CN101877034A - Land utilization automatic division method - Google Patents

Abstract

The invention relates to a land utilization automatic division method, and belongs to the field of land utilization planning. The automatic division method comprises the following steps: extracting and integrating the basic data of land utilization division; constructing a land utilization automatic division model based on a particle swarm optimization algorithm, establishing a mapping relationship between an unknown question and a particle swarm by taking the data as model input data and taking a land utilization patch as a data processing unit, finally solving a land utilization optimized layout result; and combining patches of adjacent same type land, and extracting the boundary of the combined patches so as to obtain a land utilization division boundary, and finally generating a land utilization division chart. The method has the advantages of well simulating the intelligent behavior of human being in the process of land utilization decision to improve the rationality and suitability of land utilization division so as to overcome the defect that the rationality of land-use zoning is poor caused by low efficiency of the conventional land utilization efficiency and hard simulation of the intelligent behavior of human being.

Description

A kind of soil utilizes the auto-partition method

Technical field

The present invention relates to a kind of soil and utilize the auto-partition method, refer to be applied to the soil especially and utilize the soil in the space optimization configuration to utilize the auto-partition method, belong to the land use planning field based on particle swarm optimization algorithm.

Background technology

It is the major technique means of land resource purposes control that the soil utilizes subregion, also is the key content that the soil utilizes the space optimization configuration.The soil utilizes subregion research to start from the regional soil that 20 th America start and utilizes the comprehensive sub-areas in the world, begun in mid-term in 20th century subsequently that the soil of land resource reasonable disposition and layout utilizes subregion research between all departments to solve, the classification subregion system of the territory planning of carrying out as Korea S, Japan.In the last few years, the soil utilized subregion research to begin to pay attention to the research of partitioning technique means and subregion theory, and subregion accuracy and practicality have also obtained certain raising.The soil of China utilizes subregion research beginning later, first representative achievement is the national present status of land utilization zoning of finishing the sixties in last century, the national potential of land resource zoning thought that the eighties, Chinese Academy of Sciences's geographical synthesis board of review proposed in the land resource map process in the establishment whole nation in 1: 100 ten thousand is divided into 9 REGION OF WATER INJECTION OILFIELD with national land resource; The soil utilizes the research of partitioning technique method to obtain paying close attention to more widely afterwards, proposed to utilize partition method than soils such as Y-factor method Y, cellular automatons based on improvement cluster analysis, space overlay analysis, planisphere method, mould, the proposition of these methods has partly overcome traditional soil and has utilized subregion based on qualitative, partition boundaries is fuzzy, subregion is random greatly, the subregion work efficiency is low, the subregion result utilizes the not strong defective of directive significance to the soil, and brought into play vital role in preceding two-wheeled overall plan for land use compilation process.Yet because the soil utilizes the complex nature of the problem, it is a land ecology economy composite system that relates to nature, society, economy that the soil utilizes system, the natural quality that the soil utilizes system can effectively be analyzed and simulate to simple measurement geography method and geographical computing system, the influence to the soil utilization at aspects such as the social economy of soil utilization and people's subjective desires then is difficult to obtain analytical effect preferably, particle swarm optimization algorithm also is the simulation that originates from simple social system, is the process that the simulation flock of birds is looked for food at first.

The tradition soil utilizes partition method, and geographical and geographical computing method are difficult to simulate the soil and utilize the complex behavior of people in the system soil to be utilized the influence of system based on metering, rationality is not high as a result to cause the soil to utilize subregion, and land resource is rationally utilized and the not strong defective of purposes control directiveness.

Summary of the invention

For addressing the above problem, the invention provides a kind of soil and utilize the auto-partition method, this method is that a kind of soil based on particle cluster algorithm utilizes the auto-partition method, utilize the newest research results of correlation techniques such as artificial intelligence and biological computation, come the anthropomorphic dummy to utilize intelligent behavior in the system in the soil, to improve rationality and the operability that the soil utilizes subregion.This method comprises the steps:

(1) the extraction soil utilizes the basic data of subregion and integrates, and the data after the integration are land use combination and each figure spot information wherein;

(2) make up the soil based on particle swarm optimization algorithm and utilize the auto-partition model, data with step (1) are model input data, with the land-use map spot is data processing unit, sets up the mapping relations wait to ask between problem and the population, finally finds the solution the soil utilization result that optimizes distribution.

(3) the soil utilization that step (2) is obtained is optimized distribution, and merges by the figure spot to contiguous identical land use pattern and handles, and extracts figure spot boundary line after merging and promptly obtains the soil and utilize zone boundary, and finally generate the soil and utilize block plan.

Described in the step (2) foundation to wait to ask the mapping relations between problem and the population as follows: the land-use map spot is abstract to be the particle in the particle swarm optimization algorithm, the position of each figure spot and land use pattern correspond to particle position (x, y) and kind (i), the soil utilizes the comprehensive benefit function to be the population fitness function.

Finding the solution described in the step (2) draws optimize distribution result's step of soil utilization and comprises:

(a) scale of population is set, the maximum flying speed (υ of particle _Xmax, υ _Ymax) and quicken weight (c ₁, c ₂), maximum iteration time, error amount, and each particle position of initialization, speed and attribute;

(b), calculate the fitness of each particle according to fitness function;

(c) calculate individual optimal value (p _b) and global optimum (p _g), and calculate the individual optimal location and the global optimum position of particle;

(d) calculate inertia weight, upgrade the flying speed of each particle, the individual optimal location and the global optimum position that draw according to step (c), the more current location of new particle;

(e) loop iteration: when satisfying Rule of judgment, search finishes, otherwise the value that step (d) is upgraded is as the input value of (b), continues to search for to obtain global optimum and global optimum position.

The current location of the described more new particle in the step (c) adopts the arest neighbors rule, and present bit is equipped with when a plurality of, and the arest neighbors rule adopts the area method that is dominant to determine and the most contiguous figure spot of current location.

Rule of judgment described in the step (d) is: the absolute value of the difference of twice global optimum in front and back is not more than error amount, and the number of times of loop iteration is not more than maximum iteration time.

The invention has the advantages that: particle swarm optimization algorithm can make full use of the computation capability of computing machine on the one hand, effectively improves soil subregion efficient; Simultaneously, utilize population intelligent optimization analog capability preferably simulating human utilize intelligent behavior in the decision process in the soil, utilize the rationality and the applicability of subregion to improve the soil, thereby solve the deficiency that current soil utilizes the subregion inefficiency, the intelligent behavior that is difficult to the anthropomorphic dummy causes subregion rationality difference.

Description of drawings

Fig. 1 model process flow diagram of the present invention.

Fig. 2 particle position of the present invention upgrades arest neighbors rule synoptic diagram.

The soil of Fig. 3 embodiment 1 utilizes subregion present situation figure.

The analog result figure of Fig. 4 embodiment 1.

Embodiment

Model flow process of the present invention such as Fig. 1 show.

This soil utilizes the auto-partition method to comprise the steps:

Step 1. extraction soil utilizes the basic data of subregion and integrates, data after the integration are land use combination and each figure spot information wherein, the extraction basic data is meant from other databases or other system obtains the data that the soil utilizes section post to need, utilize suitability evaluation data, statistical yearbook data etc. as present status of land utilization data, soil, integral data is meant that these data are data of multiple source, different-format, to unify standardization processing to it, form a unified basic database.

Step 2. makes up the soil based on particle swarm optimization algorithm and utilizes the auto-partition model, data with step (1) are model input data, with the land-use map spot is data processing unit, sets up the mapping relations wait to ask between problem and the population, finally finds the solution the soil utilization result that optimizes distribution.The land-use map spot is abstract to be particle in the particle swarm optimization algorithm, and the position of each figure spot and land use pattern correspond to particle position (x, y) and kind (i), the soil utilizes the comprehensive benefit function to be the population fitness function.

Step 3. is provided with the population scale, the maximum flying speed (υ of particle _Xmax, υ _Ymax) and quicken weight (c ₁, c ₂), maximum iteration time, error amount, each particle position of initialization, speed and attribute;

$\left\{\begin{array}{c}{v}_{\mathrm{xij}}=\mathrm{rand}\left(\mathrm{0,1}\right)*v_{x\max }\\ v_{\mathrm{yij}}=\mathrm{rand}\left(\mathrm{0,1}\right)*v_{y\max }\end{array}\right.$

In the formula: υ _XijAnd υ _YijBe respectively the x of particle and the speed on the y direction of principal axis.

Step 4. is calculated the fitness of each particle according to fitness function.

The fitness function of particle is as follows:

$\mathrm{Minimize}{C}_k=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{c}_{\mathrm{ik}}{x}_{\mathrm{ik}}$

$\mathrm{Minimize}{S}_k=\underset{i|x_{\mathrm{ik}}=1}{\min }\left(s_{\mathrm{ik}}{x}_{\mathrm{ik}}\right)$

$\mathrm{Minmize}{Z}_k=\underset{i|x_{\mathrm{ik}}=1,j|x_{\mathrm{jk}}=0,j\∈T_i}{\mathrm{\Σ}}\underset{h=1}{\overset{n_{\mathrm{ij}}}{\mathrm{\Σ}}}{l}_{\mathrm{ijh}}{x}_{\mathrm{ik}}$

F＝(C _k，S _k，Z _k)

Satisfy:

x _Ik∈ 0,1}

In the formula: n is a land-use map spot sum; c _IkThe land use that is i figure spot changes the needed expense of k kind land use pattern into; s _IkIt is the suitability evaluation index of the land use k of i land-use map spot; T _iIt is the set that the adjacent map spot of i land-use map spot is constituted; n _IjCommon edge number for land-use map spot i and figure spot j; l _IjhThe length of side for the h bar common edge of figure spot i and figure spot j; a _iLand area for figure spot i; A _1kAnd A _2kBe respectively required minimum of k kind land use pattern and maximum area; If i land unit is x during land use pattern among the k _Ik=1, otherwise x _Ik=0, C _kBe soil change expense target; S _kIt is the land suitability target of k kind land use; Z _kFor figure spot compactedness is the shape target; F is the multiple goal composite evaluation function.

Step 5. is calculated individual optimal value p _bWith global optimum p _g, and calculate the individual optimal location and the global optimum position of particle, suppose the fitness function determined among the f (X), the optimum position formula of the particle after so each the renewal is:

Hypothetical particle group scale is s, global optimum p _g, then:

P _g(t)∈{P _1b(t)，P _1b(t)，........，P _sb(t)}＝min{f(P _1b(t)，f(P _2b(t))，.......，f(P _sb(t)))}

Step 6. is calculated inertia weight, upgrades the flying speed of each particle, draws individual optimal location and global optimum position according to step 5, upgrades the current location of each particle.

The function of inertia weight is: ω (t)=ω _Max-t (ω _Max-ω _Min)/I _Max

In the formula: t is iterations t=1,2 ..., I _Max, I wherein _MaxBe maximum iteration time.ω _MaxBe maximum inertia weight, ω _MinBe minimum inertia weight.

The flying speed renewal function is:

$\left\{\begin{array}{c}{v}_{\mathrm{ij}}(t+1)={\mathrm{wv}}_{\mathrm{ij}}\left(t\right)+c_1{r}_{1j}\left(t\right)(P_{\mathrm{ijb}}\left(t\right)-x_{\mathrm{ij}}\left(t\right))+c_2{r}_{2j}\left(t\right)(P_{\mathrm{ig}}\left(t\right)-x_{\mathrm{ij}}\left(t\right))\\ x_{\mathrm{ij}}(t+1)={\mathrm{wx}}_{\mathrm{ij}}\left(t\right)+v_{\mathrm{ij}}(t+1)\end{array}\right.$

Wherein: " i " expression particle i, the j dimension of " j " expression particle, w is an inertia weight, t represents iterations, c ₁, c ₂The expression accelerator coefficient, c ₁Regulate the step-length of particle self flying speed, c ₁Then regulate particle and fly to the step-length of overall desired positions, r ₁～U (0,1), r ₂～U (0,1) is separate random function, and a moment speed also is influence coefficient to the influence degree of present speed on the expression particle.

In order to prevent the unlimited increase of particle flying speed, present the blast disordered state, need to increase by one group of constraint condition:

$\left\{\begin{array}{cc}{\mathrm{\&upsi;}}_i={\mathrm{\&upsi;}}_{\max }& \mathrm{if\&upsi;}>{\mathrm{\&upsi;}}_{\max }\\ {\mathrm{\&upsi;}}_i=-{\mathrm{\&upsi;}}_{\max }& \mathrm{if\&upsi;}<-{\mathrm{\&upsi;}}_{\max }\end{array}\right.$

Particle maximum flying speed υ _MaxLimited the flying speed υ of particle _i, υ _MaxDetermined particle search accuracy in the space, when value is too big, then particle is crossed optimum solution easily; Work as υ _MaxValue is too little, and then the particle flying speed is slow, is absorbed in the Local Search space easily and can't obtains globally optimal solution.

The current location arest neighbors rule of new particle more, if present bit is equipped with when a plurality of, the arest neighbors rule adopts the area method that is dominant.This rule proposes at proximity relations how to determine scrambling figure spot in the model, is the criterion that is used for judging the next position of particle flight when upgrading particle position.When the current location of known particle and speed, be the center of circle with the figure spot center of current location correspondence, the present speed vector is that radius is done circle, then it next certain figure spot that possible position should be and this circle intersects constantly.When having a plurality of intersection graph spot, at first calculate the area that each intersection graph spot falls into circle and account for the ratio of this figure spot area; the center at figure spot place of getting the ratio maximum is as its next position; as shown in Figure 2; when the current location of known particle and speed; its next possible position should be to be the center of circle (A) with the current location; velocity is on the circle of radius; the dashed circle shown in the accompanying drawing 2; then its next position may be a figure spot 114; 111; 204 or 112; according to the arest neighbors rule of the method that is dominant based on area; getting the center that the area that falls into circle in these figure spots accounts for that figure spot place of figure spot area ratio maximum is its next position, is A1 position among the figure.

Step 7. loop iteration, when satisfying Rule of judgment, the absolute value of the difference of twice global optimum is not more than error amount promptly, and when the number of times of loop iteration is not more than maximum iteration time, search finishes, otherwise the value that will upgrade continues search and obtains global optimum and global optimum position as the input value of step 4.

Step 8. pair step 2) the soil utilization that obtains is optimized distribution, and merges by the figure spot to contiguous identical land use pattern and handles, and extracts figure spot boundary line after merging and promptly obtains the soil and utilize zone boundary, and finally generate the soil and utilize block plan.

Embodiment 1:

1. extract the basic data of a certain small towns soil utilization and integrate, the data after the integration are land use combination and each figure spot information wherein, as shown in Figure 3.

2. use particle swarm optimization algorithm to above-mentioned data-optimized, carry out modeling and select optimum soil to utilize layout.

3. 20 of population scales are set, maximum flying speed of particle (3,4) and acceleration weight (1.44,1.44), maximum iteration time is 200 times, error amount is 10 ^-6, the position of primary correspondence is determined by the barycentric coordinates of figure spot, the corresponding ten kinds of land use patterns of attribute, and scope is value between (0,10), and initial velocity is that maximum flying speed multiply by the speed that the random number between (0,1) produces at random.

4. according to fitness function, calculate the fitness of each particle.

F＝(C _k，S _k，Z _k)

Wherein: $\mathrm{Minimize}{C}_k=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{c}_{\mathrm{ik}}{x}_{\mathrm{ik}}$

$\mathrm{Minimize}{S}_k=\underset{i|x_{\mathrm{ik}}=1}{\min }\left(s_{\mathrm{ik}}{x}_{\mathrm{ik}}\right)$

$\mathrm{Minmize}{Z}_k=\underset{i|x_{\mathrm{ik}}=1,j|x_{\mathrm{jk}}=0,j\&Element;T_i}{\mathrm{\&Sigma;}}\underset{h=1}{\overset{n_{\mathrm{ij}}}{\mathrm{\&Sigma;}}}{l}_{\mathrm{ijh}}{x}_{\mathrm{ik}}$

5. calculate individual optimal value p _bWith global optimum p _g, and calculate the individual optimal location and the global optimum position of particle.

6. calculating inertia weight upgrades the flying speed of each particle, draws individual optimal location and global optimum position according to step 5, upgrades the current location of each particle.

Inertia weight w regards the linear function of iterations as, its from 0.98 to 0.48 linearity is set reduces, and maximum inertia weight is 0.98, and minimum inertia weight is 0.48, and the function of inertia weight is:

$w\left(t\right)=0.98-\frac{t}{I_{\max }}\&times;0.5$

7. loop iteration.When satisfying Rule of judgment, promptly the absolute value of the difference of twice global optimum in front and back is not more than 10 ^-6, and the number of times of loop iteration is when being not more than maximum iteration time 200, search finishes, otherwise the value that will upgrade is as the input value of step 4, continues to search for to obtain global optimum and global optimum position.

8. the extraction of optimization result's processing, zone boundary is with definite.To the soil utilization of finishing the loop iteration result figure that optimizes distribution, be close to the merging of similar figure spot and handle, extract different land used categories subareas boundary line, make the soil and utilize subregion figure as a result, as shown in Figure 4.

Claims (6)

1. a soil utilizes the auto-partition method, it is characterized in that this method comprises the steps:

(1) the extraction soil utilizes the basic data of subregion and integrates, and the data after the integration are land use combination and each figure spot information wherein;

(2) make up the soil based on particle swarm optimization algorithm and utilize the auto-partition model, data with step (1) are model input data, with the land-use map spot is data processing unit, sets up the mapping relations wait to ask between problem and the population, finally finds the solution the soil utilization result that optimizes distribution.

(3) the soil utilization that step (2) is obtained is optimized distribution, and merges by the figure spot to contiguous identical land use pattern and handles, and extracts figure spot boundary line after merging and promptly obtains the soil and utilize zone boundary, and finally generate the soil and utilize block plan.

2. soil according to claim 1 utilizes the auto-partition method, it is as follows to it is characterized in that foundation in the step (2) waits to ask the mapping relations between problem and the population: the land-use map spot is abstract to be the particle in the particle swarm optimization algorithm, the position of each figure spot and land use pattern correspond to particle position (x, y) and kind (i), the soil utilizes the comprehensive benefit function to be the population fitness function.

3. soil according to claim 1 utilizes the auto-partition method, it is characterized in that finding the solution in the step (2) drawing optimize distribution result's step of soil utilization and comprising:

(a) scale of population is set, the maximum flying speed (υ of particle _Xmax, υ _Ymax) and quicken weight (c ₁, c ₂), maximum iteration time, error amount, and each particle position of initialization, speed and attribute;

(b), calculate the fitness of each particle according to fitness function;

(c) calculate individual optimal value (p _b) and global optimum (p _g), and calculate the individual optimal location and the global optimum position of particle;

(d) calculate inertia weight, upgrade the flying speed of each particle, the individual optimal location and the global optimum position that draw according to step (c), the more current location of new particle;

(e) loop iteration: when satisfying Rule of judgment, search finishes, otherwise the value that step (d) is upgraded is as the input value of (b), continues to search for to obtain global optimum and global optimum position.

4. soil according to claim 3 utilizes the auto-partition method, it is characterized in that the current location of the more new particle in the step (c) adopts the arest neighbors rule.

5. soil according to claim 4 utilizes the auto-partition method, it is characterized in that: present bit is equipped with when a plurality of, and the arest neighbors rule adopts the area method that is dominant.

6. soil according to claim 3 utilizes the auto-partition method, and it is characterized in that Rule of judgment is in the step (e): the absolute value of the difference of twice global optimum in front and back is not more than error amount, and the number of times of loop iteration is not more than maximum iteration time.

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