CN106372740A - Calculation method and system of pasturing area water-land-pasture-livestock balance model - Google Patents

Abstract

The invention discloses a calculation method and system of a pasturing area water-land-pasture-livestock balance model, and relates to the technical field of mathematical model calculation. The method takes the bearing capacities of water resources, land resources and pasture resources as bottom lines, takes the comprehensive benefit maximum of economic benefits and ecological benefits as a target and takes total balance among pasturing area ''water-land-pasture-livestock'' as a criterion to reasonably determine a water and land resource development scale, an agriculture and husbandry plantation structure, an animal husbandry production way and a proper grazing capacity, and the sustainable utilization of pasturing area water-land-pasture resources, the benign development of the ecological environment and the sustainable development of social economy are realized.

Description

A kind of computational methods of pastoral area water and soil Forage-Livestock Balance model and system

Technical field

The present invention relates to mathematical model computing technique field, particularly to a kind of calculating of pastoral area water and soil Forage-Livestock Balance model Method and system.

Background technology

China's pastoral area drought, shortage of water resources, water and soil resources mismatch, and Forage-Livestock Balance is particularly thorny, Grassland Desertification Degenerate serious, ecological environment is very fragile, overgraze that to nibble with agro-farming economy be cause that grassland ecology degenerates mainly artificial Factor.

The problem that current pastoral area EQUILIBRIUM CALCULATION FOR PROCESS exists:

(1) it is by water-grass-poultry EQUILIBRIUM CALCULATION FOR PROCESS conventional research, for the material impact of Irrigated artificial pasture plantation more The consideration of factor soil is not enough；

(2) studied in the past how to be calculated as target using maximization of economic benefit；

(3) studied mostly is to enter row constraint to irrigate utilized water resources in the past, irrigates utilized water resources and can supply water for water resource Definite value after amount deduction other industry water consumption, irrigating utilized water resources in different water configuration targets and scheme should be change 's；

(4) conventional Forage-Livestock Balance research only considers quantitative balance, and have ignored natural meadow forage with manually excellent Mass balance between matter fodder grass.

Content of the invention

Embodiments provide computational methods and the system of a kind of pastoral area water and soil Forage-Livestock Balance model, existing in order to solve There is problem present in technology.

A kind of computational methods of pastoral area water and soil Forage-Livestock Balance model, methods described includes:

The first preset relation between parameter according to input and described parameter determines object function, described object function Including comprehensive beneficial function and the preferential order function of resource of water supply, described comprehensive beneficial function is economic benefit and ecological benefits sum Maximum；

Constraints is determined according to the second preset relation between described parameter and described parameter, described constraints bag Include resource bearing capacity constraints, equilibrium of supply and demand class constraints, living guarantee class constraints, fairness constraint condition and Condition of Non-Negative Constrains；

According to penkeeping mode, according to the incremental mode of artificial supplementary feeding quota by the natural water and soil grass herding full drylot feeding Poultry balance arranges the value of multigroup described parameter, and the value of every group of parameter combines described object function and constraints constitutes a side Case, multiple described scheme composition proposal collection；

Solve described scheme collection, obtain the optimal solution set of each described scheme；

The optimal solution set being obtained according to different described computation schemes, is contrasted by multi-proxy investigations, analyzed area is sent out The limiting factor of exhibition, selects a described scheme most beneficial for regional development as preferred plan, with described preferred plan Optimal solution set is as Regional Soil grass poultry development threshold value；

Wherein, solve described scheme collection, the optimal solution set obtaining each described scheme includes:

The solution of the described object function of each described scheme is corresponded on [0,1] interval, obtains the coding shape of all solutions Formula, the coding form of described solution is referred to as individuality；

Generate multiple described individualities at random, individuality each described is carried out with reasonableness test, retain and meet described constraint bar As initial individuals, multiple described initial individuals form initial population to the individuality of part；

Calculate the fitness of each described initial individuals in described initial population；

Using the initial individuals in described initial population as parent individuality, and generated according to the fitness of described parent individuality Corresponding offspring individual；

N the most forward for the fitness of first generation evolution generation filial generation group of individuals is protected as existing Noninferior Solution Set Deposit, one by one for each solution in n best offspring individual and described existing Noninferior Solution Set produced by the evolution of every generation later It is compared, retain excellent solution and replace inferior solution, obtain Noninferior Solution Set；

When evolution number of times reaches default evolution number of times, and when iterationses also reach default iterationses, the warp of reservation Cross and replace the described Noninferior Solution Set obtaining, as optimal solution set.

Preferably, when described iterationses are not up to described default iterationses, re-start next iteration, directly To described iterationses reach described default iterationses.

Preferably, described comprehensive beneficial function is determined according to below equation:

f_c=max (obe+oben)

In formula, f_cFor described comprehensive beneficial function, obe is described economic benefit, and oben is described ecological benefits；

Described economic benefit obe is determined by below equation:

Obe=anb+inb

In formula, anb is farming and animal husbandry net benefits, and inb is non-farming and animal husbandry net benefits；

Described farming and animal husbandry net benefits is determined by below equation:

Anb=anba+anbl

In formula, anba irrigates net benefits for planting industry, and anbl is penkeeping net benefits；

Described planting industry is irrigated net benefits and is determined by below equation:

$anba=\underset{f}{\σ}aca\left(f\right)\·\left(p\right(f)\·y(f)-c(f)-wp\·wn(f\left)\right)$

In formula, aca (f) is the irrigated area of f class long-term cropping, and p (f), y (f) and c (f) are respectively f class long-term cropping Yield univalent, per mu and planting cost, wp is water price, and wn (f) is the gross irrigation quota of f class long-term cropping；

Described penkeeping net benefits is determined by below equation:

$anbl=lsl\·\left(\underset{i}{\σ}p\right(l)\·y(l)\·\mathrm{\ω}-c\·(1+0.5\mathrm{\ω})-wp\·wnl\·365/1000)$

In formula, lsl is penkeeping amount, i.e. standard sheep unit, and p (l) and y (l) is respectively domestic animal standard sheep unit the 1st class The yield of product and unit price, ω is rate of domestic animals for sale, and c is the cost of domestic animal standard sheep unit, and wnl is that domestic animal standard sheep unit is drunk Water quota；

Described non-farming and animal husbandry net benefits inb is expressed as with water for industrial use net benefits:

Inb=iav ψ δ

In formula, iav is industrial added value, and ψ is the ratio that industrial net value accounts for the gross output value, and δ is water for industrial use benefitsharing system Number；

Described ecological benefits oben are determined by below equation:

$oben=\underset{k}{\σ}{\mathrm{ana}}_k\·\frac{obend}{area}\·\mathrm{\ξ}\left(k\right)$

In formula, ana_kUtilize area for kth class natural pasture, obend is dynamic grassland ecology value of services, area is sky So pasture can utilize area, and ξ (k) is the conversion factor under the corresponding grazing rate of kth class natural pasture；

Described dynamic grassland ecology value of services is determined by below equation:

Obend=l r obens

In formula, l is relative willingness to pay, and r is natural grasslands scarcity of resources degree, can take according to natural grasslands degree of degeneration Value, span [0,1], obens is static grassland ecology value of services；

Described willingness to pay l expression formula relatively is:

$l=\frac{l}{1+{\mathrm{ae}}^{-bt}}$

In formula, l is the described maximum of willingness to pay l relatively, represents the willingness to pay in the stage that is rich in, value for 1, t is Time variable, represents the socio-economic development stage, and a, b are constant, and value is 1, e is natural logrithm；

Wherein, described time variable t expression formula is:

$t=\frac{1}{e_n}-3$

In formula, e_nFor Engel's coefficient；

Described static state grassland ecology value of services is determined by below equation:

$obens=\underset{i}{\σ}{\mathrm{area}}_i\·{\mathrm{vpa}}_i$

In formula, area_iFor i class natural grasslands area, vpa_iFor the grassland ecology service under i class natural grasslands native state It is worth unit price.

Preferably, the preferential order function of described resource of water supply is determined according to below equation:

$f_w=max\left(obw\right)=\underset{i}{\σ}{\mathrm{\α}}_i\·\underset{j}{\σ}{\mathrm{\β}}_j\·\underset{t}{\σ}wsl(i,j,t)$

In formula, f_wFor the preferential order function of described resource of water supply, obw is the preferential ordinal number of resource of water supply, α_iAnd β_jIt is respectively i row The water supply precedence coefficient at industry water supply weight coefficient and j water source, wsl (i, j, t) is the output at t period i industry j water source.

Preferably, described resource bearing capacity constraints includes water resources carrying capacity constraints, grassland resources holds Loading capability constraints and land resource bearing capacity constraints；

Described water resources carrying capacity constraints expression formula is:

$\underset{i}{\σ}\underset{j}{\σ}\underset{t}{\σ}wsl(i,j,t)\≤min(wsg{\left(t\right)}_{max},\underset{t}{\σ}(\underset{j}{\σ}wll\left(j,t\right)+wtl\left(t\right)),wui)$

In formula, wsg (t)_maxFor t period water supply project maximum water supply capacity, wll (j, t) is can be utilized of t period j water source Amount, wtl (t) is t period outer water diversion volume, and wui is regional water use overall control index；

Described grassland resources bearing capacity constraints expression formula is:

lsl·ln_k·t_k≤ana_k·ncl_k

lsl·iln_l·t_l≤iga_l·cl_l

$\underset{k}{\σ}{\mathrm{ana}}_k\≤area$

In formula, ln_kSearch for food by norm for k class natural pasture sheep unit, t_kFor the raising natural law of k class natural pasture, ncl_kFor k The hay yield of class natural pasture, iln_lSearch for food by norm for l class Irrigated artificial pasture sheep unit, t_lRaising for l class irrigated grasslands Natural law, iga_lFor the area of l class irrigated grasslands, cl_lHay yield for l class irrigated grasslands；

Described land resource bearing capacity constraints expression formula is:

$aca=\underset{m}{\σ}{\mathrm{fca}}_m+\underset{l}{\σ}{\mathrm{iga}}_l+\underset{n}{\σ}{\mathrm{eca}}_n\≤afl\·r$

In formula, aca is long-term cropping irrigated area, fca_mFor m class cereal crops irrigated area, eca_nFor the n-th class economy Crop irrigation area, afl is that cultivated area can be utilized, and r is multiple crop index；

Described equilibrium of supply and demand class constraints includes water supply and water demand constraints and the constraint of the fodder grass equilibrium of supply and demand Condition, the expression formula of described water supply and water demand constraints is:

$\underset{j}{\σ}wsl(i,j,t)=wrl(i,t)$

Wrl (i, t)=ei (i, t) wn (i, t)

In formula, wrl (i, t) is the water requirement of t period the i-th class industry, and ei (i, t) is that the economy of t period the i-th class industry refers to Mark, wn (i, t) is the water demand quota of t period the i-th class industry；

The expression formula of described fodder grass equilibrium of supply and demand constraints is:

lsl·iln_l·t_l=iga_l·cl_l；

Described living guarantee class constraints includes the minimum constraints of cereal crops and animal feeding-stuff basic number constraint bar Part.The expression formula of the minimum constraints of described cereal crops is:

$\underset{m}{\σ}{\mathrm{fca}}_m\·{\mathrm{fcd}}_m\&greaterequal;pgd\·pop$

In formula, fcd_mFor m class cereal crops yield per unit area, pgd is grain demand amount per capita, and pop is that population is total Number；

The expression formula of described animal feeding-stuff basic number constraints is:

lsl≥lsl_min

In formula, lsl_minRaise quantity for domestic animal basis；

The expression formula of described fairness constraint condition is:

ei_min(i, t)≤ei (i, t)≤ei_max(i, t)

wsl_min(i, j, t)≤wsl (i, j, t)≤wsl_max(i, j, t)

In formula, ei_min(i, t) is the minimum of t period i industrial economy index, ei_max(i, t) is t period i industrial economy The highest limit of index, wsl_min(i, j, t) is the minimum of t period i industry j water source output, wsl_max(i, j, t) is t period i The highest limit of industry j water source output.

Preferably, the coding form of described solution is determined by below equation:

X (j)=a (j)+y (j) [(b (j)-a (j))] (j=1,2 ..., p)

In formula, [a (j), b (j)] is that the initial change of j-th optimized variable x (j) pre-setting is interval, and y (j) is x J () corresponds to the real number on [0,1], be called gene, and described coding form is expressed as (y (1), y (2) ..., y (p)), wherein Described optimized variable x (j) is the solution of the corresponding described object function of each scheme.

Preferably, the fitness of described initial individuals is determined by below equation:

$f_t\left(i\right)=\left\{\begin{array}{cc}{\mathrm{kn}}^2& r_t\left(i\right)=1\\ {(n-r_t(i\left)\right)}^2& r_t\left(i\right)>1\end{array}\right.,t=1,2,...n$

$f\left(i\right)=\underset{t=1}{\overset{n}{\σ}}{f}_t\left(i\right),i=1,2,...n$

In formula: r_tI () is i-th individuality sequence sequence number to object function t, f in described initial population_tI () is described first I-th individuality fitness to target t gained in beginning population, f (i) be in described initial population i-th individuality to target complete The comprehensive fitness degree of function, k is the constant in (1,2), for increasing the individual adaptation degree of performance optimum, obtains more participation Chance, n is described object function number, and n is individual number in described initial population.

Present invention also offers a kind of computing system of pastoral area water and soil Forage-Livestock Balance model, described system includes:

Object function determining module, determines for the first preset relation between the parameter according to input and described parameter Object function, described object function includes comprehensive beneficial function and the preferential order function of resource of water supply, and described comprehensive beneficial function is Economic benefit and the maximum of ecological benefits sum；

Constraints determining module, for determining about according to the second preset relation between described parameter and described parameter Bundle condition, described constraints includes resource bearing capacity constraints, equilibrium of supply and demand class constraints, the constraint of living guarantee class Condition, fairness constraint condition and Condition of Non-Negative Constrains；

Scheme collection setup module, for according to penkeeping mode, according to the incremental mode of artificial supplementary feeding quota by natural The water and soil Forage-Livestock Balance herding full drylot feeding arranges the value of multigroup described parameter, the value of every group of parameter combine described object function and Constraints constitutes a scheme, multiple described scheme composition proposal collection；

Scheme collection solves module, for solving described scheme collection, obtains the optimal solution set of each scheme；

Preferred plan determining module, for calculating, according to different schemes, the optimal solution set obtaining, is divided by comprehensive multi-index Analysis contrast, the limiting factor of analyzed area development, select a scheme most beneficial for regional development as preferred plan, with The optimal solution set of good scheme is as Regional Soil grass poultry development threshold value；

Wherein, described scheme collection solves module and includes:

Individual UVR exposure submodule, for the solution of the described object function of each described scheme is corresponded on [0,1] interval, Obtain the coding form of all solutions, the coding form of described solution is referred to as individuality；

Population generates and initialization submodule, generates multiple described individualities for random, individuality each described is closed Rationality is checked, and retains the individuality meeting described constraints as initial individuals, and multiple described initial individuals form initial population；

Fitness calculating sub module, for calculating the fitness of each described initial individuals in described initial population；

Offspring individual generates submodule, for using the initial individuals in described initial population as parent individuality, and according to The fitness of described parent individuality generates corresponding offspring individual；

Noninferior Solution Set calculating sub module, n the most forward offspring individual of the fitness for producing first generation evolution Set preserves as existing Noninferior Solution Set, later existing with described for best n offspring individual produced by the evolution of every generation There is each solution in Noninferior Solution Set to be compared one by one, retain excellent solution and replace inferior solution, obtain Noninferior Solution Set；

When evolution number of times reaches default evolution number of times, optimal solution set calculating sub module, for judging whether iterationses reach To default iterationses, the described Noninferior Solution Set obtaining through replacement if so, retaining, as optimal solution set；Otherwise again Carry out next iteration, till iterationses reach default iterationses；After obtaining the optimal solution set of a scheme, Repeat to evolve and calculate the optimal solution set of next scheme with iterative step.

In the embodiment of the present invention computational methods of pastoral area water and soil Forage-Livestock Balance model and system with water resource, land resource and Grassland resources bearing capacity be bottom line, target is to the maximum with economic benefit and ecological benefits comprehensive benefit, with pastoral area " water-soil-grass- Overall balance between poultry " is criterion, rationally determines water and soil resources exploitation scale, farming and animal husbandry pattern of farming, Animal husbandry production side Formula and suitable animal number, realize pastoral area water, soil, grass resource sustainable use, ecological environment benign development and social economy Sustainable development.

Brief description

In order to be illustrated more clearly that inventive embodiments of the present invention or technical scheme of the prior art, below will be to embodiment Or in description of the prior art the accompanying drawing of required use be briefly described it should be apparent that, drawings in the following description are only It is some embodiments of present invention invention, for those of ordinary skill in the art, in the premise not paying creative work Under, other accompanying drawings can also be obtained according to these accompanying drawings.

Fig. 1 is a kind of steps flow chart of the computational methods of pastoral area provided in an embodiment of the present invention water and soil Forage-Livestock Balance model Figure；

Fig. 2 is the flow chart of steps solving scheme collection in Fig. 1；

Fig. 3 is a kind of functional module of the computing system of pastoral area provided in an embodiment of the present invention water and soil Forage-Livestock Balance model Figure.

Specific embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Site preparation description is it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.It is based on Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of not making creative work Embodiment, broadly falls into the scope of protection of the invention.

See figures.1.and.2, embodiments provide a kind of computational methods of pastoral area water and soil Forage-Livestock Balance model, should Method includes:

Step 100, the preset relation between the parameter according to user input and parameter determines object function, described mesh Scalar functions include comprehensive beneficial function and the preferential order function of resource of water supply.

Described comprehensive beneficial function be economic benefit and ecological benefits sum maximum it may be assumed that

f_c=max (obe+oben)

In formula, f_cFor described comprehensive beneficial function, obe is described economic benefit, and oben is described ecological benefits.

Described economic benefit obe is determined by below equation:

Obe=anb+inb

In formula, anb is farming and animal husbandry net benefits, and inb is non-farming and animal husbandry net benefits.

Described farming and animal husbandry net benefits is determined by below equation:

Anb=anba+anbl

In formula, anba irrigates net benefits for planting industry, and anbl is penkeeping net benefits.

Described planting industry is irrigated net benefits and is determined by below equation:

$anba=\underset{f}{\σ}aca\left(f\right)\·\left(p\right(f)\·y(f)-c(f)-wp\·wn(f\left)\right)$

In formula, aca (f) is the irrigated area of f class long-term cropping, and p (f), y (f) and c (f) are respectively f class long-term cropping Yield univalent, per mu and planting cost, wp is water price, and wn (f) is the gross irrigation quota of f class long-term cropping.

Described penkeeping net benefits is determined by below equation:

$anbl=lsl\·\left(\underset{i}{\σ}p\right(l)\·y(l)\·\mathrm{\ω}-c\·(1+0.5\mathrm{\ω})-wp\·wnl\·365/1000)$

In formula, lsl is penkeeping amount, i.e. standard sheep unit, and p (l) and y (l) is respectively domestic animal standard sheep unit the 1st class The yield of product (Carnis caprae seu ovis, cashmere, Corii Caprae seu Oviss etc.) and unit price, ω is rate of domestic animals for sale, and c is the cost of domestic animal standard sheep unit, wnl Drink water by norm for domestic animal standard sheep unit.

Described non-farming and animal husbandry net benefits inb is expressed as with water for industrial use net benefits:

Inb=iav ψ δ

In formula, iav is industrial added value, and ψ is the ratio that industrial net value accounts for the gross output value, and δ is water for industrial use benefitsharing system Number.

Described ecological benefits oben are determined by below equation:

$oben=\underset{k}{\σ}{\mathrm{ana}}_k\·\frac{obend}{area}\·\mathrm{\ξ}\left(k\right)$

In formula, ana_kUtilize area for kth class natural pasture, obend is dynamic grassland ecology value of services, area is sky So pasture can utilize area, and ξ (k) is the conversion factor under the corresponding grazing rate of kth class natural pasture.

Described dynamic grassland ecology value of services is determined by below equation:

Obend=l r obens

In formula, l is relative willingness to pay, and r is natural grasslands scarcity of resources degree, can take according to natural grasslands degree of degeneration Value, span [0,1], obens is static grassland ecology value of services.

Described willingness to pay l expression formula relatively is:

$l=\frac{l}{1+{\mathrm{ae}}^{-bt}}$

In formula, l is the described maximum of willingness to pay l relatively, represents the willingness to pay in the stage that is rich in, value for 1, t is Time variable, represents the socio-economic development stage, and a, b are constant, and value is 1, e is natural logrithm.

Wherein, described time variable t expression formula is:

$t=\frac{1}{e_n}-3$

In formula, e_nFor Engel's coefficient.

Described static state grassland ecology value of services is determined by below equation:

$obens=\underset{i}{\σ}{\mathrm{area}}_i\·{\mathrm{vpa}}_i$

In formula, area_iFor i class natural grasslands area, vpa_iFor the grassland ecology service under i class natural grasslands native state It is worth unit price.

The preferential order function of described resource of water supply is determined by below equation:

$f_w=max\left(obw\right)=\underset{i}{\σ}{\mathrm{\α}}_i\·\underset{j}{\σ}{\mathrm{\β}}_j\·\underset{t}{\σ}wsl(i,j,t)$

In formula, f_wFor the preferential order function of described resource of water supply, obw is the preferential ordinal number of resource of water supply, α_iAnd β_jIt is respectively i row The water supply precedence coefficient at industry water supply weight coefficient and j water source, wsl (i, j, t) is the output at t period i industry j water source.

Step 110, according to user input parameter and parameter between preset relation determine constraints, described about Bundle condition includes resource bearing capacity constraints, equilibrium of supply and demand class constraints, living guarantee class constraints, fairness about Bundle condition and Condition of Non-Negative Constrains.

Described resource bearing capacity constraints includes water resources carrying capacity constraints, grassland resources bearing capacity again Constraints and land resource bearing capacity constraints.

Described water resources carrying capacity constraints expression formula is:

$\underset{i}{\σ}\underset{j}{\σ}\underset{t}{\σ}wsl(i,j,t)\≤min(wsg{\left(t\right)}_{max},\underset{t}{\σ}(\underset{j}{\σ}wll\left(j,t\right)+wtl\left(t\right)),wui)$

In formula, wsg (t)_maxFor t period water supply project maximum water supply capacity, wll (j, t) is can be utilized of t period j water source Amount, wtl (t) is t period outer water diversion volume, and wui is regional water use overall control index.

Described grassland resources bearing capacity constraints expression formula is:

lsl·ln_k·t_k≤ana_k·ncl_k

lsl·iln_l·t_l≤iga_l·cl_l

$\underset{k}{\σ}{\mathrm{ana}}_k\≤area$

In formula, ln_kSearch for food by norm for k class natural pasture sheep unit, t_kFor the raising natural law of k class natural pasture, ncl_kFor k The hay yield of class natural pasture, iln_lSearch for food by norm for l class Irrigated artificial pasture sheep unit, t_lRaising for l class irrigated grasslands Natural law, iga_lFor the area of l class irrigated grasslands, cl_lHay yield for l class irrigated grasslands.

Described land resource bearing capacity constraints expression formula is:

$aca=\underset{m}{\σ}{\mathrm{fca}}_m+\underset{l}{\σ}{\mathrm{iga}}_l+\underset{n}{\σ}{\mathrm{eca}}_n\≤afl\·r$

In formula, aca is long-term cropping irrigated area, fca_mFor m class cereal crops irrigated area, eca_nFor the n-th class economy Crop irrigation area, afl is that cultivated area can be utilized, and r is multiple crop index.

Described equilibrium of supply and demand class constraints includes water supply and water demand constraints and the constraint of the fodder grass equilibrium of supply and demand Condition.The expression formula of described water supply and water demand constraints is:

$\underset{j}{\σ}wsl(i,j,t)=wrl(i,t)$

Wrl (i, t)=ei (i, t) wn (i, t)

In formula, wrl (i, t) is the water requirement of t period the i-th class industry, and ei (i, t) is that the economy of t period the i-th class industry refers to Mark, wn (i, t) is the water demand quota of t period the i-th class industry.

The expression formula of described fodder grass equilibrium of supply and demand constraints is:

lsl·iln_l·t_l=iga_l·cl_l

Described living guarantee class constraints includes the minimum constraints of cereal crops and animal feeding-stuff basic number constraint bar Part.The expression formula of the minimum constraints of described cereal crops is:

$\underset{m}{\σ}{\mathrm{fca}}_m\·{\mathrm{fcd}}_m\&greaterequal;pgd\·pop$

In formula, fcd_mFor m class cereal crops yield per unit area, pgd is grain demand amount per capita, and pop is that population is total Number.

The expression formula of described animal feeding-stuff basic number constraints is:

lsl≥lsl_min

In formula, lsl_minRaise quantity for domestic animal basis.

The expression formula of described fairness constraint condition is:

ei_min(i, t)≤ei (i, t)≤ei_max(i, t)

wsl_min(i, j, t)≤wsl (i, j, t)≤wsl_max(i, j, t)

In formula, ei_min(i, t) is the minimum of t period i industrial economy index, ei_max(i, t) is t period i industrial economy The highest limit of index, wsl_min(i, j, t) is the minimum of t period i industry j water source output, wsl_max(i, j, t) is t period i The highest limit of industry j water source output.

Constraints above condition is Condition of Non-Negative Constrains.

Step 120, plan of establishment collection.Specifically, according to penkeeping mode (natural herd, warm season herds cold-worked hole, Warm season herds housing, warm season supplementary feeding housing, full drylot feeding), put by natural according to the mode that artificial supplementary feeding quota is incremented by The water and soil Forage-Livestock Balance herding full drylot feeding arranges the value of multigroup parameter, and every group of parameter measured value combines above-mentioned object function and constraint Condition constitutes a scheme, multiple scheme composition proposal collection.

Step 130, scheme collection solves and calculates.

Specifically, the solution of scheme collection is included scheme is concentrated with the solution one by one of each scheme, the solution of each scheme Calculate and include following sub-step:

Sub-step 131, individual UVR exposure, using real number coding method, encoded using following linear transformation:

X (j)=a (j)+y (j) [(b (j)-a (j))] (j=1,2 ..., p)

In formula, [a (j), b (j)] is j-th optimized variable x (j) pre-setting, i.e. each scheme corresponding target letter The solution of number, initial change interval, y (j) corresponds to the real number on [0,1] for x (j), is called gene.The all changes of optimization problem Measure corresponding gene successively connect together constitute solution coding form (y (1), y (2) ..., y (p)), referred to as chromosome or Individual.Encoded, all optimized variables are all unified to [0,1] interval, directly the gene forms of each optimized variable are carried out respectively Plant genetic manipulation.

Sub-step 132, population generates and initializes.Specifically, according to sub-step 131 method random generate many each and every one Body, the group of individuals of multiple random generations becomes colony.For ensureing the feasibility of population, the reasonability of colony need to be tested, row Except undesirable individuality, that is, it is unsatisfactory for the individuality of described constraints.If feasible retain as initial individuals, otherwise Regenerate individuality to check again, till population at individual is all feasible, initial individuals form initial population.

Sub-step 133, calculates the fitness of initial individuals in initial population.Specifically, multi-objective genetic algorithm is individual suitable The determination principle of response is to make the excellent individuality of general performance obtain larger fitness, by all for initial population initial individuals pair Each object function is ranked up, and obtains the ordinal matrix based on object function, according to following supply calculating initial individuals fitness:

$f_t\left(i\right)=\left\{\begin{array}{cc}{\mathrm{kn}}^2& r_t\left(i\right)=1\\ {(n-r_t(i\left)\right)}^2& r_t\left(i\right)>1\end{array}\right.,t=1,2,...n$

$f\left(i\right)=\underset{t=1}{\overset{n}{\σ}}{f}_t\left(i\right),i=1,2,...n$

In formula: r_tI () is the sequence sequence number to object function t for i-th individuality of population, f_tI () is that i-th individuality of population is right The fitness of target t gained, f (i) is the comprehensive fitness degree to target complete function for i-th individuality of population, and k is in (1,2) Constant, for increasing the individual adaptation degree of performance optimum, obtains more participation opportunities, n is object function number, and n is initial Individual number in population.

Sub-step 134, using the individuality in initial population as parent individuality, and produces offspring individual according to parent individuality.

Wherein, the mode selecting parent individuality is selection operation, and the mode generating offspring individual includes crossover operation and change ETTHER-OR operation.

Selection operation particularly as follows: taking ratio selection mode, then the select probability p of i-th parent individuality y (j, i)_sI () is

$p_s\left(i\right)=f\left(i\right)/\underset{i=1}{\overset{n}{\σ}}f\left(i\right)$

OrderThen sequence { p (i) | i=1,2 ..., n } is divided into n subinterval [0,1] interval, these Subinterval is corresponded with n parent individuality.Generate a random number u, if u is in interval [p (i-1), p (i)], then i-th Parent individuality y (j, i) is selected.

Crossover operation is particularly as follows: for real coding system, one optimized variable of a gene representation, for keeping colony Multiformity, randomly chooses a pair of parent individuality y (j, i according to select probability₁) and y (j, i₂) as parents, and carry out random as follows Linear combination, produces an offspring individual y₂(j, i):

$\left.\begin{array}{c}{y}_2(j,i)=u_{1}y(j,i_1)+(1-u_1)y(j,i_2),u_3<0.5\\ y_2(j,i)=u_{2}y(j,i_1)+(1-u_2)y(j,i_2),u_3\&greaterequal;0\mathrm{.5}\end{array}\right\}$

In formula: u₁, u₂, u₃It is all random number, by such crossover operation, common property gives birth to n offspring individual.

Particularly as follows: any one parent individuality y (j, i), if its fitness function value f (i) is less, it selects mutation operation Select Probability p_sI () is less, then this individuality is entered with the Probability p of row variation_mI () also should be bigger.Therefore, mutation operation is, using p Random number is with p_m(i)=1-p_sThe probability of (i) replacing individual y (j, i), thus obtaining offspring individual y₃(j, i), j=1, 2 ..., p.

$\left.\begin{array}{cc}{y}_3(j,i)=u\left(j\right),& u_m<p_m\left(i\right)\\ y_3(j,i)=y(j,i),& u_m\&greaterequal;p_m\left(i\right)\end{array}\right\}$

In formula, and u (j) (j=1,2 ..., p) and u_mFor the uniform random number between [0,1], p_mI () is mutation probability.

Sub-step 135, calculates Noninferior Solution Set.

Substantial amounts of feasible solution is had according to generation every in genetic algorithm, that is, offspring individual produces this feature it is considered to pass through The method being compared to each other superseded inferior solution between feasible solution is finally approached to Noninferior Solution Set to reach.First the first generation is evolved and produce Best, that is, n the most forward feasible solution of fitness preserves as existing Noninferior Solution Set, later for every generation evolve produced Raw n best feasible solution is compared one by one with each solution in Noninferior Solution Set, retains excellent solution and replaces inferior solution, obtains noninferior solution Collection.

Sub-step 136, calculates optimal solution set.

Judge when evolution number of times reaches default evolution number of times whether iterationses reach default iterationses, if so, The Noninferior Solution Set obtaining through replacement retaining, as optimal solution set.Otherwise return sub-step 132, until iterationses reach Till default iterationses.After obtaining the optimal solution set of a scheme, repeat above sub-step 131 and count to sub-step 136 Calculate the optimal solution set of next scheme.

Step 140, calculates, according to different schemes, the optimal solution set obtaining, by comprehensive benefit, water total amount, folk prescription water effect Benefit, the multi-proxy investigations such as penkeeping level, harmonious development of ecological economy degree contrast, analyzed area development restriction because Element, selects a scheme most beneficial for regional development as preferred plan, using the optimal solution set of preferred plan as regional water Soil grass poultry development threshold value.Described development threshold value includes natural grasslands development and utilization level, water consumption, folk prescription Wat er benefit, soil are opened The scale of sending out, irrigation Forage land planting scale, penkeeping mode, penkeeping amount etc..

Based on same inventive concept, embodiments provide a kind of calculating system of pastoral area water and soil Forage-Livestock Balance model System, as shown in figure 3, because this system solves the principle of technical problem and a kind of computational methods of pastoral area water and soil Forage-Livestock Balance model Similar, the enforcement of therefore this system can refer to the enforcement of method, repeats no more in place of repetition.

Object function determining module 200, for the first preset relation between the parameter according to input and described parameter Determine object function, described object function includes comprehensive beneficial function and the preferential order function of resource of water supply, described comprehensive benefit letter Number is the maximum of economic benefit and ecological benefits sum；

Constraints determining module 210, for true according to the second preset relation between described parameter and described parameter Determine constraints, described constraints includes resource bearing capacity constraints, equilibrium of supply and demand class constraints, living guarantee class Constraints, fairness constraint condition and Condition of Non-Negative Constrains；

Scheme collection setup module 220, for according to penkeeping mode, according to the incremental mode of artificial supplementary feeding quota by sky The water and soil Forage-Livestock Balance so herding full drylot feeding arranges multigroup parameter, and every group of parameter combines above-mentioned object function and constraints Constitute a scheme, multiple scheme composition proposal collection；

Scheme collection solves module 230, for solving described scheme collection, obtains the optimal solution set of each scheme.Described scheme Collection solves module 230 and includes:

Individual UVR exposure submodule 231, for corresponding to the solution of the object function of each scheme on [0,1] interval, obtains The coding form of all solutions, the coding form of described solution is referred to as individuality；

Population generates and initialization submodule 232, generates multiple individualities for random, carries out reasonability inspection to each individuality Test, as initial individuals, multiple initial individuals form initial population to the individuality retaining meet the constraint condition；

Fitness calculating sub module 233, for calculating the fitness of each described initial individuals in described initial population；

Offspring individual generates submodule 234, for using the initial individuals in described initial population as parent individuality, and root Fitness according to described initial individuals generates corresponding offspring individual；

Noninferior Solution Set calculating sub module 235, n the most forward feasible solution of the fitness for producing first generation evolution is made Preserve for existing Noninferior Solution Set, later in n best feasible solution and existing Noninferior Solution Set produced by the evolution of every generation Each solution be compared one by one, retain excellent solution replace inferior solution, obtain Noninferior Solution Set；

Optimal solution set calculating sub module 236, for judging that when evolution number of times reaches default evolution number of times iterationses are The no Noninferior Solution Set obtaining through replacement reaching default iterationses, if so, retaining, as optimal solution set.Otherwise again Carry out next iteration, till iterationses reach default iterationses.After obtaining the optimal solution set of a scheme, Repeat to evolve and calculate the optimal solution set of next scheme with iterative step.

Preferred plan determining module 240, for calculating, according to different schemes, the optimal solution set obtaining, by comprehensive multi-index Analysis contrast, the limiting factor of analyzed area development, select scheme most beneficial for regional development as preferred plan, with The optimal solution set of preferred plan is as Regional Soil grass poultry development threshold value.

It should be appreciated that the module that includes of the computing system of one of the above pastoral area water and soil Forage-Livestock Balance model only according to this is The logical partitioning that the function that system is realized is carried out, in practical application, can carry out superposition or the fractionation of above-mentioned module.And this enforcement One kind that the function that a kind of computing system of pastoral area water and soil Forage-Livestock Balance model that example provides is realized is provided with above-described embodiment The computational methods of pastoral area water and soil Forage-Livestock Balance model correspond, the more detailed handling process realized for this system, Said method embodiment one is described in detail, is not described in detail herein.

Those skilled in the art are it should be appreciated that embodiments of the invention can be provided as method, system or computer program Product.Therefore, the present invention can be using complete hardware embodiment, complete software embodiment or the reality combining software and hardware aspect Apply the form of example.And, the present invention can be using in one or more computers wherein including computer usable program code The upper computer program implemented of usable storage medium (including but not limited to disk memory, cd-rom, optical memory etc.) produces The form of product.

The present invention is the flow process with reference to method according to embodiments of the present invention, equipment (system) and computer program Figure and/or block diagram are describing.It should be understood that can be by each stream in computer program instructions flowchart and/or block diagram Flow process in journey and/or square frame and flow chart and/or block diagram and/or the combination of square frame.These computer programs can be provided The processor instructing general purpose computer, special-purpose computer, Embedded Processor or other programmable data processing device is to produce A raw machine is so that produced for reality by the instruction of computer or the computing device of other programmable data processing device The device of the function of specifying in present one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame.

These computer program instructions may be alternatively stored in and can guide computer or other programmable data processing device with spy Determine in the computer-readable memory that mode works so that the instruction generation inclusion being stored in this computer-readable memory refers to Make the manufacture of device, this command device realize in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or The function of specifying in multiple square frames.

These computer program instructions also can be loaded in computer or other programmable data processing device so that counting On calculation machine or other programmable devices, execution series of operation steps to be to produce computer implemented process, thus in computer or On other programmable devices, the instruction of execution is provided for realizing in one flow process of flow chart or multiple flow process and/or block diagram one The step of the function of specifying in individual square frame or multiple square frame.

Although preferred embodiments of the present invention have been described, but those skilled in the art once know basic creation Property concept, then can make other change and modification to these embodiments.So, claims are intended to be construed to including excellent Select embodiment and fall into being had altered and changing of the scope of the invention.

Obviously, those skilled in the art can carry out the various changes and modification essence without deviating from the present invention to the present invention God and scope.So, if these modifications of the present invention and modification belong to the scope of the claims in the present invention and its equivalent technologies Within, then the present invention is also intended to comprise these changes and modification.

Claims (8)

1. a kind of computational methods of pastoral area water and soil Forage-Livestock Balance model are it is characterised in that methods described includes:

The first preset relation between parameter according to input and described parameter determines object function, and described object function includes Comprehensive beneficial function and the preferential order function of resource of water supply, described comprehensive beneficial function be economic benefit and ecological benefits sum Big value；

Constraints is determined according to the second preset relation between described parameter and described parameter, described constraints includes providing Source bearing capacity constraints, equilibrium of supply and demand class constraints, living guarantee class constraints, fairness constraint condition and non-negative Constraints；

According to penkeeping mode, put down by the natural water and soil grass poultry herding full drylot feeding according to the mode that artificial supplementary feeding quota is incremented by Weighing apparatus arranges the value of multigroup described parameter, and the value of every group of parameter combines described object function and constraints constitutes a scheme, many Individual described scheme composition proposal collection；

Solve described scheme collection, obtain the optimal solution set of each described scheme；

The optimal solution set being obtained according to different described computation schemes, is contrasted by multi-proxy investigations, analyzed area development Limiting factor, selects a described scheme most beneficial for regional development as preferred plan, with the optimum of described preferred plan Disaggregation is as Regional Soil grass poultry development threshold value；

Wherein, solve described scheme collection, the optimal solution set obtaining each described scheme includes:

The solution of the described object function of each described scheme is corresponded on [0,1] interval, obtains the coding form of all solutions, institute The coding form stating solution is referred to as individuality；

Generate multiple described individualities at random, individuality each described is carried out with reasonableness test, retain and meet described constraints As initial individuals, multiple described initial individuals form initial population to individuality；

Calculate the fitness of each described initial individuals in described initial population；

Using the initial individuals in described initial population as parent individuality, and generated corresponding according to the fitness of described parent individuality Offspring individual；

N the most forward for the fitness of first generation evolution generation filial generation group of individuals is preserved as existing Noninferior Solution Set, with Afterwards for every generation evolve produced by each solution in best n offspring individual and described existing Noninferior Solution Set one by one than Relatively, retain excellent solution and replace inferior solution, obtain Noninferior Solution Set；

When evolution number of times reaches default evolution number of times, and when iterationses also reach default iterationses, the process of reservation is replaced The described Noninferior Solution Set got in return, as optimal solution set.

2. the method for claim 1 is not up to described default iterationses it is characterised in that working as described iterationses When, re-start next iteration, till described iterationses reach described default iterationses.

3. the method for claim 1 is it is characterised in that determine described comprehensive beneficial function according to below equation:

f_c=max (obe+oben)

In formula, f_cFor described comprehensive beneficial function, obe is described economic benefit, and oben is described ecological benefits；

Described economic benefit obe is determined by below equation:

Obe=anb+inb

In formula, anb is farming and animal husbandry net benefits, and inb is non-farming and animal husbandry net benefits；

Described farming and animal husbandry net benefits is determined by below equation:

Anb=anba+anbl

In formula, anba irrigates net benefits for planting industry, and anbl is penkeeping net benefits；

Described planting industry is irrigated net benefits and is determined by below equation:

$anba=\underset{f}{\mathrm{\&sigma;}}aca\left(f\right)\&centerdot;\left(p\right(f)\&centerdot;y(f)-c(f)-wp\&centerdot;wn(f\left)\right)$

In formula, aca (f) is the irrigated area of f class long-term cropping, the list of p (f), y (f) and c (f) respectively f class long-term cropping Valency, yield per mu and planting cost, wp is water price, and wn (f) is the gross irrigation quota of f class long-term cropping；

Described penkeeping net benefits is determined by below equation:

$anbl=lsl\&centerdot;\left(\underset{l}{\mathrm{\&sigma;}}p\right(l)\&centerdot;y(l)\&centerdot;\mathrm{\&omega;}-c\&centerdot;(1+0.5\mathrm{\&omega;})-wp\&centerdot;wnl\&centerdot;365/1000)$

In formula, lsl is penkeeping amount, i.e. standard sheep unit, and p (l) and y (l) is respectively domestic animal standard sheep unit l class product Yield and unit price, ω is rate of domestic animals for sale, and c is the cost of domestic animal standard sheep unit, and wnl is that domestic animal standard sheep unit drinking-water is fixed Volume；

Described non-farming and animal husbandry net benefits inb is expressed as with water for industrial use net benefits:

Inb=iav ψ δ

In formula, iav is industrial added value, and ψ is the ratio that industrial net value accounts for the gross output value, and δ is water for industrial use benefit-shared coefficient；

Described ecological benefits oben are determined by below equation:

$oben=\underset{k}{\&sigma;}{\mathrm{ana}}_k\&centerdot;\frac{obend}{area}\&centerdot;\mathrm{\&xi;}\left(k\right)$

In formula, ana_kUtilize area for kth class natural pasture, obend is dynamic grassland ecology value of services, area is natural herding Field can utilize area, and ξ (k) is the conversion factor under the corresponding grazing rate of kth class natural pasture；

Described dynamic grassland ecology value of services is determined by below equation:

Obend=l r obens

In formula, l is relative willingness to pay, and r is natural grasslands scarcity of resources degree, can according to natural grasslands degree of degeneration value, Span [0,1], obens is static grassland ecology value of services；

Described willingness to pay l expression formula relatively is:

$l=\frac{l}{1+{\mathrm{ae}}^{-bt}}$

In formula, l is the described maximum of willingness to pay l relatively, represents the willingness to pay in the stage that is rich in, value is 1, t is the time Variable, represents the socio-economic development stage, and a, b are constant, and value is 1, e is natural logrithm；

Wherein, described time variable t expression formula is:

$t=\frac{1}{e_n}-3$

In formula, e_nFor Engel's coefficient；

Described static state grassland ecology value of services is determined by below equation:

$obens=\underset{i}{\&sigma;}{\mathrm{area}}_i\&centerdot;{\mathrm{vpa}}_i$

In formula, area_iFor i class natural grasslands area, vpa_iFor the grassland ecology value of services under i class natural grasslands native state Unit price.

4. method as claimed in claim 3 is it is characterised in that determine described resource of water supply precedence letter according to below equation Number:

$f_w=max\left(obw\right)=\underset{j}{\&sigma;}{\mathrm{\&alpha;}}_i\&centerdot;\underset{j}{\&sigma;}{\mathrm{\&beta;}}_j\&centerdot;\underset{t}{\&sigma;}wsl(i,j,t)$

In formula, f_wFor the preferential order function of described resource of water supply, obw is the preferential ordinal number of resource of water supply, α_iAnd β_jIt is respectively i industry to supply The water supply precedence coefficient at water weight coefficient and j water source, wsl (i, j, t) is the output at t period i industry j water source.

5. method as claimed in claim 4 is it is characterised in that described resource bearing capacity constraints includes water resource carrying Capacity consistency condition, grassland resources bearing capacity constraints and land resource bearing capacity constraints；

Described water resources carrying capacity constraints expression formula is:

$\underset{i}{\&sigma;}\underset{j}{\&sigma;}\underset{t}{\&sigma;}wsl(i,j,t)\&le;min(wsg{\left(t\right)}_{max},\underset{t}{\&sigma;}(\underset{j}{\&sigma;}wll\left(j,t\right)+wtl\left(t\right)),wui)$

In formula, wsg (t)_maxFor t period water supply project maximum water supply capacity, wll (j, t) is the amount usable at t period j water source, Wtl (t) is t period outer water diversion volume, and wui is regional water use overall control index；

Described grassland resources bearing capacity constraints expression formula is:

lsl·ln_k·t_k≤ana_k·ncl_k

lsl·iln_l·t_l≤iga_l·cl_l

$\underset{k}{\&sigma;}{\mathrm{ana}}_k\&le;area$

In formula, ln_kSearch for food by norm for k class natural pasture sheep unit, t_kFor the raising natural law of k class natural pasture, ncl_kFor k class sky So hay yield in pasture, iln_lSearch for food by norm for l class Irrigated artificial pasture sheep unit, t_lRaising sky for l class irrigated grasslands Number, iga_lFor the area of l class irrigated grasslands, cl_lHay yield for l class irrigated grasslands；

Described land resource bearing capacity constraints expression formula is:

$aca=\underset{m}{\&sigma;}{\mathrm{fca}}_m+\underset{l}{\&sigma;}{\mathrm{iga}}_l+\underset{n}{\&sigma;}{\mathrm{eca}}_n\&le;afl\&centerdot;r$

In formula, aca is long-term cropping irrigated area, fca_mFor m class cereal crops irrigated area, eca_nFor the n-th class industrial crops Irrigated area, afl is that cultivated area can be utilized, and r is multiple crop index；

Described equilibrium of supply and demand class constraints includes water supply and water demand constraints and fodder grass equilibrium of supply and demand constraints, The expression formula of described water supply and water demand constraints is:

$\underset{j}{\&sigma;}wsl(i,j,t)=wrl(i,t)$

Wrl (i, t)=ei (i, t) wn (i, t)

In formula, wrl (i, t) is the water requirement of t period the i-th class industry, and ei (i, t) is the economic indicator of t period the i-th class industry, Wn (i, t) is the water demand quota of t period the i-th class industry；

The expression formula of described fodder grass equilibrium of supply and demand constraints is:

lsl·iln_l·t_l=iga_l·cl_l；

Described living guarantee class constraints includes the minimum constraints of cereal crops and animal feeding-stuff basic number constraints；Institute The expression formula stating the minimum constraints of cereal crops is:

$\underset{m}{\&sigma;}{\mathrm{fca}}_m\&centerdot;{\mathrm{fcd}}_m\&greaterequal;pgd\&centerdot;pop$

In formula, fcd_mFor m class cereal crops yield per unit area, pgd is grain demand amount per capita, and pop is population；

The expression formula of described animal feeding-stuff basic number constraints is:

lsl≥lsl_min

In formula, lsl_minRaise quantity for domestic animal basis；

The expression formula of described fairness constraint condition is:

ei_min(i, t)≤ei (i, t)≤ei_max(i, t)

wsl_min(i, j, t)≤wsl (i, j, t)≤wsl_max(i, j, t)

In formula, ei_min(i, t) is the minimum of t period i industrial economy index, ei_max(i, t) is t period i industrial economy index Highest limit, wsl_min(i, j, t) is the minimum of t period i industry j water source output, wsl_max(i, j, t) is t period i industry The highest limit of j water source output.

6. the method for claim 1 it is characterised in that determine the coding form of described solution by below equation:

X (j)=a (j)+y (j) [(b (j)-a (j))] (j=1,2 ..., p)

In formula, [a (j), b (j)] is that the initial change of j-th optimized variable x (j) pre-setting is interval, and y (j) is that x (j) is right The real number on [0,1] should be arrived, be called gene, described coding form is expressed as (y (1), y (2) ..., y (p)), wherein said excellent Change the solution that variable x (j) is the corresponding described object function of each scheme.

7. the method for claim 1 it is characterised in that determine the fitness of described initial individuals by below equation:

$f_t\left(i\right)=\left\{\begin{array}{cc}{\mathrm{kn}}^2& r_t\left(i\right)=1\\ {(n-r_t(i\left)\right)}^2& r_t\left(i\right)>1\end{array}\right.,t=1,2,...n$

$f\left(i\right)=\underset{t=1}{\overset{n}{\&sigma;}}{f}_t\left(i\right),i=1,2,...n$

In formula: r_tI () is i-th individuality sequence sequence number to object function t, f in described initial population_tI () is described initial kind I-th individuality fitness to target t gained in group, f (i) be in described initial population i-th individuality to target complete function Comprehensive fitness degree, k is the constant in (1,2), the individual adaptation degree optimum for increasing performance, obtains and more participates in machines Meeting, n is described object function number, and n is individual number in described initial population.

8. a kind of computing system of pastoral area water and soil Forage-Livestock Balance model is it is characterised in that described system includes:

Object function determining module, determines target for the first preset relation between the parameter according to input and described parameter Function, described object function includes comprehensive beneficial function and the preferential order function of resource of water supply, and described comprehensive beneficial function is economy Benefit and the maximum of ecological benefits sum；

Constraints determining module, for determining constraint bar according to the second preset relation between described parameter and described parameter Part, described constraints includes resource bearing capacity constraints, equilibrium of supply and demand class constraints, living guarantee class constraint bar Part, fairness constraint condition and Condition of Non-Negative Constrains；

Scheme collection setup module, for according to penkeeping mode, being herded by natural according to the mode that artificial supplementary feeding quota is incremented by Water and soil Forage-Livestock Balance to full drylot feeding arranges the value of multigroup described parameter, and the value of every group of parameter combines described object function and constraint Condition constitutes a scheme, multiple described scheme composition proposal collection；

Scheme collection solves module, for solving described scheme collection, obtains the optimal solution set of each scheme；

Preferred plan determining module, for calculating, according to different schemes, the optimal solution set obtaining, by multi-proxy investigations pair Than, the limiting factor of analyzed area development, select a scheme most beneficial for regional development as preferred plan, with most preferably square The optimal solution set of case is as Regional Soil grass poultry development threshold value；

Wherein, described scheme collection solves module and includes:

Individual UVR exposure submodule, for corresponding to the solution of the described object function of each described scheme on [0,1] interval, obtains The coding form of all solutions, the coding form of described solution is referred to as individuality；

Population generates and initialization submodule, generates multiple described individualities for random, carries out reasonability to individuality each described Inspection, retains the individuality meeting described constraints as initial individuals, and multiple described initial individuals form initial population；

Fitness calculating sub module, for calculating the fitness of each described initial individuals in described initial population；

Offspring individual generates submodule, for using the initial individuals in described initial population as parent individuality, and according to described The fitness of parent individuality generates corresponding offspring individual；

Noninferior Solution Set calculating sub module, n the most forward filial generation group of individuals of the fitness for producing first generation evolution Preserve as existing Noninferior Solution Set, later existing non-with described for best n offspring individual produced by the evolution of every generation Each solution that inferior solution is concentrated is compared one by one, retains excellent solution and replaces inferior solution, obtains Noninferior Solution Set；

Optimal solution set calculating sub module, pre- for judging when evolution number of times reaches default evolution number of times whether iterationses reach If iterationses, if so, retain through replacing the described Noninferior Solution Set that obtains, as optimal solution set；Otherwise re-start Next iteration, till iterationses reach default iterationses；After obtaining the optimal solution set of a scheme, repeat Evolve and calculate the optimal solution set of next scheme with iterative step.