CN115271153A - Multi-objective planning-based regional industry structure adjustment optimization method and system - Google Patents
Multi-objective planning-based regional industry structure adjustment optimization method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for adjusting and optimizing a regional industrial structure based on multi-objective planning, wherein the method comprises the steps of calculating an industrial association coefficient based on an input-output table, and sequencing industrial departments according to the industrial association coefficient to obtain a department classification result; constructing an economic-energy-environment multi-target planning model; solving the economic-energy-environment multi-target planning model to obtain a regional industrial structure adjustment path; and optimizing the regional industrial structure adjustment path according to the department classification result. The method considers the correlation among industries, and can improve the industrial structure adjustment and optimization efficiency in the regional development process; and the economic benefit maximization and the carbon emission minimization are selected as target functions, and the constraint environment of structure adjustment is established, so that the energy-saving effect of an industrial department, the industrial structure adjustment and the regional economic development can be more accurately, reasonably and comprehensively designed and optimized.
Description
Technical Field
The invention relates to the technical field of urban planning and industrial structure adjustment, in particular to a regional industrial structure adjustment optimization method and system based on multi-objective planning.
Background
The coal proportion and the carbon emission strength of China are gradually reduced, but the industrial structure still has many problems, and the most important problem is that the industrial economic value can not be matched with the consumed energy and the discharged carbon emission. By reasonably adjusting the industrial structure, the utilization efficiency of energy is improved, and the optimal economic development of energy conservation and emission reduction is realized.
The task of low-carbon transformation optimization of regional industrial structure energy is to find out an optimal industrial transformation scheme in an economic-energy-environment cooperative management mode. The scheme includes the purpose and path of transformation. The existing industrial transformation optimization method generally constructs the original problem into a linear planning problem or a dynamic production planning problem which describes cost minimization and is constrained by the total amount of energy, and the optimized decision variables are capital investment, production scale, energy consumption and the like. The method is at a microscopic view angle, is not beneficial to solving the problems of economy, energy and environment in the middle and macro, and can not clearly output the emission reduction target and the industrial structure adjustment path.
However, the development of the economic society can not be achieved at the cost of energy consumption, climate change or ineffective utilization, and particularly, china just steps into a new stage of building a modern energy system at present, strives to achieve the goals of carbon peak reaching and carbon neutralization as soon as possible, and promotes the high-quality development of the social economy.
Since objective functions are diverse and interactive, the optimization model becomes a multi-objective planning problem. Obviously, the existing linear programming problem or dynamic production programming algorithm cannot be applied to the multi-objective programming problem, and a new optimization method needs to be proposed urgently.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a regional production structure adjustment and optimization method and system based on multi-objective planning.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
in a first aspect, the invention provides a method for adjusting and optimizing a regional industrial structure based on multi-objective planning, which comprises the following steps:
s1, calculating an industry association coefficient based on an input-output table, and sequencing industry departments according to the industry association coefficient to obtain a department classification result;
s2, constructing an economic-energy-environment multi-target planning model by taking economic growth maximization and carbon emission minimization as target functions and taking energy consumption constraint, driving force oriented economic inhibition constraint, driving force oriented economic support constraint and industrial structure and policy oriented fitness constraint as constraint conditions;
s3, solving the economic-energy-environment multi-target planning model to obtain a regional industrial structure adjustment path;
and S4, optimizing the regional industrial structure adjustment path according to the department classification result.
Optionally, the step S1 of calculating the industry association coefficient based on the input-output table specifically includes:
calculating a Lyonefields inverse matrix based on an input-output table;
and respectively calculating the industry driving coefficient and the industry pushing coefficient of each industry department based on the Lyoney inverse matrix.
Optionally, the calculation manner of the risc inverse matrix is represented as:
wherein li,iElements representing the ith row and ith column in the inverse Lyontigh matrix, zi,iIndicating that the ith industry department obtains the currency unit input by the ith industry department required by the output of one currency unit.
Optionally, the calculation manner of the industry driving coefficient is represented as:
wherein li,jRepresenting the elements of the ith row and ith column in the inverse reonverger matrix.
Optionally, the industry promotion coefficient is calculated by:
wherein li,jRepresenting the elements of the ith row and ith column in the inverse reonverger matrix.
Optionally, in step S2, the industry departments are ranked according to the industry association coefficient to obtain a department classification result, which specifically includes:
sorting according to the size of the industry driving coefficients of all the industry departments, classifying the industry development corresponding to the first 50% of the departments as high, and classifying the industry development corresponding to the last 50% of the departments as low;
sorting according to the size of the industry promotion coefficients of all the industry departments, classifying the industry development corresponding to the first 50% of the departments as high, and classifying the industry development corresponding to the last 50% of the departments as low;
according to the classification result of each industry department, the industry types of all the industry departments are divided into four types, and the industry development selection of the industry departments of various industry types is determined.
Optionally, the economic-energy-environment multi-objective planning model constructed in step S2 is specifically:
f(x)＝[f1(x),f2(x)]
wherein f (x) represents the overall objective function, f1(x) Representing an economic growth maximization objective function, f2(x) Represents the carbon rejection minimization objective function, viIndicates the added value rate, x, of the ith industrial sectoriRepresents the yield of the i-th industrial sector, qiDenotes the carbon emission coefficient, wiRepresents the amount of energy consumption per ten-thousand-dollar economic increment of the ith industry division, C represents the total amount of energy consumption, and deltamA variable of 0-1 indicating whether development is restricted, m indicates the number of the industrial division whose development is to be restricted, s indicates the scale of the industrial scale for suppressing development, δnA variable 0-1 for supporting development, n for the number of the department of industry to be supported, t for the scale of the industry to encourage development, A, B, C, D for the industry of the department, and thetalIndicating the proportion of industry development representing the current planning period.
Optionally, the step S3 of solving the economic-energy-environment multi-target planning model to obtain the regional industrial structure adjustment path specifically includes:
solving decision parameters for regional industrial structure adjustment of the economic-energy-environment multi-target planning model, wherein the decision parameters comprise 0-1 variable of whether the development is limited or not, 0-1 variable of whether the development is supported or not and the output of each industrial department;
and determining the adjustment path of the regional industrial structure according to the decision parameters for adjusting the regional industrial structure.
Optionally, the step S4 of optimizing the regional industry structure adjustment path according to the department classification result specifically includes:
judging whether each industry department in the adjustment path of the regional industry structure is supported to develop or not; if yes, carrying out the next judgment; otherwise, continuing to judge the next industrial department;
judging whether the industry type of the industry department is an uncertain industry development choice; if so; then the next judgment is carried out; otherwise, returning to the previous step for judgment;
judging whether the energy consumption of each ten-thousand-yuan economic added value of the industrial department is larger than a set energy consumption threshold value or not; if yes, modifying a 0-1 variable of whether the development is supported or not of the industry department into 0; otherwise, continuing to save the regional industrial structure adjustment path.
In a second aspect, the present invention provides a system for adjusting and optimizing regional industrial structure based on multi-objective planning, including:
the classification module is used for calculating an industry association coefficient based on the input-output table, and sequencing industry departments according to the industry association coefficient to obtain a department classification result;
the modeling module is used for constructing an economic-energy-environment multi-target planning model by taking economic growth maximization and carbon emission minimization as objective functions and taking energy consumption constraint, economic inhibition constraint with power guide, economic support constraint with driving force guide and fitness constraint of industrial structure and policy guide as constraint conditions;
the solving module is used for solving the economic-energy-environment multi-target planning model to obtain a regional industrial structure adjusting path;
and the optimization module is used for optimizing the regional industrial structure adjustment path according to the department classification result.
The invention has the following beneficial effects:
the method considers the correlation among industries, namely the driving force and the promotion of a certain department in the economic development, and considers the energy consumption characteristics, so that the industrial structure adjustment and optimization efficiency in the regional development process can be improved; and economic benefit maximization and carbon emission minimization are selected as target functions, and a structural adjustment constraint environment is established, so that energy-saving effect, industrial structure adjustment and regional economic development of industrial departments can be more accurately, reasonably and comprehensively designed and optimized, and further the key factors for policy making are used, the problems facing economy, energy and environment are comprehensively and deeply analyzed, and reference is provided for making more effective industrial structure adjustment, energy conservation and emission reduction measures.
Drawings
FIG. 1 is a schematic flow chart of a method for adjusting and optimizing a regional industrial structure based on multi-objective planning in embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of a system for adjusting and optimizing a regional industry structure based on multi-objective planning in embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a method for adjusting and optimizing a regional industrial structure based on multi-objective planning, including the following steps S1 to S4:
s1, calculating an industry association coefficient based on an input-output table, and sequencing industry departments according to the industry association coefficient to obtain a department classification result;
in an alternative embodiment of the present invention, the present invention first obtains the input-output tables of each industry department that needs to perform regional industry structure adjustment and optimization, as shown in table 1.
TABLE 1 input-output Table
After the input-output table of each industrial department is obtained, the industrial association coefficient is calculated based on the input-output table, and the method specifically comprises the following steps:
calculating a Lyonefields inverse matrix based on an input-output table;
and respectively calculating the industry driving coefficient and the industry pushing coefficient of each industry department based on the Lyonberg inverse matrix.
Then, the industrial departments are ranked according to the industrial association coefficient to obtain a department classification result, and the method specifically comprises the following steps:
sorting according to the industrial driving coefficients of all industrial departments according to the sizes, classifying the industrial development corresponding to the first 50% of the departments as high, and classifying the industrial development corresponding to the last 50% of the departments as low;
sorting according to the industry promotion coefficients of all the industry departments according to the sizes, classifying the industry development corresponding to the first 50% of the departments as high, and classifying the industry development corresponding to the last 50% of the departments as low;
according to the classification result of each industrial department, the industrial types of all the industrial departments are divided into four types, and the industrial development selection of the industrial departments of various industrial types is determined.
Specifically, the invention uses the influence factor as the industry driving coefficient to represent the influence of one department on another department, namely, the pulling level of the yield value of the other department when the department i adds one code unit. Coefficient of industrial drive MiCan be calculated by a LyonFr inverse matrix. Wherein lijThe values of the ith row and jth column in the Lyinger inverse matrix.
The calculation mode of the LyonFref inverse matrix is represented as follows:
wherein li,iElements representing the ith row and ith column in the inverse Lyontigh matrix, zi,iIndicates the ith department of industry required for the ith department of industry to obtain the output of one currency unitThe monetary units that the door invests.
The calculation of the industry drive coefficient is expressed as:
wherein li,jRepresenting the elements of the ith row and ith column in the inverse reonverger matrix.
The larger the industry driving coefficient is, the larger the dragging effect of the department on other departments is. When M isi>1, the dragging effect of the economic development of the department i on other departments is larger than the average level of the society, and when M is higheriWhen the term is 1, the economic development of the industry A unit has the dragging effect on other departments except the department i equal to the average social level, and Mi<1, it indicates that the economic development of the department i has less tractive effect on other departments except the department i than the average social level.
Therefore, the investment situation of a department with high industrial driving coefficient is increased, and the economic development situation of the whole industrial unit can be motivated. Particularly, aiming at the influence of emergencies (epidemic situations), the development of enterprises is frustrated, the consumption enthusiasm of residents is not high, and the government can utilize the characteristic of the industrial driving coefficient to stimulate industrial units with high industrial driving coefficient and drive the economic development.
The invention adopts the sensitivity factor as the industry promotion factor to express the promotion effect of the development condition of each department to a certain department, and the industry promotion factor NjIt can also be calculated from the inverse of the lyon tighren matrix.
The calculation mode of the industry promotion coefficient is expressed as follows:
wherein li,jRepresenting the elements of the ith row and ith column in the inverse reonverger matrix.
The high and low of the industry promotion coefficient represents the promotion effect of the development condition of each department on a certain department at a certain level, the higher the industry promotion coefficient is, the more indispensable the industry is, the more other industry departments are away from the industry, and the development of the industry department seriously restricts the development of the whole national economic system. The industry push factor is compared to the standard "1", which is representative of the average social level,
for example: industry promotion factor N of department jj>1, indicating that department j receives a demand response greater than the average level, and so on. Therefore, the method can be known that the investment situation of the industry with high industry promotion coefficient is increased, and the restriction effect of the department j on other departments can be limited, so that the structural upgrade of the whole industry is optimized. Particularly, when the economic development situation is rapid, the demand induction degree developed by the department j needs to be matched with the final use condition of other departments, so that the continuous, stable and healthy development of the economy can be ensured.
Based on this, the industry departments are ranked according to the industry driving coefficient and the industry pushing coefficient, as shown in table 2:
TABLE 2 industry Association measure coefficient ranking
The present invention classifies the first 50% of departments as high and the last 50% as low according to the ranking results, thereby classifying the industrial development into four types, as shown in table 3 below.
TABLE 3 Industrial Scale adjustment Table under different Industrial Association metrics
As can be seen from table 3, all the departments are divided into four groups, and the production scale of the department can be enlarged regardless of the size for the high department, and the development of the department should be restricted for the case where both are small, and the production scale should be reduced and the import replacement should be selected for the case of the energy-intensive industry and the energy-saving industry depending on the situation of the department depending on whether the industry is the energy-intensive industry or the energy-saving industry.
S2, constructing an economic-energy-environment multi-target planning model by taking economic growth maximization and carbon emission minimization as target functions and taking energy consumption constraint, driving force oriented economic inhibition constraint, driving force oriented economic support constraint and industrial structure and policy oriented fitness constraint as constraint conditions;
in an optional embodiment of the invention, the industrial adjustment feature recognition based on the industrial correlation coefficient can process the regional industrial structure adjustment problems of the types I, II and IV, but in the face of the type III, the invention describes the original problem as a multi-objective optimization problem, designs an economic-energy-environment multi-objective planning algorithm and outputs a regional industrial adjustment path with the energy-saving and emission-reducing goal.
The economic-energy-environment multi-target planning model constructed by the invention specifically comprises the following steps:
f(x)＝[f1(x),f2(x)]
wherein f (x) represents the overall objective function, f1(x) Representing an economic growth maximization objective function, f2(x) Representing a carbon rejection minimization objective function; v. ofiThe increase rate of the ith industry department can be obtained according to the previous historical input-output table, and the increase rate is the economic increase value of the i department divided by the total input, so vixiRepresents an economic added value of department i; x is a radical of a fluorine atomiRepresents the yield of the ith industrial department; q. q.siA carbon row representing a carbon emission coefficient, i.e., a unit energy consumption of the department i; w is aiEnergy consumption amount representing the economic added value per ten thousand yuan of the ith industrial department; c represents the total amount of energy consumption, deltamA variable 0-1 for indicating whether the development is limited, m indicates the serial number of the industry department which needs to be limited, s indicates the development inhibiting proportion of the industry scale, and the specific numerical value is determined according to the proportion of the yield value of the department which needs to be limited to develop to the total yield value; deltanA variable 0-1 for indicating whether the development is supported, n is the serial number of the industry department which needs to be supported, t is the development encouraging proportion of the industry scale, and the specific numerical value is determined according to the proportion of the yield value of the department which needs to be encouraged to the total yield value; a, B, C and D represent industries of departments, and respectively represent agriculture, industry, energy production and supply and service industry; thetalIndicating the proportion of industry development representing the current planning period.
S3, solving the economic-energy-environment multi-target planning model to obtain a regional industrial structure adjustment path;
in an optional embodiment of the present invention, the solving of the economic-energy-environment multi-target planning model to obtain the regional industrial structure adjustment path specifically includes:
solving decision parameters for regional industrial structure adjustment of the economic-energy-environment multi-target planning model, wherein the decision parameters comprise 0-1 variable of whether the development is limited or not, 0-1 variable of whether the development is supported or not and the output of each industrial department;
and determining the adjustment path of the regional industrial structure according to the decision parameters for adjusting the regional industrial structure.
Specifically, the invention adopts a fast non-dominated sorting genetic algorithm (NSGA-II) which utilizes an elite strategy to solve. The decision variable is δm，δn，xi。δmIs a variable of 0-1, indicating whether development is restricted, δnIs a 0-1 variable indicating whether or not development is supported. x is the number ofi(I =1, 2.., I = 42) represents the yield of department I.
And S4, optimizing the regional industrial structure adjustment path according to the department classification result.
In an optional embodiment of the present invention, the optimizing the adjustment path of the regional industry structure according to the department classification result specifically includes:
judging whether each industry department in the adjustment path of the regional industry structure is supported to develop or not; if yes, carrying out the next judgment; otherwise, continuing to judge the next industrial department;
judging whether the industry type of the industry department is an uncertain industry development choice; if so; then the next judgment is carried out; otherwise, returning to the previous step for judgment;
judging whether the energy consumption of each ten-thousand-yuan economic added value of the industrial department is larger than a set energy consumption threshold value or not; if yes, modifying the 0-1 variable of the industry department whether being supported to develop into 0; otherwise, continuing to save the regional industry structure adjustment path.
Example 2
As shown in fig. 2, based on the adjustment and optimization scheme for the regional industry structure based on multi-objective programming described in embodiment 1, the present invention provides a system for adjustment and optimization for the regional industry structure based on multi-objective programming, which includes:
the classification module is used for calculating an industry association coefficient based on the input-output table, and sequencing industry departments according to the industry association coefficient to obtain a department classification result;
the modeling module is used for constructing an economic-energy-environment multi-target planning model by taking economic growth maximization and carbon emission minimization as objective functions and taking energy consumption constraint, economic inhibition constraint with power guide, economic support constraint with driving force guide and fitness constraint of industrial structure and policy guide as constraint conditions;
the solving module is used for solving the economic-energy-environment multi-target planning model to obtain a regional industrial structure adjusting path;
and the optimization module is used for optimizing the regional industrial structure adjustment path according to the department classification result.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be changes in the specific embodiments and the application scope, and in view of the above, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention, and it is to be understood that the scope of the invention is not to be limited to such specific statements and embodiments. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (10)
1. A regional industrial structure adjustment optimization method based on multi-objective programming is characterized by comprising the following steps:
s1, calculating an industry association coefficient based on an input-output table, and sequencing industry departments according to the industry association coefficient to obtain a department classification result;
s2, constructing an economic-energy-environment multi-target planning model by taking economic growth maximization and carbon emission minimization as target functions and taking energy consumption constraint, economic inhibition constraint with power guide, economic support constraint with driving force guide and fitness constraint with industrial structure and policy guide as constraint conditions;
s3, solving the economic-energy-environment multi-target planning model to obtain a regional industrial structure adjustment path;
and S4, optimizing the regional industrial structure adjustment path according to the department classification result.
2. The multi-objective planning-based regional industry structure adjustment and optimization method according to claim 1, wherein the step S1 of calculating the industry association coefficient based on the input-output table specifically comprises:
calculating a Riontighren inverse matrix based on the input-output table;
and respectively calculating the industry driving coefficient and the industry pushing coefficient of each industry department based on the Lyoney inverse matrix.
3. The multi-objective planning-based regional industry structure adjustment optimization method according to claim 2, wherein the calculation mode of the Reed-Frov inverse matrix is represented as follows:
wherein li,iElements representing the ith row and ith column in a LyonFr inverse matrix, zi,iIndicating that the ith industry division obtains the currency unit input by the ith industry division required for outputting one currency unit.
4. The multi-objective planning-based regional industry structure adjustment optimization method according to claim 2, wherein the industry drive coefficient is calculated in a manner as follows:
wherein li,jRepresenting the elements of row i and column i in the reenvolve inverse matrix.
5. The multi-objective planning-based regional industry structure adjustment optimization method according to claim 2, wherein the industry push coefficient is calculated by:
wherein li,jRepresenting the elements of the ith row and ith column in the inverse reonverger matrix.
6. The multi-objective programming-based regional industry structure adjustment optimization method of claim 2, wherein in the step S2, the industry departments are ranked according to the industry association coefficients to obtain a department classification result, and the method specifically comprises:
sorting according to the size of the industry driving coefficients of all the industry departments, classifying the industry development corresponding to the first 50% of the departments as high, and classifying the industry development corresponding to the last 50% of the departments as low;
sorting according to the size of the industry promotion coefficients of all the industry departments, classifying the industry development corresponding to the first 50% of the departments as high, and classifying the industry development corresponding to the last 50% of the departments as low;
according to the classification result of each industrial department, the industrial types of all the industrial departments are divided into four types, and the industrial development selection of the industrial departments of various industrial types is determined.
7. The multi-objective planning-based regional industry structure adjustment optimization method according to claim 1, wherein the economic-energy-environment multi-objective planning model constructed in the step S2 is specifically:
f(x)＝[f1(x),f2(x)]
wherein f (x) represents the overall objective function, f1(x) Representing an economic growth maximization objective function, f2(x) Represents the carbon rejection minimization objective function, viIndicates the added value rate, x, of the ith industrial sectoriRepresents the yield of the i-th industrial sector, qiDenotes the carbon emission coefficient, wiRepresents the amount of energy consumption per ten-thousand-dollar economic increment of the ith industry division, C represents the total amount of energy consumption, and deltamA variable of 0-1 indicating whether development is restricted, m indicates the number of the industrial division whose development is to be restricted, s indicates the scale of the industrial scale for suppressing development, δnA variable 0-1 for indicating whether development is supported, n is the serial number of the industry department needing supported development, t is the scale of the industry to encourage development, A, B, C and D are the industry of the department, thetalIndicating the proportion of industry development representing the current planning period.
8. The multi-objective planning-based regional industrial structure adjustment optimization method according to claim 1, wherein the step S3 of solving the economic-energy-environment multi-objective planning model to obtain the regional industrial structure adjustment path specifically comprises:
solving decision parameters for regional industrial structure adjustment of the economic-energy-environment multi-target planning model, wherein the decision parameters comprise 0-1 variable of whether the development is limited or not, 0-1 variable of whether the development is supported or not and the output of each industrial department;
and determining the adjustment path of the regional industrial structure according to the decision parameters for adjusting the regional industrial structure.
9. The multi-objective programming-based regional industrial structure adjustment optimization method according to claim 1, wherein the step S4 of optimizing the regional industrial structure adjustment path according to the department classification result specifically comprises:
judging whether each industry department in the adjustment path of the regional industry structure is supported to develop or not; if yes, carrying out the next judgment; otherwise, continuing to judge the next industrial department;
judging whether the industry type of the industry department is an uncertain industry development choice; if so; then the next judgment is carried out; otherwise, returning to the previous step for judgment;
judging whether the energy consumption of each ten thousand yuan of economic added value of the industry department is greater than a set energy consumption threshold value or not; if yes, modifying the 0-1 variable of the industry department whether being supported to develop into 0; otherwise, continuing to save the regional industrial structure adjustment path.
10. A system for adjusting and optimizing regional industrial structure based on multi-objective programming is characterized by comprising:
the classification module is used for calculating an industry association coefficient based on the input-output table and sequencing industry departments according to the industry association coefficient to obtain a department classification result;
the modeling module is used for constructing an economic-energy-environment multi-target planning model by taking economic growth maximization and carbon emission minimization as objective functions and taking energy consumption constraint, economic inhibition constraint with power guide, economic support constraint with driving force guide and fitness constraint of industrial structure and policy guide as constraint conditions;
the solving module is used for solving the economic-energy-environment multi-target planning model to obtain a regional industrial structure adjustment path;
and the optimization module is used for optimizing the regional industrial structure adjustment path according to the department classification result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202210621318.6A CN115271153A (en) | 2022-06-02 | 2022-06-02 | Multi-objective planning-based regional industry structure adjustment optimization method and system |
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| CN116029759A (en) * | 2023-01-31 | 2023-04-28 | 砼联数字科技有限公司 | Dynamic intelligent optimization method and system for concrete production industry chain |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106355268A (en) * | 2016-08-22 | 2017-01-25 | 中交天津港航勘察设计研究院有限公司 | Optimization method for urban industrial structure based on environmental carrying capacity |
| CN107609686A (en) * | 2017-08-25 | 2018-01-19 | 西安理工大学 | A kind of Stands in Arsenic Sandstone Area sand ground agricultural development utilizes the determination method of adaptability scale |
| US20180100385A1 (en) * | 2016-10-11 | 2018-04-12 | Encline Artificial Lift Technologies LLC | Liquid Piston Compressor System |
| CN109784582A (en) * | 2019-02-15 | 2019-05-21 | 黄河勘测规划设计研究院有限公司 | A kind of regional economy department water distribution equalization methods and system |
| CN113449924A (en) * | 2021-07-09 | 2021-09-28 | 中国人民解放军国防科技大学 | National economy mobilization ability optimization analysis method and device and computer equipment |
-
2022
- 2022-06-02 CN CN202210621318.6A patent/CN115271153A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106355268A (en) * | 2016-08-22 | 2017-01-25 | 中交天津港航勘察设计研究院有限公司 | Optimization method for urban industrial structure based on environmental carrying capacity |
| US20180100385A1 (en) * | 2016-10-11 | 2018-04-12 | Encline Artificial Lift Technologies LLC | Liquid Piston Compressor System |
| CN107609686A (en) * | 2017-08-25 | 2018-01-19 | 西安理工大学 | A kind of Stands in Arsenic Sandstone Area sand ground agricultural development utilizes the determination method of adaptability scale |
| CN109784582A (en) * | 2019-02-15 | 2019-05-21 | 黄河勘测规划设计研究院有限公司 | A kind of regional economy department water distribution equalization methods and system |
| CN113449924A (en) * | 2021-07-09 | 2021-09-28 | 中国人民解放军国防科技大学 | National economy mobilization ability optimization analysis method and device and computer equipment |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116029759A (en) * | 2023-01-31 | 2023-04-28 | 砼联数字科技有限公司 | Dynamic intelligent optimization method and system for concrete production industry chain |
| CN116029759B (en) * | 2023-01-31 | 2024-03-19 | 砼联数字科技有限公司 | Dynamic intelligent optimization method and system for concrete production industry chain |
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