CN115860189A - Method and system for optimizing land utilization spatial pattern under low-carbon target - Google Patents
Method and system for optimizing land utilization spatial pattern under low-carbon target Download PDFInfo
- Publication number
- CN115860189A CN115860189A CN202211431291.0A CN202211431291A CN115860189A CN 115860189 A CN115860189 A CN 115860189A CN 202211431291 A CN202211431291 A CN 202211431291A CN 115860189 A CN115860189 A CN 115860189A
- Authority
- CN
- China
- Prior art keywords
- land
- target
- utilization
- carbon
- land utilization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a method and a system for optimizing a spatial pattern of underground utilization under a low-carbon target, which comprises the following steps: acquiring an original data set, and constructing an objective function and a constraint condition for land use optimization based on the original data set; debugging is carried out through a differential evolution method to obtain the optimal solution of the target function under the constraint condition; the optimal solution is a quantity structure of land utilization under a target year low-carbon target; and importing the optimal solution, the driving factors and the limited area in the original data set into PLUS software to perform future land utilization space simulation, and obtaining a target year low-carbon target underground utilization space pattern optimization result. The method is simple, objective and strong in repeatability, the influence of land utilization change on carbon emission is disclosed, the optimal land utilization quantity structure is identified under the low-carbon limit, and the spatial pattern of the structure is simulated. And reliable theory and technical support are provided for green development of land utilization.
Description
Technical Field
The invention relates to the technical field of future land utilization optimization, in particular to a low-carbon target underground utilization space pattern optimization method and system.
Background
With the development of human socioeconomic resources, global carbon emissions are continuously increasing, and changes in land use are important factors affecting atmospheric carbon emissions.
At present, for the optimization of the land utilization space pattern, the content mainly comprises the optimization of land utilization quantity structure and the optimization of the land utilization space pattern, and in most developing countries, with the acceleration of the urbanization process, the carbon emission caused by the change of the land utilization is continuously increased. Most of the existing research focuses on analyzing and evaluating the carbon emission effects under different land use patterns and simulating and calculating the carbon emission of the land use patterns for a certain period in the future. The land use pattern under the low-carbon target in a certain period of time in the future is rarely optimized.
Disclosure of Invention
The invention aims to provide a method and a system for optimizing a land utilization space pattern under a low-carbon target.
In order to achieve the purpose, the invention provides the following scheme:
a land utilization spatial pattern optimization method under a low-carbon target comprises the following steps:
acquiring an original data set, and constructing an objective function and a constraint condition of land use optimization based on the original data set; wherein the raw data set comprises land utilization data, driving factors, a restricted area, energy consumption data and agricultural data;
debugging is carried out through a differential evolution method to obtain the optimal solution of the target function under the constraint condition; the optimal solution is a quantitative structure of land utilization under a low-carbon target of a target year;
and importing the optimal solution, the driving factors and the limited area in the original data set into PLUS software, and carrying out future land utilization space simulation to obtain a target year low-carbon target underground utilization space pattern optimization result.
Preferably, the raw data set is acquired, comprising:
determining a target area, acquiring natural, social and economic data of the target area, and processing the data to obtain the original data set; the driving factors in the raw data set include: DEM, slope, incline, temperature, annual average precipitation, GDP, population density, distance from river, distance from high speed, distance from railway, distance from highway, distance from administrative center, the restricted area comprising: ecological protection red line, permanent basic farmland and town development boundary.
Preferably, constructing an objective function for land use optimization comprises:
and weighting the target annual land utilization carbon emission function, the land utilization economic benefit function and the land utilization ecological benefit function by optimizing the land space pattern to obtain the target function of land utilization optimization.
Preferably, constructing a land space pattern optimization target annual land use carbon emission function comprises:
acquiring carbon emission coefficients of various land utilization types, and estimating the carbon emission of the construction land according to the combustion quantity of energy consumption and a default emission factor;
obtaining a construction land carbon emission coefficient based on the ratio of the construction land carbon emission to the construction land area, and predicting through a grey early warning model to obtain a target annual construction land carbon emission coefficient;
multiplying the area of each land use type by the carbon emission coefficient of the target annual construction land, and summing to obtain the carbon emission function of land use.
Preferably, constructing the land use economic benefit function comprises:
dividing the total economic yield of the industry corresponding to each land use type in each year by the GDP of the corresponding year to obtain the relative interest coefficient of each land use type in each year, and fitting and predicting the relative interest coefficient of each land use type in the target year through a linear regression trend to further determine the land use economic benefit objective function; wherein the land use types include cultivated land, woodland, grassland, water area, construction land and bare land.
Preferably, constructing the land use ecological benefit function comprises:
and constructing the ecological benefit function based on an ecological service value economic evaluation method, correcting the ecological benefit function by combining with the actual situation of the target area to obtain the ecological service values of different ecosystems in unit area according with the actual situation of the target area, and further determining the land utilization ecological benefit target function.
Preferably, obtaining the target annual low-carbon target soil utilization space pattern optimization result comprises:
acquiring a land use expansion image through a land use image, and importing the land use expansion image and the driving factor into a land use expansion analysis strategy module to acquire a land use development probability atlas; inputting the initial annual land utilization image of the target area, the land utilization development probability atlas, the limiting area and the target annual land utilization quantity into a CA model based on multiple types of random plaque seeds to obtain a target annual low-carbon target underground utilization spatial pattern optimization result.
In order to achieve the above object, the present invention further provides a system for optimizing a spatial pattern of a low-carbon target underground utilization, comprising:
the system comprises a data acquisition module, a low-carbon target lower objective function construction and constraint condition construction module, a target annual land utilization quantity structure optimization module and a target annual land utilization space pattern simulation module;
the data acquisition module is used for acquiring an original data set;
the low-carbon target lower objective function construction and constraint condition construction module is used for weighting the land use carbon emission function, the land use economic benefit function and the land use ecological benefit function to obtain an objective function, and meanwhile, constructing constraint conditions according to the actual situation of a target area;
the target annual land utilization quantity structure optimization module is used for obtaining a target annual land utilization quantity structure by compiling a differential evolution algorithm under a constraint condition;
the target year land utilization spatial pattern simulation module is used for obtaining a land utilization expansion image through the two-stage land utilization image.
Preferably, the target year land use spatial pattern simulation module includes:
the land use expansion analysis strategy unit is used for obtaining a land use development probability atlas by importing the land use expansion image and the driving factor;
and the CA model unit based on the various random plaque seeds is used for obtaining a target year low-carbon target soil-discharging utilization space pattern optimization result by inputting an initial year land utilization image, a land utilization development probability atlas, a limited area and target year land utilization quantity.
The invention has the beneficial effects that:
the method is simple, objective and strong in repeatability, the influence of land utilization change on carbon emission is disclosed, the optimal land utilization quantity structure is identified under the low-carbon limit, and the spatial pattern of the structure is simulated. And reliable theory and technical support are provided for green development of land utilization.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for optimizing a land use spatial pattern under a low carbon goal in an embodiment of the invention;
FIG. 2 is a schematic diagram of a system for optimizing the spatial pattern of the low-carbon target soil utilization according to an embodiment of the present invention;
fig. 3 is a diagram of an optimization result in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The example proposes a land use spatial pattern optimization method under a low-carbon target, as shown in fig. 1, the steps of which include:
acquiring various data of a target area, and acquiring an original data set;
wherein the raw data comprises: land utilization data; driving factors (DEM, slope, temperature, annual average precipitation, GDP, population density, distance from river, distance from high speed, distance from railway, distance from highway, distance from administrative center); restricted areas (ecological protection red line, permanent basic farmland, town development boundaries); energy consumption data and agricultural data.
Secondly, constructing an objective function and constraint conditions for land use optimization based on the original data set;
the objective function is obtained by weighting a land space pattern optimization target annual land utilization carbon emission function, a land utilization economic benefit function and a land utilization ecological benefit function.
(1) Construction of land utilization carbon emission function: the carbon emission coefficient of each land utilization type is firstly obtained, wherein cultivated land, woodland, grassland, bare land and water area can be consulted through the existing data documents, and in addition, the carbon emission of the construction land is estimated through the combustion quantity of energy consumption and a default emission factor.
In the formula, C j Carbon emission for construction land; e i Converting the consumption of the ith energy into standard coal for calculation; f. of i The carbon emission coefficient of the ith energy source.
And finally, predicting the carbon emission coefficient of the construction land for the target year by a grey early warning model GM (1, 1).
Wherein the GM (1, 1) model comprises the following steps:
(1) For data sequence X (0) ＝{x (0) (1),x (0) (2),...,x (0) (N) is generated by one-time accumulation to obtain X (1) ＝{x (1) (1),x (1) (2),...,x (1) (N), }, in which
(2) Solving the parameter a by using a least square method:
a＝|a|＝(B T B) -1 B T Y N
in the formula, Y N Is a column vector Y N ＝[x 1 (0) (2),x 1 (0) (3),...,x 1 (0) (N)] T (ii) a B is a structural matrix, and B is a structural matrix,
(3) Substituting the gray parameter into the time function:
x(t+1)＝(x (0) (1)-u/a)e -at +u/a
(4) To X (1) The derivation reduction is carried out to obtain:
x (0) (t+1)＝-a(x (0) (1)-u/a)e -at
(5) And (4) residual error detection: absolute error and relative error;
(6) And (3) post residual error detection: firstly calculating the observed data dispersion S 1 Deviation S of sum residual 2 ；
(7) Then calculating a variance ratio and a small error probability;
the model is diagnosed according to the posterior ratio c and the small error probability P, when P is more than 0.95 and c is less than 0.35, the model is considered to be reliable, prediction can be carried out, otherwise, the formula (4) needs to be corrected through analysis of a residual sequence.
And finally, multiplying the area of each land use type by the carbon emission coefficient, and then adding to obtain a land use carbon emission function.
In the formula (f) 1 (x) → min represents that the net carbon emission of land utilization tends to be minimized; i representsA type of ground; k is a radical of i Net carbon emission coefficient, S, representing the type of plot of the i-th land i Is the area of the ith plot type. Wherein the carbon emission coefficient of the construction land for the target year is obtained by prediction through a GM (1, 1) model.
(2) Constructing a land utilization economic benefit function: and dividing the total economic yield of the industry corresponding to each land use type in each year by the GDP of the corresponding year to obtain the relative interest coefficient of each land use type in each year, and predicting the relative interest coefficient of each land use type in the target year through linear regression trend fitting.
The corresponding industry is the farmland corresponding to the total economic output value of agriculture and animal husbandry; forest land corresponding to the forestry production value; the grassland corresponds to the total value of the animal husbandry, the water area corresponds to the fishery and the construction land corresponds to the total value of the second industry and the third industry; the unutilized relative gain factor is directly set to 0.
e i And the economic benefit coefficient represents the unit economic output representation of the type of the i-th land.
(3) Ecological benefit function of land utilization: and constructing an ecological benefit function by adopting an ecological service value economic evaluation method, and correcting by combining the actual situation of the target area, so as to obtain the ecological service values of different ecosystems in unit area according with the actual situation of the target area, thereby determining the land utilization ecological benefit target function.
p i And the ecological benefit coefficient represents the unit economic output representation of the type of the i-th land.
Thirdly, compiling codes through a Differential Evolution (DE) algorithm, and continuously debugging to obtain an optimal solution of the target function under the constraint condition, namely a quantitative structure of land utilization under the target year low-carbon target;
the basic flow of the DE algorithm is as follows:
(1) Initializing a population
Firstly, determining basic parameters of a DE algorithm, including a space dimension N, a population size NP, an iteration number G, a variation factor F, a cross factor CR and a lower limit of a search space
And an upper limit of the search space->
Then initially colonizing
The method comprises the following steps of (1) randomly generating, wherein a specific expression is as follows:
in the formula (I), the compound is shown in the specification,
a j-dimension component representing an i-th individual of generation 0; rand (0, 1) is [0,1 ]]Random numbers evenly distributed over the interval.
(2) Mutation operations
For each individual x in the population ij (i =1,2, \ 8230;, NP; j =1,2, \ 8230;, N) (referred to as the target vector) to generate variant offspring by adding the difference between any two individuals in the population to one another
The specific expression is as follows:
in the formula (I), the compound is shown in the specification,
representing the generated variation vector; />
And &>
3 individuals randomly selected from the group, wherein r1 is not equal to r2 is not equal to r3 is not equal to i; f denotes a mutation factor, which controls the difference vector->
The magnitude of the magnitude is scaled.
(3) Crossover operation
To maintain population diversity, variant progeny are generated
And a target vector +>
Making a crossover as follows to produce offspring>
(also called test vector), the specific formula is as follows: />
In the formula, rand j Is [0,1 ]]Random numbers uniformly distributed in the interval; j is a function of rand ∈[1,2,…,N]And is a random integer; CR ∈ [0,1 ]]Indicating the crossover factor. In the crossover operator, j = j rand Guarantee test vector
At least in one component with the target vector x ij And meanwhile, the diversity of the population is maintained.
(4) Selection operation
The basic principle of the selection operation is to compare a target vector with a test vector, if the fitness value of the test vector is superior to that of the target vector, the test vector is used for replacing the target vector in the next generation, otherwise, the target vector is still stored, and the specific expression is as follows:
in the formula (I), the compound is shown in the specification,
and &>
Are respectively individual>
And &>
The fitness value (objective function value).
And step four, importing the obtained quantity, each driving factor and each obstacle factor into PLUS software to carry out future land utilization space simulation. And obtaining the optimization result of the low-carbon target underground utilization space pattern in the target year.
Firstly, a land use expansion image is obtained through a two-stage land use image, secondly, a land use development probability map set is obtained after the land use expansion image and a driving factor are introduced into a land use expansion analysis strategy module (LEAS), and finally, an initial year land use image, the land use development probability map set, a limiting area and the target year land use quantity are input into a CA model (CARS) based on multiple types of random plaque seeds to obtain a low-carbon target lower target year land use spatial pattern optimization result.
Fig. 3 is a schematic diagram of the optimized spatial pattern of the land utilization in the three gorges city in 2030 year according to this embodiment.
Example two
The invention also provides a low-carbon target underground utilization space pattern optimization system, which comprises the following steps:
the data acquisition module is used for acquiring various related data;
wherein the raw data comprises: land utilization data; driving factors (DEM, slope, temperature, annual average precipitation, GDP, population density, distance from river, distance from high speed, distance from railway, distance from highway, distance from administrative center); restricted areas (ecological protection red line, permanent basic farmland, town development boundaries); energy consumption data and agricultural data.
An objective function construction and constraint condition construction module under a low-carbon target,
a target function is obtained by weighting a land utilization carbon emission function, a land utilization economic benefit function and a land utilization ecological benefit function, and meanwhile, constraint conditions are constructed according to the actual situation of a target area.
(1) Construction of land utilization carbon emission function: first, carbon emission coefficients of respective land use types are acquired, wherein cultivated land, woodland, grassland, bare land and water area are referred to by existing data documents, and in addition, carbon emission amount of construction land is estimated by combustion amount of energy consumption and default emission factor.
In the formula, C j Carbon emission for construction land; e i Converting the consumption of the ith energy into standard coal for calculation; f. of i The carbon emission coefficient of the ith energy source.
And finally, predicting through a grey early warning model GM (1, 1) to obtain the carbon emission coefficient of the construction land for the target year.
Wherein the GM (1, 1) model has the following steps;
(1) For data sequence X (0) ＝{x (0) (1),x (0) (2),...,x (0) (N) is generated by one-time accumulation to obtain X (1) ＝{x (1) (1),x (1) (2),...,x (1) (N), }, in which
(2) Solving the parameter a by using a least square method:
a＝|a|＝(B T B) -1 B T Y N
in the formula, Y N Is a column vector
B is a structural matrix, and B is a structural matrix,
(3) Substituting the gray parameter into the time function:
x(t+1)＝(x (0) (1)-u/a)e -at +u/a
(4) To X (1) The derivation reduction is carried out to obtain:
x (0) (t+1)＝-a(x (0) (1)-u/a)e -at
(5) And (4) residual error detection: absolute error and relative error;
(6) And (3) post residual error detection: firstly calculating the observed data dispersion S 1 Deviation S from residual 2 ；
(7) Then calculating a variance ratio and a small error probability;
diagnosing the model according to the posterior ratio c and the small error probability P when P>0.95 and c<0.35, the model is considered reliable, prediction can be performed, otherwise, the residual sequence is required to be processed
The formula x (t + 1) = (x) was corrected after analysis (0) (1)-u/a)e -at + u/a.
And finally, multiplying the area of each land use type by the carbon emission coefficient, and then adding to obtain a land use carbon emission function.
In the formula (f) 1 (x) → min represents that the net carbon emission of land utilization tends to be minimized. Wherein the carbon emission coefficient of the construction land for the target year is obtained by prediction through a GM (1, 1) model.
(2) Constructing a land utilization economic benefit function: and dividing the total economic yield of the industry corresponding to each land use type in each year by the GDP of the corresponding year to obtain the relative interest coefficient of each land use type in each year, and predicting the relative interest coefficient of each land use type in the target year through linear regression trend fitting.
(3) Ecological benefit function of land utilization: and constructing an ecological benefit function by adopting an ecological service value economic evaluation method, and correcting by combining the actual situation of the target area, so as to obtain the ecological service values of different ecosystems in unit area according with the actual situation of the target area, thereby determining the land utilization ecological benefit target function.
And the target annual land utilization quantity structure optimization module obtains a target annual optimized land utilization quantity structure by compiling a differential evolution algorithm under constraint conditions.
The basic flow of the DE algorithm is as follows:
(1) Initializing a population
Firstly, determining basic parameters of a DE algorithm, including a space dimension N, a population size NP, an iteration number G, a variation factor F, a cross factor CR and a lower limit of a search space
And an upper limit of the search space->
Then initially colonizing
The method comprises the following steps of (1) randomly generating, wherein a specific expression is as follows:
in the formula (I), the compound is shown in the specification,
a j-dimension component representing an i-th individual of generation 0; rand (0, 1) is [0,1 ]]Random numbers evenly distributed over the interval.
(2) Mutation operations
For each individual x in the population ij (i =1,2, \ 8230;, NP; j =1,2, \ 8230;, N) (referred to as the target vector) to generate variant offspring by adding the difference between any two individuals in the population to one another
The specific expression is as follows:
in the formula (I), the compound is shown in the specification,
representing the generated variation vector; />
And &>
3 individuals randomly selected from the group, wherein r1 is not equal to r2 is not equal to r3 is not equal to i; f denotes a mutation factor, which controls the difference vector->
The magnitude of the magnitude is scaled.
(3) Crossover operation
To maintain population diversity, variant progeny are generated
And the target vector->
Crossover as follows to generate a progeny->
(also called test vector), the specific formula is as follows:
in the formula, rand j Is [0,1 ]]Random numbers uniformly distributed in the interval; j is a function of rand ∈[1,2,…,N]And is a random integer; CR is in the range of [0,1 ]]Indicating the crossover factor. In the crossover operator, j = j rand Guaranteed test vector
At least on one component with the target vector x ij And meanwhile, the diversity of the population is maintained. />
(4) Selection operation
The basic principle of the selection operation is to compare a target vector with a test vector, if the fitness value of the test vector is superior to that of the target vector, the test vector is used for replacing the target vector in the next generation, otherwise, the target vector is still stored, and the specific expression is as follows:
in the formula (I), the compound is shown in the specification,
and &>
Are respectively individual>
And &>
The fitness value (objective function value).
A land utilization space pattern simulation module for the target year;
the method comprises the steps of obtaining a land utilization expansion image by using a two-stage land utilization image, obtaining a land utilization development probability map set by introducing the land utilization expansion image and a driving factor into a land utilization expansion analysis strategy module (LEAS), and inputting an initial annual land utilization image, the land utilization development probability map set, a limiting area and a target annual land utilization quantity into a CA model (CARS) based on multiple types of random plaque seeds to obtain a low-carbon target lower target annual land utilization spatial pattern optimization result.
The method is simple, objective and highly repeatable, reveals the influence of land utilization change on carbon emission, identifies the optimal land utilization quantity structure under the low-carbon limit, and simulates the spatial pattern. And reliable theory and technical support are provided for green development of land utilization.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (9)
1. A land utilization space pattern optimization method under a low-carbon target is characterized by comprising the following steps of:
acquiring an original data set, and constructing an objective function and a constraint condition of land use optimization based on the original data set; wherein the raw data set comprises land utilization data, driving factors, a restricted area, energy consumption data and agricultural data;
debugging is carried out through a differential evolution method to obtain the optimal solution of the target function under the constraint condition; the optimal solution is a quantitative structure of land utilization under a low-carbon target of a target year;
and importing the optimal solution, the driving factors and the limited area in the original data set into PLUS software to perform future land utilization space simulation, and obtaining a target year low-carbon target underground utilization space pattern optimization result.
2. The method of claim 1, wherein obtaining the raw data set comprises:
determining a target area, acquiring natural, social and economic data of the target area, and processing the data to obtain the original data set; the driving factors in the raw data set include: DEM, slope, temperature, annual average precipitation, GDP, population density, distance from a river, distance from a high speed, distance from a railway, distance from a highway, distance from an administrative center, the restricted area comprising: ecological protection red line, permanent basic farmland and town development boundary.
3. The method of claim 1, wherein constructing a land use optimization objective function comprises:
and weighting the target annual land utilization carbon emission function, the land utilization economic benefit function and the land utilization ecological benefit function by optimizing the land space pattern to obtain the target function for optimizing the land utilization.
4. The method for optimizing a low-carbon target land use spatial pattern according to claim 3, wherein constructing a land use spatial pattern optimization target annual land use carbon emission function comprises:
acquiring carbon emission coefficients of various land utilization types, and estimating the carbon emission of the construction land according to the combustion quantity of energy consumption and a default emission factor;
obtaining a carbon emission coefficient of the construction land based on the ratio of the carbon emission of the construction land to the area of the construction land, and predicting the carbon emission coefficient of the construction land for the target year through a gray early warning model;
multiplying the area of each land use type by the carbon emission coefficient of the target annual construction land, and summing to obtain the carbon emission function of land use.
5. The method for optimizing land use spatial pattern under a low carbon goal of claim 3, wherein constructing the land use economic benefit function comprises:
dividing the total economic yield of the industry corresponding to each land use type in each year by the GDP of the corresponding year to obtain the relative interest coefficient of each land use type in each year, and fitting and predicting the relative interest coefficient of each land use type in the target year through a linear regression trend to further determine the land use economic benefit objective function; wherein the land utilization types comprise cultivated land, forest land, grassland, water areas, construction land and bare land.
6. The method for optimizing the land use spatial pattern under the low carbon goal of claim 3, wherein constructing the land use ecological benefit function comprises:
and constructing the ecological benefit function based on an ecological service value economic evaluation method, correcting the ecological benefit function by combining with the actual situation of the target area to obtain the ecological service values of different ecosystems in unit area according with the actual situation of the target area, and further determining the land utilization ecological benefit target function.
7. The method of claim 1, wherein obtaining the target annual low carbon target earthed utilization spatial pattern optimization result comprises:
obtaining a land utilization expansion image through a land utilization image, and importing the land utilization expansion image and the driving factor into a land utilization expansion analysis strategy module to obtain a land utilization development probability atlas; inputting the initial annual land utilization image of the target area, the land utilization development probability atlas, the limiting area and the target annual land utilization quantity into a CA model based on multiple types of random plaque seeds to obtain a target annual low-carbon target underground utilization spatial pattern optimization result.
8. A low carbon target earth utilization spatial pattern optimization system, comprising:
the system comprises a data acquisition module, a low-carbon target lower objective function construction and constraint condition construction module, a target annual land utilization quantity structure optimization module and a target annual land utilization space pattern simulation module;
the data acquisition module is used for acquiring an original data set;
the low-carbon target lower objective function construction and constraint condition construction module is used for weighting the land use carbon emission function, the land use economic benefit function and the land use ecological benefit function to obtain an objective function, and meanwhile, constructing constraint conditions according to the actual situation of a target area;
the target annual land utilization quantity structure optimization module is used for obtaining a target annual land utilization quantity structure by compiling a differential evolution algorithm under a constraint condition;
the target year land utilization spatial pattern simulation module is used for obtaining a land utilization expansion image by using the two-stage land utilization image.
9. The low carbon target earthmoving space pattern optimization system of claim 8, in which the target annual land use space pattern simulation module comprises:
the land use expansion analysis strategy unit is used for obtaining a land use development probability atlas by importing the land use expansion image and the driving factor;
and the CA model unit based on the various random plaque seeds is used for obtaining a target year low-carbon target soil-discharging utilization space pattern optimization result by inputting an initial year land utilization image, a land utilization development probability atlas, a limited area and target year land utilization quantity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211431291.0A CN115860189A (en) | 2022-11-14 | 2022-11-14 | Method and system for optimizing land utilization spatial pattern under low-carbon target |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211431291.0A CN115860189A (en) | 2022-11-14 | 2022-11-14 | Method and system for optimizing land utilization spatial pattern under low-carbon target |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115860189A true CN115860189A (en) | 2023-03-28 |
Family
ID=85663649
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211431291.0A Pending CN115860189A (en) | 2022-11-14 | 2022-11-14 | Method and system for optimizing land utilization spatial pattern under low-carbon target |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115860189A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116720614A (en) * | 2023-06-05 | 2023-09-08 | 中国科学院地理科学与资源研究所 | Land layout optimization method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577897A (en) * | 2013-11-14 | 2014-02-12 | 武汉大学 | Population initialization method for intelligent land utilization space layout optimization |
| CN107248029A (en) * | 2017-05-26 | 2017-10-13 | 河南城建学院 | A kind of multi-target improvement differential evolution model of Land use structure type |
| CN114357874A (en) * | 2021-12-24 | 2022-04-15 | 华南理工大学 | Method for optimizing large-scale multi-source water supply pipe network by applying differential evolution and divide-and-conquer algorithm |
| CN114971328A (en) * | 2022-06-02 | 2022-08-30 | 郑州轻工业大学 | Shared automobile region scheduling method based on PSO-DE |
| CN114997480A (en) * | 2022-05-24 | 2022-09-02 | 福州大学 | Low-carbon guide provincial territory space partition optimization simulation method based on MOP-PLUS model |
-
2022
- 2022-11-14 CN CN202211431291.0A patent/CN115860189A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577897A (en) * | 2013-11-14 | 2014-02-12 | 武汉大学 | Population initialization method for intelligent land utilization space layout optimization |
| CN107248029A (en) * | 2017-05-26 | 2017-10-13 | 河南城建学院 | A kind of multi-target improvement differential evolution model of Land use structure type |
| CN114357874A (en) * | 2021-12-24 | 2022-04-15 | 华南理工大学 | Method for optimizing large-scale multi-source water supply pipe network by applying differential evolution and divide-and-conquer algorithm |
| CN114997480A (en) * | 2022-05-24 | 2022-09-02 | 福州大学 | Low-carbon guide provincial territory space partition optimization simulation method based on MOP-PLUS model |
| CN114971328A (en) * | 2022-06-02 | 2022-08-30 | 郑州轻工业大学 | Shared automobile region scheduling method based on PSO-DE |
Non-Patent Citations (3)
| Title |
|---|
| 王世东, 李立: "基于 DE PSO 模型的土地资源优化配置研究", 农业机械学报, vol. 53, no. 6, pages 1 - 10 * |
| 王凯;甘畅;王梦晗;朱芳书;邓楚雄;: "湖北省旅游扶贫效率时空格局及其影响因素", 长江流域资源与环境, no. 04 * |
| 王福岭;: "基于协同差分演化算法的梯级水库短期发电优化调度研究", 中国农村水利水电, no. 06 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116720614A (en) * | 2023-06-05 | 2023-09-08 | 中国科学院地理科学与资源研究所 | Land layout optimization method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects | |
| Chen et al. | Assessing potential ecosystem service dynamics driven by urbanization in the Yangtze River Economic Belt, China | |
| CN108536908B (en) | Method for evaluating watershed water environment safety based on non-point source nitrogen and phosphorus loss risk | |
| CN111178786B (en) | Emission source position determining method and system for guaranteeing regional air quality | |
| Lin et al. | What is the influence of landscape metric selection on the calibration of land-use/cover simulation models? | |
| CN112131731B (en) | Urban growth cellular simulation method based on spatial feature vector filtering | |
| CN112669976B (en) | Crowd health assessment method and system based on ecological environment change | |
| CN114254802B (en) | Prediction method for vegetation coverage space-time change under climate change drive | |
| CN113902580A (en) | Historical farmland distribution reconstruction method based on random forest model | |
| CN115860189A (en) | Method and system for optimizing land utilization spatial pattern under low-carbon target | |
| Rocha et al. | Early prediction of durum wheat yield in Spain using radial basis functions interpolation models based on agroclimatic data | |
| Chen et al. | Identifying the drivers of land expansion and evaluating multi-scenario simulation of land use: A case study of Mashan County, China | |
| Feng et al. | Evaluation of soil suitability for cultivation based on back-propagation artificial neural network: The case of Jiangxia district | |
| CN110716998A (en) | Method for spatializing fine-scale population data | |
| Monteil et al. | Regional CO 2 inversions with LUMIA, the Lund University modular inversion algorithm, v1. 0 | |
| Sipahioğlu et al. | Scenario-Based Cellular Automata and Artificial Neural Networks in Urban Growth Modeling | |
| Döll et al. | Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling | |
| Tsai et al. | From parameter calibration to parameter learning: Revolutionizing large-scale geoscientific modeling with big data | |
| Pérez et al. | Model testing and assessment: perspectives from a swarm intelligence, agent-based model of forest insect infestations | |
| Lu et al. | Incorporating eco-evolutionary information into species distribution models provides comprehensive predictions of species range shifts under climate change | |
| Suriadikusumaha et al. | Prediction of Bandung District Land Use Change Using Markov Chain Modeling | |
| Helliwell et al. | The present is the key to the past, but what does the future hold for the recovery of surface waters from acidification? | |
| Koo et al. | Estimating soil carbon in agricultural systems using ensemble Kalman filter and DSSAT-CENTURY | |
| Sangpradid | APPLICATION OF A MULTI-LAYER PERCEPTRON NEURAL NETWORK TO SIMULATE SPATIAL-TEMPORAL LAND USE AND LAND COVER CHANGE ANALYSIS BASED ON CELLULAR AUTOMATA IN BURIRAM PROVINCE, THAILAND. | |
| Behrens et al. | A Bayesian analysis of the efficiency of growth and inflation forecasts for Germany |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |