CN105069287A - Prediction method of forest growth of overhead transmission line passageway - Google Patents
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Abstract
The invention belongs to the technology of the hidden danger prediction and management field of the forest of the overhead transmission line passageway, and particularly relates to a prediction method of the forest growth of the overhead transmission line passageway. The prediction method comprises the following steps: conducting a field survey, and establishing a hidden danger database of the forest of a whole-network transmission line passageway; referring to a natural growth rule of the tree species of the whole-network overhead transmission line passageway and the hidden danger database of the forest to generate a tree height and DBH (Diameter at Breast Height) curve of the forest, and determining an initial value of a Richard growth equation; and on the basis of the prior information and the sample information of the forest of the overhead transmission line passageway, according to the prior distribution of a Bayes estimation method, establishing a tree height growth prediction model. The prediction method realizes the precise prediction of tree height growth, is used for guiding a transmission line operation unit to make a strategy of line periphery tree obstacle state patrol and hidden danger elimination, and provides a decision basis for management departments to deploy transmission line passageway governance work.
Description
Technical field
The invention belongs to overhead power transmission line passage forest hidden danger control and prediction technical field, particularly a kind of Forecasting Methodology of overhead power transmission line passage tree growth.
Background technology
In recent years, China's Development of Forestry Industry is rapid, in addition the enforcement of national multi-line regression model and the raising to environmental requirement, no matter be newly-built circuit or the circuit of having gone into operation, the trees problem threatening line security to run under electric transmission line channel has become very outstanding, within arboreal growth to transmission line of electricity safe distance scope, just likely there is line tripping fault, threaten the safety and stability of whole electrical grid.Owing to planting tree families in study area, there is diversity, each species grow cycle is different, cause the maintenance work of power transmission line passage forest hidden danger very difficult, therefore study automatic investigation that a kind of dominant tree for overhead power transmission line passage forest height growth prediction model is forest hidden danger and early warning provides accurate basic data to have important more practical value; When extraneous perturbed force degree is less, arboreal growth speed changes with the increase of the age of tree, namely slowly-vigorous-slowly-stop, there is flex point in arboreal growth curve, the curve of reflection total increment change procedure is " S " type curve growth equation, and conventional theoretical growth equation has Richards equation, Logistic equation, Single molecular equation, Gompertz equation etc.Wherein, Richards growth equation strong adaptability, accuracy are high, each parameter in equation has certain biological significance, at home and abroad grow during results are estimated and be used widely, and conventional model parameter estimation method is nonlinear least square method, only utilizes sample information, using parameter as fixed value, process and the randomness of arboreal growth of its estimation are disagreed, poor to the estimation effect of parameter uncertainty, and the prediction effect especially in particular cases tree growth height is poorer.Such as, compared to the tree growth process in common forest zone, the growth of electric transmission line channel forest has certain singularity, different negative growths, positive growth or distortion can be presented along with the difference of transmission line of electricity voltage level, and fully effectively cannot consider these region random parameters in the process estimated at model of traditional model parameter estimating and measuring method, cause the estimation precision of model parameter generally on the low side.
Summary of the invention
Object of the present invention is the problems referred to above solving prior art, provide and a kind ofly automatically investigate the Forecasting Methodology with the overhead power transmission line passage tree growth of accurate early warning accurately predicting for forest hidden danger, to achieve these goals, the technical solution used in the present invention is as follows:
A kind of Forecasting Methodology of overhead power transmission line passage tree growth, it is characterized in that: in conjunction with the whole network overhead power transmission line passage forest file generated incipient fault data data bank, set up Tree height growth forecast model, realize the accurately predicting of Tree height growth, comprise the following steps:
Step1: the type of demarcating the dominant tree of electric transmission line channel forest survey region, sets up the whole network overhead power transmission line passage forest incipient fault data storehouse;
Step2: for a certain dominant tree, in conjunction with the sample ground information at these seeds all age of stands in the self-sow rule of dominant tree in the whole network overhead power transmission line passage and forest incipient fault data storehouse, and diameter of a cross-section of a tree trunk 1.3 meters above the ground d is horizontal ordinate, height of tree h is ordinate, generate tree height-Thoracic sympathicectomy curve, then determine the initial value of Richard growth equation; The initial value of described Richards growth equation is estimated by following formula:
In above formula, H is the height of crop, and t is the age of stand, parameter A, B, in k, x, A > 0, k > 0, wherein, A is the maximal value of tree growth, and B is growth factors, and x is the flex point value of tree growth equation, and k is the growth rate of forest;
Step3: for a certain dominant tree, based on prior imformation and the sample information of the whole network overhead power transmission line passage forest, builds prior distribution and the marginal distribution of this dominant tree in Bayes's estimation method;
Step4: estimate according to Bayes the Posterior distrbutionp that method calculates unknown parameter, obtain the value of Richard equation unknown parameter;
Step5: assess according to the precision of checking sample to tried to achieve unknown parameter, if parameters precision is defective, the unknown parameter value utilizing current calculating to try to achieve is revised the initial value of unknown parameter in Richard growth course, then return Step3 and carry out iterative loop, again call Bayes and estimate the value that method calculates unknown parameter; Otherwise stop iterative loop, the method namely by optimizing adjustment obtaining the best estimated value of unknown parameter, the best value of all unknown parameters being substituted in Richard growth equation, final Tree height growth forecast model can be obtained.
Preferably, described forest relative tree height is estimated by following formula with the relative diameter of a cross-section of a tree trunk 1.3 meters above the ground:
d
r＝d/D
0,h
r＝h/H
0,
In above formula, d
rand h
rrepresent the relative diameter of a cross-section of a tree trunk 1.3 meters above the ground and the relative tree height of forest respectively, d and h represents the actual diameter of a cross-section of a tree trunk 1.3 meters above the ground and the height of tree of forest respectively, D
0represent mean DBH increment, H
0represent forest mean height.
Preferably, described Bayes to estimate method analysis process as follows:
A), select the dominant tree selecting electric transmission line channel from forest incipient fault data storehouse, and in conjunction with the initial value of Richard growth equation, calculate prior distribution;
B), for the conditional probability distribution of initial value possible arbitrarily, namely the Posterior distrbutionp of required parameter is estimated by following formula,
Wherein,
In above formula: p is probability distribution function or density function, p (A
j| be y) that unknown parameter A value is A under the prerequisite of certain dominant tree sample information y given
jcondition distribution, i.e. the Posterior distrbutionp of required parameter; P (y
i| A
j),
(i=1,2,3 ... n, j=1,2,3 ... m), expression parameter value is A
jwhen, sample information is y
iprobability, can calculate according to sample information; P (A
j) be unknown parameter A value be A
jprobability, i.e. prior distribution; P (y
i) represent that the marginal distribution of certain dominant tree calculates;
C), compare the Posterior distrbutionp of unknown parameter A, namely comparing unknown parameter A value under the prerequisite of certain dominant tree sample information y given is A
j(j=1,2,3 ..., all conditions distribution p (A m)
j| y), all p (A
j| the A y) corresponding to value the maximum
jbe the best value of unknown parameter A;
D), other parameter k in Richard growth equation are calculated according to above-mentioned same method, m, the Posterior distrbutionp of B, and try to achieve best value, in the Richard growth equation that the method is tried to achieve, the best value of unknown parameter has taken into full account sample information and the prior imformation of transmission of electricity county paths forest, is convenient to the high-precision forecast of tree growth height.
In sum, the present invention is owing to have employed above technical scheme, and the present invention has following beneficial effect:
(1) the present invention passes through overhead transmission line different brackets voltage to the distortion effects of passage tree growth, taking into full account the regional characteristic of electric transmission line channel forest, self-sow characteristic, human intervention mode, and when the random parameter such as electric pressure impact, the distribution curve setting up the standing forest height of tree and the diameter of a cross-section of a tree trunk 1.3 meters above the ground in forest incipient fault data data bank estimates the initial value of Richard growth equation, then based on the dynamic contribution margin of Bayesian Estimation theoretical appraisal multiple random variables in model iteration optimization, and realize the optimal estimation of unknown parameter in Richard equation, set up Tree height growth forecast model, realize the accurately predicting of Tree height growth.
(2) the present invention takes into full account the singularity of electric transmission line channel tree growth, realize the optimal fitting of any dimension random sum sample information, can the Tree height growth of accurately predicting overhead power transmission line passage forest dominant tree, and can the contribution margin of dynamic evaluation electric transmission line channel forest multiple random variables in model iteration optimization.
Accompanying drawing explanation
In order to be illustrated more clearly in example of the present invention or technical scheme of the prior art, introduce doing accompanying drawing required in embodiment or description of the prior art simply below, apparently, accompanying drawing in the following describes is only examples more of the present invention, to those skilled in the art, do not paying under creationary prerequisite, other accompanying drawing can also obtained according to these accompanying drawings.
Fig. 1 is the Forecasting Methodology process flow diagram of a kind of overhead power transmission line passage tree growth of the present invention.
Fig. 2 is the tree height-Thoracic sympathicectomy curve map of the Forecasting Methodology of a kind of overhead power transmission line passage tree growth of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in example of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
As shown in Figure 1, a kind of Forecasting Methodology of overhead power transmission line passage tree growth, in conjunction with the whole network overhead power transmission line passage forest file generated incipient fault data data bank, set up Tree height growth forecast model, realize the accurately predicting of Tree height growth, comprise the following steps:
Step1: carry out on-site inspection, demarcates the forest kind of dominant tree and the type of typical seeds of electric transmission line channel forest survey region, and sets up the whole network overhead power transmission line passage forest incipient fault data storehouse; Concrete steps are as follows:
1), the generation in power line protection district: according to electric pressure, setting power line protection district, the sideline outer distance corresponding according to different electric pressure, as shown in table 1, buffer zone is generated with the line of vector layer file (tran_line.shp) of overhead transmission line, obtain overhead transmission line protected location (scope of overhead transmission line protected location comprises horizontal-extending outside traverse side alignment, and the region in two parallel surfaces formed perpendicular to ground);
The sideline extension distance table that table 1 electric pressure is corresponding
2), forest incipient fault data storehouse is generated: the forest incipient fault data in overhead transmission line protected location is added up, gather the essential information (comprising the present level Distance geometry vertical range of seeds, the age of tree, canopy density, quantity, the current height of tree, the current diameter of a cross-section of a tree trunk 1.3 meters above the ground, distance of wire) of typical seeds, for single seeds, sampling point need cover all age of trees, forms the whole network electric transmission line channel forest incipient fault data storehouse;
Step2:: for a certain dominant tree, in conjunction with self-sow rule and the forest incipient fault data storehouse of the seeds of the whole network overhead power transmission line passage, generates tree height-Thoracic sympathicectomy curve, determines the initial value of Richard growth equation; Wherein, the dominant tree of overhead power transmission line passage refers to that seeds that accumulation proportion is larger (such as: for Guangxi, advantage arbor species are eucalyptus, Birch, rubber tree, China fir, pine, bamboo grove etc.), the concrete steps of tree height-Thoracic sympathicectomy curve distribution are as follows:
S1), the generation of the height of tree-Thoracic sympathicectomy curve: in conjunction with sample ground information and diameter of a cross-section of a tree trunk 1.3 meters above the ground d be horizontal ordinate, height of tree h is ordinate, generate tree height-Thoracic sympathicectomy curve map, as shown in Figure 2, as can be seen from Figure, along with the increase gradually of the diameter of a cross-section of a tree trunk 1.3 meters above the ground, the height of tree also increases gradually, reject the sampling point that around curve, error is excessive, mean DBH increment is obtained according to the sample ground information after rejecting unreasonable sampling point, mean stand height, the relative diameter of a cross-section of a tree trunk 1.3 meters above the ground and relative tree height, therefore, mean DBH increment can be obtained by this curve, mean stand height, the relative diameter of a cross-section of a tree trunk 1.3 meters above the ground and relative tree height, parameter k in Richard equation can be determined based on above-mentioned value, m, A, the initial value of B, the sampling point information of bigger error is deleted according to distribution curve, and then calculate mean DBH increment D
0, mean height H
0, relative diameter of a cross-section of a tree trunk 1.3 meters above the ground d
rwith relative tree height h
r, the described forest relative tree height diameter of a cross-section of a tree trunk 1.3 meters above the ground relative to forest is estimated by formula 1:
D
r=d/D
0, h
r=h/H
0, (formula 1),
In formula 1, d
rand h
rrepresent the relative diameter of a cross-section of a tree trunk 1.3 meters above the ground and the relative tree height of forest respectively, d and h represents the actual diameter of a cross-section of a tree trunk 1.3 meters above the ground and the height of tree of forest respectively, D
0represent mean DBH increment, H
0represent forest mean height.
S2), the determination of Richard growth equation initial value: in conjunction with relative diameter of a cross-section of a tree trunk 1.3 meters above the ground d
r, mean DBH increment D
0, relative tree height h
r, mean height H
0, the age of stand and thickness of stand determination parameter k, the initial value of m, A, B.The initial value of described Richards growth equation is estimated by formula 2:
In formula 2, H is the height of crop, and t is the age of stand, in parameter A, B, k, x, and A > 0, k > 0, wherein, A is the end value of equation, is the maximal value of tree growth; B is growth factors, decides the size of growth factor during t=0; X is the flex point value of tree growth equation, and flex point degree of accuracy is higher, then precision is higher; K is the growth rate of forest.
Step3: for a certain dominant tree (if the type of demarcating seeds is China fir, pine etc., the impact etc. of the arboreal growths such as assessment seeds, present level, self-sow characteristic, regional disparity, manual intervention, plant spacing, electric pressure), based on prior imformation and the sample information of the whole network overhead power transmission line passage forest, build prior distribution and marginal distribution that Bayes estimates method, posterior information is calculated again according to prior distribution, finally use posterior information to infer the distribution of unknown parameter in Richard equation, unknown parameter comprises the distribution of k, x, A and B;
Below for unknown parameter A, the flow process that use Bayes estimates method analysis unknown parameter A is as follows:
A), the dominant tree selecting electric transmission line channel from forest incipient fault data storehouse is selected, in conjunction with the factor such as impact of the kind of these seeds, present level, growth natural characteristic, regional disparity, manual intervention (weeding, fertilising), the arboreal growth such as plant spacing and electric pressure, first make y=(y
1, y
2, y
3..., y
n) be data vector, represent the sample information of dominant tree in overhead power transmission line passage, and in conjunction with the initial value of A, the incremental steps when span of given A and iterative loop, obtains the value set A=(A of unknown parameter A
1, A
2, A
2..., A
m), be parameter vector, represent the possible value set for this dominant tree unknown parameter A, and calculate prior distribution p (A
j);
B), for arbitrary possibility value A
j(j=1,2,3 ..., conditional probability distribution m), namely the Posterior distrbutionp of required parameter is estimated by formula 3,
Wherein,
(formula 4),
In formula 3 and formula 4: p is probability distribution function or density function, p (A
j| be y) that unknown parameter A value is A under the prerequisite of certain dominant tree sample information y given
jcondition distribution, i.e. the Posterior distrbutionp of required parameter; P (y
i| A
j), (i=1,2,3 ... n, j=1,2,3 ... m), expression parameter value is A
jwhen, sample information is y
iprobability, can calculate according to sample information; P (A
j) be unknown parameter A value be A
jprobability, i.e. prior distribution; P (y
i) represent that the marginal distribution of certain dominant tree is calculated by formula 4.
C), according to the sample information of this dominant tree, p (y is calculated
i| A
j), (i=1,2,3 ... n, j=1,2,3 ... m); And to calculate unknown parameter A value based on formula 3 and formula 4 be A
jcondition distribution
and all p (A
j| y), (j=1,2,3 ..., m), value maximal value represents under the prerequisite of given dominant tree sample information y and prior imformation, A=A
jpossibility maximum, i.e. the best value of unknown parameter A;
D), other parameter k in Richard growth equation are calculated according to above-mentioned same method, m, the Posterior distrbutionp of B, and try to achieve best value, in the Richard growth equation that the method is tried to achieve, the best value of unknown parameter has taken into full account sample information and the prior imformation of transmission of electricity county paths forest, is convenient to the high-precision forecast of tree growth height;
Step4: estimate according to Bayes the Posterior distrbutionp that method calculates unknown parameter, obtain the value of Richard equation unknown parameter;
Step5: assess according to the precision of checking sample to tried to achieve Richard equation unknown parameter, if parameters precision is defective, the unknown parameter value utilizing current calculating to try to achieve is to the initial value of unknown parameter in Richard growth course, revise the initial value of original equation unknown parameter, then return Step3 and carry out iterative loop, again call Bayes and estimate the value that method calculates unknown parameter; Otherwise, stop iterative loop, object is the precision progressively being improved tried to achieve unknown parameters ' value by iterative loop, namely the method by optimizing adjustment obtains the best estimated value of unknown parameter, the best value of all unknown parameters is substituted in Richard growth equation, final Tree height growth forecast model can be obtained.
The foregoing is only the preferred embodiment of invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (3)
1. the Forecasting Methodology of an overhead power transmission line passage tree growth, it is characterized in that: in conjunction with the whole network overhead power transmission line passage forest file generated incipient fault data data bank, set up Tree height growth forecast model, realize the accurately predicting of Tree height growth, comprise the following steps:
Step1: the type of demarcating the dominant tree of electric transmission line channel forest survey region, sets up the whole network overhead power transmission line passage forest incipient fault data storehouse;
Step2: for a certain dominant tree, in conjunction with the sample ground information at these seeds all age of stands in the self-sow rule of dominant tree in the whole network overhead power transmission line passage and forest incipient fault data storehouse, and diameter of a cross-section of a tree trunk 1.3 meters above the ground d is horizontal ordinate, height of tree h is ordinate, generate tree height-Thoracic sympathicectomy curve, then determine the initial value of Richard growth equation; The initial value of described Richards growth equation is estimated by following formula:
In above formula, H is the height of crop, and t is the age of stand, parameter A, B, in k, x, A > 0, k > 0, wherein, A is the maximal value of tree growth, and B is growth factors, and x is the flex point value of tree growth equation, and k is the growth rate of forest;
Step3: for a certain dominant tree, based on prior imformation and the sample information of the whole network overhead power transmission line passage forest, builds prior distribution and the marginal distribution of this dominant tree in Bayes's estimation method;
Step4: estimate the Posterior distrbutionp that method calculates unknown parameter according to Bayes; Obtain the value of Richard equation unknown parameter;
Step5: assess according to the precision of checking sample to tried to achieve unknown parameter, if parameters precision is defective, the unknown parameter value utilizing current calculating to try to achieve is revised the initial value of unknown parameter in Richard growth course, then return Step3 and carry out iterative loop, again call Bayes and estimate the value that method calculates unknown parameter; Otherwise stop iterative loop, the method namely by optimizing adjustment obtaining the best estimated value of unknown parameter, the best value of all unknown parameters being substituted in Richard growth equation, final Tree height growth forecast model can be obtained.
2. the Forecasting Methodology of a kind of overhead power transmission line passage tree growth according to claim 1, is characterized in that: described forest relative tree height is estimated by following formula with the relative diameter of a cross-section of a tree trunk 1.3 meters above the ground:
d
r＝d/D
0,h
r＝h/H
0,
In above formula, d
rand h
rrepresent the relative diameter of a cross-section of a tree trunk 1.3 meters above the ground and the relative tree height of forest respectively, d and h represents the actual diameter of a cross-section of a tree trunk 1.3 meters above the ground and the height of tree of forest respectively, D
0represent mean DBH increment, H
0represent forest mean height.
3. the Forecasting Methodology of a kind of overhead power transmission line passage tree growth according to claim 1, is characterized in that: it is as follows that described Bayes estimates method analysis process:
A), select the dominant tree selecting electric transmission line channel from forest incipient fault data storehouse, and in conjunction with the initial value of Richard growth equation, calculate prior distribution;
B), for the conditional probability distribution of initial value possible arbitrarily, namely the Posterior distrbutionp of required parameter is estimated by following formula,
Wherein,
In above formula: p is probability distribution function or density function, p (A
j| be y) that unknown parameter A value is A under the prerequisite of certain dominant tree sample information y given
jcondition distribution, i.e. the Posterior distrbutionp of required parameter; P (y
i| A
j), (i=1,2,3 ... n, j=1,2,3 ... m), expression parameter value is A
jwhen, sample information is y
iprobability, can calculate according to sample information; P (A
j) be unknown parameter A value be A
jprobability, i.e. prior distribution; P (y
i) represent that the marginal distribution of certain dominant tree calculates;
C), compare the Posterior distrbutionp of unknown parameter A, namely comparing unknown parameter A value under the prerequisite of certain dominant tree sample information y given is A
j(j=1,2,3 ..., all conditions distribution p (A m)
j| y), all p (A
j| the A y) corresponding to value the maximum
jbe the best value of unknown parameter A;
D), other parameter k in Richard growth equation are calculated according to above-mentioned same method, m, the Posterior distrbutionp of B, and try to achieve best value, in the Richard growth equation that the method is tried to achieve, the best value of unknown parameter has taken into full account sample information and the prior imformation of transmission of electricity county paths forest, is convenient to the high-precision forecast of tree growth height.
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