CN103425513A - Automatic update method for forest operating decision support model - Google Patents
Automatic update method for forest operating decision support model Download PDFInfo
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Abstract
The invention discloses an automatic update method for a forest operating decision support model. The method comprises the following steps: acquiring latest sample plot investigation data, introducing into a sample plot data sheet, and updating the data update data in the data sheet to the date of introduction of the sample plot investigation data; if record of a model update date is unavailable in a schedule or a recorded model update date is earlier than the data update date, updating the model update date in the schedule according to a current date; reading a model independent variable and a model dependent variable in the sample plot data sheet according to the types of data in the sample plot investigation data, performing model fitting on model prototypes in a model selection table by using a regression method to obtain the parameters and relevant coefficients of each model, and updating the parameters and relevant coefficients of each model prototype in the model selection table; and selecting a model prototype with a largest correlation coefficient and parameters thereof from the model selection table, constructing a model expression according to the model prototype and parameters thereof, and updating a corresponding model expression in the model table.
Description
Technical field
The present invention relates to field of forestry, in particular to a kind of orest management decision support template automatic update method.
Background technology
Model in forest reserves management management decision support system is along with the modeling data of accumulation increases matching again or reselects, to improve the correctness of decision-making.Current technology is when artificially judging whether modeling data changes, if change, then determines whether or to reselect model matching again.If determine and need to or reselect model matching again, need the professional to adopt the modeling data after statistical software instrument utilization changes to re-start matching, whether analytical model changes, once determine that model changes, to the system source program, the compiling of modifying just can complete the modification to model again to need specialized procedure person, or in the manual modification model bank, model and parameter completes the modification of model.The prior art model modification lags behind, and complex operation, need the professional to carry out, and cost is higher, for these problems, consults related data and does not find the research related to the present invention in this field and report, proposes accordingly the present invention.
Summary of the invention
The invention provides a kind of orest management decision support template automatic update method, in order to overcome at least one problem existed in prior art.
For achieving the above object, the invention provides a kind of orest management decision support template automatic update method, comprise the following steps:
Obtain up-to-date sample ground enquiry data, it is imported in sample ground tables of data, and the date when Data Update date in timetable is updated to the enquiry data importing of sample ground;
If do not have the record on model modification date or the model modification date of record to be less than the Data Update date in timetable, according to current date, the model modification date in timetable upgraded;
Classification according to data in sample ground enquiry data, read model independent variable and model dependent variable in sample ground tables of data, utilize homing method respectively all model prototypes in the alternative table of model to be carried out to models fitting, obtain parameter and the related coefficient of each model, and parameter and the related coefficient of each model prototype in the alternative table of model are upgraded respectively to related coefficient wherein
R in formula
2Mean related coefficient, y
iThe field actual measured value that means the model dependent variable,
The mean value that means the field actual measured value of model dependent variable,
Expression is by the theoretical value of gained model dependent variable after model independent variable substitution model, and m is natural number;
Select model prototype and the parameter thereof of related coefficient maximum from the alternative table of model, according to this model prototype and parametric configuration model tormulation formula thereof, and by the model tormulation formula of the correspondence in this model tormulation formula Renewal model table;
Date when the model modification date of wherein, storing in timetable is the update method operation; The Data Update date is the date during by sample ground data importing sample ground tables of data; What the model prototype in the alternative table of model was stored is the model that there is no the design parameter value; What the model tormulation formula in the model table was stored is by the model after parameter substitution model prototype.
Optionally, if the model modification date of recording in timetable is not less than the Data Update date, the model tormulation formula in the model table does not need to upgrade.
Optionally, in the alternative table of model, each model prototype and parameter thereof, Relation Parameters are to preserve according to numbering, and the model tormulation formula in the model table is also to preserve according to the numbering of corresponding model prototype.
Optionally, model is site index curves or growth results and prediction model.
In the above-described embodiments, by the orest management decision support template is upgraded automatically, the model such as guaranteed site index, the growth results in the model bank of forest reserves management management decision support systems, the forest reserves systems of operation and management and estimate is constantly in last state, in the situation that data change also without reprogramming, greatly improved efficiency; Control by the time, system can be carried out matching to sample ground data automatically; And the present invention can realize automatically selecting excellent, and optimal model can be provided, and has improved the accuracy rate of result.
The accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, below will the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain according to these accompanying drawings other accompanying drawing.
The orest management decision support template automatic update method process flow diagram that Fig. 1 is one embodiment of the invention;
The orest management decision support template automatic update method program realization flow figure 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, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Embodiment based in the present invention, those of ordinary skills, not paying under the creative work prerequisite the every other embodiment obtained, belong to the scope of protection of the invention.
The orest management decision support template automatic update method process flow diagram that Fig. 1 is one embodiment of the invention.When specific implementation the present embodiment, can adopt in advance relational database to build model bank, the list structure of model bank comprises:
Timetable, for preservation model update date and Data Update date;
Sample ground tables of data, for preserving sample ground numbering, seeds, model independent variable, model dependent variable etc.;
The alternative table of model, for the numbering of preservation model, the model prototype, parameter 1, parameter 2 ..., parameter n, coefficient R
2
The model table, for the numbering of preservation model, the model tormulation formula.
Wherein, memory model update date in timetable, i.e. date during model update method operation; The Data Update date, the date while being about to sample ground data importing sample ground tables of data.In the alternative table of model, the numbering of model is identical with the numbering of model in the model table.What the model prototype in the alternative table of model was stored is the model that there is no the design parameter value.In the model table, the storage of model tormulation formula is by model after parameter substitution model prototype.
As shown in Figure 1, this orest management decision support template automatic update method comprises the following steps:
S110, obtain up-to-date sample ground enquiry data, it imported in sample ground tables of data, and the date when Data Update date in timetable is updated to the enquiry data importing of sample ground;
S120, if do not have the record on model modification date or the model modification date of record to be less than the Data Update date in timetable, upgraded the model modification date in timetable according to current date;
When this step of specific implementation, can first judge the record whether the model modification date is arranged in timetable, if there is no record, illustrate that this model automatic update method is to move first, the model modification date in timetable using current date; If the record on model modification date is arranged in timetable, so the model modification date of judgement record whether be less than the Data Update date, if the model modification date is less than the Data Update date, illustrate that modeling data changes, need to be upgraded model, according to current date, the model modification date in timetable is upgraded, and then carry out subsequent step model is upgraded; If the model modification date is not less than the Data Update date, illustrate that modeling data does not change, do not need model is upgraded.
S130, classification according to data in sample ground enquiry data, read model independent variable and model dependent variable in sample ground tables of data, utilize homing method respectively all model prototypes in the alternative table of model to be carried out to models fitting, obtain parameter and the related coefficient of each model, and parameter and the related coefficient of each model prototype in the alternative table of model are upgraded respectively;
Wherein sample ground enquiry data is preserved according to classification, and when this step of specific implementation, type that can be corresponding with the model dependent variable according to the model independent variable in sample ground tables of data is carried out reading of data, and then carries out the regression fit of model.
Wherein, related coefficient is for meaning the field survey value of dependent variable and the degree of closeness of theoretical value, related coefficient R
2Mean, specific formula for calculation is
Y in formula
iThe field actual measured value that means dependent variable,
The mean value that means the field actual measured value of dependent variable,
Expression is by the theoretical value of gained dependent variable after independent variable substitution model, and m is natural number.Field survey value and the more approaching higher R of correlativity that shows of theoretical value when dependent variable
2Larger, on the contrary R
2Less.Can decision model fitting degree size by related coefficient, i.e. this models fitting degree of the larger expression of related coefficient is higher, with this, reaches the whether applicable foundation of decision model, thereby uses this model more can reflect the problem that will study.
S140 selects model prototype and the parameter thereof of related coefficient maximum from the alternative table of model, according to this model prototype and parametric configuration model tormulation formula thereof, and by the model tormulation formula of the correspondence in this model tormulation formula Renewal model table.
For example, in above-described embodiment, in the alternative table of model, each model prototype and parameter thereof, Relation Parameters are to preserve according to numbering, and the model tormulation formula in the model table is also to preserve according to the numbering of corresponding model prototype, is convenient to fast finding and the coupling of data.
The orest management decision support template automatic update method program realization flow figure that Fig. 2 is a preferred embodiment of the invention.As shown in Figure 2, this program realizes specifically comprising the following steps:
(1) obtain new sample ground enquiry data as the user, it is imported in sample ground tables of data, the date while then " Data Update date " in timetable being revised as to the enquiry data importing of sample ground;
(2) while the automatic refresh routine of model being installed for the first time, can be by " the model modification date " in current date write time table after program initialization, and call fitting data and start to upgrade operation; If not installing and using for the first time, start the automatic refresh routine of model, in the 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 is not carried out model modification, exits this program; If the model modification date is not more than the Data Update date, program is carried out model modification;
(3) if the model modification date is less than the Data Update date, program is by " model modification date " attribute in showing update time on current system date; " the model independent variable " that read in sample ground tables of data distinguishes assignment to variable corresponding in the automatic refresh routine of model with " model dependent variable ", call the recurrence program and respectively all models in the alternative table of model are carried out to models fitting, obtain parameter and the coefficient R of each model
2, upgraded respectively and to parameter and the related coefficient of model prototype in the alternative table of model;
(4) select the record of related coefficient maximum from the alternative table of model, read numbering, model prototype and parameter, utilize model prototype and parametric configuration model tormulation formula, then use the model tormulation formula of the reference numeral in this model tormulation formula substitution model table, complete renewal.
(statistical software called of the automatic refresh routine of model can have multiple the automatic refresh routine of model of realizing with the development technique of .NET technology Calling MATLAB software, not only only limit MATLAB), adopt above-mentioned " a kind of orest management decision support template automatic update method " to be updated to example to the site quality evaluation model, the principle that the automatic refresh routine of model is realized is as follows:
(1) after the first installation and operation initialization of model modification program, get Date, (relation schema is Time(UpdateTime to the 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 tables of data, TreeName, age, hight_avg), wherein ydid is sample ground numbering, TreeName is seeds, model independent variable age is Stand Age, model dependent variable hight_avg is the flat wooden mean height of standing forest dominant tree) in fitting data, and use SQL(Structured Query Language, Structured Query Language (SQL)) statement reads Stand Age in fitting data (age) and standing forest average high of superior tree (hight_avg) data recording.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) Calling MATLAB software carries out matching to the equation prototype, obtains parameter and coefficient R
2, and (relation schema is bxmx(ID, mx, a, b to the alternative table of model, c, xgxs), ID representative model numbering, mx represents equation prototype, a, b, the c representation parameter, xgxs represents related coefficient) in parameter (a, b, c) and the related coefficient (xgxs) of equation prototype upgraded.
(3) select the record of related coefficient maximum from the alternative table of model, read numbering (ID), model prototype (mx) and parameter (a, b, c), utilize model prototype and parametric configuration site index curves, then (relation schema is Model(ID to the substitution model table, mxbds), the site index curves (mxbds) of the reference numeral ID representative model numbering, mxbds representative model expression formula), complete renewal.
(4) the model modification program is not while installing and using for the first time, after program starts in the automatic discrimination timetable " model modification date " (UpdateTime) whether be greater than " Data Update date " (DataTime), if be greater than directly exit, otherwise read the fitting data in reading database sample ground tables of data, start to carry out model modification.
Below that the computer program that the site index evaluation model upgrades automatically adopts the method for .NET Calling MATLAB algorithm to realize.
1. newly-built several .m files in MATLAB software.A model prototype in the alternative table of the corresponding model of each .m file..m in file, code is as follows, and the 1st line code in this document reads the data in sample ground tables of data, and the 2nd line code is the model prototype.
function?f=curve_fun(x,Adata)
f=x(1)*(1-exp(-x(2)*Adata)).^x(3);
2. use MATLAB DeployTool by step several .Net assemblies of several .m file generateds in 1..
3. add quoting of MATLAB assembly in the .NET development platform.
using?MathWorks;
using?MathWorks.MATLAB;
using?MathWorks.MATLAB.NET.WebFigures;
using?MathWorks.MATLAB.NET.Arrays;
using?MathWorks.MATLAB.NET.Utility;
using?MLApp;
using?curve_fun;
4. obtain system operation time write into Databasce
String?date=DateTime.Now.ToString(“D”);
Insert?into?Time(UpdateTime)values(date)
5. read the fitting data in sample ground database.
6. the C# data type conversion becomes the MWArray type, and the dynamic link library that Calling MATLAB generates carries out models fitting.
MWNumericArray?Adata=new?MWNumericArray(x);
MWNumericArray?Hdata=new?MWNumericArray(y);
curve_fun.cs?A=new?cs();
MWArray?Result=null;
Result=A.cs1(Adata,Hdata);
7. fitting result is converted to the C# data type, and model in the alternative table of model is upgraded
8. select the record of related coefficient maximum from the alternative table of model, read numbering, model prototype and parameter, utilize model prototype and parametric configuration site index curves, then the site index curves of the reference numeral in the substitution model table, complete renewal.
9. judge in timetable whether the model modification date is greater than the Data Update date, if be less than call refresh routine and upgraded, otherwise finish.
In the above-described embodiments, by the orest management decision support template is upgraded automatically, the model such as guaranteed site index, the growth results in the model bank of forest reserves management management decision support systems, the forest reserves systems of operation and management and estimate is constantly in last state, in the situation that data change also without reprogramming, greatly improved efficiency; Control by the time, system can be carried out matching to sample ground data automatically; And the present invention can realize automatically selecting excellent, and optimal model can be provided, and has improved the accuracy rate of result.
One of ordinary skill in the art will appreciate that: accompanying drawing is the schematic diagram of an embodiment, and the module in accompanying drawing or flow process might not be that enforcement the present invention is necessary.
One of ordinary skill in the art will appreciate that: the module in the device in embodiment can be described and be distributed in the device of embodiment according to embodiment, also can carry out respective change and be arranged in the one or more devices that are different from the present embodiment.The module of above-described embodiment can be merged into a module, also can further split into a plurality of submodules.
Finally it should be noted that: above embodiment only, in order to technical scheme of the present invention to be described, is not intended to limit; Although with reference to previous embodiment, the present invention is had been described in detail, those of ordinary skill in the art is to be understood that: its technical scheme that still can put down in writing previous embodiment is modified, or part technical characterictic wherein is equal to replacement; And these modifications or replacement do not make the essence of appropriate technical solution break away from the spirit and scope of embodiment of the present invention technical scheme.
Claims (4)
1. an orest management decision support template automatic update method, is characterized in that, comprises the following steps:
Obtain up-to-date sample ground enquiry data, it is imported in sample ground tables of data, and the date when Data Update date in timetable is updated to the enquiry data importing of sample ground;
If do not have the record on model modification date or the described model modification date of record to be less than the described Data Update date in described timetable, according to current date, the described model modification date in described timetable upgraded;
Classification according to data in described sample ground enquiry data, read model independent variable and model dependent variable in described sample ground tables of data, utilize homing method respectively all model prototypes in the alternative table of model to be carried out to models fitting, obtain parameter and the related coefficient of each model, and parameter and the related coefficient of each model prototype in the alternative table of described model are upgraded respectively to related coefficient wherein
R in formula
2Mean related coefficient, y
iThe field actual measured value that means the model dependent variable,
The mean value that means the field actual measured value of model dependent variable,
Expression is by the theoretical value of gained model dependent variable after model independent variable substitution model, and m is natural number;
Select model prototype and the parameter thereof of related coefficient maximum from the alternative table of described model, according to this model prototype and parametric configuration model tormulation formula thereof, and by the model tormulation formula of the correspondence in this model tormulation formula Renewal model table;
Date when the described model modification date of wherein, storing in described timetable is described update method operation; The described Data Update date is the date during by the described sample of sample ground data importing ground tables of data; What the described model prototype in the alternative table of described model was stored is the model that there is no the design parameter value; What the described model tormulation formula in described model table was stored is by the model after parameter substitution model prototype.
2. update method according to claim 1, is characterized in that, if the described model modification date of recording in described timetable is not less than the described Data Update date, the model tormulation formula in described model table does not need to upgrade.
3. update method according to claim 1, is characterized in that, in the alternative table of described model, each model prototype and parameter thereof, Relation Parameters are to preserve according to numbering, and the model tormulation formula in described model table is also to preserve according to the numbering of corresponding model prototype.
4. update method according to claim 1, is characterized in that, described model is site index curves or growth results and prediction model.
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| CN201310362330.0A CN103425513B (en) | 2013-08-19 | 2013-08-19 | Model automatic update method is supported in a kind of forest business decision |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106372277A (en) * | 2016-05-13 | 2017-02-01 | 新疆农业大学 | Variation function model optimization method in forest site index spatial-temporal estimation |
| CN110831029A (en) * | 2018-08-13 | 2020-02-21 | 华为技术有限公司 | Model optimization method and analysis network element |
-
2013
- 2013-08-19 CN CN201310362330.0A patent/CN103425513B/en not_active Expired - Fee Related
Non-Patent Citations (2)
| Title |
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| 吴保国，李成赞，马驰: "《森林培育专家决策支持系统的研究》", 《北京林业大学学报》 * |
| 谢小魁，苏东凯，代力民，周莉，于大炮，欧阳锴: "《森林经营决策支持系统的设计与实现及在采伐中的应用》", 《生态学杂志》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106372277A (en) * | 2016-05-13 | 2017-02-01 | 新疆农业大学 | Variation function model optimization method in forest site index spatial-temporal estimation |
| CN106372277B (en) * | 2016-05-13 | 2021-12-28 | 新疆农业大学 | Method for optimizing variation function model in forest land index space-time estimation |
| CN110831029A (en) * | 2018-08-13 | 2020-02-21 | 华为技术有限公司 | Model optimization method and analysis network element |
| CN110831029B (en) * | 2018-08-13 | 2021-06-22 | 华为技术有限公司 | Model optimization method and analysis network element |
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