CN109086359A - A kind of forest against wave wash tree species appraisal procedure based on big data - Google Patents
A kind of forest against wave wash tree species appraisal procedure based on big data Download PDFInfo
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Abstract
The forest against wave wash tree species appraisal procedure based on big data that the invention discloses a kind of, comprising: the evaluation index for determining several tree species, each evaluation index data to gather tree seeds and classification obtain real number class and text class data；It is normalized, real number class data after being standardized；To text class data according to opinion rating progress and assignment, to obtain the assessment real number value of each text class data；Using hierarchical clustering method classification is carried out to the real number class data of each evaluation index and the assessment real number value of each text class data respectively and obtains several classifications, and take maximum value and minimum value in of all categories that the boundary value between adjacent category is calculated respectively, to establish the assessment table of each evaluation index of tree species；The tree species data to be assessed of acquisition are mapped to the assessment table of each evaluation index of tree species, with the assessment result of determination tree species to be assessed.Precise classification assessment can be achieved in the present invention, without relying on manual evaluation, improves assessment efficiency, has very strong practicability and wide applicability.
Description
Technical field
The forest against wave wash tree species appraisal procedure based on big data that the present invention relates to a kind of, belongs to the technical field of forest against wave wash.
Background technique
River and lake environmental health receives more and more attention, and ecological revetment is more applied in dike construct.Wave resistance
Woods --- the vegetated floodplain before dike, can not only reduce stormy waves to the threat of dyke, extend the dyke service life, ring can also be beautified
Border maintains and improves river ecosystem.In China, great rivers and lakes and beach are widely used.
Since wave absorbing effect depends on certain features of selected plant, and different floristics has respective growth to practise
Property, the factors such as other effects of vegetation for thering is specific requirement and policymaker to consider planting area, therefore, it is necessary to right
Forest against wave wash tree species are screened.Previous tree species screening technique is all based on evaluation criteria given by man, such standard system
Fixed very subjectiveization, does not have universality, and the forest against wave wash tree species evaluation criteria that studying can promote has very important practice to anticipate
Justice.
Summary of the invention
It is a kind of anti-based on big data technical problem to be solved by the present invention lies in overcoming the deficiencies of the prior art and provide
Unrestrained woods tree species appraisal procedure solves tree species screening technique in the prior art and is all based on evaluation criteria given by man, causes to comment
Estimate not and have standardization, and can not combined data processing technique intelligent high-efficiency assessment, reduce assessment efficiency the problem of.
The present invention specifically uses following technical scheme to solve above-mentioned technical problem:
A kind of forest against wave wash tree species appraisal procedure based on big data, comprising the following steps:
Step 1, the evaluation index for determining several tree species, each evaluation index number to be gathered tree seeds using web crawlers technology
According to, and classify to each evaluation index data of the tree species of acquisition, obtain the real number class data and text of each evaluation index of tree species
Class data, wherein text class data are the opinion rating under each evaluation index to tree species；
The real number class data of each evaluation index of tree species are normalized in step 2, and tree species are respectively commented after being standardized
Estimate the real number class data of index；
Step 3 arranges the text class data of each evaluation index of tree species according to opinion rating, counts each evaluation index
The species number of lower opinion rating, and assignment is carried out according to the text class data sequence of arrangement, to obtain each text under each evaluation index
The assessment real number value of this class data；
Step 4, the real number class data using hierarchical clustering method to each evaluation index of tree species after standardization and each text class number
According to assessment real number value carry out classification respectively and obtain respective several classifications, and take the maximum value and minimum in of all categories respectively
The boundary value between adjacent category is calculated in value, and determine therefrom that numberical range corresponding to each classification of real number class data and
Numberical range corresponding to each classification of assessment real number value of text class data, to establish the assessment table of each evaluation index of tree species；
Step 5, the assessment table that the tree species data to be assessed of acquisition are mapped to each evaluation index of tree species, it is to be assessed with determination
The assessment result of tree species.
Further, as a preferred technical solution of the present invention: real number class of the step 2 to tree species evaluation index
Data are normalized including real number class data are divided into more bigger more excellent type and smaller more excellent type, and respectively to more bigger more excellent
Type and smaller more excellent type real number class data are normalized.
Further, as a preferred technical solution of the present invention, normalized in the step 2 specifically:
Formula is used to the real number class data normalization processing of more bigger more excellent type:
rij=(xij-ximin)/(ximax-ximin)
Formula is used to the real number class data normalization processing of smaller more excellent type:
rij=(ximax-xij)/(ximax-ximin)
Wherein, rijIndicate the value under i-th of index after j-th of value standardization；xijIt is data set in i-th of index
Under j-th of value；ximaxIndicate the maximum value in data set under i-th of index；ximinIt indicates in data set under i-th of index
Minimum value.
Further, as a preferred technical solution of the present invention: according to the text class data of arrangement in the step 3
The range that sequence carries out assignment is [0,1].
Further, as a preferred technical solution of the present invention: being divided in the step 4 using hierarchical clustering method
Class, comprising:
Using the assessment real number value of the real number class data of each evaluation index of tree species after standardization or each text class data as sample
This, and all samples are classified as a class cluster；
According to required clusters number, two group clusters that a class cluster is divided into are determined；
The distance between sample two-by-two is calculated in a class cluster, finds out two farthest samples of distance and is respectively allocated to
In two group clusters being divided to；
The distance for calculating each remaining sample two sample farthest with distance is found out respectively, sentences according to the distance of calculating
In one to break in the sample clustering to two group clusters being divided to.
Cluster iteration is repeated, with group cluster corresponding to each sample of determination.
The present invention by adopting the above technical scheme, can have the following technical effects:
Forest against wave wash tree species appraisal procedure based on big data of the invention, this method utilize all kinds of tree species big datas and level
Clustering method proposes objective, reproducible standard formulation method, by data acquisition, the processing of data, data classification, comments
Estimate and formulate four steps, realizes the rapid evaluation to tree species, treatment process is efficiently realized using big data processing technique,
The boundary value and range of data classification under each index can be accurately obtained, precise classification assessment is realized, without relying on manual evaluation, mentions
High assessment efficiency, has very strong practicability and wide applicability.
Classification thresholds determine excessively subjective, operating process rule during this method can overcome the problems, such as previous standard formulation
Generalized.This method is absorbed in the determination of each metrics-thresholds, does not influence the selection process of standard, has very strong independence, just
Field is formulated in being generalized to other standards, there is wide applicability.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow diagrams of the forest against wave wash tree species appraisal procedure of big data.
Specific embodiment
Embodiments of the present invention are described with reference to the accompanying drawings of the specification.
As shown in Figure 1, the present invention devises a kind of forest against wave wash tree species appraisal procedure based on big data, this method is mainly related to
And the acquisition of data available, the processing of data, data classification, evaluation criteria formulation Four processes, specifically includes the following steps:
Step 1, the evaluation index for determining several tree species, each evaluation index number to be gathered tree seeds using web crawlers technology
According to as adopted to the data available of the Intranets such as internet and professional science, yearbook database using web crawlers technology
Collection；And classify to each evaluation index data of the tree species of acquisition, obtain the real number class data and text of each evaluation index of tree species
Class data, wherein text class data are the opinion rating under each evaluation index to tree species, and are stored respectively.
The real number class data of each evaluation index of tree species are normalized in step 2, wherein real number class data are divided into
More bigger, more excellent type and smaller more excellent type, and place is normalized to more bigger more excellent type and smaller more excellent type real number class data respectively
Reason, the real number class data of each evaluation index of tree species after being standardized.
Formula is used to the real number class data normalization processing of more bigger more excellent type:
rij=(xij-ximin)/(ximax-ximin)
Formula is used to the real number class data normalization processing of smaller more excellent type:
rij=(ximax-xij)/(ximax-ximin)
Wherein, rijIndicate the value under i-th of index after j-th of value standardization；xijIt is data set in i-th of index
Under j-th of value；ximaxIndicate the maximum value in data set under i-th of index；ximinIt indicates in data set under i-th of index
Minimum value.
Step 3 arranges the text class data of each evaluation index of tree species according to opinion rating, counts each evaluation index
The species number of lower opinion rating, and assignment is carried out according to the text class data sequence of arrangement, to obtain each text under each evaluation index
The assessment real number value of this class data, wherein the range of assignment is [0,1].
Such as to i-th of evaluation index, all opinion ratings are pressed by difference to good arrangement, and the species number n of statistical estimation, it is right
Opinion rating is worst text evaluation assignment 0, is time assignment of difference to opinion ratingAnd so on, opinion rating is
Best assessment assignment 1.
Step 4, the real number class data using hierarchical clustering method to each evaluation index of tree species after standardization and each text class number
According to assessment real number value carry out classification respectively and obtain respective several classifications.The maximum value and minimum value in of all categories are taken respectively
The boundary value between adjacent category is calculated, and determines therefrom that numberical range and text corresponding to each classification of real number class data
Numberical range corresponding to each classification of assessment real number value of this class data, to establish the assessment table of each evaluation index of tree species.
To different evaluation indexes, needed for data are divided by the disintegrating method for the foundation distance being respectively adopted in hierarchical clustering method
If Ganlei, the present embodiment specifically counts the data classification method of a certain index by taking 4 poor, medium, preferable, good classes as an example
Calculation method is as follows:
Firstly, using the real number class data of each evaluation index of tree species after standardization or each text class data as sample, by institute
There is sample to be classified as a class cluster, is denoted as U；
Then, according to 4 required clusters numbers, i.e. U1, U2, U3, U4；First cluster determines and divides needed for a class cluster
At group cluster be 2, i.e. group cluster U1 and U4；Secondly, cluster iteration determines group cluster corresponding to each sample, comprising:
(1) distance between sample two-by-two is calculated in a class cluster U, finds out distance farthest two samples a, b；
(2) sample a, b are distributed into class cluster in group the cluster U1 and U4 at both ends respectively；
(3) it iterates to calculate remaining sample in class cluster U and finds out the distance apart from farthest two samples a, b, according to meter
The group cluster of the Distance Judgment of the calculation sample clustering extremely, if the distance dis (a) of i.e. sample distance a < dis (b), then by sample
Point is grouped into group cluster U1, is otherwise grouped into U4.
(4) until the number iteration for reaching cluster terminates.In the present embodiment by taking four classes as an example, first time iteration divides sample
For two class of U1 and U4, U1 is divided into U1 and U2 according to step (3) again by second of iteration, calculate distribution to corresponding classification for sample
In U1 or U2.U4 is divided into U3 and U4, sample calculate distribution into corresponding classification U3 or U4.Meter is completed after iteration twice
It calculates.
Determination for boundary value between adjacent category.To different indexs, taken in all kinds of respectively in 4 classes divided
Maximum value and minimum value, such asWherein,Indicate i-th of index
Under " poor " class minimum value,The maximum value of " poor " class is indicated under i-th of index.Remaining and so on.Then under i-th of index
" poor " class and " in " boundary value of class may be defined as:Phase under each evaluation index can be calculated separately accordingly
Boundary value between adjacent two classes, and determine therefrom that numberical range corresponding to each classification of real number class data and text class data
Numberical range corresponding to each classification under real number value is assessed, to establish the assessment table of each evaluation index of tree species.
Step 5, the assessment table that the tree species data to be assessed of acquisition are mapped to each evaluation index of tree species, it is to be assessed with determination
The assessment result of tree species, the index generic including assessment, such as poor, medium, preferable, good middle any sort.
Tree species assessment can be fast implemented in order to verify the method for the present invention, a verifying example is enumerated and is illustrated, the verifying
Example is specific as follows by taking forest against wave wash construction engineering is administered in Heilongjiang Province Nenjiang as an example:
Step 1: listing evaluation index, acquisition and storing data.
Since forest against wave wash is located at river upstream face, as the main purpose with wave resistance, and there are seasonal ponding, and vegetation object in flood season
The quality and wave resistance effect of the selection direct relation forest against wave wash engineering construction of kind.River forest against wave wash Tree Species Selection must satisfy following
Condition: 1. resistance to water logging, in humid conditions also can normal growth, this is most basic condition；2. well developed root system, main root is deeper,
It is not easy to lodge；3. branch flexibility is good, anti-current-rush；4. the speed of growth is fast, closing can be formed as early as possible, make forest against wave wash system
Wave resistance benefit is played as early as possible；5. sprouting ability is strong, rigid trees tree crown is larger more plentiful；6. being with Native species best in quality
It is main, select that ornamental value is higher, the higher vegetation and plant species of economic value as far as possible under the conditions of possible, it is graceful aquatic to create
State environment meets the needs of people are to high-quality life.
By crawler technology obtain 172 hybridization willows, 105 clone poplars, 106 clone poplars, 121 clone poplars,
203 clone poplars, camplotheca acuminata, middle mountain China fir, bald cypress, Koelreuteria bipinnata Franchet, Bischofia javanica Bl, Cortex Eucommiae, meaning poplar, dry land willow, metasequoia, taxodium ascendens, Soviet Union
The data of the magnanimity tree species such as willow J172, Su Liu J194, Su Liu J799, including survival rate, main root length, the diameter of a cross-section of a tree trunk 1.3 meters above the ground, hat width, branch are soft
The evaluation indexes such as toughness, the speed of growth, pest species, landscape value index, cold-resistant temperature, soil fertility requirement.Acquisition
Text class data are under each evaluation index to the opinion ratings of tree species, as it is poor, in, it is good, excellent.Data are identified, data
It is stored respectively by real number class data and text class data.
Step 2: real data normalized.
The processing of real data.In general, real data index can be divided into more bigger more excellent type and smaller more excellent type, right
More bigger, the real data of more excellent type is handled as follows:
rij=(xij-ximin)/(ximax-ximin)
The real data of smaller more excellent type is handled as follows:
rij=(ximax-xij)/(ximax-ximin)
Wherein: xijIt is j-th value of the data set under i-th of index.ximaxInstitute under i-th of index in expression data set
There is the maximum value of value；ximinExpression scheme concentrates the minimum value of all values under i-th of index.rijIndicate that j-th of value exists
The value after standardization under i-th of index.
Step 3: the processing of text data is needed text evaluation digitization.To i-th of index, by all evaluation grades
It is arranged by by difference to good degree, and the species number n of statistical estimation, the tax to worst text evaluation assignment 0, to secondary difference
ValueAnd so on, best assessment assignment 1.
Step 4: evaluation criteria is established.
1), the classification of data.To different indexs, the disintegrating method for the foundation distance being respectively adopted in hierarchical clustering method will be counted
According to being divided into poor, medium, preferable, 4 classes.It is as follows to the data classification method circular of a certain index:
Input: all tree species treated data under the index；Clusters number: 4.
Output: cluster result
Start to calculate: all samples being classified as a class cluster, are denoted as U；
It is iterated:
The distance between sample two-by-two is being calculated in U, finds out distance farthest two samples a, b；
Sample a, b are assigned in different class cluster U1 and U4；
The distance of remaining other sample points and a, b in former class cluster U is calculated, if the distance dis (a) of sample distance a <
Sample point is then grouped into U1 by dis (b), is otherwise grouped into U4；
Sample is divided into two class of U1 and U4 by first time iteration, U1 is divided into U1 and U2 again by second of iteration, by sample
Distribution calculate into corresponding classification U1 or U2.U4 is divided into U3 and U4, sample calculate distribution to corresponding classification U3
Or in U4.It completes to calculate after iteration twice, until: reach the number of cluster.
2), the determination of standard boundary value.To different indexs, maximum value in class and most is taken respectively in 5 classes divided
Small valueWherein,The minimum of " poor " class is indicated under i-th of index
Value,The maximum value of " poor " class is indicated under i-th of index.Remaining and so on.Then under i-th of index " poor " class and " in " class
Boundary value may be defined as:The boundary under each index between adjacent two class can be calculated separately accordingly
Value, these boundary values constitute complete assessment table, as shown in table 1.
1 Nenjiang mainstream forest against wave wash tree species adaptability target system of table
| Index | It is good | Preferably | In | Difference |
| Continuously flood 15d survival rate/% | [95,100] | [85,95) | [75,85) | [0,75) |
| Main root length/m | [1.5,+∞) | [1,1.5) | [0.6,1) | (0,0.6) |
| The diameter of a cross-section of a tree trunk 1.3 meters above the ground/m | [0.2,+∞) | [0.15,0.2) | [0.1,0.15) | (0,0.1) |
| Hat width/m | [4.5,+∞) | [3,4.5) | [1.5,3) | (0,1.5) |
| Branch flexibility | [80,100] | [60,80) | [40,60) | [0,40) |
| The speed of growth/(m/a) | [1.5,+∞) | [1,1.5) | [0.5,1) | (0,0.5) |
| Pest species/ | [0,1] | (1,4] | (4.9] | (9,+∞) |
| Landscape value index | [90,100] | [70,90) | [40,70) | [0,40) |
| Cold-resistant temperature/DEG C | (- ∞, -30] | [-20,-30) | [-10,-20) | (-10,+∞) |
| Soil fertility requires index | [0,20] | (20,40] | (40,60] | (60,+∞) |
Step 5: after obtaining table 1, this appraisement system can be applied to be denoted as with table middle finger as assessed value progress
The Tree Species Selection decision domain of assessment, obtains assessment result, such as the assessment situation of each index of tree species, as survival rate is good, main root
Length preferably etc. assessment results.After the above index value for obtaining any several tree species to be assessed, can by analytic hierarchy process (AHP),
A variety of methods such as expert point rating method, entropy assessment select tree species to be assessed, and the present invention is not limited thereof.
To sum up, the method for the present invention utilizes all kinds of tree species big datas and hierarchy clustering method, realizes to the fast of tree species index
Speed assessment, efficiently realizes treatment process using big data processing technique, can accurately obtain the side of data classification under each index
Dividing value and range realize precise classification assessment, without relying on manual evaluation, improve assessment efficiency, have very strong practicability
And wide applicability.
Embodiments of the present invention are explained in detail above in conjunction with attached drawing, but the present invention is not limited to above-mentioned implementations
Mode within the knowledge of a person skilled in the art can also be without departing from the purpose of the present invention
It makes a variety of changes.
Claims (5)
1. a kind of forest against wave wash tree species appraisal procedure based on big data, which comprises the following steps:
Step 1, the evaluation index for determining several tree species, each evaluation index data to be gathered tree seeds using web crawlers technology,
And classify to each evaluation index data of the tree species of acquisition, obtain the real number class data and text class number of each evaluation index of tree species
According to wherein text class data are the opinion rating under each evaluation index to tree species；
The real number class data of each evaluation index of tree species are normalized in step 2, and tree species are respectively assessed and refer to after being standardized
Target real number class data；
Step 3 arranges the text class data of each evaluation index of tree species according to opinion rating, counts and comments under each evaluation index
The species number of valence grade, and assignment is carried out according to the text class data sequence of arrangement, to obtain each text class under each evaluation index
The assessment real number value of data；
Step 4, using hierarchical clustering method to the real number class data of each evaluation index of tree species after standardization and each text class data
Assessment real number value carries out classification respectively and obtains several respective classifications, and takes the maximum value and minimum value meter in of all categories respectively
Calculation obtains the boundary value between adjacent category, and determines therefrom that numberical range and text corresponding to each classification of real number class data
Numberical range corresponding to each classification of class data, to establish the assessment table of each evaluation index of tree species；
Step 5, the assessment table that the tree species data to be assessed of acquisition are mapped to each evaluation index of tree species, with determination tree species to be assessed
Assessment result.
2. the forest against wave wash tree species appraisal procedure based on big data according to claim 1, which is characterized in that the step 2 is right
The real number class data of tree species evaluation index be normalized including by real number class data be divided into more bigger more excellent type and it is smaller more
Excellent type, and more bigger more excellent type and smaller more excellent type real number class data are normalized respectively.
3. the forest against wave wash tree species appraisal procedure based on big data according to claim 2, which is characterized in that in the step 2
Normalized specifically:
Formula is used to the real number class data normalization processing of more bigger more excellent type:
rij=(xij-ximin)/(ximax-ximin)
Formula is used to the real number class data normalization processing of smaller more excellent type:
rij=(ximax-xij)/(ximax-ximin)
Wherein, rijIndicate the value under i-th of index after j-th of value standardization；xijIt is of data set under i-th of index
J value；ximaxIndicate the maximum value in data set under i-th of index；ximinIndicate the minimum in data set under i-th of index
Value.
4. the forest against wave wash tree species appraisal procedure based on big data according to claim 1, which is characterized in that in the step 3
Carrying out the range of assignment according to the text class data sequence of arrangement is [0,1].
5. the forest against wave wash tree species appraisal procedure based on big data according to claim 1, which is characterized in that in the step 4
Classified using hierarchical clustering method, comprising:
Using the assessment real number value of the real number class data of each evaluation index of tree species after standardization or each text class data as sample, and
All samples are classified as a class cluster；
According to required clusters number, two group clusters that a class cluster is divided into are determined；
The distance between sample two-by-two is calculated in a class cluster, finds out two farthest samples of distance and is respectively allocated to point
Two group clusters in；
The distance for calculating each remaining sample two sample farthest with distance is found out respectively, should according to the Distance Judgment of calculating
In one in two group clusters that sample clustering is extremely divided to；
Cluster iteration is repeated, with group cluster corresponding to each sample of determination.
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