面向人工林经营的模型库和方法库服务平台

doi:10.11707/j.1001-7488.20200109

Abstract

Abstract:

Objective: There are many models of management in artificial forests, and they have strong dependence on statistical methods. The coupling between the traditional assistant decision-making support systems for artificial forest management and model libraries and method libraries is relatively high, and the commonality of model libraries is not high. The problems of high coupling of business code and method code also exist. Based on the above-mentioned statuses, combined with the diversity of forestry mathematical models, this paper establishes a model library and method library platform of artificial forest to realize the scientific management and information service of the model and method of artificial forest. Method: Based on the invocation mechanisms of different model representations and methods, the relational database construction theory was used to design the relational model between the model library and the method library. For equation expression models, lexical analysis, grammar analysis, semantic analysis, etc., were introduced in order to realize the analytical calculation of the equation model; for the procedural model, the model parsing calculation was implemented by calling the package; the call and management of the method library were realized based on the package call method of the package. Using the idea of data coupling, the model library, method library, and decision-making application system were decoupled to achieve independent management and standardized shared services of models and methods. Result: The model base system could realize the storage, management and analysis of mathematical formula model and program block model; the method base system could realize the storage and management of method program block, the user-oriented model and method oriented computing service, and application-oriented API calling service is, respectively, realized by building the interaction format of model and method data. Conclusion: A general model and method data interaction structure is designed, and a service platform of model base and method base for plantation management is also constructed. The management and analysis of model and method based on web is realized, and the sharing and calling function of model and method is also provided.

Key words: plantation management, model library, method library, data coupling, shared services

CLC Number:

S750

Dong Chen,Baoguo Wu,Shanshan Wang,Xiaohui Su,Yuling Chen,Yijin Li. Model Library and Method Library Service Platform for Plantation Management[J]. Scientia Silvae Sinicae, 2020, 56(1): 87-102.

Figures/Tables 20

Table 1

The decision support model classification of artificial forest management"

模型分类 Model classification	具体说明 Explain	表达形式 Expression form	示例 Example
立地质量评价模型 Site quality evaluation model	有林地立地质量评价：通常利用代数差分法推导出的差分型地位指数模型；无林地立地质量评价：各立地因子得到的数量化地位指数模型Site quality evaluation of forest land: a differential site index model usually derived by algebraic difference method; site quality evaluation of non-forest land: a quantitative site index model obtained by each site factor	数学方程 Mathematical equation	华北落叶松地位指数模型表达方程(韩焱云，2015)： HT×{[1-exp(-0.056×30)]/[1-exp(-0.056×t)]}^1.36 Expression equation of site index model of Larix principis-rupprechtii: HT×{[1-exp(-0.056×30)]/[1-exp(-0.056×t)]}^1.36
生长收获模型 Growth harvest model	包括全林分模型、径阶模型和单木模型。全林分模型：一般构建林分平均高、平均胸径、断面积生长方程、蓄积收获方程；径阶分布模型：通常利用Weibull分布函数和正态分布函数等拟合的经阶分布模型；单木生长模型：采用生长量修正法或经验方程法建立与距离有关或与距离无关的单木生长模型Including the whole stand model, the diameter step model and the single tree model. The whole stand model: generally build the average height, the average DBH, the growth equation of the sectional area and the accumulation harvest equation of the stand; the diameter step distribution model: generally use the step distribution model fitted by Weibull distribution function and the normal distribution function; the single tree growth model: use the growth amount correction method or the experience equation method to build the distance the growth model of single tree is closed or independent of distance	数学方程 Mathematical equation	广西马尾松人工林全林整体模型(孟宪宇，2013)：其中包含3个基本函数关系式： ①林分断面积公式：$G=\frac{ \mathsf{π}}{40 \; 000} {ND}_{\mathrm{g}}^{2}$ ②林分密度指数公式：SDI=N×(D_I/D_g)^-β ③林分蓄积公式：M=FH·G 还包括5个统计模型，分别为断面积生长方程、不同立地条件上林分优势高生长曲线、现实林分自然稀疏模型、平均高预估模型以及形高与平均高关系方程 The whole forest model of Pinus massoniana plantation in Guangxi： There are three basic function relations： ①Formula of stand sectional area：$G=\frac{ \mathsf{π}}{40 \; 000} {ND}_{\mathrm{g}}^{2}$ ②Stand density index formula：SDI=N×(D_I/D_g)^-β ③Stand accumulation formula：M=FH·G It also includes five statistical models, which are the growth equation of sectional area, the growth curve of dominant height of stand on different site conditions, the natural sparse model of real stand, the prediction model of average height and the relationship equation between form height and average height
形态生长模型 Morphological growth model	主要包括削度方程、整枝、木材质量等；还包括胸径形态生长模型、树高形态生长模型、冠幅形态生长模型、冠长形态生长模型等在内的单木形质模型It mainly includes cutting degree equation, pruning, wood quality, etc; it also includes DBH morphological growth model, tree height morphological growth model, crown width morphological growth model, crown length morphological growth model, etc	数学方程 Mathematical equation	Kozak等(1969)提出的树干削度方程等： $\frac{d^{2}}{D^{2}}=a+b\left(\frac{h}{H}\right)+c\left(\frac{h}{H}\right)^{2}$ Equation of tree trunk cutting degree proposed: $\frac{d^{2}}{D^{2}}=a+b\left(\frac{h}{H}\right)+c\left(\frac{h}{H}\right)^{2}$
效益评价模型 Benefit evaluation model	包括经济效益和生态效益模型Including economic and ecological benefit model	程序块 Program block	如以下形式： ①目标函数：max(min)z=c₁x₁ +c₂x₂ +…+c_nx_n ②约束函数：a₁₁x₁ +a₁₂x₂ +…+a₁_nx_n(>)b₁ As follows: ①objective function：max(min)z=c₁x₁ +c₂x₂ +…+c_nx_n ②Constraint function：a₁₁x₁ +a₁₂x₂ +…+a₁_nx_n(>)b₁
其他模型 Other models	神经网络模型等Neural network model, etc	程序块 Program block	黑盒模型，通常采用一段封装的程序块表示 Black box model, usually represented by a block of encapsulation

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Table 3

A list of methods programs based on the R language"

方法名 Method name	方法描述 Method description	R语言程序表达 R language program expression	R程序包 R packages
一元线性回归 Univariate linear regression	一元线性回归分析用于建立2个变量之间的关系模型，2个变量通过方程联系，当绘制曲线图时关系呈一条直线。一元线性回归的数学方程一般为：y=ax+b Univariate linear regression analysis is used to establish the relationship model between two variables. Two variables of univariatelinear regression are related through the equation. When drawing the curve, the relationship is in a straight line. The generally mathematical equation of linear regression is y=ax+b	R语言中用lm( )函数来创建预测变量和响应变量之间的关系模型：lm(formula, data)。创建关系模型时输入x、y的一系列值，使用lm(y~x)来得出x和y的线性关系，获取系数 In R language, lm( ) function is used to create the relationship model between prediction variable and response variable: lm(formula, data). When creating a relational model, input a series of values of x and y, use lm(y ~ x) to get the linear relationship between x and y, and obtain the coefficient	包名Package name：linearregression.R 核心程序代码Core program code： result < -lm(y~x)
多元线性回归 Multiple linear regression	多元线性回归是一元线性回归的延伸，具有2个以上的预测值变量，响应变量由几个预测值变量共同决定。一般数学方程为：y=a+b₁x₁+b₂x₂+…+b_nx_n Multiple linear regression is an extension of one variable linear regression, which has more than two predictor variables. The response variable is determined by several predictor variables. The general mathematical equation is y=a+b₁x₁+b₂x₂+…+b_nx_n	R语言中使用lm( )函数创建回归模型, lm( )函数在多元线性回归中的基本语法是：lm(y~x₁+x₂+x₃…, data) The lm( ) function is also used to create regression model in R language. The basic syntax of lm( ) function in multivariate linear regression is: lm (y~ x₁ + x₂ + x₃, data)	包名Package name：multiplelinearregression.R 核心程序代码Core program code：result < - lm(y~x1+x2+x3, data)
广义线性回归 Generalized linear regression	广义线性模型包括Logistic回归模型、对数线性模型和Cox比例风险模型等The generalized linear model includes logistic regression model, logarithmic linear model and Cox proportional risk model, etc	R语言中使用glm(formula, family=Gaussian, data, …)；formula为公式，即要拟合的模型，family为分布簇，包括正太分布、二项分布、泊松分布等，data为可选择的数据 glm(formula, family=Gaussian, data, …) used in R language，formula is the model to be fitted, family is a distribution cluster, including positive distribution, binomial distribation, Poisson distribution, etc, data is the optional data	包名Package name：generalizedlinearregression.R 核心程序代码Core program code：glm(formula, family= Gaussian, data)
非线性回归 Nonlinear regression	非线性回归包括一元非线性回归模型和多元非线性回归模型Nonlinear regression includes one-way nonlinear regression model and multi-way nonlinear regression model	一元非线性回归包括曲线回归和可直线化的曲线回归，其主要思路是画出三点图，然后找到适合的曲线形式进行拟合或转化成线性回归处理，一般使用plot( )函数作出散点图，再利用线性回归函数进行拟合；多元非线性回归一般使用nls(formula, data, start, …)函数进行拟合，formula是包括变量和参数的非线性拟合公式；data为可选择的数据框，不能是矩阵，start是初始值，用列表形式给出Univariate non-linear regression includes curve regression and linear curve regression. Its main idea is to draw three-point graph, and then find a suitable curve form to fit or convert to linear regression. Generally, plot( ) function is used to make scatter plot, and linear regression function is used to fit; nls(formula, data, start, ...) is used for multivariate non-linear regression, function to fit, formula is a non-linear fitting formula including variables and parameters, data is an optional data box, not a matrix, and start is the initial value, which is given in the form of a list	一元非线性回归的思路是作出散点图，然后利用线性回归包进行处理 The idea of univariate nonlinear regression is to make a scatter diagram, and then use the linear regression package to deal with it 多元非线性回归的处理包名 Package name of multivariate nonlinear regression：Multivariatenonlinearregression.R 其核心程序代码Core program code： nls(formula, data, start)
混合效应模型 Mixed effect model	有研究将单水平混合效应模型应用于树高曲线、单木生长、树干削度、枝条特征模型的构建(姜立春等，2014) Some researches have applied the single level mixed effect model to the construction of tree height curve, single tree growth, trunk cutting degree and branch characteristic model	—	—

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模型编号 Model number	模型分类 Model classification	模型名称 Model name	适用地区 Zone of application	树种 Tree species	模型形式 Model form	模型代数表达式 Model algebra expression	模型程序包存放地址 Model package storage address	模型变量列表 Model variable name list
1	立地质量评价模型 Site quality evaluation model	地位指数 Status index	福建省 Fujian	杉木 Chinese fir	数据表示 Data representation	28.992*(HT/28.992)^(t/20)	无 Null value	HT, t
2	生长收获模型 Growth harvest model	全林分平均胸径 Average DBH of the whole stand	福建省 Fujian	杉木 Chinese fir	数据表示 Data representation	7.697(SI^0.547)(1-exp(-0.213(N^-0.221)t)^1.078)	无 Null value	SI, N, t
3	生长收获模型 Growth harvest model	林分密度指数 Stand density index	福建省 Fujian	杉木 Chinese fir	数据表示 Data representation	N*(D_g/20)^1.323	无 Null value	D_g
4	生长收获模型 Growth harvest model	林分断面积 Stand area	福建省 Fujian	杉木 Chinese fir	数据表示 Data representation	8.051(SI^0.673)(1-exp((-10.510(SDI/10 000)^2.502)(t-t₀)))^0.644	无 Null value	SI, SDI, t, t₀
5	生长收获模型 Growth harvest model	林分蓄积模型 Stand accumulation model	福建省 Fujian	杉木 Chinese fir	数据表示 Data representation	exp(1.147 +0.016*SI-5.033/t+1.196 lG)	无 Null value	SI, t, G
6	生长收获模型 Growth harvest model	BP神经网络小班蓄积量模型 Small class volume model of BP neural network	河北省 Hebei	华北落叶松 Larix principis-rupprechtii	程序表示 Program representation	无 Null value	/programmodel/NeuralNetworks.R	t, n, SI
…	…	…	…	…	…	…	…	…

说明Explain	内容Content
请求地址Request address	http://211.71.149.64:8080/model/modelcalculate
请求方式Request mode	POST/GET
参数类别Parameter category	int
参数示例Parameter example	SI=18;t=10, 15, 20, 25, 30;N=2 000
输出结果类型Output result type	字符
调用URL Call URL	http://211.71.149.64:8080/model/modelcalculate/modelid=9&SI=18&N=2000&t=10, 15, 20, 25, 30
调用结果Call result	{ForestryModel:{ModelDic:{ModelId:9, ModelName：全林分胸径模型, ModelClassName:生长收获模型, ModelTreeName:杉木, ModelArea:福建, ModelExplain:暂无, ModelAuthors:董晨, Country:中国, Email:暂无, Remark:暂无}, ModelDes:{ModelId:9, ModelAlgebraicExpression: 7.697(SI^0.547)(1-exp(-0.213(N^-0.221)t)^1.078), ModelParams:SI, N, t; Remark:暂无}, ModelResult:[11.4, 16.3, 20.6, 21.4, 24.3, 25.0, 27.5], Time:2018-04-21 14:44:23}}
若无权限Without permission	{error：“对不起，您无权访问此接口”}

年龄 Age/a	10	15	20	25	30
林分平均胸径 Mean DBH/cm	13.0	17.7	21.5	24.6	27.1

说明 Explain	内容 Content
请求地址 Request address	http://211.71.149.64:8080/method/methodcalculate
请求方式 Request mode	POST/GET
参数类别 Parameter category	Double数组
参数示例 Parameter example	formula=‘HT₂~HT₁{{[1-exp(-bT₂)]/[1-exp(-b*T₁)]}^c}’； b=0.105 636, c=1.139 241；T₁=14, 16, 18, 20, 14, …；T₂=19, 21, 23, 25, 19, …；HT₁=3.2, 7.6, 8.6, 7.5, 8.1, …；HT₂=5.9, 5, 5, 8.5, 13.5, …；
输出结果类型 Output result type	字符
调用URL Call URL	http://211.71.149.64:8080/method/methodcalculate/methodid=5&formula=‘HT2~HT1(((1-exp(-bT2))/(1-exp(-b*T1)))^c}’&b=0.105636 &c=1.139241&T1=14, 16, 18, 20, 14, …&T2=19, 21, 23, 25, 19, …&HT1=3.2, 7.6, 8.6, 7.5, 8.1, …&HT2=5.9, 5, 5, 8.5, 13.5, …
调用结果 Call result	{ForestryAlgorithm:{AlgorithmDic:{AlgorithmId:5, AlgorithmName:非线性回归, AlgorithmExplain:暂无, AlgorithmParams:Doubel数组, AlgorithmOutputType:字符串公式, IsGroup:否, GroupType:否, Remark:暂无}, AlgorithmDes:{AlgorithmId:5, Remark:暂无}, AlgorithmResult:y={b=0.105 50, Pr>\|t\|:7.48e^-10;c=1.138 01, Pr>\|t\|:Pr>\|t\|:3.50e^-16}，Time:2018-04-21 12:16:45}}
若无权限 Without permission	{error：“对不起，您无权访问此接口”}

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地块号Block number	T₁	T₂	HT₁	HT₂
100	14	19	8.1	13.5
113	13	18	8.9	10.0
118	34	39	16.5	14.5
122	17	22	7.1	8.7
123	18	18	6.1	8.0
132	16	21	10.1	11.0
…	…	…	…	…