous embodiment still can be modified by it, or wherein part technology feature is carried out equivalent replacement; And these amendments or replacement, do not make the spirit and scope of the essence disengaging embodiment of the present invention technical scheme of appropriate technical solution.
Claims (4)
1. model automatic update method is supported in a forest business decision, it is characterised in that, comprise the following steps:
Obtain up-to-date sample-plot survey data, imported in sample ground data sheet, and the date when Data Update date in timetable is updated to sample-plot survey data importing;
If described timetable not having the record on model modification date or the described model modification date of record are less than the described Data Update date, then according to the current date, the described model modification date in described timetable is upgraded;
Classification according to data in described sample-plot survey data, read the model independent variable(s) in described sample ground data sheet and model dependent variable, homing method is utilized respectively model prototypes all in the alternative table of model to be carried out model-fitting, obtain parameter and the relation conefficient of each model, and the parameter and relation conefficient to model prototype each in the alternative table of described model upgrades respectively, wherein relation conefficientR in formula2Represent relation conefficient, yiRepresent the field actual measured value of model dependent variable,Represent the mean value of the field actual measured value of model dependent variable,Represent that m is natural number by the theoretical value of gained model dependent variable after model independent variable(s) substitution model;
The model prototype selecting relation conefficient maximum from the alternative table of described model and parameter thereof, according to this model prototype and parametric configuration model expression thereof, and the model expression by the correspondence in this model expression Renewal model table;
Wherein, the described model modification date stored in described timetable is the date during operation of described update method; The described Data Update date be by sample ground data importing described in sample ground data sheet time date; What the described model prototype in the alternative table of described model stored is the model not having concrete parameter value; What the described model expression in described model table stored is the model after parameter substitutes into model prototype.
2. update method according to claim 1, it is characterised in that, if the described model modification date of record is not less than the described Data Update date in described timetable, then the model expression in described model table does not need to upgrade.
3. update method according to claim 1, it is characterised in that, in the alternative table of described model, each model prototype and parameter thereof, relation conefficient preserve according to numbering, and the model expression in described model table is also preserve according to the numbering of corresponding model prototype.
4. update method according to claim 1, it is characterised in that, described model is that site index curves or growth are gathered in the crops and prediction model.
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| 《森林培育专家决策支持系统的研究》;吴保国，李成赞，马驰;《北京林业大学学报》;20091115;第31卷;全文 * |
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