Industrial transfer space decision-making method and system based on ANN-GIS-CA architecture

Abstract

The invention provides an industrial transfer space decision-making method and system based on an ANN-GIS-CA architecture, and belongs to the field of land and space resource utilization. The method comprises the following steps: firstly, dividing an industrial transfer undertaking place into a plurality of grids based on a GIS (Geographic Information System), judging sub-regions to which the grids belong, then constructing an evaluation index system of industrial transfer undertaking capacity, calculating an undertaking capacity score, constructing an undertaking effect evaluation index system structure, and calculating a social economic effect score and an ecological environment effect score; calculating whether six state probabilities of all grids belong to a prohibited development area and Euclidean distances from the grids to the existing industrial grids; and constructing a partition asynchronous cellular automaton model by taking the calculation result as an attribute, substituting the basic year grid data to iterate the cellular automaton model for multiple times, after the precision reaches a threshold value, dividing future industry transfer into five different scenes for simulation, and then making a decision based on a simulation result. According to the invention, the accuracy and effectiveness of industrial space decision making during industrial transfer are improved.

Description

Technical Field

[0001] The present invention belongs to the field of land and space resource utilization, and specifically relates to an industry transfer spatial decision-making method and system based on ANN-GIS-CA architecture. Background Art

[0002] Spatial layout is one of the basic issues that geographers have long been concerned about. It involves how to reasonably arrange the elements in the geographic space to achieve specific social, economic and environmental goals. Reasonable spatial layout can promote the efficient use of resources, improve the quality of life of residents, protect the natural environment, and promote regional sustainable development. Spatial optimization decision-making is a key issue in the field of geographic information systems and is usually defined as multi-objective optimization with constraints. Industrial transfer is the change and transformation of the spatial layout of the industry. In essence, it is the process of matching the type of industry to be transferred to a specific geographical unit to achieve the optimal resource and environmental effect. There is a relationship of mutual dependence and mutual promotion between spatial layout, spatial optimization decision-making and industrial transfer. Industrial transfer is the embodiment of the dynamic adjustment of spatial layout. Spatial layout is the basis for achieving spatial optimization decision-making, and spatial optimization decision-making is the key means to ensure that industrial transfer and spatial layout achieve the expected social, economic and environmental goals. Since the application of spatial optimization methods in the field of geography was first proposed in 1959, it has been continuously developed and improved, and has formed a rich theoretical system and application results.

[0003] At the same time, the advancement of Geographic Information System (GIS) technology has provided powerful tools and platforms for geography. It not only provides a wealth of spatial data analysis tools for geographical research, but also allows for visualization, exploratory analysis and model building of geographic data, allowing spatial optimization methods to be widely used and deeply developed. Through GIS technology, researchers can quickly and accurately process and analyze spatial data, providing basic data and decision support for subsequent spatial optimization. In addition, GIS can provide decision makers with intuitive map displays and spatial analysis results, helping them better understand problems and make decisions.

[0004] At present, GIS mainly combines two optimization algorithms to solve the problem of spatial layout optimization. One is the numerical optimization model, and the other is the intelligent optimization model. The former is prone to large computational complexity and low precision when solving complex nonlinear combination optimization problems of spatial optimization decisions, while the intelligent algorithm can obtain an approximate optimal solution within an acceptable time. Although these studies have laid the foundation for the spatial optimization of industrial transfer, they did not incorporate the influencing factors and the spatial differences themselves into the establishment of the model. At the same time, they ignored the fact that the human and natural environment are changing dynamically.

[0005] In contrast, the Cellular Automata (CA) model can be used to simulate the impact of dynamic changes in the human and natural environments on the research object. The CA model has powerful spatial computing capabilities and is widely used in the study of the evolution of self-organizing systems. The three core features of this model - the discreteness of time, space and state, as well as the local grid dynamics, make it an ideal tool for simulating the spatiotemporal evolution of complex systems. In recent years, scholars have achieved many meaningful research results in urban system simulation using the CA model. These research results show that complex urban spatial structures can be simulated through simple local transformation rules, reflecting the essence of complexity science that "complex systems come from the interaction of simple subsystems", and providing a reliable basis for theoretical research in geography and other fields.

[0006] Many phenomena in geography belong to the category of dynamic complex systems, which are characterized by openness, dynamism, self-organization and dissipative structures in non-equilibrium states. Taking the urban system as an example, its development and evolution are jointly influenced by many factors such as natural conditions, social structure, economic level, cultural elements, political background and legal framework, showing a highly complex behavior pattern. In view of this complexity, the urban CA model must take these factors into consideration. Although CA has potential in simulating certain characteristics of urban systems, a single form of CA model is difficult to fully simulate the diverse characteristics of cities. Therefore, the academic community has proposed a variety of CA models to simulate the broad characteristics of cities from multiple perspectives.

[0007] In early CA models, the use of spatial information was quite limited, which hindered their effective integration with GIS. However, since the 1990s, scholars have begun to try to combine CA with GIS. This integration has significantly improved the accuracy and reliability of CA in simulating actual urban conditions. When dealing with complex spatial relationships, the functions of existing GIS have certain limitations. In order to better study the complex spatiotemporal dynamic characteristics of geographic systems, it is necessary to couple dynamic models in GIS. CA has powerful spatial modeling and computing capabilities, and can simulate complex dynamic systems with spatiotemporal characteristics, which just makes up for the lack of GIS.

[0008] However, the CA model still has some limitations when simulating the dynamic changes of geographical phenomena. On the one hand, many CA models use a unified transformation rule to drive the evolution of all cells, ignoring the spatial heterogeneity of geographical phenomena and their related factors, thus limiting the simulation accuracy of the geographical CA model; on the other hand, these models usually assume that all cells evolve at the same rate, which is inconsistent with the dynamic change law of actual geographical phenomena and also limits the improvement of simulation accuracy. When the CA model is used to simulate real urban development, especially when simulating changes in different land use types, the complexity of the model increases significantly, and the accuracy and reality of the simulation effect cannot be guaranteed. At the same time, there are many studies on the optimization of industrial spatial layout with resources and environment as constraints in the existing technology, and the research results generally only include qualitative descriptions, and there is no operational planning strategy for spatial optimization; although there are a few methods for evaluating ecological carrying capacity combined with GIS zoning, they only guide industrial layout with the concept of ecological carrying capacity, and have not formed a regulatory model for the interaction between ecological carrying capacity and industrial layout. Summary of the invention

[0009] In view of the above-mentioned defects or deficiencies in the prior art, the present invention aims to provide a spatial decision-making method and system for industrial transfer based on the ANN-GIS-CA architecture. On the basis of fully considering the industrial transfer acceptance capacity and the socio-economic effects and ecological and environmental effects of industrial transfer, the spatiotemporal impact process of industrial acceptance capacity on industrial transfer and the spatiotemporal process of industrial transfer effects on resources and the environment are mapped to the geographic information environment GIS, and a spatiotemporal information mechanism for analyzing industrial transfer is constructed. Starting from the spatial differences of industrial transfer, the constraint CA and partitioned asynchronous CA methods are integrated to construct an optimization model for industrial spatial layout, thereby improving the accuracy and effectiveness of spatial layout optimization.

[0010] In order to achieve the above object, the embodiment of the present invention adopts the following technical solution:

[0011] In a first aspect, an embodiment of the present invention provides an industry transfer spatial decision-making method based on an ANN-GIS-CA architecture, the method comprising the following steps:

[0012] Step S1, based on the geographic information system GIS, the industrial transfer receiving area is divided into several grids to form cells, and the existing industrial transfer situation is marked in the grid by manual interpretation, so as to generate grid data of the basic year and the verification year based on GIS; the grid data includes spatial information and attribute information; at the same time, the sub-area to which the grid belongs is determined according to the preset rules, and the attribution result is used as the attribute information of the grid data;

[0013] Step S2, obtaining the original data of the industrial transfer undertaking capacity of all grids based on the Geographic Information System (GIS), and constructing an evaluation index system for the industrial transfer undertaking capacity;

[0014] Step S3, based on the constructed industrial transfer acceptance capacity evaluation index system, calculate the industrial transfer acceptance capacity scores of all grids, and use the Gaussian mixture model to identify the industry type that each grid is suitable for accepting, and use the score and the industry type that is suitable for accepting as the attribute information of the grid data;

[0015] Step S4, based on GIS, obtain the original data of the social and economic effects and ecological and environmental effects of all grids undertaking industrial transfer, and construct an evaluation index system for the social and economic effects and ecological and environmental effects of undertaking industrial transfer; the constructed evaluation index system structure for undertaking effect includes primary indicators, secondary indicators, and the source and direction attributes of the secondary indicators; the direction attributes include positive indicators and negative indicators; wherein, a positive indicator refers to an indicator with a larger attribute value, the better, and a negative indicator refers to an indicator with a smaller attribute value, the better;

[0016] Step S5, respectively calculating the social and economic effect scores and the ecological and environmental effect scores of all grids according to the secondary indicators of the social and economic effect and the ecological and environmental effect in the undertaking effect and the directional attributes of the secondary indicators, and using the scores as the attribute information of the grid data;

[0017] Step S6, selecting grids with existing industrial transfers in all base years, and randomly selecting a predetermined number of grids without existing industrial transfers in the base year, extracting the social and economic effect scores and ecological and environmental effect scores of these grids as samples, constructing an artificial neural network ANN model, and calculating the state probabilities of all grids evolving into six states: resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industrial transfer, and using the six states and state probabilities as attribute information of the grid data;

[0018] Step S7, using GIS to determine whether each grid belongs to a development-prohibited area, and using the determination result as attribute information of the grid data;

[0019] Step S8, using GIS to calculate the Euclidean distance between each grid and the existing industrial grid based on the grid spatial information of the base year, and using the Euclidean distance as the attribute information of the grid data;

[0020] Step S9, taking the grid as a cell, taking resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industry as the six states of the cell, taking the industry type that each grid is suitable for undertaking, the six state probabilities, whether each grid is a prohibited development zone, and the Euclidean distance of each grid from the existing industry grid as the attributes of the cell, constructing a partitioned asynchronous cellular automaton model;

[0021] Step S10, based on the grid data of the base year and the verification year, calculate the asynchronous evolution rate of the sub-region from the base year to the verification year; substitute the grid data of the base year, and iterate the cellular automaton model multiple times; the cellular automaton model will traverse all cells during iteration, and the cell will be accessed only when the number of iterations is an integer multiple of the evolution rate and the cell state is that there is no existing industry transfer; when the total number of cells belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states is greater than or equal to the ratio threshold of the total number of grids belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states in the verification year, the cellular automaton model suspends iteration;

[0022] Step S11, setting an accuracy threshold, and verifying the accuracy of the iterative result of the cellular automaton model; if the accuracy reaches the accuracy threshold, the process proceeds to step S12, and if the accuracy does not reach the accuracy threshold, the process returns to step S10;

[0023] Step S12, divide the future industrial transfer scenarios into five different simulation scenarios; then use the Markov chain method to predict the demand quantity of five types of industries, namely resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive, under each future scenario, and use the demand quantity as the termination condition of the model to simulate the spatiotemporal process of future industrial transfer under five different scenarios; wherein the five different simulation scenarios include a baseline scenario, a major infrastructure construction scenario, an ecological protection priority scenario, an economic growth priority scenario, and a coordinated development scenario of ecological protection and economic growth.

[0024] As a preferred embodiment of the present invention, the carrying capacity evaluation index system structure constructed in step S2 includes a primary index, a secondary index, a tertiary index and the source and direction attributes of the tertiary index; the direction attribute includes a positive index and a negative index; wherein a positive index refers to an index with a larger attribute value, the better, and a negative index refers to an index with a smaller attribute value, the better;

[0025] When calculating the industrial transfer acceptance scores of all grids, the global principal component analysis (GPCA) method is used for calculation based on the three-level indicators and their directional attributes.

[0026] As a preferred embodiment of the present invention, in step S5, the social economic effect score and the ecological environmental effect score are calculated based on the secondary indicators and their directional attributes, and the calculation process specifically includes the following steps:

[0027] Step S51, respectively performing dimensionless normalization processing on the positive index and the negative index in the secondary index using different range normalization formulas to obtain a standardized secondary index;

[0028] Step S52, respectively determine the preliminary weights of the secondary indicators by principal component analysis GPCA method, entropy weight EWM method and hierarchical analysis AHP method, and integrate the three preliminary weights by approximating the ideal solution ranking TOPSIS method and weighted average method to obtain two integrated weights; then calculate the average value of the two integrated weights as the final comprehensive weight of each secondary indicator;

[0029] Step S53, according to the normalized secondary indicators and the comprehensive weights of the secondary indicators, the socio-economic effect score and the ecological environmental effect score of each grid are calculated respectively, and the socio-economic effect and the ecological environmental effect of each grid are quantified by the scores.

[0030] As a preferred embodiment of the present invention, step S52 further includes:

[0031] Step S521, calculating the first preliminary weight of the secondary index by GPCA method;

[0032] Step S522, using the EWM method to calculate the second preliminary weight of the secondary index;

[0033] Step S523, using the AHP method to calculate the third preliminary weight of the secondary index;

[0034] Step S524, using the TOPSIS method, integrating the first preliminary weight, the second preliminary weight and the third preliminary weight to obtain a first integrated weight;

[0035] Step S525, using a weighted average weight method, integrating the first preliminary weight, the second preliminary weight and the third preliminary weight to obtain a second integrated weight;

[0036] Step S526, combining the first integrated weight and the second integrated weight to obtain the integrated weight of each secondary indicator; the first integrated weight represents the weight obtained by the objective weighting method, and the second integrated weight represents the weight obtained by the subjective weighting method.

[0037] As a preferred embodiment of the present invention, the prohibited development area in step S7 includes three situations: the water area in the grid is greater than 50% of the total area of ​​the grid; the grid is located in a protected area, and any form of development is prohibited in these places according to relevant policies; the average slope of the grid is greater than 15°.

[0038] As a preferred embodiment of the present invention, in step S9, the partitioned asynchronous cellular automaton model is composed of cells, cell spaces, neighborhoods and conversion rules, wherein the conversion rules adopt a roulette wheel selection rule;

[0039] The partitioned asynchronous cellular automaton model introduces socio-economic effects, ecological environmental effects and Euclidean distance as external factor constraints, and uses grid data based on a GIS data platform to enhance the ability to express complex geographical phenomena.

[0040] As a preferred embodiment of the present invention, the specific steps of the roulette selection rule are as follows:

[0041] Step S91, calculating the sum of fitness: firstly calculating the sum of fitness of all individuals in the population;

[0042] Step S92, calculating the selection probability: the selection probability of each individual is equal to its fitness divided by the total fitness;

[0043] Step S93, constructing a roulette wheel: mapping the selection probability of each individual onto a roulette wheel to form a cumulative probability distribution;

[0044] Step S94, select an individual: by generating a random number between 0 and 1, determine the position on the roulette wheel and select the corresponding individual.

[0045] As a preferred embodiment of the present invention, in step S10, when a cell is visited, the probability of the cell evolving into six states of resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industry transfer is substituted into the roulette selection model to randomly select an industry type; then it is determined whether the cell is a prohibited development zone, and whether the Euclidean distance of the cell from the existing industry transfer grid is less than the average Euclidean distance between the existing industry grids of the administrative district where the cell is located; after these two conditions are met, if the industry type randomly selected by the roulette selection model is compatible with the industry type that the cell is suitable for undertaking, the state of the cell is transformed into the industry type randomly selected by the roulette selection rule, otherwise the state of the cell remains no existing industry transfer; for each iteration, the model calculates the total number of cells belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states.

[0046] As a preferred embodiment of the present invention, when running the five different simulated scenarios in step S11, the simulation processes are as follows:

[0047] Run the baseline scenario to simulate the future based on the changing patterns of industrial transfer in the base year and the verification year, that is, do not make any changes to the cellular automaton model, and continue to run the suspended cellular automaton in S10 until the quantity requirements are met;

[0048] Run the major transportation infrastructure construction scenario. Based on the baseline scenario, this scenario takes into account the transportation infrastructure that may be newly built in the industrial transfer receiving area in the future. The area within the predetermined range of the newly added transportation infrastructure will also be set as a transferable area. That is, a new rule is added based on the model in S9, and then the suspended cellular automaton in S10 is continued to run until the quantity requirement is met;

[0049] Run the ecological protection priority scenario. This scenario classifies the ecological and environmental effects of industrial transfer of cells according to the standard deviation classification method. Areas with higher ecological and environmental effects are regarded as areas where industrial transfer will occur in the future. That is, based on the model described in S9, "whether the Euclidean distance of the cell from the existing industrial transfer grid is less than or equal to the average Euclidean distance between the existing industrial grids in the administrative area where the cell is located in S8" is changed to "the ecological and environmental effect value of the cell is greater than 1.5 standard deviations of all ecological and environmental effect values", and then continue to run the suspended cellular automaton in S10 until the quantity requirements are met;

[0050] Run the economic growth priority scenario. This scenario classifies the socioeconomic effects of industrial transfer of cells according to the standard deviation classification method. Regions with higher socioeconomic effects are regarded as regions where industrial transfer will occur in the future. That is, based on the model described in S9, the "whether the Euclidean distance of the cell from the existing industrial transfer grid is less than or equal to the average Euclidean distance between the existing industrial grids in the administrative district where the cell is located in S8" is changed to "the socioeconomic effect value of the cell is greater than 1.5 standard deviations of all socioeconomic effect values", and then continue to run the suspended cellular automaton in S10 until the quantity requirement is met;

[0051] Running the scenario of coordinated development of ecological protection and economic growth, areas with higher ecological and environmental effects and socio-economic effects are regarded as areas where industrial transfer will occur in the future, which are equivalent to the overlapping parts of the areas described in the ecological protection priority scenario and the economic growth priority scenario. On the basis of the model described in S9, "whether the Euclidean distance of the cell from the existing industrial transfer grid is less than or equal to the average Euclidean distance between the existing industrial grids in the administrative region where the cell is located in S8" is changed to "the ecological and environmental effect value of the cell is greater than 1.5 standard deviations of all ecological and environmental effect values, and the socio-economic effect value of the cell is greater than 1.5 standard deviations of all socio-economic effect values", and then continue to run the suspended cellular automaton in S10 until the quantity requirements are met.

[0052] In the second aspect, the embodiment of the present invention further provides an industrial transfer spatial decision-making system based on the ANN-GIS-CA architecture, the system comprising: a grid division module, a carrying capacity evaluation index construction module, a carrying capacity calculation module, a carrying effect evaluation index construction module, a carrying effect evaluation module, a state probability calculation module, a forbidden area determination module, a Euclidean distance calculation module, a CA model construction module, a CA model iteration module, an accuracy verification module and a scenario simulation module; wherein,

[0053] The grid division module is used to divide the industrial transfer receiving area into several grids based on the geographic information system GIS to form cells, and use manual interpretation to mark the existing industrial transfer situation in the grid, and generate grid data of the basic year and the verification year based on GIS; the grid data includes spatial information and attribute information; it is also used to determine the sub-area to which the grid belongs according to preset rules, and use the attribution result as the attribute information of the grid data;

[0054] The module for constructing the evaluation index of industrial transfer capacity is used to obtain the original data of industrial transfer capacity of all grids based on the geographic information system GIS, and to construct an evaluation index system of industrial transfer capacity;

[0055] The undertaking capacity calculation module is used to calculate the industrial transfer undertaking capacity scores of all grids based on the constructed industrial transfer undertaking capacity evaluation index system, and use the Gaussian mixture model to identify the industry type that each grid is suitable for undertaking, and use the scores and the industry type that is suitable for undertaking as the attribute information of the grid data;

[0056] The construction module of the evaluation index of the undertaking effect is used to obtain the original data of the social and economic effects and the ecological and environmental effects of all grids undertaking industrial transfer based on GIS, and to construct the evaluation index system of the social and economic effects and the ecological and environmental effects of undertaking industrial transfer; the constructed evaluation index system structure of the undertaking effect includes the primary index, the secondary index, and the source and direction attributes of the secondary index; the direction attributes include positive index and negative index; wherein the positive index refers to the index whose attribute value is larger, the better, and the negative index refers to the index whose attribute value is smaller, the better;

[0057] The undertaking effect evaluation module is used to calculate the social and economic effect scores and the ecological and environmental effect scores of all grids according to the secondary indicators of the social and economic effect and the ecological and environmental effect in the undertaking effect and the directional attributes of the secondary indicators, and use the scores as the attribute information of the grid data;

[0058] The state probability calculation module is used to select grids with existing industrial transfers in all base years, and randomly select a predetermined number of grids without existing industrial transfers in the base years, extract the social and economic effect scores and ecological and environmental effect scores of these grids as samples, construct an artificial neural network ANN model, and respectively calculate the state probabilities of all grids evolving into six states: resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industrial transfer, and use the six states and state probabilities as attribute information of the grid data;

[0059] The prohibited development area determination module is used to use GIS to determine whether each grid belongs to a prohibited development area, and use the determination result as the attribute information of the grid data;

[0060] The Euclidean distance calculation module is used to calculate the Euclidean distance of each grid from the existing industrial grid using GIS based on the grid spatial information of the basic year, and use the Euclidean distance as the attribute information of the grid data;

[0061] The CA model construction module is used to construct a partitioned asynchronous cellular automation model with grids as cells, resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industries as six states of cells, and the types of industries that each grid is suitable for undertaking, the probabilities of the six states, whether each grid is a prohibited development zone, and the Euclidean distance of each grid from the existing industrial grid as attributes of the cells;

[0062] The CA model iteration module is used to calculate the asynchronous evolution rate of the sub-region from the basic year to the verification year based on the grid data of the basic year and the verification year; substitute the grid data of the basic year and iterate the cellular automaton model multiple times; the cellular automaton model will traverse all cells during iteration, and the cells will be accessed only when the number of iterations is an integer multiple of the evolution rate and the cell state is that there is no existing industry transfer; when the total number of cells belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states is greater than or equal to the ratio threshold of the total number of grids belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states in the verification year, the cellular automaton model suspends iteration;

[0063] The accuracy verification module is used to set an accuracy threshold and perform accuracy verification on the iteration result of the cellular automaton model; if the accuracy reaches the accuracy threshold, the scenario simulation module is started, and if the accuracy does not reach the accuracy threshold, the CA model iteration module is started;

[0064] The scenario simulation module is used to divide the future industrial transfer scenarios into five different simulation scenarios; then the Markov chain method is used to predict the demand quantity of five types of industries, namely resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive, under each future scenario, and the demand quantity is used as the termination condition of the model to simulate the spatiotemporal process of future industrial transfer under five different scenarios; among them, the five different simulation scenarios include a baseline scenario, a major infrastructure construction scenario, an ecological protection priority scenario, an economic growth priority scenario, and a scenario of coordinated development of ecological protection and economic growth.

[0065] The technical solution provided by the embodiment of the present invention has the following beneficial effects:

[0066] The industrial transfer spatial decision-making method and system based on the ANN-GIS-CA architecture provided in the embodiment of the present invention integrates ANN, GIS and CA to construct an industrial spatial layout optimization model during industrial transfer, and predicts the spatiotemporal patterns of future industrial transfer under different scenarios; uses the CA model to simulate industrial transfer; and more accurately simulates the spatiotemporal optimization process of industrial transfer by combining qualitative, quantitative and positioning, and combining subjective and objective methods, predicts the development trend of industrial transfer under different development models, improves the optimization effect of industrial spatial layout during industrial transfer, reasonably arranges industrial transfer space, and realizes regional industrial sustainable development.

[0067] Of course, it is not necessary to achieve all of the advantages described above at the same time to implement any product or method of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0069] Figure 1 It is a flow chart of the spatial decision-making method for industrial transfer based on the ANN-GIS-CA architecture provided by an embodiment of the present invention;

[0070] Figure 2 is a schematic diagram of the structure of a cellular automaton model used in an embodiment of the present invention;

[0071] Figure 3 It is a schematic diagram of the structure of the geographic cellular automaton model constructed in the embodiment of the present invention. DETAILED DESCRIPTION

[0072] After discovering the above problems, the inventors of this application conducted a detailed study on the optimization of industrial spatial layout caused by the existing industrial transfer. The study found that when coupling CA's powerful spatial modeling and computing capabilities with the GIS database platform to improve the optimization of industrial spatial layout, it is necessary to consider and integrate more external factors as model constraints in the CA model to improve the accuracy and reality of the simulation.

[0073] The traditional CA model has the same state transition rules for all cells and only considers the neighborhood effect, which cannot depict the spatial differentiation of geographical objects and the influence of other factors. Especially in the context of industrial transfer, the application of CA can significantly depict the impact of industrial migration on land use patterns. In each iteration, the transformation of land use is determined by the joint action of all transformation functions. By integrating planning objectives (including the trend and objectives of industrial transfer) into the transformation function, changes in land use can be effectively guided and controlled.

[0074] In addition, in the past few decades, satellite remote sensing technology has become an important data source for many geographical studies, providing a large amount of valuable information about surface features. Satellite remote sensing images provide key land use information for CA models, which is essential for model initialization. Since remote sensing data is essentially a raster structure, it can be seamlessly integrated as input data for CA models, greatly improving the accuracy and practicality of CA models in simulating complex geographical phenomena.

[0075] However, industrial transfer in different regions needs to consider different constraints due to the specific land resource utilization, geography, economy, human environment and other factors at that time. For example, when the region faces a series of severe environmental problems such as water pollution, land degradation, biodiversity loss and frequent natural disasters, these problems become rigid conditions that restrict economic development. As an important aspect of economic development, industrial transfer is also constrained by resources and the environment. Therefore, how to spatially arrange industrial transfer is an urgent problem to achieve sustainable development of regional industries. How to establish a multi-scenario spatial optimization model of industrial transfer based on the constrained CA model under the influence of resources and environment is a key problem that needs to be solved in spatial optimization decision support.

[0076] It should be noted that the defects existing in the solutions in the above-mentioned prior art are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above-mentioned problems and the solutions proposed in the embodiments of the present invention for the above-mentioned problems below should all be the contributions made by the inventor to the present invention in the process of the present invention.

[0077] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations. It should be noted that the embodiments of the present invention and the features in the embodiments can also be combined with each other without conflict.

[0078] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In the description of the present invention, the terms "first", "second", "third", "fourth", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0079] After the above in-depth analysis, the embodiment of the present invention provides a spatial decision-making method and system for industrial transfer based on the ANN-GIS-CA architecture, the method includes: first, on the basis of the industrial transfer acceptance capacity and the socio-economic and ecological environmental effects of industrial transfer, the spatio-temporal impact process of industrial acceptance capacity on industrial transfer and the spatio-temporal process of industrial transfer on resource and environmental effects need to be mapped to the geographic information environment, and a spatio-temporal information mechanism for analyzing industrial transfer is constructed. Secondly, starting from the spatial differences of industrial transfer, the constraint CA and partition asynchronous CA methods are integrated, and the constraints of industrial acceptance capacity, socio-economic and ecological environmental effects of industrial transfer, political policies, etc. on industrial transfer are comprehensively considered to construct an industrial transfer spatial optimization decision support model. Finally, on the basis of the decision support model, different industrial transfer scenarios are set, and the advantages and problems of different industrial transfers under different scenarios are analyzed. On this basis, the model parameters are continuously adjusted to realize the spatial optimization configuration analysis of industrial transfer.

[0080] Specifically, Figure 1 As shown, the industrial transfer spatial decision-making method based on the ANN-GIS-CA architecture includes the following steps:

[0081] Step S1, based on the geographic information system GIS, the industrial transfer receiving area is divided into several grids to form cells, and the existing industrial transfer situation is marked in the grid by manual interpretation, generating grid data of the basic year and the verification year based on GIS; the grid data includes spatial information and attribute information; at the same time, the sub-area to which the grid belongs is determined according to preset rules, and the attribution result is used as the attribute information of the grid data.

[0082] In this step, the preset rules include division by administrative attributes, division by geographic coordinates, etc. Finally, the receiving area will be divided into several square grids, and the location information and transaction information of the enterprise will be found using the enterprise list. The grid where the enterprise is located will be determined based on the location information, and the type of enterprise will be determined based on the transaction information. Then all grids will be marked as resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, or no existing industry transfer. The data of the basic year and the verification year are marked separately.

[0083] The GIS is used to collect, store, manage, calculate, analyze, display and describe the geographical distribution data in the space of the earth's surface (including the atmosphere) with the support of computer hardware and software systems. Many geographical phenomena belong to typical dynamic complex systems, with dissipative structural characteristics such as openness, dynamism, self-organization and non-equilibrium.

[0084] The grid data is a type of vector data based on GIS, including spatial information and attribute information, wherein the spatial information includes coordinates, etc., and the attribute information includes sub-area affiliation, etc. At the same time, the attribute information will increase attribute items and corresponding values ​​as the grid information is calculated, such as the corresponding carrying capacity score, six states, etc. Normally, the calculated values ​​of the grid will be included as attribute items in the attribute information of the grid data to ensure the integrity and applicability of the grid data.

[0085] Step S2, based on the geographic information system GIS, obtain the original data of the industrial transfer acceptance capacity of all grids, and construct an evaluation index system for the industrial transfer acceptance capacity; the constructed acceptance capacity evaluation index system structure includes primary indicators, secondary indicators, tertiary indicators and the source and direction attributes of the tertiary indicators; the direction attributes include positive indicators and negative indicators; among them, a positive indicator refers to an indicator whose attribute value is larger, the better, while a negative indicator refers to an indicator whose attribute value is smaller, the better.

[0086] In this step, the space to be optimized is the destination of industrial transfer, that is, the receiving area. Transferring industries to receiving areas involves changes and optimizations in the spatial layout of receiving areas. The first thing to do is to evaluate the ability of the area to receive the transferred industries, that is, the receiving capacity, which is related to the natural, historical and technical organizational conditions of the area. For example, for the manufacturing industry as the primary industry, the ability to receive the transfer of manufacturing means that the receiving area must have a certain attraction to the relevant manufacturing industry and be able to "pull it in". At the same time, there must be certain basic conditions that enable the enterprise to survive and have the potential for sustainable development.

[0087] Based on this, the industrial transfer acceptance capacity proposed in this embodiment includes three aspects: attraction, support and development. Among them, the political factors, natural resource endowment, economic development level, market attractiveness, labor quality and cost input of the receiving area determine the attractiveness of the transferred manufacturing industry and can bring in related industries; support is the ability of the receiving industry to survive. To introduce the transferred industry into the region, integrate it into the regional economy and survive, it is necessary to fully consider the urbanization level, transportation infrastructure, communication infrastructure and environmental acceptance of the receiving area; development is the ability of the transferred industry to integrate with the original industry in the region to promote the optimization and upgrading of the industrial structure, and to continuously lengthen the regional characteristic industrial chain, thereby forming a competitive advantage. It is generally determined by the receiving area's ability to attract foreign investment, economic growth rate and information development potential.

[0088] In a specific embodiment, the raw data of the receiving capacity at least includes the following data of the receiving area: greenhouse gas, land use, population density, night lights, traffic network, PM2.5, communication base stations, mineral resources, panel data, etc. As shown in Table 1, each type of data is further divided into specific detailed types and their related sources. For example, the panel data further includes: total water resources, number of labor, total number of middle school enrollment, employee remuneration, cost of business start-up process, natural resource rent, GDP growth rate, per capita GNI, net inflow of foreign direct investment, electricity access rate, number of secure Internet servers, and urbanization rate, etc.

[0089] Table 1 Original data types and sources of the carrying capacity of the receiving area

[0090]

[0091] Based on the acquired raw data of carrying capacity, the constructed carrying capacity evaluation index system structure includes primary indicators, secondary indicators, tertiary indicators and indicator directions. The primary indicators include several secondary indicators, the secondary indicators include several tertiary indicators, and the indicator direction is set for each type of tertiary indicator, that is, the consistency between the indicator value change and the direction of target achievement. The directional attributes of the indicators include positive indicators or negative indicators; among them, positive indicators refer to the higher the value, the better, while negative indicators refer to the lower the value, the better.

[0092] In a specific embodiment, as shown in Table 2, the primary indicators include: attraction, support and development. Attraction refers to the degree of attraction of a region or country to external enterprises and industries during the industrial transfer process, which is one of the key factors affecting the industrial transfer acceptance capacity; support refers to the various support conditions and guarantee capabilities that a region has when receiving and stably developing newly transferred industries; development is the ability of a region to effectively promote the sustainable development of new industries and continuously improve its competitiveness after receiving and integrating foreign industrial transfers.

[0093] The attractiveness index includes at least the following secondary indicators: political factors, natural resource endowment, economic development level, market attractiveness, labor quality and cost input; the supporting capacity index includes at least the following secondary indicators: urbanization level, transportation infrastructure, communication infrastructure and environmental carrying capacity; the development capacity index includes at least the following secondary indicators: ability to attract foreign investment, economic growth rate and information development potential.

[0094] Table 2 Industrial transfer acceptance index system

[0095]

[0096]

[0097] Step S3, based on the constructed industrial transfer acceptance capacity evaluation index system, calculate the industrial transfer acceptance capacity scores of all grids, and use the Gaussian mixture model to identify the industry type that each grid is suitable for accepting, and use the scores and the industry types suitable for accepting as attribute information of the grid data.

[0098] In this step, the calculation of the industrial transfer acceptance capacity of the receiving area adopts the time series global principal component analysis (GPCA) method. The specific calculation process is as follows:

[0099] Step S31, respectively, dimensionlessly normalize the positive indicators and negative indicators in the three-level indicators using different range standardization formulas to obtain standard three-level indicators.

[0100] Step S32, based on the standard three-level indicators and according to the time series global principal component analysis method, construct a time series space subject carrying capacity data table; and solve the matrix of the data table.

[0101] Step S33, converting the data table into a matrix form, and calculating the covariance matrix V of the matrix, then solving the eigenvalues ​​and corresponding eigenvectors of the data table matrix through the covariance matrix, and calculating the contribution rate and cumulative contribution rate of the eigenvector.

[0102] When calculating the cumulative contribution rate, the contribution rates of all eigenvectors are first sorted from large to small; based on the contribution rates of the sorted eigenvectors, the cumulative contribution rate is calculated.

[0103] Step S34, taking the standard three-level indicators whose cumulative contribution rate is greater than a preset threshold (for example, 80%) as evaluation indicators; and calculating the weights of all the standard three-level indicators used as evaluation indicators according to the contribution rate of the feature vector.

[0104] Step S35, constructing a scoring calculation function for the current city's industrial transfer acceptance capacity, substituting all the standard third-level indicators and corresponding weights used as evaluation indicators into the scoring calculation function, and calculating the acceptance capacity scores of all grids.

[0105] Step S35, based on the acceptance score of each grid, a Gaussian mixture model (GMM) is used to calculate the industry type that each grid is suitable for accepting.

[0106] In this step, the GMM is a multivariate Gaussian distribution function composed of a linear combination of multiple independent Gaussian distribution functions, which can fit any type of distribution. It is usually used to solve the situation where the data in the same set contains multiple different distributions, and uses the maximum expected algorithm to estimate the parameters of the model, which can provide strong descriptive ability and can accurately quantify the research object. Through the industrial layout optimization model constructed based on the Gaussian mixture model, the receiving area is clustered according to the first indicator type (attractiveness, support and development power) of the administrative region, so that the clustering results are more in line with objective reality, helping all walks of life to carry out industrial transfer scientifically and rationally. At the same time, because it uses a combination of multiple Gaussian distributions to fit the data, it can adapt to data distributions of various shapes and sizes.

[0107] The specific operations for constructing a Gaussian mixture model are as follows:

[0108] Suppose there are n observation data x1, x2, …, x n , divided into k categories, the Gaussian mixture model can be expressed as:

[0109]

[0110] In formula (1), φ(x|θ k ) is the probability density of mixed Gaussian distribution, θ k =(μ k ,σ k 2 ), μ k and σ k 2 are the mean and variance of the kth Gaussian distribution, α j is the mixing coefficient of Gaussian distribution, that is, the weight of single Gaussian distribution, 0<α j <1,∑α j =1; p(x|θ) is the probability that x is classified into the kth class, and θ is the model parameter to be estimated, which is μ here. k , σ k 2 , α j 3 in total.

[0111] In this step, θ is estimated using the maximum likelihood method (Expectation Maximization, EM), that is, the parameter θ obtained should maximize the value of p(x|θ). The optimization objective function is set as:

[0112]

[0113] In formula (2), N is the total number of samples, N k is the number of samples in the kth category.

[0114] The specific calculation steps of the EM algorithm are as follows: Step S351, determine the number of categories: one category corresponds to one Gaussian distribution, which is combined into a multivariate mixed Gaussian distribution function; Step S352, determine the initial value of the function parameter: randomly assign the mean and variance of each Gaussian distribution function, and combine them into a mixing coefficient of the mixed Gaussian distribution function; Step S353, calculate the probability of each sample under each Gaussian distribution; Step S354, find the maximum likelihood function of the mean value of the kth Gaussian distribution; Step S355, find the maximum likelihood function of the variance of the kth Gaussian distribution; Step S356, find the maximum likelihood function of the mixing coefficient; Step S357, repeat steps S352 to S355 until the model converges. The above EM algorithm is a prior art, and the specific parameters are set according to the needs of this embodiment, which will not be repeated here.

[0115] From the steps of the EM algorithm, we can know that its core is to update the parameters according to the likelihood value after the initial value of the given variable, and iterate and optimize step by step to gradually increase the likelihood function value, and finally find the optimal parameters, so that the model likelihood function value is the largest. By using the EM algorithm to fit the data, the posterior probability of each component density is calculated based on each observation.

[0116] Step S4, based on GIS, obtain the original data of the socio-economic effects and eco-environmental effects of all grids undertaking industrial transfer, and construct an evaluation index system for the socio-economic effects and eco-environmental effects of undertaking industrial transfer; the constructed undertaking effect evaluation index system structure includes primary indicators, secondary indicators, and the source and directional attributes of the secondary indicators; the directional attributes include positive indicators and negative indicators; wherein, a positive indicator refers to an indicator whose attribute value is as large as possible, while a negative indicator refers to an indicator whose attribute value is as small as possible,

[0117] In this step, when evaluating the socio-economic effects and eco-environmental effects of all grids, the socio-economic effects and eco-environmental effects are used as two primary indicators to construct an evaluation index system for the receiving effect of industrial transfer in the receiving area.

[0118] As shown in Table 3, the socio-economic effect evaluation includes several secondary evaluation indicators such as changes in regional employed population, population density, industrial employed population density, per capita GDP, industrial employment growth, industrial added value, labor productivity and education expenditure.

[0119] The production factor costs, market potential and social development brought about by industrial transfer will change the regional employment situation. Industrial transfer can promote the leap of regional industrial structure, shorten the time of industrial upgrading, accelerate the process of industrialization, create new employment opportunities for the receiving areas, promote the transfer of surplus rural labor to towns, and the transfer of primary industry employees to the secondary and tertiary industries, which will drive the growth of employment rate. Change the spatial distribution of population density and employment rate. GDP represents the total market value of all final goods and services produced by a country in a certain period of time. Per capita GDP is usually used as an indicator to measure the average living standard of residents. Its changes can reflect the adjustment of economic structure and the economic benefits brought about by industrial upgrading. Input-production efficiency refers to the ratio of output (usually products or services) generated by each unit of input (such as labor, capital, raw materials, etc.) in the production process. It is an important indicator for evaluating the economic benefits of industrial transfer. High input-production efficiency means that with a certain amount of resources invested, higher output can be obtained, thereby improving the overall economic benefits. The fixed asset investment and enterprise mergers brought about by industrial transfer increase the capital stock of the receiving area and improve the quality of capital; in addition, industrial transfer also brings technical support and talent transfer to the receiving area, and optimizes and upgrades the industrial structure by improving economies of scale and resource utilization efficiency. Industrial added value refers to the total value added in the production process of various industrial sectors in a country or region within a certain period of time, which is equal to the total output of the industry minus the intermediate consumption (i.e. the cost of raw materials and services used in the production process). According to different industrial sectors, industrial added value can be divided into: primary industry added value, secondary industry added value, and tertiary industry added value. By analyzing the added value of different industries, we can understand the economic structure of a country or region. Industries with high added value usually have higher added value, reflecting the quality and sustainability of economic growth. Industrial transfer can drive the flow of technology from developed regions to underdeveloped regions and promote the improvement of local technological levels. By promoting technological spillovers, the innovation ability of enterprises is enhanced. Labor productivity can be expressed by the number of products produced by the same labor in a unit of time. The more products produced in a unit of time, the higher the labor productivity. It can also be expressed by the labor time spent on producing a unit of product. The less labor time required to produce a unit of product, the higher the labor productivity. Total labor productivity can reflect the production efficiency of an enterprise and the economic benefits of labor input. The total amount of public expenditure on education generally refers to the total expenditure of the government on education in a specific period (usually a fiscal year). These expenditures mainly include: basic education expenditures, higher education expenditures, teachers' wages and benefits, education expenditure facilities, education and scientific research, etc.Education expenditure is constrained by a country's economic development. The following four indicators are usually used internationally to measure a country's education expenditure: the proportion of education expenditure in national income, the proportion of education expenditure in gross domestic product, the proportion of education expenditure in total national fiscal expenditure, and the proportion of education infrastructure expenditure in total infrastructure expenditure. Education expenditure can reflect the degree of importance a country or region places on education. A high proportion of education expenditure usually indicates that the region regards education as a priority area for development.

[0120] As shown in Table 3, the ecological and environmental effect evaluation includes several secondary evaluation indicators such as water quality, air pollutants, carbon storage changes, changes in land use type area, and changes in terrestrial ecosystem productivity.

[0121] During the industrial transfer process, the transfer of different types of industries such as mining, heavy industry, and agricultural development may lead to deforestation and land degradation, directly affecting the ecosystem in the basin and threatening biodiversity. And with the rapid development of industry, the number of enterprises discharging sewage and waste has increased, and the water quality of rivers may be seriously polluted, affecting the drinking water safety and agricultural water use of local residents. The transfer of some polluting industries may lead to increased local air pollution, especially industries that rely on fossil fuels, which may release a large amount of PM2.5, SO2 and other atmospheric pollutants, affecting air quality and public health. The population mobility caused by rapid industrial transfer may lead to a decline in the environmental carrying capacity of local communities, resulting in over-exploitation of resources and conflicts between local communities and enterprises.

[0122] Table 3 Indicator system and data sources for socio-economic and ecological environmental effects

[0123]

[0124]

[0125] Step S5, respectively calculating the socio-economic effect scores and eco-environmental effect scores of all grids according to the secondary indicators of socio-economic effect and eco-environmental effect in the undertaking effect and the directional attributes of the secondary indicators, and using the scores as the attribute information of the grid data.

[0126] In this step, the socio-economic effect score and the ecological environmental effect score are calculated based on the secondary indicators and their directional attributes. The calculation process specifically includes the following steps:

[0127] Step S51, respectively, dimensionlessly normalize the positive index and the negative index in the secondary index using different range normalization formulas to obtain a standardized secondary index.

[0128] In this step, the dimensionless normalization process is the same as the existing data normalization process.

[0129] Step S52, respectively determine the preliminary weights of the secondary indicators by GPCA method, Entropy Weight Method (EWM) method and Analytic Hierarchy Process (AHP) method, and integrate the three preliminary weights by using the Tchnique for Order Preference by Similarity to Ideal Solution (TOPSIS) and weighted average method to obtain two integrated weights; then calculate the average value of the two integrated weights as the final comprehensive weight of each secondary indicator.

[0130] Specifically include:

[0131] Step S521, calculating the first preliminary weight of the secondary index by GPCA method.

[0132] In this step, the GPCA method is an existing method and will not be described in detail here.

[0133] Step S522, using the EWM method to calculate the second preliminary weight of the secondary indicator.

[0134] In this step, EWM is a kind of objective weighting method. The smaller the entropy value of the indicator, the greater the variation of the indicator value, the greater the amount of information provided, and the greater the weight of the indicator.

[0135] Step S523, using the AHP method to calculate the third preliminary weight of the secondary index.

[0136] In this step, the AHP method does not require a priori probability estimation and is a system analysis method that combines qualitative and quantitative methods. The steps of AHP to determine the weight of evaluation indicators are as follows: first, establish a hierarchical structure; second, construct a pairwise judgment matrix, and use a scaling method of 1 to 7 and its reciprocal to quantify the relative importance of pairwise elements; third, calculate the subjective weight of the indicator w' i ; Fourth, the consistency is tested. When the consistency ratio (CR) is <0.1, it indicates that the consistency of the judgment matrix is ​​reasonable; when CR ≥ 0.1, it indicates that the judgment matrix is ​​unreasonable and needs to be re-tested for consistency.

[0137] Step S524: using the TOPSIS method, the first preliminary weight, the second preliminary weight and the third preliminary weight are integrated to obtain a first integrated weight.

[0138] In this step, the TOPSIS method is a multi-objective decision analysis method that selects a certain number of evaluation indicators according to different evaluation objects. The ideal value of each indicator is selected, and the distance between each solution and the ideal value (i.e., the proximity) is calculated. Therefore, the TOPSIS method can be used to calculate the comprehensive weight of the subjective and objective weighting methods. The specific steps include:

[0139] Normalize the weight matrix;

[0140] Determine ideal solutions and negative ideal solutions;

[0141] Calculate the distance between each index value and the positive and negative ideal solutions;

[0142] Calculate the relative proximity, based on the distances to the ideal solution and the negative ideal solution;

[0143] According to the relative proximity and the weight matrix, the first preliminary weight, the second preliminary weight and the third preliminary weight are integrated to obtain a first integrated weight.

[0144] Step S525 , using a weighted average weight method, the first preliminary weight, the second preliminary weight and the third preliminary weight are integrated to obtain a second integrated weight.

[0145] In this step, in addition to the TOPSIS model, in order to make the weight result closer to the actual situation, the preliminary weights obtained by the GPCA, EMW and AHP methods can be weighted and then the weighted average weight can be calculated. Preferably, GPCA is weighted 0.5, EMW is weighted 0.3, and AHP method is weighted 0.2, and the weighted average weight is calculated:

[0146] w i =0.5w GPCA +0.3w EMW +0.2w AHP (3)

[0147] In formula (3), w i is the weighted average weight of indicator i.

[0148] Step S526, the first integrated weight and the second integrated weight are combined to obtain the integrated weight of each secondary indicator. The first integrated weight represents the weight obtained by the objective weighting method, and the second integrated weight represents the weight obtained by the subjective weighting method. Therefore, the obtained integrated weight can comprehensively consider the importance of the indicator from both subjective and objective aspects.

[0149] In this step, the indicator weights calculated by TOPSIS are combined and the average of the two is taken as the final weight:

[0150]

[0151] In formula (4), W i is the comprehensive weight of indicator i; w 1 i is the weight of index i calculated by the TOPSIS model, w 2 i is the weighted average weight of indicator i.

[0152] Step S53, according to the normalized secondary indicators and the comprehensive weights of the secondary indicators, the socio-economic effect score and the ecological environmental effect score of each grid are calculated respectively, and the socio-economic effect and the ecological environmental effect of each grid are quantified by the scores.

[0153] In this step, after obtaining the comprehensive weight, the secondary indicators are weighted and summed to finally obtain the socio-economic effect and ecological environmental effect of each grid.

[0154] Step S6, select the grids with existing industrial transfer in all base years, and randomly select a predetermined number of grids without existing industrial transfer in the base year, extract the socio-economic effect scores and ecological environmental effect scores of these grids as samples, construct an artificial neural network ANN model, and calculate the state probabilities of all grids evolving into six states: resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industrial transfer.

[0155] In this step, the input values ​​of the ANN model are the socio-economic effect scores and the eco-environmental effect scores of the sample grids, and the output values ​​are the probability values ​​of all grids evolving into six states: resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industry transfer. In this step, the ANN performs calculations and simulations by imitating the functions of the human brain. Neural networks are organized by a series of neurons and have a powerful ability to perform complex parallel calculations. Neural network cellular automata are composed of simple networks. The model contains two relatively independent modules: model training and simulation. These two modules use the same neural network. In the model training module, the parameters of the model are automatically obtained using the training data; then the parameters are input into the simulation module for simulation calculations.

[0156] Step S7, using GIS to determine whether each grid belongs to a development-prohibited area, and using the determination result as attribute information of the grid data.

[0157] The prohibited development areas in this step include three situations: the water area in the grid is greater than 50% of the total area of ​​the grid, mainly considering the protection of water sources; the grid is located in a protected area, and any form of development is prohibited in these places according to relevant policies; the average slope of the grid is greater than 15°, and it is generally difficult to build large-scale infrastructure in these areas.

[0158] Step S8, based on the grid spatial information of the base year, use GIS to calculate the Euclidean distance of each grid from the existing industrial grid, and use the Euclidean distance as the attribute information of the grid data.

[0159] Step S9, with the grid as the cell, with resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industry as the six states of the cell, with the type of industry that each grid is suitable for undertaking, the probabilities of the six states, whether each grid is a prohibited development zone, and the Euclidean distance of each grid from the existing industry grid as the attributes of the cell, to construct a partitioned asynchronous cellular automaton model.

[0160] In this step, the cellular automaton is a grid dynamics model that divides the space into discrete cells, each of which is assigned a discrete state and evolves in a discrete time step. The cellular automaton model can better express the local temporal causality and spatial interaction. The cellular automaton model can well simulate the evolution of the self-organizing system, and it also has strong spatial computing capabilities. The CA model can simulate the spatiotemporal evolution of complex systems through local interactions and temporal causality.

[0161] like Figure 2 As shown in Figure 1, the cellular automaton model consists of four basic parts: cells, cellular space, neighborhood, and transition rules. The cellular automaton model consists of a discrete cellular space and cells with discrete states in this cellular space, neighborhoods, and transition rules.

[0162] The cellular automaton model can express the concepts of time and space at the same time, and express and simulate the spatiotemporal evolution of complex systems through discrete cellular space divisions, discrete state divisions, and discrete evolution steps. The geographical system is a complex giant system, and the spatiotemporal dynamic evolution of complex geographical phenomena can be better expressed and simulated using the cellular automaton model, which is the geographic cellular automaton model.

[0163] like Figure 3As shown in the figure, the geographic cellular automaton model is a cellular automaton model used to solve the dynamic simulation and expression of complex geographical phenomena. Therefore, like the general cellular automaton model, the geographic cellular automaton model is not a specific model, but only a model framework. Under the framework of the geographic cellular automaton model, the model can be concretized according to the specific geographical problems that need to be analyzed and simulated, so as to meet the needs of geographical analysis. In fact, the geographic cellular automaton model is an extension and concretization of the general cellular automaton model, and is an effective means to simulate and analyze the spatiotemporal evolution laws of complex geographical phenomena. By integrating the cellular automaton model with various theories and methods such as geographic information system technology, multi-agent model, data mining method, probabilistic reasoning method and fuzzy logic, the spatiotemporal dynamic process of complex geographical phenomena can be scientifically analyzed and objectively simulated. Therefore, the geographic cellular automaton model can explore the spatiotemporal evolution laws of complex geographical phenomena, predict the future development trend and pattern of geographical phenomena, and can also be included in the research framework of "Digital Earth". The partitioned asynchronous CA model of this embodiment introduces multiple external factors such as social and economic effects, ecological and environmental effects, and Euclidean distance as constraints, and combines it with the GIS data platform to enhance the expression ability of the partitioned asynchronous CA model for complex geographical phenomena.

[0164] In order to make the cellular automaton model be used to simulate complex geographical phenomena, it is necessary to expand and improve the cellular automaton model. At the same time, it is necessary to integrate the theory of geographical concepts into the cellular automaton model to make it meet the requirements of geographical simulation, so as to obtain geographical cells. Figure 3 As shown, the geographic cell partitions the cellular space composed of the geographic space through spatial data mining, and determines conversion rules for each partition, combining different evolution rates to simulate geographical phenomena.

[0165] The conversion rule adopts a roulette selection mechanism. The roulette selection is a selection mechanism commonly used in genetic algorithms, which simulates the roulette process and is used to select individuals from a population for reproduction. The probability of being selected is determined according to the fitness of the individual. The higher the fitness of the individual, the greater the probability of being selected. The specific steps of the roulette model selection are as follows:

[0166] Step S91, calculate the sum of fitness: first calculate the sum of fitness of all individuals in the population.

[0167] Step S92, calculate the selection probability: the selection probability of each individual is equal to its fitness divided by the total fitness.

[0168] Step S93, constructing a roulette wheel: mapping the selection probability of each individual onto a roulette wheel to form a cumulative probability distribution.

[0169] Step S94, select an individual: by generating a random number between 0 and 1, determine the position on the roulette wheel and select the corresponding individual.

[0170] Through roulette wheel selection, it is ensured that individuals with high fitness have a greater chance of being selected, but individuals with lower fitness are also given a certain chance, thus maintaining the diversity of the population.

[0171] Step S10, based on the grid data of the base year and the verification year, calculate the asynchronous evolution rate of the sub-region from the base year to the verification year; substitute the grid data of the base year, and iterate the cellular automaton model multiple times; the cellular automaton model will traverse all cells during iteration, and the cells will only be accessed when the number of iterations is an integer multiple of the evolution rate and the cell state is that there is no existing industrial transfer; when the total number of cells belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states is greater than or equal to the proportion threshold of the total number of grids belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states in the verification year, the cellular automaton model suspends iteration.

[0172] In this step, when a cell is visited, the probability of the cell evolving into the six states of resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industry transfer will be substituted into the roulette selection model to randomly select an industry type; next, it is determined whether the cell is a prohibited development zone, and whether the Euclidean distance of the cell from the existing industry transfer grid is less than the average Euclidean distance between the existing industry grids in the administrative area where the cell is located; after these two conditions are met, if the industry type randomly selected by the roulette selection rule is compatible with the industry type that the cell is suitable for, the state of the cell is transformed into the industry type randomly selected by the roulette selection model, otherwise the state of the cell remains no existing industry transfer. For each iteration, the model calculates the total number of cells in the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states.

[0173] In this step, the ratio threshold is selected according to actual conditions. Preferably, the ratio threshold in this step is 90%.

[0174] Step S11, setting an accuracy threshold, and verifying the accuracy of the iterative result of the cellular automaton model; if the accuracy reaches the accuracy threshold, proceed to step S12, and if the accuracy does not reach the accuracy threshold, return to S10.

[0175] In this step, the data of the base year is used to simulate the data of the verification year and verify the model accuracy. When the model accuracy meets the standard, the model can be used to simulate the future industrial transfer distribution. The accuracy threshold is set to determine whether the model accuracy meets the standard.

[0176] Step S12, divide the future industrial transfer scenarios into five different simulation scenarios; then use the Markov chain method to predict the demand quantity of five types of industries, namely resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive, under each future scenario, and use the demand quantity as the termination condition of the model to simulate the spatiotemporal process of future industrial transfer under five different scenarios; wherein the five different simulation scenarios include a baseline scenario, a major infrastructure construction scenario, an ecological protection priority scenario, an economic growth priority scenario, and a coordinated development scenario of ecological protection and economic growth.

[0177] In this step, the evolution rate refers to the ratio of the number of grids with no existing industry transfer converted into any type of industry in a certain administrative region, i.e., a sub-region, to the total number of grids in the administrative region from the base year to the verification year.

[0178] When running the five different scenarios simulated, the simulation process is as follows:

[0179] Run the baseline scenario to simulate the future based on the changing patterns of industrial transfer in the base year and the verification year, that is, do not make any changes to the cellular automaton model, and continue to run the suspended cellular automaton in S10 until the quantity requirements are met;

[0180] Running the major transportation infrastructure construction scenario. Based on the baseline scenario, this scenario takes into account the transportation infrastructure (railways, canals, etc.) that may be newly built in the future in the industrial transfer recipient area. The area within the predetermined distance from the newly added transportation infrastructure is also set as a transferable area. That is, a new rule is added based on the model in S9, and then the suspended cellular automaton in S10 is continued to run until the quantity requirement is met;

[0181] Run the ecological protection priority scenario. This scenario classifies the ecological and environmental effects of industrial transfer of cells according to the standard deviation classification method. Areas with higher ecological and environmental effects are regarded as areas where industrial transfer will occur in the future. That is, based on the model described in S9, "whether the Euclidean distance of the cell from the existing industrial transfer grid is less than or equal to the average Euclidean distance between the existing industrial grids in the administrative area where the cell is located in S8" is changed to "the ecological and environmental effect value of the cell is greater than 1.5 standard deviations of all ecological and environmental effect values", and then continue to run the suspended cellular automaton in S10 until the quantity requirements are met;

[0182] Run the economic growth priority scenario. This scenario classifies the socioeconomic effects of industrial transfer of cells according to the standard deviation classification method. Regions with higher socioeconomic effects are regarded as regions where industrial transfer will occur in the future. That is, based on the model described in S9, the "whether the Euclidean distance of the cell from the existing industrial transfer grid is less than or equal to the average Euclidean distance between the existing industrial grids in the administrative district where the cell is located in S8" is changed to "the socioeconomic effect value of the cell is greater than 1.5 standard deviations of all socioeconomic effect values", and then continue to run the suspended cellular automaton in S10 until the quantity requirement is met;

[0183] Run the scenario of coordinated development of ecological protection and economic growth. In this scenario, areas with higher ecological and environmental effects and socio-economic effects are regarded as areas where industrial transfer will occur in the future, that is, the overlapping parts of the areas described in the ecological protection priority scenario and the economic growth priority scenario. That is, on the basis of the CA model described in S9, "whether the Euclidean distance of the cell from the existing industrial transfer grid is less than or equal to the average Euclidean distance between the existing industrial grids in the administrative region where the cell is located in S8" is changed to "the ecological and environmental effect value of the cell is greater than 1.5 standard deviations of all ecological and environmental effect values, and the socio-economic effect value of the cell is greater than 1.5 standard deviations of all socio-economic effect values", and then continue to run the suspended cellular automaton in S10 until the quantity requirement is met.

[0184] The five scenarios proposed in this step (resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive) combine socio-economic and ecological environmental effects, and can fully reflect the diversity and complexity of industrial transfer. This scenario setting not only focuses on the spatial distribution of industrial types, but also considers the temporal and spatial evolution laws under different scenarios. In addition, the scenario setting process uses a combination of qualitative, quantitative, and positioning methods, as well as a combination of subjective and objective ideas, which can more accurately simulate and predict the temporal and spatial optimization process of industrial transfer.

[0185] Among the above five scenarios, one is run each time and the future prediction results under the current scenario are obtained; according to actual needs, one of the scenarios is selected as the decision, so as to obtain the industrial transfer space decision that meets the needs.

[0186] Based on the same idea, the embodiment of the present invention also provides an industrial transfer spatial decision-making system based on the ANN-GIS-CA architecture, the system comprising: a grid division module, a carrying capacity evaluation index construction module, a carrying capacity calculation module, a carrying effect evaluation index construction module, a carrying effect evaluation module, a state probability calculation module, a forbidden area determination module, a Euclidean distance calculation module, a CA model construction module, a CA model iteration module, an accuracy verification module and a scenario simulation module; wherein,

[0187] The grid division module is used to divide the industrial transfer receiving area into several grids based on the geographic information system GIS to form cells, and use manual interpretation to mark the existing industrial transfer situation in the grid, and generate grid data of the basic year and the verification year based on GIS; the grid data includes spatial information and attribute information; it is also used to determine the sub-area to which the grid belongs according to preset rules, and use the attribution result as the attribute information of the grid data;

[0188] The module for constructing the evaluation index of industrial transfer capacity is used to obtain the original data of industrial transfer capacity of all grids based on the geographic information system GIS, and to construct an evaluation index system of industrial transfer capacity;

[0189] The undertaking capacity calculation module is used to calculate the industrial transfer undertaking capacity scores of all grids based on the constructed industrial transfer undertaking capacity evaluation index system, and use the Gaussian mixture model to identify the industry type that each grid is suitable for undertaking, and use the scores and the industry type that is suitable for undertaking as the attribute information of the grid data;

[0190] The construction module of the evaluation index of the undertaking effect is used to obtain the original data of the social and economic effects and the ecological and environmental effects of all grids undertaking industrial transfer based on GIS, and to construct the evaluation index system of the social and economic effects and the ecological and environmental effects of undertaking industrial transfer; the constructed evaluation index system structure of the undertaking effect includes the primary index, the secondary index, and the source and direction attributes of the secondary index; the direction attributes include positive index and negative index; wherein the positive index refers to the index whose attribute value is larger, the better, and the negative index refers to the index whose attribute value is smaller, the better;

[0191] The undertaking effect evaluation module is used to calculate the social and economic effect scores and the ecological and environmental effect scores of all grids according to the secondary indicators of the social and economic effect and the ecological and environmental effect in the undertaking effect and the directional attributes of the secondary indicators, and use the scores as the attribute information of the grid data;

[0192] The state probability calculation module is used to select grids with existing industrial transfers in all base years, and randomly select a predetermined number of grids without existing industrial transfers in the base years, extract the social and economic effect scores and ecological and environmental effect scores of these grids as samples, construct an artificial neural network ANN model, and respectively calculate the state probabilities of all grids evolving into six states: resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industrial transfer, and use the six states and state probabilities as attribute information of the grid data;

[0193] The prohibited development area determination module is used to use GIS to determine whether each grid belongs to a prohibited development area, and use the determination result as the attribute information of the grid data;

[0194] The Euclidean distance calculation module is used to calculate the Euclidean distance of each grid from the existing industrial grid using GIS based on the grid spatial information of the basic year, and use the Euclidean distance as the attribute information of the grid data;

[0195] The CA model construction module is used to construct a partitioned asynchronous cellular automation model with grids as cells, resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industries as six states of cells, and the types of industries that each grid is suitable for undertaking, the probabilities of the six states, whether each grid is a prohibited development zone, and the Euclidean distance of each grid from the existing industrial grid as attributes of the cells;

[0196] The CA model iteration module is used to calculate the asynchronous evolution rate of the sub-region from the basic year to the verification year based on the grid data of the basic year and the verification year; substitute the grid data of the basic year and iterate the cellular automaton model multiple times; the cellular automaton model will traverse all cells during iteration, and the cells will be accessed only when the number of iterations is an integer multiple of the evolution rate and the cell state is that there is no existing industry transfer; when the total number of cells belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states is greater than or equal to the ratio threshold of the total number of grids belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states in the verification year, the cellular automaton model suspends iteration;

[0197] The accuracy verification module is used to set an accuracy threshold and perform accuracy verification on the iteration result of the cellular automaton model; if the accuracy reaches the accuracy threshold, the scenario simulation module is started, and if the accuracy does not reach the accuracy threshold, the CA model iteration module is started;

[0198] The scenario simulation module is used to divide the future industrial transfer scenarios into five different simulation scenarios; then the Markov chain method is used to predict the demand quantity of five types of industries, namely resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive, under each future scenario, and the demand quantity is used as the termination condition of the model to simulate the spatiotemporal process of future industrial transfer under five different scenarios; among them, the five different simulation scenarios include a baseline scenario, a major infrastructure construction scenario, an ecological protection priority scenario, an economic growth priority scenario, and a scenario of coordinated development of ecological protection and economic growth.

[0199] In this embodiment, each module is implemented by a processor, and a memory is appropriately added when storage is required. The processor may be, but is not limited to, a microprocessor MPU, a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components, etc. The memory may include a random access memory (RAM) and may also include a non-volatile memory (NVM).

[0200] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions.

[0201] It should also be noted that the industrial transfer spatial decision-making system based on the ANN-GIS-CA architecture described in this embodiment corresponds to the industrial transfer spatial decision-making method based on the ANN-GIS-CA architecture. The description and limitation of the method are also applicable to the system and will not be repeated here.

[0202] It can be seen from the above technical solutions that the industrial transfer spatial decision-making method and system based on the ANN-GIS-CA architecture provided by the embodiment of the present invention integrates ANN, GIS and CA to construct an industrial spatial layout optimization model during industrial transfer, and predicts the spatiotemporal pattern of future industrial transfer under different scenarios; uses the CA model to simulate industrial transfer; and more accurately simulates the spatiotemporal optimization process of industrial transfer by combining qualitative, quantitative and positioning, and combining subjective and objective methods, predicts the development trend of industrial transfer under different development models, improves the optimization effect of industrial spatial layout during industrial transfer, reasonably arranges industrial transfer space, and realizes regional industrial sustainable development.

[0203] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. It is not intended to limit the scope of the invention claimed for protection, but only represents the preferred embodiment of the present invention. Those skilled in the art should understand that the scope of the invention involved in the present invention is not limited to the technical solution formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the inventive concept. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

Claims

1. A spatial decision-making method for industrial transfer based on ANN-GIS-CA architecture, characterized by: The method comprises the following steps: Step S1, based on the geographic information system GIS, the industrial transfer receiving area is divided into several grids to form cells, and the existing industrial transfer situation is marked in the grid by manual interpretation, so as to generate grid data of the basic year and the verification year based on GIS; the grid data includes spatial information and attribute information; at the same time, the sub-area to which the grid belongs is determined according to the preset rules, and the attribution result is used as the attribute information of the grid data; Step S2, obtaining the original data of the industrial transfer undertaking capacity of all grids based on the Geographic Information System (GIS), and constructing an evaluation index system for the industrial transfer undertaking capacity; Step S3, based on the constructed industrial transfer acceptance capacity evaluation index system, calculate the industrial transfer acceptance capacity scores of all grids, and use the Gaussian mixture model to identify the industry type that each grid is suitable for accepting, and use the score and the industry type that is suitable for accepting as the attribute information of the grid data; Step S4, based on GIS, obtain the original data of the social and economic effects and ecological and environmental effects of all grids undertaking industrial transfer, and construct an evaluation index system for the social and economic effects and ecological and environmental effects of undertaking industrial transfer; the constructed evaluation index system structure for undertaking effect includes primary indicators, secondary indicators, and the source and direction attributes of the secondary indicators; the direction attributes include positive indicators and negative indicators; wherein, a positive indicator refers to an indicator with a larger attribute value, the better, and a negative indicator refers to an indicator with a smaller attribute value, the better; Step S5, respectively calculating the social and economic effect scores and the ecological and environmental effect scores of all grids according to the secondary indicators of the social and economic effect and the ecological and environmental effect in the undertaking effect and the directional attributes of the secondary indicators, and using the scores as the attribute information of the grid data; Step S6, selecting grids with existing industrial transfers in all base years, and randomly selecting a predetermined number of grids without existing industrial transfers in the base year, extracting the social and economic effect scores and ecological and environmental effect scores of these grids as samples, constructing an artificial neural network ANN model, and calculating the state probabilities of all grids evolving into six states: resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industrial transfer, and using the six states and state probabilities as attribute information of the grid data; Step S7, using GIS to determine whether each grid belongs to a development-prohibited area, and using the determination result as attribute information of the grid data; Step S8, using GIS to calculate the Euclidean distance between each grid and the existing industrial grid based on the grid spatial information of the base year, and using the Euclidean distance as the attribute information of the grid data; Step S9, taking the grid as a cell, taking resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industry as the six states of the cell, taking the industry type that each grid is suitable for undertaking, the six state probabilities, whether each grid is a prohibited development zone, and the Euclidean distance of each grid from the existing industry grid as the attributes of the cell, constructing a partitioned asynchronous cellular automaton model; Step S10, based on the grid data of the base year and the verification year, calculate the asynchronous evolution rate of the sub-region from the base year to the verification year; substitute the grid data of the base year, and iterate the cellular automaton model multiple times; the cellular automaton model will traverse all cells during iteration, and the cell will be accessed only when the number of iterations is an integer multiple of the evolution rate and the cell state is that there is no existing industry transfer; when the total number of cells belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states is greater than or equal to the ratio threshold of the total number of grids belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states in the verification year, the cellular automaton model suspends iteration; Step S11, setting an accuracy threshold, and verifying the accuracy of the iterative result of the cellular automaton model; if the accuracy reaches the accuracy threshold, the process proceeds to step S12, and if the accuracy does not reach the accuracy threshold, the process returns to step S10; Step S12, divide the future industrial transfer scenarios into five different simulation scenarios; then use the Markov chain method to predict the demand quantity of five types of industries, namely resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive, under each future scenario, and use the demand quantity as the termination condition of the model to simulate the spatiotemporal process of future industrial transfer under five different scenarios; wherein the five different simulation scenarios include a baseline scenario, a major infrastructure construction scenario, an ecological protection priority scenario, an economic growth priority scenario, and a coordinated development scenario of ecological protection and economic growth.

2. The method according to claim 1, characterized in that The carrying capacity evaluation index system structure constructed in step S2 includes primary indexes, secondary indexes, tertiary indexes and the source and direction attributes of the tertiary indexes; the direction attributes include positive indexes and negative indexes; wherein, a positive index refers to an index with a larger attribute value, the better, while a negative index refers to an index with a smaller attribute value, the better; When calculating the industrial transfer acceptance scores of all grids, the global principal component analysis (GPCA) method is used for calculation based on the three-level indicators and their directional attributes.

3. The method according to claim 1, characterized in that In step S5, the social and economic effect scores and the ecological and environmental effect scores are calculated based on the secondary indicators and their directional attributes. The calculation process specifically includes the following steps: Step S51, respectively performing dimensionless normalization processing on the positive index and the negative index in the secondary index using different range normalization formulas to obtain a standardized secondary index; Step S52, respectively determine the preliminary weights of the secondary indicators by principal component analysis GPCA method, entropy weight EWM method and hierarchical analysis AHP method, and integrate the three preliminary weights by approximating the ideal solution ranking TOPSIS method and weighted average method to obtain two integrated weights; then calculate the average value of the two integrated weights as the final comprehensive weight of each secondary indicator; Step S53, according to the normalized secondary indicators and the comprehensive weights of the secondary indicators, the socio-economic effect score and the ecological environmental effect score of each grid are calculated respectively, and the socio-economic effect and the ecological environmental effect of each grid are quantified by the scores.

4. The method according to claim 3, characterized in that Step S52 further includes: Step S521, calculating the first preliminary weight of the secondary index by GPCA method; Step S522, using the EWM method to calculate the second preliminary weight of the secondary index; Step S523, using the AHP method to calculate the third preliminary weight of the secondary index; Step S524, using the TOPSIS method, integrating the first preliminary weight, the second preliminary weight and the third preliminary weight to obtain a first integrated weight; Step S525, using a weighted average weight method, integrating the first preliminary weight, the second preliminary weight and the third preliminary weight to obtain a second integrated weight; Step S526, combining the first integrated weight and the second integrated weight to obtain the integrated weight of each secondary indicator; the first integrated weight represents the weight obtained by the objective weighting method, and the second integrated weight represents the weight obtained by the subjective weighting method.

5. The method according to claim 1, characterized in that The prohibited development area in step S7 includes three situations: the water area in the grid is greater than 50% of the total area of ​​the grid; the grid is located in a protected area, where any form of development is prohibited according to relevant policies; and the average slope of the grid is greater than 15°.

6. The method according to claim 1, characterized in that In step S9, the partitioned asynchronous cellular automation model is composed of cells, cell spaces, neighborhoods, and conversion rules, wherein the conversion rules adopt a roulette wheel selection rule; The partitioned asynchronous cellular automaton model introduces socio-economic effects, ecological environmental effects and Euclidean distance as external factor constraints, and uses grid data based on a GIS data platform to enhance the ability to express complex geographical phenomena.

7. The method according to claim 6, characterized in that The specific steps of the roulette selection rule are as follows: Step S91, calculating the sum of fitness: firstly calculating the sum of fitness of all individuals in the population; Step S92, calculating the selection probability: the selection probability of each individual is equal to its fitness divided by the total fitness; Step S93, constructing a roulette wheel: mapping the selection probability of each individual onto a roulette wheel to form a cumulative probability distribution; Step S94, select an individual: by generating a random number between 0 and 1, determine the position on the roulette wheel and select the corresponding individual.

8. The method according to claim 1, characterized in that: In step S10, when a cell is visited, the probability of the cell evolving into the six states of resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industry transfer is substituted into the roulette selection model to randomly select an industry type; then it is determined whether the cell is a prohibited development zone, and whether the Euclidean distance of the cell from the existing industry transfer grid is less than the average Euclidean distance between the existing industry grids of the administrative district where the cell is located; after these two conditions are met, if the industry type randomly selected by the roulette selection model is compatible with the industry type that the cell is suitable for undertaking, the state of the cell is transformed into the industry type randomly selected by the roulette selection rule, otherwise the state of the cell remains no existing industry transfer; for each iteration, the model calculates the total number of cells belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states.

9. The method according to claim 1, characterized in that: When running the five different scenarios simulated in step S11, the simulation process is as follows: Run the baseline scenario to simulate the future based on the changing patterns of industrial transfer in the base year and the verification year, that is, do not make any changes to the cellular automaton model, and continue to run the suspended cellular automaton in S10 until the quantity requirements are met; Run the major transportation infrastructure construction scenario. Based on the baseline scenario, this scenario takes into account the transportation infrastructure that may be newly built in the industrial transfer receiving area in the future. The area within the predetermined range of the newly added transportation infrastructure will also be set as a transferable area. That is, a new rule is added based on the model in S9, and then the suspended cellular automaton in S10 is continued to run until the quantity requirement is met; Run the ecological protection priority scenario. This scenario classifies the ecological and environmental effects of industrial transfer of cells according to the standard deviation classification method. Areas with higher ecological and environmental effects are regarded as areas where industrial transfer will occur in the future. That is, based on the model described in S9, "whether the Euclidean distance of the cell from the existing industrial transfer grid is less than or equal to the average Euclidean distance between the existing industrial grids in the administrative area where the cell is located in S8" is changed to "the ecological and environmental effect value of the cell is greater than 1.5 standard deviations of all ecological and environmental effect values", and then continue to run the suspended cellular automaton in S10 until the quantity requirements are met; Run the economic growth priority scenario. This scenario classifies the socioeconomic effects of industrial transfer of cells according to the standard deviation classification method. Regions with higher socioeconomic effects are regarded as regions where industrial transfer will occur in the future. That is, based on the model described in S9, "whether the Euclidean distance of the cell from the existing industrial transfer grid is less than or equal to the average Euclidean distance between the existing industrial grids in the administrative area where the cell is located in S8" is changed to "the socioeconomic effect value of the cell is greater than 1.5 standard deviations of all socioeconomic effect values", and then continue to run the suspended cellular automaton in S10 until the quantity requirement is met; Running the scenario of coordinated development of ecological protection and economic growth, the areas with higher ecological and environmental effects and socio-economic effects are regarded as the areas where industrial transfer will occur in the future, which are equivalent to the overlapping parts of the areas described in the ecological protection priority scenario and the economic growth priority scenario. On the basis of the model described in S9, "whether the Euclidean distance of the cell from the existing industrial transfer grid is less than or equal to the average Euclidean distance between the existing industrial grids in the administrative region where the cell is located in S8" is changed to "the ecological and environmental effect value of the cell is greater than 1.5 standard deviations of all ecological and environmental effect values, and the socio-economic effect value of the cell is greater than 1.5 standard deviations of all socio-economic effect values", and then continue to run the suspended cellular automaton in S10 until the quantity requirements are met.

10. An industry transfer spatial decision-making system based on ANN-GIS-CA architecture, characterized by: The system includes: a grid division module, a carrying capacity evaluation index construction module, a carrying capacity calculation module, a carrying effect evaluation index construction module, a carrying effect evaluation module, a state probability calculation module, a forbidden area determination module, a Euclidean distance calculation module, a CA model construction module, a CA model iteration module, an accuracy verification module and a scenario simulation module; wherein, The grid division module is used to divide the industrial transfer receiving area into several grids based on the geographic information system GIS to form cells, and use manual interpretation to mark the existing industrial transfer situation in the grid, and generate grid data of the basic year and the verification year based on GIS; the grid data includes spatial information and attribute information; it is also used to determine the sub-area to which the grid belongs according to preset rules, and use the attribution result as the attribute information of the grid data; The module for constructing the evaluation index of industrial transfer capacity is used to obtain the original data of industrial transfer capacity of all grids based on the geographic information system GIS, and to construct an evaluation index system of industrial transfer capacity; The undertaking capacity calculation module is used to calculate the industrial transfer undertaking capacity scores of all grids based on the constructed industrial transfer undertaking capacity evaluation index system, and use the Gaussian mixture model to identify the industry type that each grid is suitable for undertaking, and use the scores and the industry type that is suitable for undertaking as the attribute information of the grid data; The construction module of the evaluation index of the undertaking effect is used to obtain the original data of the social and economic effects and the ecological and environmental effects of all grids undertaking industrial transfer based on GIS, and to construct the evaluation index system of the social and economic effects and the ecological and environmental effects of undertaking industrial transfer; the constructed evaluation index system structure of the undertaking effect includes the primary index, the secondary index, and the source and direction attributes of the secondary index; the direction attributes include positive index and negative index; wherein the positive index refers to the index whose attribute value is larger, the better, and the negative index refers to the index whose attribute value is smaller, the better; The undertaking effect evaluation module is used to calculate the social and economic effect scores and the ecological and environmental effect scores of all grids according to the secondary indicators of the social and economic effect and the ecological and environmental effect in the undertaking effect and the directional attributes of the secondary indicators, and use the scores as the attribute information of the grid data; The state probability calculation module is used to select grids with existing industrial transfers in all base years, and randomly select a predetermined number of grids without existing industrial transfers in the base years, extract the social and economic effect scores and ecological and environmental effect scores of these grids as samples, construct an artificial neural network ANN model, and respectively calculate the state probabilities of all grids evolving into six states: resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industrial transfer, and use the six states and state probabilities as attribute information of the grid data; The prohibited development area determination module is used to use GIS to determine whether each grid belongs to a prohibited development area, and use the determination result as the attribute information of the grid data; The Euclidean distance calculation module is used to calculate the Euclidean distance of each grid from the existing industrial grid using GIS based on the grid spatial information of the basic year, and use the Euclidean distance as the attribute information of the grid data; The CA model construction module is used to construct a partitioned asynchronous cellular automation model with grids as cells, resource-intensive, labor-intensive, technology-intensive, capital-intensive, comprehensive, and no existing industries as six states of cells, and the types of industries that each grid is suitable for undertaking, the probabilities of the six states, whether each grid is a prohibited development zone, and the Euclidean distance of each grid from the existing industrial grid as attributes of the cells; The CA model iteration module is used to calculate the asynchronous evolution rate of the sub-region from the basic year to the verification year based on the grid data of the basic year and the verification year; substitute the grid data of the basic year and iterate the cellular automaton model multiple times; the cellular automaton model will traverse all cells during iteration, and the cells will be accessed only when the number of iterations is an integer multiple of the evolution rate and the cell state is that there is no existing industry transfer; when the total number of cells belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states is greater than or equal to the ratio threshold of the total number of grids belonging to the resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive states in the verification year, the cellular automaton model suspends iteration; The accuracy verification module is used to set an accuracy threshold and perform accuracy verification on the iteration result of the cellular automaton model; if the accuracy reaches the accuracy threshold, the scenario simulation module is started, and if the accuracy does not reach the accuracy threshold, the CA model iteration module is started; The scenario simulation module is used to divide the future industrial transfer scenarios into five different simulation scenarios; then the Markov chain method is used to predict the demand quantity of five types of industries, namely resource-intensive, labor-intensive, technology-intensive, capital-intensive, and comprehensive, under each future scenario, and the demand quantity is used as the termination condition of the model to simulate the spatiotemporal process of future industrial transfer under five different scenarios; among them, the five different simulation scenarios include a baseline scenario, a major infrastructure construction scenario, an ecological protection priority scenario, an economic growth priority scenario, and a scenario of coordinated development of ecological protection and economic growth.

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