林业科学 ›› 2020, Vol. 56 ›› Issue (1): 87-102.doi: 10.11707/j.1001-7488.20200109
陈栋1,2,吴保国1,*,王姗姗3,苏晓慧1,陈玉玲1,李宜瑾1
收稿日期:
2017-10-23
出版日期:
2020-01-25
发布日期:
2020-02-24
通讯作者:
吴保国
基金资助:
Dong Chen1,2,Baoguo Wu1,*,Shanshan Wang3,Xiaohui Su1,Yuling Chen1,Yijin Li1
Received:
2017-10-23
Online:
2020-01-25
Published:
2020-02-24
Contact:
Baoguo Wu
Supported by:
摘要:
目的: 人工林经营模型众多,对统计学方法依赖性较强,传统人工林经营辅助决策支持系统与模型库、方法库耦合性较高,针对模型库通用性不高、方法代码与业务代码耦合度过高等问题,结合林业数学模型多样性等特点,构建人工林经营模型库和方法库服务平台,以实现人工林经营模型与方法的科学化管理和信息化服务。方法: 基于不同的模型表现形式和方法的调用机制,采用关系型数据库构建理论,设计模型库和方法库的关系模式;针对方程式表达模型,引入词法分析、语法分析和语义分析等方法,实现方程式模型解析计算;针对程序化模型,采用调用程序包方式实现模型解析计算;基于程序包的封装调用方法,实现方法库的调用和管理。利用数据耦合思想,将模型库、方法库与决策应用系统解耦,实现模型、方法的独立管理和标准化共享服务。结果: 模型库系统可实现数学公式型和程序块型模型的存储、管理与解析;方法库系统可实现对方法程序块的统一存储与管理,且通过构建模型与方法调用数据交互格式,可分别实现面向用户的模型和方法的计算服务与面向应用程序的API调用服务。结论: 设计通用性较好的模型、方法调用数据交互结构,构建面向人工林经营的模型库和方法库服务平台,实现基于Web的模型、方法的管理与解析,提供模型和方法的共享调用功能。
中图分类号:
陈栋,吴保国,王姗姗,苏晓慧,陈玉玲,李宜瑾. 面向人工林经营的模型库和方法库服务平台[J]. 林业科学, 2020, 56(1): 87-102.
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.
表1
人工林经营决策支持模型分类"
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( 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( 其中包含3个基本函数关系式: ①林分断面积公式: ②林分密度指数公式:SDI=N×(DI/Dg)-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: ②Stand density index formula:SDI=N×(DI/Dg)-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 | Equation of tree trunk cutting degree proposed: |
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 +…+cnxn ②约束函数:a11x1 +a12x2 +…+a1nxn(>)b1 As follows: ①objective function:max(min)z=c1x1 +c2x2 +…+cnxn ②Constraint function:a11x1 +a12x2 +…+a1nxn(>)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 |
表2
模型表中的存储示例"
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*(Dg/20)^1.323 | 无 Null value | Dg |
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 |
… | … | … | … | … | … | … | … | … |
表3
基于R语言的方法程序①"
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+…+bnxn 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+…+bnxn | 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( 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 | — | — |
表4
模型API调用说明"
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”} |
表7
方法API调用说明"
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(-b*T2)]/[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(-b*T2))/(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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