Land space planning-oriented land utilization simulation method and system, electronic equipment and storage medium

Abstract

The invention belongs to the field of land planning, and discloses a land space planning-oriented land utilization simulation method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting land utilization form current situation data, social economic data and environmental data, and carrying out the spatialization processing and data preprocessing through a GIS, and obtaining the preprocessed data; constructing a multi-target optimization weight of land utilization planning by using a multi-standard decision analysis method; constructing a land utilization change model based on the GIS, the preprocessed data and the multi-objective optimization weight; and inputting a land utilization form, and performing simulation by using the land utilization change model to obtain a land utilization simulation result. The method is helpful for reasonable planning and utilization of land resources, has important significance for promoting sustainable development, optimizing land resource allocation and improving land utilization efficiency, and also provides powerful scientific support for land management decision.

Description

Technical Field

[0001] The present invention relates to the technical field of land planning, and particularly relates to a land use simulation method, system, electronic device and storage medium for land spatial planning. Background Art

[0002] With the growth of the global population and the expansion of economic activities, land resources, as the basic elements to support social and economic development, the importance of their rational utilization and planning has become increasingly prominent. Land use planning refers to the scientific arrangement and design of the development, utilization, management and protection of land resources within a certain area according to the natural characteristics of land resources and social and economic conditions. This process involves multiple aspects such as the allocation of land resources, urban development, environmental protection, etc., aiming to achieve the sustainable utilization and optimal allocation of land resources.

[0003] Traditional land use planning methods mainly rely on expert experience and static data analysis. These methods often cannot accurately predict the future change trends of land use and are also difficult to adapt to the rapidly changing social and economic conditions. In addition, due to the lack of the ability to simulate and predict the dynamic process of land use change, these methods are unable to handle complex land use problems effectively. For example, conflicts between different land use demands such as urban expansion, agricultural development, and ecological protection require more refined and dynamic planning methods to coordinate and solve.

[0004] In land use planning practice, problems of conversion between multiple land use types often occur, such as the conversion of agricultural land to construction land, and the conversion of forests to farmland. These conversions not only involve physical changes in land resources, but also involve problems at multiple levels such as land use benefits, ecological environment impacts, and social and economic development. Therefore, land use planning needs to comprehensively consider these factors to achieve the balance and optimization of multiple objectives.

[0005] In addition, with the development of information technology, the application of Geographic Information System (GIS) technology in land use planning has become more and more extensive. GIS technology can process and analyze a large amount of spatial data, providing powerful data support for land use planning. However, GIS technology still has limitations in the dynamic simulation and prediction of land use changes and needs to be combined with other technologies to improve the scientificity and accuracy of planning.

[0006] In summary, traditional land use planning methods have obvious deficiencies in dealing with complex and dynamic land use problems, and there is an urgent need to develop new technical methods to improve the scientificity, dynamics and multi-objective optimization ability of land use planning. These methods should not only be able to process and analyze a large amount of spatial data, but also be able to simulate the dynamic process of land use change and provide a scientific basis for decision-making. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides a land use simulation method for land space planning, including the following steps:

[0008] Collect the current land use form data, socio-economic data, and environmental data, and perform spatial processing and data preprocessing through GIS to obtain the preprocessed data;

[0009] Use the multi-criteria decision analysis method to construct the multi-objective optimization weights for land use planning;

[0010] Based on the GIS, the preprocessed data, and the multi-objective optimization weights, construct a land use change model;

[0011] Input the land use form and use the land use change model for simulation to obtain the land use simulation result.

[0012] Preferably, the method for obtaining the preprocessed data includes:

[0013] Normalize the land use current situation data, the socio-economic data, and the environmental data from different sources, and integrate them into the GIS;

[0014] Use the GIS to convert non-spatial data into spatial data and associate the data with space through geocoding;

[0015] Clean and denoise the data to obtain the preprocessed data.

[0016] Preferably, the method for obtaining the multi-objective optimization problem includes:

[0017] Determine the decision variables affecting land use planning;

[0018] Determine evaluation indicators for each decision variable;

[0019] Through the Delphi method, assign weights to each of the evaluation indicators to obtain the multi-objective optimization weights.

[0020] Preferably, the method for constructing the land use change model includes:

[0021] Construct a FLUS model, and the structure of the FLUS model includes: an artificial neural network, a system dynamics module, and a cellular automata module;

[0022] Combine the multi-objective optimization weights with the preprocessed data to obtain a training dataset;

[0023] Use the training dataset to train the FLUS model to obtain the land use change model.

[0024] The present invention also provides a land use simulation system for land space planning, which is used to implement the method described in any one of the above, and includes: a data collection and processing module, a weight allocation module, a model construction module, and a simulation module;

[0025] The data collection and processing module is used to collect the current situation data of land use forms, social and economic data, and environmental data, and perform spatial processing and data preprocessing through GIS to obtain preprocessed data;

[0026] The weight allocation module constructs multi-objective optimization weights for land use planning by using multi-criteria decision analysis methods;

[0027] The model construction module constructs a land use change model based on the GIS, the preprocessed data, and the multi-objective optimization weights;

[0028] The simulation module is used to input the land use form and perform simulation by using the land use change model to obtain the land use simulation result.

[0029] Preferably, the data collection and processing module includes: a data collection unit, a spatial transformation unit, and a denoising unit;

[0030] The data collection unit normalizes the land use current situation data, the social and economic data, and the environmental data from different sources and integrates them into the GIS;

[0031] The spatial transformation unit uses the GIS to convert non-spatial data into spatial data and associates the data with space through geocoding;

[0032] The denoising unit cleans and denoises the data to obtain the preprocessed data.

[0033] Preferably, the weight allocation module includes: a variable determination unit, an index generation unit, and a weight allocation module;

[0034] Determine the decision variables affecting land use planning;

[0035] Determine evaluation indexes for each decision variable;

[0036] Allocate weights to each of the evaluation indexes through the Delphi method to obtain multi-objective optimization weights.

[0037] Preferably, the model construction module includes: a model construction unit, a data processing unit, and a model training unit;

[0038] The model construction unit is used to construct a FLUS model, and the structure of the FLUS model includes: an artificial neural network, a system dynamics module, and a cellular automaton module;

[0039] The data processing unit is configured to combine the multi-objective optimization weights with the preprocessed data to obtain a training data set;

[0040] The model training unit trains the FLUS model using the training data set to obtain the land use change model.

[0041] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, a land use simulation method for land space planning is implemented.

[0042] The present invention also provides a computer-readable storage medium storing a computer program, which when executed, implements a land use simulation method for land space planning.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] By comprehensively applying GIS technology, multi-criteria decision analysis method, and FLUS model, the present invention realizes the efficient simulation of land use change; by collecting and preprocessing land use status, socio-economic, and environmental data, the accuracy and availability of the data are ensured. The multi-objective optimization weights constructed by the multi-criteria decision analysis method make land use planning more scientific and reasonable, capable of comprehensively considering various factors, improving the comprehensiveness and adaptability of the planning; the construction of the FLUS model combines artificial neural network, system dynamics module, and cellular automata module, improving the accuracy and flexibility of the simulation. The present invention contributes to the rational planning and utilization of land resources, is of great significance for promoting sustainable development, optimizing land resource allocation, and improving land use efficiency, and also provides strong scientific support for land management decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention;

[0047] Figure 2 It is a schematic structural diagram of the electronic device according to an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] 1010, Processor; 1020, Memory; 1030, Input / Output Interface; 1040, Communication Interface; 1050, Bus. Detailed Implementation Manner

[0050] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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 the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the embodiments of this disclosure should have the ordinary meaning understood by those of ordinary skill in the art in the field to which this disclosure belongs. The "first", "second" and similar terms used in the embodiments of this disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0052] Embodiment 1

[0053] In this embodiment, as Figure 1 shown, a land use simulation method for land space planning includes the following steps:

[0054] S1. Collect the current situation data of land use forms, social and economic data, and environmental data, and perform spatial processing and data preprocessing through GIS to obtain preprocessed data.

[0055] The method for obtaining the preprocessed data includes: normalizing the land use current situation data, social and economic data, and environmental data from different sources and integrating them into GIS; using GIS to convert non-spatial data into spatial data and associating the data with space through geocoding; cleaning and denoising the data to obtain the preprocessed data.

[0056] In this embodiment, first, a comprehensive survey of the current land use situation is carried out, including but not limited to the distribution and area of various land use types such as cultivated land, forest land, construction land, water areas, etc. These data can be obtained through remote sensing images, current land use maps, field surveys, etc.; then, socioeconomic data are obtained through government statistical departments, social survey reports, and economic analysis reports. Socioeconomic data are crucial for understanding the driving forces of land use change and include information such as population distribution, economic growth rate, industrial structure, transportation network, etc.; then, environmental data are obtained through environmental monitoring stations and ecological surveys, including but not limited to climate data, soil quality, water resource status, biodiversity, etc.; finally, these data are normalized and standardized. Secondly, the standardized data of various types are integrated into a Geographic Information System (GIS). For non-spatial data, it is associated with specific geographical locations through geocoding. Specifically: (1) Address standardization: Standardize the address information in the collected socioeconomic data and environmental data. For example, convert "No. 11, Fuxing Road, Haidian District, Beijing" into a standardized address format; (2) Address matching: Next, use a fuzzy matching algorithm to match the standardized address with the existing standard address library in the GIS to ensure that even if the address information is not completely accurate, the closest matching item can be found; (3) Geocoding quantization: Once a matching address record is found, the GIS system will use a quantization method to determine the geographical coordinates corresponding to each address. This usually involves algorithmic programs such as interpolation algorithms to ensure the accurate conversion of text addresses into geographical coordinates. Finally, operations such as removing outliers, filling in missing values, and correcting incorrect data are performed on the spatially associated data to obtain preprocessed data.

[0057] S2. Use a multi-criteria decision analysis method to construct the multi-objective optimization weights for land use planning.

[0058] The methods for obtaining the multi-objective optimization problem include: determining the decision variables that affect land use planning; determining evaluation indicators for each decision variable; and allocating weights to each evaluation indicator through the Delphi method to obtain the multi-objective optimization weights.

[0059] In this embodiment, it is first necessary to determine which factors will affect land use planning. These factors may include, but are not limited to: land use types (such as agricultural land, industrial land, residential land, etc.), the sustainability of land use, environmental impacts, and social and economic benefits. For each decision variable, a series of evaluation indicators need to be determined: for land use types, the evaluation indicators include: the economic benefits of land use, the environmental impacts of land use, the social benefits of land use, and the sustainability of land use; for sustainability, the evaluation indicators include: duration; for environmental impacts, the evaluation indicators include: climate impacts, land pollution, and water pollution; for social and economic benefits, the evaluation indicators include: economic growth rate, economic increment, and economic stability. The weight of each evaluation indicator is assigned through the Delphi method:

[0060]

[0061] Among them, w j represents the weight of the j-th indicator, p ij represents the importance score of the i-th expert for the j-th indicator, k represents a constant, usually taking n represents the number of experts, and both i and j represent natural numbers.

[0062] S3. Construct a land use change model based on GIS, preprocessed data, and multi-objective optimization weights.

[0063] The methods for constructing a land use change model include: constructing a FLUS model, and the structure of the FLUS model includes: an artificial neural network, a system dynamics module, and a cellular automata module; combining the multi-objective optimization weights with the preprocessed data to obtain a training data set; using the training data set to train the FLUS model to obtain a land use change model.

[0064] In this embodiment, the FLUS model is composed of an artificial neural network (ANN), system dynamics (SD), and cellular automata (CA). The artificial neural network is used to estimate the occurrence probability of each land use type, that is, the suitability probability of the land use type, and the formula is as follows:

[0065] P ij = f(X ij ),

[0066] Among them, P ij represents the suitability probability of the i-th land use type in the j-th grid cell, X ij represents the driving factors affecting land use, and f represents the ANN model function; the system dynamics (SD) module is used to simulate the dynamic feedback mechanism within the land use system and predict the quantity of land use demand, and the formula is as follows:

[0067] St+1 = S t + ΔS t ,

[0068] where S t represents the land use status at time t, and ΔS t represents the land use change amount at time t; the Cellular Automata (CA) module combines the suitability probability estimated by ANN, the land use conversion cost, the neighborhood condition, and the competition factors between different land use types to comprehensively estimate the merging probability of each land grid cell. The formula is as follows:

[0069]

[0070] C ij represents the merging probability of the i-th land use on the j-th grid cell, N ij represents the neighborhood condition factor, and k represents a constant. Combining the multi-objective optimization weights with the preprocessed data to obtain the training dataset; using the training dataset to train the FLUS model to obtain the land use change model.

[0071] S4. Input the land use form and use the land use change model to simulate to obtain the land use simulation result.

[0072] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by the cooperation of multiple devices. In this case of a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiments of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0073] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention. The actions or steps recited in the claims can be executed in an order different from that in the above embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In certain embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0074] Embodiment 2

[0075] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the present disclosure also provides a land use simulation system for land space planning, including: a data collection and processing module, a weight allocation module, a model construction module, and a simulation module.

[0076] The data collection and processing module is used to collect the current situation data of land use forms, socioeconomic data, and environmental data, and perform spatial processing and data preprocessing through GIS to obtain preprocessed data.

[0077] The data collection and processing module includes: a data collection unit, a spatial transformation unit, and a denoising unit; the data collection unit normalizes the land use current situation data, socioeconomic data, and environmental data from different sources and integrates them into GIS; the spatial transformation unit uses GIS to convert non-spatial data into spatial data and associates the data with space through geocoding; the denoising unit cleans and denoises the data to obtain preprocessed data.

[0078] The weight allocation module uses a multi-criteria decision analysis method to construct multi-objective optimization weights for land use planning.

[0079] The weight allocation module includes: a variable determination unit, an index generation unit, and a weight allocation module; determines the decision variables affecting land use planning; determines evaluation indicators for each decision variable; assigns weights to each evaluation indicator through the Delphi method to obtain multi-objective optimization weights.

[0080] The model construction module constructs a land use change model based on GIS, preprocessed data, and multi-objective optimization weights.

[0081] The model construction module includes: a model construction unit, a data processing unit, and a model training unit; the model construction unit is used to construct a FLUS model, and the structure of the FLUS model includes: an artificial neural network, a system dynamics module, and a cellular automata module; the data processing unit is used to combine multi-objective optimization weights with preprocessed data to obtain a training data set; the model training unit uses the training data set to train the FLUS model to obtain a land use change model.

[0082] The simulation module is used to input the land use form and perform simulation using the land use change model to obtain a land use simulation result.

[0083] The system of the above embodiment is used to implement the corresponding land use simulation method for land space planning in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0084] It should be noted that the above land use simulation system for land spatial planning is embodied in the form of functional units. The term "module" here can be implemented in the form of software and / or hardware, and no specific limitation is made thereto.

[0085] For example, the "module" can be a software program, a hardware circuit, or a combination of both that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a proprietary processor, or a group of processors, etc.) for executing one or more software or firmware programs, and a memory, a merged logic circuit, and / or other suitable components that support the described functions.

[0086] Embodiment III

[0087] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the land use simulation method for land spatial planning described in any one of the above embodiments.

[0088] Figure 2 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0089] The processor 1010 can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0090] The memory 1020 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and called and executed by the processor 1010.

[0091] The input / output interface 1030 is used to connect to the input / output module to implement information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0092] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to implement communication and interaction between this device and other devices. Among them, the communication module can implement communication through a wired method (such as USB (Universal Serial Bus), network cable, etc.) or through a wireless method (such as a mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0093] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0094] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solutions of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0095] The system of the above embodiment is used to implement the land use simulation method for land space planning corresponding to any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0096] Embodiment Four

[0097] Based on the same inventive concept, corresponding to any of the above-described method embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the land use simulation method for land space planning as described in any of the above embodiments.

[0098] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0099] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the land use simulation method for land space planning as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0100] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the concept of the present disclosure, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.

[0101] In addition, for simplicity of explanation and discussion, and so as not to make the embodiments of the present disclosure difficult to understand, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that details regarding the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be entirely within the understanding of those skilled in the art). In cases where specific details (such as circuits) are set forth to describe exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be practiced without these specific details or with variations of these specific details. Accordingly, these descriptions should be regarded as illustrative rather than restrictive.

[0102] Although the present disclosure has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0103] Therefore, the units of the examples described in the embodiments of this application can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0104] The embodiments of the present disclosure are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A land use simulation method for land space planning, characterized in that: The following steps are involved: Collect the current land use data, socio-economic data and environmental data, and perform spatial processing and data preprocessing through GIS to obtain preprocessed data; Use multi-criteria decision analysis methods to construct multi-objective optimization weights for land use planning; Constructing a land use change model based on the GIS, the preprocessed data and the multi-objective optimization weights; The land use form is input, and the land use change model is used for simulation to obtain a land use simulation result.

2. A land use simulation method for land space planning according to claim 1, characterized in that: The method for obtaining the preprocessed data includes: Normalizing the land use status data, the socio-economic data and the environmental data from different sources and integrating them into the GIS; Converting non-spatial data into spatial data using the GIS and associating the data with space through geocoding; The data is cleaned and denoised to obtain the preprocessed data.

3. A land use simulation method for land space planning according to claim 1, characterized in that: The method for obtaining the multi-objective optimization problem includes: Identify decision variables that affect land use planning; Determine the evaluation index for each decision variable; The Delphi method is used to assign weights to each evaluation index to obtain multi-objective optimization weights.

4. The land use simulation method for land space planning according to claim 1, characterized in that: The method for constructing the land use change model includes: Constructing a FLUS model, wherein the structure of the FLUS model includes: an artificial neural network, a system dynamics module and a cellular automation module; Combining the multi-objective optimization weights with the pre-processed data to obtain a training data set; The FLUS model is trained using the training data set to obtain the land use change model.

5. A land use simulation system for land space planning, the system being used to implement the method according to any one of claims 1 to 4, characterized in that: include: Data collection and processing module, weight distribution module, model building module and simulation module; The data collection and processing module is used to collect the current status data of land use forms, social economic data and environmental data, and perform spatial processing and data preprocessing through GIS to obtain preprocessed data; The weight allocation module uses a multi-criteria decision analysis method to construct multi-objective optimization weights for land use planning; The model building module builds a land use change model based on the GIS, the preprocessed data and the multi-objective optimization weights; The simulation module is used to input the land use form, perform simulation using the land use change model, and obtain land use simulation results.

6. A land use simulation system for land space planning according to claim 5, characterized in that: The data collection and processing module includes: a data collection unit, a space conversion unit and a denoising unit; The data collection unit normalizes the land use status data, the socio-economic data and the environmental data from different sources and integrates them into the GIS; The spatial conversion unit converts non-spatial data into spatial data using the GIS and associates the data with space through geocoding; The denoising unit cleans and denoises the data to obtain the preprocessed data.

7. A land use simulation system for land space planning according to claim 5, characterized in that: The weight distribution module includes: a variable determination unit, an indicator generation unit and a weight distribution module; Identify decision variables that affect land use planning; Determine the evaluation index for each decision variable; The Delphi method is used to assign weights to each evaluation index to obtain multi-objective optimization weights.

8. The land use simulation system for land space planning according to claim 5, characterized in that: The model building module includes: a model building unit, a data processing unit and a model training unit; The model building unit is used to build a FLUS model, and the structure of the FLUS model includes: an artificial neural network, a system dynamics module and a cellular automation module; The data processing unit is used to combine the multi-objective optimization weights with the pre-processed data to obtain a training data set; The model training unit trains the FLUS model using the training data set to obtain the land use change model.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 4 is implemented.

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