Land space planning method based on big data

Abstract

The invention discloses a territorial space planning method based on big data, and belongs to the technical field of territorial space planning. Comprising the following steps: step 1, constructing a territorial space knowledge graph; step 2, constructing a multi-dimensional territorial space planning solution space, identifying key constraints and determining an optimization path; 3, searching and generating an optimal planning scheme in a solution space; 4, performing quantification and grading processing on hard constraints and soft constraints in territorial space planning to realize multi-objective comprehensive optimization; step 5, performing multi-dimensional confidence evaluation and validity verification on the planning scheme generated by optimization; and step 6, realizing continuous optimization and adaptive evolution of territorial space planning.

Description

Technical Field

[0001] The present application relates to the technical field of national land space planning, and more specifically, to a national land space planning method based on big data. Background Art

[0002] How to scientifically and rationally conduct national land space planning, ensuring the efficient use of land resources while balancing ecological protection, social development, and economic development, has become a major issue facing us today. Traditional national land space planning methods often rely on manual experience and statistical models, failing to fully consider various constraints and multi-objective optimization requirements, resulting in limited planning effectiveness and inefficient implementation. Therefore, how to improve the accuracy, feasibility, and sustainability of planning through modern information technology and data analysis has become a technical challenge that needs to be addressed urgently.

[0003] The development of big data technology has provided new opportunities for national land spatial planning. Through in-depth mining and analysis of multi-source, heterogeneous data, we can obtain more comprehensive and accurate national land spatial information, providing data support for planning decisions. Furthermore, with the continuous advancement of artificial intelligence and machine learning technologies, agent-based planning methods have gradually become an important research direction. By introducing intelligent optimization algorithms and adaptive learning mechanisms, planning schemes can be flexibly adjusted under multi-dimensional and multi-constraint conditions, improving the efficiency and effectiveness of planning implementation.

[0004] However, existing land and space planning methods still face many challenges, such as a lack of dynamic adjustment mechanisms, difficulty adapting to changes in complex constraints, and an inability to efficiently optimize planning schemes using large datasets. Therefore, achieving comprehensive optimization and adaptive adjustment of land and space planning while ensuring planning objectives and policy requirements has become a pressing technical issue. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the purpose of this application is to provide a land space planning method based on big data, which includes the following steps:

[0006] Step 1: Build a national land space knowledge map;

[0007] Step 2: Construct a multi-dimensional national land space planning solution space, identify key constraints and determine the optimization path;

[0008] Step 3: Search and generate the optimal planning solution in the solution space;

[0009] Step 4: Quantify and classify the hard and soft constraints in national land space planning to achieve comprehensive optimization of multiple objectives;

[0010] Step 5: Conduct multi-dimensional confidence evaluation and effectiveness verification on the optimized planning scheme;

[0011] Step 6: Realize continuous optimization and adaptive evolution of national land space planning.

[0012] Furthermore, the construction of the national land space knowledge graph includes:

[0013] Standardize multi-source heterogeneous national land spatial data, including: adopting unified metadata specifications, converting the format and coordinating the scale of spatial data, attribute data and unstructured data to achieve data consistency and interoperability;

[0014] Constructing a national land space domain ontology, specifically including: defining basic concepts, attributes, and their relationships in the national land space domain based on ontology engineering and semantic web technology, and building a hierarchical ontology system to support the semantic expression and integration of multi-source data;

[0015] Conduct knowledge extraction and semantic enhancement processing, specifically including: semantic mining of unstructured text data related to land space, identifying and extracting entities, relationships, and events, and converting implicit knowledge into structured information to enhance the semantic expression capability of the knowledge graph;

[0016] Constructing a semantic relationship network between data elements, specifically including: automatically identifying semantic associations between different data sources through semantic alignment and entity linking technology, establishing logical connection relationships between data elements, and forming an association map that reflects the interaction between national land space objects;

[0017] Expanding the static knowledge graph into a dynamic spatiotemporal graph includes: introducing temporal and spatial attributes, dynamically modeling knowledge nodes and their relationships, and supporting the expression and reasoning of the evolution laws of national spatial structure and processes;

[0018] Construct an interactive application interface for knowledge services, specifically including: designing a graph interaction platform that supports complex semantic retrieval, spatial reasoning and knowledge recommendation, to achieve efficient query, visual expression and intelligent push of national land space knowledge.

[0019] Furthermore, step 2 includes the following steps:

[0020] Constructing a multi-dimensional national land space planning solution space, specifically including: Based on the entity relationships and attribute constraints in the national land space knowledge map, constructing a high-dimensional solution space covering land use, ecological environment, economic development, population distribution and infrastructure dimensions, where each dimension is defined by a corresponding indicator system and constraint conditions to determine the feasible domain of the planning scheme;

[0021] Conduct topological structure analysis of the solution space, specifically including: topological structure modeling and analysis of the constructed high-dimensional solution space, calculating the centrality index, clustering coefficient, and path connectivity of network nodes, identifying core nodes and key links with key influence, and revealing the coupling relationship and system structure characteristics between different planning dimensions;

[0022] Conduct hierarchical classification and weight assignment of constraints, specifically including: classifying and weighting various hard and soft constraints that affect the planning solution space, analyzing their scope and impact, identifying key constraint factors that have a decisive impact on the solution space boundary and structure, and clarifying the key control variables in the planning process;

[0023] Constructing a solution space goal-oriented evaluation system, specifically including: setting global optimization goals and local optimization goals based on the national land space strategic orientation and development needs, forming a hierarchical goal system to guide the optimization process of planning paths in the solution space;

[0024] Determine the optimal path in the solution space, specifically including: under the constraints, calculate the set of feasible paths under multiple constraint combinations, evaluate the execution cost, resource allocation efficiency and expected benefits of each path, and then screen out the optimal path plan that meets the multi-objective optimization requirements, ensuring that the generated planning plan achieves the optimal solution for overall resource allocation within the constraints.

[0025] Furthermore, the hierarchical classification of the constraints adopts a three-level system: the first level is hard constraints such as laws, regulations and red line control, the second level is constraints such as technical standards and specifications, and the third level is soft constraints such as policy orientation and development goals. The weight coefficients of the constraints at each level are assigned through the hierarchical analysis method. The weight of the first-level constraint is not less than 0.5, the weight of the second-level constraint is not less than 0.3, and the weight of the third-level constraint is not higher than 0.2.

[0026] Furthermore, step 3 includes the following steps:

[0027] Deployment of regional exploration agents, specifically including: based on macro-planning goals and spatial strategic orientation, performing global search and zoning exploration tasks in the national land space planning solution space, using spatial clustering and zoning differentiation algorithms to identify functional blocks with similar geographical characteristics and development potential, generating a preliminary framework plan for spatial layout, and establishing the development positioning and spatial structure of each region;

[0028] Construct a constraint-verifying agent, specifically including: introducing and loading legal constraints and technical specifications, performing constraint consistency checks on the preliminary solutions generated by the regional exploration agent, identifying spatial units that do not meet rigid constraints, marking conflicting areas, and outputting correction suggestions to ensure that the generated solutions meet the compliance requirements of laws, regulations, and policies;

[0029] Configure a refined optimization agent, specifically: after the preliminary plan passes the constraint verification, make local fine-tuning of the land use structure, industrial function layout and infrastructure network, improve the efficiency of spatial resource allocation and the rationality of organizational form based on micro-scale optimization algorithms, and achieve refined adjustment and optimization of the planning scheme;

[0030] Establish a multi-agent communication and collaboration mechanism, specifically: building a multi-level communication network based on a message-passing mechanism to enable information exchange and feedback loops between regional exploration, constraint verification, and fine-tuning agents. Reconciling decision-making differences among agents through iterative negotiation strategies, building a closed-loop collaborative process of "macro-exploration-constraint verification-micro-optimization" to ensure the logical consistency and integrity of the planning scheme.

[0031] Develop an adaptive learning module, specifically: building a case library and rule library based on historical planning cases, extracting typical planning patterns and strategies through deep learning and pattern mining, and empowering various intelligent agents with experience transfer and strategy update capabilities to continuously optimize planning search paths and agent behavior rules;

[0032] The evolutionary generation mechanism of planning schemes is realized, specifically including: introducing an evolutionary algorithm based on a population diversity maintenance mechanism to generate multiple alternative planning schemes with differences and diversity, and using the Pareto optimal principle to screen and combine non-dominated solutions to form an optimal solution cluster that meets multi-objective constraints and planning requirements.

[0033] Furthermore, the regional exploration agent adopts a hierarchical reinforcement learning architecture, including a three-level decision-making mechanism: strategic, tactical, and operational. The strategic layer is responsible for macro-spatial structure and functional positioning, the tactical layer is responsible for meso-regional division and functional organization, and the operational layer is responsible for micro-land use unit configuration. The three layers of agents coordinate with each other through hierarchical reward signals, forming a planning and decision-making process that combines top-down and bottom-up approaches.

[0034] The constraint verification agent uses a reasoning engine based on a knowledge graph, loaded with no less than 2,000 planning regulations and technical standards, and supports both forward chain reasoning and backward chain reasoning. The verification efficiency reaches 10,000 spatial units per second.

[0035] The refined optimization agent uses a suite of multi-objective optimization algorithms, including the Pareto frontier-based NSGA-II algorithm, the multi-objective particle swarm optimization algorithm, and the multi-objective differential evolution algorithm. It adaptively selects the most suitable algorithm for local optimization problems of varying complexity, achieving fine-tuning and optimization of planning schemes.

[0036] The multi-agent communication and collaboration mechanism adopts a distributed architecture based on federated learning, including a point-to-point communication protocol and a centralized coordination node, to achieve knowledge sharing and behavior coordination among different types of agents, and ensure continuous operation in the event of a single point of failure;

[0037] The adaptive learning module contains a structured knowledge base of at least 5,000 historical planning cases. It adopts a hybrid learning strategy that combines case reasoning and deep reinforcement learning to enable the intelligent agent to use analogical reasoning and experience transfer when encountering new problems. The learning efficiency grows logarithmically with the expansion of the case library.

[0038] Furthermore, step 4 includes the following steps:

[0039] Construct a hierarchical planning constraint system, specifically defining legally mandatory requirements as first-level hard constraints, physical and environmental restrictions as second-level hard constraints, and development-oriented goals as soft constraints. This will form a multi-level constraint system with clear priorities and a hierarchical structure, which will be used to standardize the constraint classification and processing logic during the planning scheme generation process.

[0040] Achieve quantitative modeling and unified expression of constraints, specifically by converting qualitatively described planning constraints into computable quantitative parameters through spatial threshold setting, mathematical inequality construction, and logical expression parsing, constructing a unified constraint violation metric function, and enabling the expression, calculation, and comparison of different types of constraints within a unified mathematical framework.

[0041] Transforming soft constraints into optimization objective functions involves: on the basis of satisfying the first- and second-level hard constraints, designing corresponding objective functions according to the development goals represented by the soft constraints, and building a multi-objective solution judgment mechanism based on the Pareto optimality principle to seek the optimal balance solution set among the objectives;

[0042] Establish a constraint sensitivity analysis model, specifically by conducting perturbation experiments on various constraint parameters to evaluate their impact on the planning solution space morphology and optimization results, and identify the key constraint factors that are most sensitive to changes in the solution space boundary, thereby providing a basis for the subsequent adjustability and robustness analysis of the planning strategy;

[0043] Establish a negotiation and reconciliation mechanism for constraint conflicts. Specifically, for conflicting constraint areas that arise during the planning process, design a negotiation strategy based on interest trade-offs and the overall goal of the system, allowing for controlled relaxation of some local soft constraints or secondary hard constraints to improve the overall feasibility and implementability of the plan.

[0044] The optimization solution method that matches the scale of the problem specifically includes: selecting an adaptive global optimization algorithm based on the scale of the planning problem and the complexity of the constraints, achieving efficient solution under the constraints, and ultimately generating an optimization planning scheme that meets multi-level constraints and has multi-objective balance characteristics.

[0045] Furthermore, the hierarchical planning constraint system further subdivides the first-level hard constraints into five subcategories: ecological protection red line, permanent basic farmland protection, historical and cultural protection, disaster risk prevention and control, and national defense security. Each subcategory has independent constraint processing rules and verification methods.

[0046] The negotiation and reconciliation mechanism for constraint conflicts adopts a multi-party negotiation model based on game theory, establishes a negotiation framework involving at least three participating parties, determines the constraint relaxation plan through Nash equilibrium solution or Pareto optimal solution, and sets the boundary conditions for constraint relaxation to ensure the realization of the overall system goals.

[0047] Furthermore, step 5 includes the following steps:

[0048] Conduct compliance verification and analysis of planning schemes, specifically including: based on the legal knowledge base and the national land and space planning standard system, conduct a comprehensive compliance check on the generated planning schemes, evaluate their coordination and consistency with the superior plan, identify potential land use conflicts and policy violations, and automatically generate a structured compliance assessment report;

[0049] Conducting plan stability and resilience analysis, specifically by conducting parameter perturbation experiments on key variables, analyzing the impact of external uncertainties on planning results, identifying highly sensitive nodes and vulnerable structural links in the plan, building a plan resilience assessment model, and quantifying the output of a resilience index;

[0050] Conduct multi-scenario simulations and adaptability verification, specifically including: constructing a set of typical scenarios covering different development trends, policy orientations, and changes in external conditions; simulating the dynamic implementation process of the planning scheme under each scenario; evaluating its adaptability, robustness, and sustainability; and verifying the effectiveness and feasibility of the scheme under various future paths;

[0051] Construct a multi-dimensional comprehensive evaluation model, specifically including: establishing a comprehensive evaluation index system for planning schemes from four dimensions: technical feasibility, economic rationality, social acceptance, and ecological and environmental compatibility; constructing a scheme confidence distribution model based on Bayesian reasoning or fuzzy mathematics methods, identifying spatial units with high uncertainty, and generating a spatial confidence distribution map reflecting the credibility of the decision;

[0052] Establishing a virtual simulation test environment includes: building a digital land space simulation platform, importing the planning scheme into the virtual operating environment for multiple rounds of dynamic simulation, tracking the system response, feedback mechanism and evolution path during the implementation of the scheme, and identifying potential systemic risks through a combination of three-dimensional visualization and quantitative indicator evaluation, so as to verify the long-term implementation effect and system stability of the planning scheme.

[0053] Furthermore, step 6 includes the following steps:

[0054] Construct a phased implementation path model, specifically by dividing the spatial planning plan into three implementation phases: short-term, medium-term, and long-term. For each phase, set phased goals and key node indicators, establish a logical dependency network between projects, and generate a planning implementation roadmap with clear time nodes, execution sequence, and priority levels.

[0055] Carry out coordinated allocation of resources in time and space, specifically including: based on a multi-stage dynamic decision-making model, coordinate the allocation of various key resources in time and space, build a hierarchical and cross-departmental organizational execution guarantee system, and improve the efficiency and coordination of the implementation of planning schemes at different stages;

[0056] Establish a real-time monitoring and early warning mechanism, specifically including: establishing a multi-dimensional indicator system covering progress execution, goal achievement, and implementation deviations; designing early warning rules based on threshold triggers; achieving multi-scale, full-process, high-frequency real-time monitoring of the plan execution process; promptly identifying execution anomalies and providing feedback and control;

[0057] Establish a dynamic planning adjustment mechanism, specifically including: regularly conducting evaluations of planning implementation effectiveness, analyzing the degree of achievement of planning goals, spatial structure evolution trends, and causes of implementation deviations, generating structured evaluation reports and optimization recommendations, and building a three-level response mechanism including routine fine-tuning, special revisions, and overall reconstruction to ensure the dynamic adjustment and adaptive evolution capabilities of the planning scheme;

[0058] Establish a scenario response and path switching mechanism, specifically including: presetting typical external change scenarios and corresponding response strategies, forming a response strategy library based on scenario identification, and designing a triggerable path switching mechanism to achieve flexible adjustment and adaptation of the planning implementation path in the face of major external disturbances;

[0059] Establish a closed-loop optimization system based on practical feedback, specifically including: converting monitoring data and decision feedback during the implementation process into reusable knowledge assets, extracting planning optimization rules and implementation evolution models, and building a closed-loop learning mechanism of "planning-implementation-monitoring-feedback-replanning" to achieve continuous self-improvement and iterative evolution of the planning system.

[0060] Furthermore, the scenario response and path switching mechanism presets at least 8 typical external change scenarios, including major industrial policy adjustments, public health emergencies, severe natural disasters, major infrastructure construction, administrative division adjustments, etc. Corresponding response strategies and path switching plans are configured for each scenario to ensure the adaptability and flexibility of the plan implementation.

[0061] Furthermore, the closed-loop optimization system based on practical feedback constructs a closed-loop architecture including a perception layer, an analysis layer, a decision-making layer and an execution layer. The frequency of data collection is no less than quarterly, and the feedback data is converted into knowledge rules after noise reduction, clustering and pattern recognition processing. The update frequency is no less than half a year, forming an intelligent planning system for self-learning and continuous optimization.

[0062] Compared with the prior art, this application has the following beneficial effects:

[0063] This application achieves continuous optimization and dynamic adaptive evolution of national land space planning by constructing a national land space knowledge graph and a multi-agent collaborative optimization mechanism, combining big data analysis and adaptive learning, thereby improving the comprehensive optimization efficiency and feasibility of the planning scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flowchart of a national land space planning method based on big data disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Throughout the drawings, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The described embodiments are only some, not all, of the embodiments of the present invention.

[0066] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0067] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0068] like Figure 1 As shown, a land space planning method based on big data includes the following steps:

[0069] Step 1: Build a national land space knowledge map;

[0070] Step 2: Construct a multi-dimensional national land space planning solution space, identify key constraints and determine the optimization path;

[0071] Step 3: Search and generate the optimal planning solution in the solution space;

[0072] Step 4: Quantify and classify the hard and soft constraints in national land space planning to achieve comprehensive optimization of multiple objectives;

[0073] Step 5: Conduct multi-dimensional confidence evaluation and effectiveness verification on the optimized planning scheme;

[0074] Step 6: Realize continuous optimization and adaptive evolution of national land space planning.

[0075] In summary, this big data-based land and space planning method achieves efficient and intelligent land and space optimization through six key steps. First, by constructing a land and space knowledge graph, it integrates multi-source, heterogeneous data to form a comprehensive spatial information system, ensuring that planning decisions are supported by solid data. Furthermore, a multidimensional land and space planning solution space is constructed, within which key constraints are identified and optimization paths are clarified, laying the foundation for subsequent optimization. Next, the method searches for and generates optimal planning solutions within the solution space, leveraging big data technology to rapidly identify the best solution that meets the objectives. Furthermore, by quantifying and hierarchically processing hard and soft constraints, the method enables precise adjustment for different types of constraints, thereby achieving comprehensive multi-objective optimization and ensuring that the planning solution meets multiple requirements while balancing various factors, such as the environment, resources, and the economy. Furthermore, the optimized planning solution undergoes multi-dimensional confidence assessment and validity verification to ensure its scientific validity and feasibility. Finally, through continuous optimization and adaptive evolution mechanisms, the method dynamically adjusts the planning solution to changing circumstances and needs during implementation, ensuring the long-term effectiveness and adaptability of land and space planning.

[0076] Furthermore, the construction of the national land space knowledge graph includes:

[0077] Standardize multi-source heterogeneous national land spatial data, including: adopting unified metadata specifications, converting the format and coordinating the scale of spatial data, attribute data and unstructured data to achieve data consistency and interoperability;

[0078] Constructing a national land space domain ontology, specifically including: defining basic concepts, attributes, and their relationships in the national land space domain based on ontology engineering and semantic web technology, and building a hierarchical ontology system to support the semantic expression and integration of multi-source data;

[0079] Conduct knowledge extraction and semantic enhancement processing, specifically including: semantic mining of unstructured text data related to land space, identifying and extracting entities, relationships, and events, and converting implicit knowledge into structured information to enhance the semantic expression capability of the knowledge graph;

[0080] Constructing a semantic relationship network between data elements, specifically including: automatically identifying semantic associations between different data sources through semantic alignment and entity linking technology, establishing logical connection relationships between data elements, and forming an association map that reflects the interaction between national land space objects;

[0081] Expanding the static knowledge graph into a dynamic spatiotemporal graph includes: introducing temporal and spatial attributes, dynamically modeling knowledge nodes and their relationships, and supporting the expression and reasoning of the evolution laws of national spatial structure and processes;

[0082] Construct an interactive application interface for knowledge services, specifically including: designing a graph interaction platform that supports complex semantic retrieval, spatial reasoning and knowledge recommendation, to achieve efficient query, visual expression and intelligent push of national land space knowledge.

[0083] In summary, this method for constructing a national spatial knowledge graph significantly enhances the processing and application capabilities of national spatial data through multiple technical means. First, by standardizing multi-source, heterogeneous data and adopting a unified metadata specification, consistency and interoperability of spatial, attribute, and unstructured data are achieved, ensuring efficient data conversion and coordination between different sources and formats. Next, an ontology system for the national spatial domain is constructed. Using ontology engineering and semantic web technologies, basic concepts, attributes, and their relationships within the domain are defined, forming a hierarchical ontology that supports the semantic expression and fusion of multi-source data. This process further utilizes knowledge extraction and semantic enhancement to perform semantic mining on unstructured text data, extracting entities, relationships, and events, transforming implicit knowledge into structured information, and enhancing the semantic expression capabilities of the knowledge graph. Furthermore, through semantic alignment and entity linking techniques, semantic associations between different data sources are automatically identified, logical connections between data elements are constructed, and a correlation graph reflecting the interactions between national spatial objects is formed. To support dynamic spatial and temporal changes, the method expands the static knowledge graph into a dynamic spatiotemporal graph, introduces temporal and spatial attributes, and dynamically models knowledge nodes and their relationships. This supports reasoning and analysis of the evolutionary patterns of national spatial structure and processes. Finally, an interactive application interface for knowledge services is designed. Through a graph interaction platform that supports complex semantic retrieval, spatial reasoning, and knowledge recommendation, efficient query, visualization, and intelligent push of national spatial knowledge are achieved.

[0084] Furthermore, the multi-source heterogeneous national land space data includes: remote sensing image data, geographic information system data, three-dimensional surveying and mapping data, socioeconomic statistical data, natural resource survey data, land use historical data, population distribution data, traffic flow data, environmental monitoring data and climate change data.

[0085] Furthermore, the metadata specification adopts an extended version of the ISO 19115 geographic information metadata standard, which includes spatial reference system, time attributes, spatial resolution, data quality, data source and responsible party information to achieve standardization and unification of data description.

[0086] Furthermore, the construction of the national land space domain ontology adopts a modular design method, including a core ontology module, a domain extension module and an application layer ontology module. The core ontology covers the concepts of regional spatial units, land use types, natural resources, infrastructure and administrative boundaries. The domain extension module includes knowledge of ecological environment, urban system, industrial layout, population distribution and transportation network. The application layer ontology module provides special concepts and relationships for planning, implementation management and monitoring and evaluation scenarios.

[0087] Furthermore, the knowledge extraction and semantic enhancement processing adopts a hybrid method that combines deep learning and rules, including using a pre-trained language model for entity recognition and relationship extraction, and combining a domain rule library for knowledge verification and completion, to achieve high-precision extraction of implicit knowledge in unstructured data.

[0088] Furthermore, step 2 includes the following steps:

[0089] Constructing a multi-dimensional national land space planning solution space, specifically including: Based on the entity relationships and attribute constraints in the national land space knowledge map, constructing a high-dimensional solution space covering land use, ecological environment, economic development, population distribution and infrastructure dimensions, where each dimension is defined by a corresponding indicator system and constraint conditions to determine the feasible domain of the planning scheme;

[0090] Conduct topological structure analysis of the solution space, specifically including: topological structure modeling and analysis of the constructed high-dimensional solution space, calculating the centrality index, clustering coefficient, and path connectivity of network nodes, identifying core nodes and key links with key influence, and revealing the coupling relationship and system structure characteristics between different planning dimensions;

[0091] Conduct hierarchical classification and weight assignment of constraints, specifically including: classifying and weighting various hard and soft constraints that affect the planning solution space, analyzing their scope and impact, identifying key constraint factors that have a decisive impact on the solution space boundary and structure, and clarifying the key control variables in the planning process;

[0092] Constructing a solution space goal-oriented evaluation system, specifically including: setting global optimization goals and local optimization goals based on the national land space strategic orientation and development needs, forming a hierarchical goal system to guide the optimization process of planning paths in the solution space;

[0093] Determine the optimal path in the solution space, specifically including: under the constraints, calculate the set of feasible paths under multiple constraint combinations, evaluate the execution cost, resource allocation efficiency and expected benefits of each path, and then screen out the optimal path plan that meets the multi-objective optimization requirements, ensuring that the generated planning plan achieves the optimal solution for overall resource allocation within the constraints.

[0094] In summary, the construction and optimization of a multidimensional solution space significantly enhances the scientificity and feasibility of national land space planning schemes. First, based on the entity relationships and attribute constraints within the national land space knowledge graph, a high-dimensional solution space was constructed encompassing multiple dimensions, including land use, ecological environment, economic development, population distribution, and infrastructure. Each dimension was assigned a corresponding indicator system and constraints to ensure the feasibility of the planning scheme under these multidimensional constraints. Next, a topological analysis of the high-dimensional solution space was performed, calculating node centrality, clustering coefficient, and path connectivity to identify key core nodes and links. This revealed the coupling relationships and overall structural characteristics between the dimensions and further clarified the mutual influence of different dimensions on the planning scheme. Finally, by hierarchically classifying and weighting hard and soft constraints, the impact of each constraint on the solution space boundary and structure was clarified, thereby identifying key control variables and providing clear guidance for subsequent optimization. Next, based on national land space strategic goals and development needs, a goal-oriented evaluation system was constructed. By setting global and local optimization objectives, it guided the selection of planning paths within the solution space. Finally, under the constraints, by calculating the set of feasible paths under different constraint combinations, evaluating the execution cost, resource allocation efficiency and expected benefits of the path, the optimal path plan that meets the multi-objective optimization requirements is screened out to ensure that the generated planning plan achieves the best balance in resource allocation and benefit realization.

[0095] Furthermore, the following steps are used to identify the core nodes and key links with key influence:

[0096] Map the high-dimensional solution space into a graph G = (V, E), where nodes v∈V represent each planning unit in the space, and edges e∈E represent the transfer channels between units. V is the set of all nodes in the graph G, corresponding to each solution in the high-dimensional solution space; E is the set of all edges in the graph G, representing the transfer between solutions.

[0097] Calculate the degree centrality C for each node in the graph D (v) = deg(v), betweenness centrality and closeness centrality It is used to quantify the direct influence, path control effect and information propagation efficiency of each node in the entire solution space, where C D (v) is the degree centrality of node v; deg(v) is the degree of node v, that is, the number of edges connected to it; C B (v) is the betweenness centrality of node v; s, t represent any two different nodes, used for the starting and ending points of the shortest path statistics; σ st is the total number of all shortest paths from node s to node t; σ st (v) represents the number of paths passing through node v in the shortest path from s to t; C C (v) is the closeness centrality of node v; u is any node in the graph G, used to represent the distance calculation object with respect to node v; d(v,u) is the shortest path distance between node v and node u;

[0098] Calculate the local clustering coefficient of each node Evaluate the connectivity density within its neighborhood to reveal the synergy and clustering characteristics of the local substructure, where C(v) is the local clustering coefficient of node v, which measures the connectivity between its neighbors; e(v) is the actual number of edges between the neighbors of node v;

[0099] Using the shortest path formula Analyze the connectivity between any pair of nodes to identify the reachability and potential bottlenecks between nodes in the graph. Π(u,v) represents the set of all possible paths from node u to node v; π is a path in the path set; w(e) represents the weight of edge e, which is used to indicate the importance of the edge.

[0100] Based on the above centrality indicators, nodes with higher values ​​of degree, betweenness and closeness centrality are selected as core nodes with key influence;

[0101] Based on path connectivity analysis and node clustering, identify the edges that occupy a bridge position in the shortest path and network connectivity, namely the key links.

[0102] Furthermore, identifying the key constraint factors that have a decisive influence on the boundary and structure of the solution space includes the following steps:

[0103] The constraints in the planning solution space are divided into hard constraints C h ={C h1 ,C h2 ,…,C hm} and soft constraint C s ={C s1 ,C s2 ,…,C sn}, and assign weight w to each constraint h,iWith w s,j , to reflect its importance and priority in the overall plan;

[0104] Define constraint C i The influence function is It is used to quantify the sensitivity of the solution space to the change of the constraint, ensuring that the impact of each constraint can be scientifically evaluated based on the change. i ) represents the constraint C i The degree of influence on the change of the planning solution space; ΔSolution represents the magnitude of the change in the solution space; ΔC i Represents constraint C i The amount of change;

[0105] Based on the assigned weights and influence functions, construct the influence measure of each constraint on the boundary of the solution space: For hard constraints, calculate BoundaryInfluence (C h,i )=w h,i Impact(C h,i ); For soft constraints, calculate BoundaryInfluence(C s,j )=w s,j Impact(C s,j ), thereby obtaining the specific scope of each constraint on the boundary, where: BoundaryInfluence(C h,i ) represents the hard constraint C h,i The degree of influence on the boundary of the solution space; w h,i Represents a hard constraint C h,i The weight of Impact(C h,i ) represents the hard constraint C h,i The strength of the influence on the solution space; BoundaryInfluence (C s,j ) represents the soft constraint C s,j The degree of influence on the boundary of the solution space; w s,j Represents soft constraint C s,j The weight of Impact(C s,j ) represents the soft constraint C s,j The intensity of the impact on the solution space;

[0106] Compute the overall influence measure of all constraints in the planning solution space by weighted summation: Among them, I total represents the overall impact measure of all constraints in the planning solution space; m represents the total number of hard constraints, that is, the constraint set C h The number of constraints in the set C; n represents the total number of soft constraints, that is, the constraint set C s The number of constraints in

[0107] According to the influence degree metric ranking, find the key constraint factors that have the most significant impact on the boundary and structure of the planning solution space, and sort them by formula Find the key constraint factors that have the most significant impact on the boundary and structure of the planning solution space, and sort them by formula key The key constraint factors that have the most significant impact on the boundary and structure of the solution space are identified.

[0108] Further, the construction of the multi-dimensional land space planning solution space adopts high-dimensional tensor representation method, and constructs N planning dimensions as N-order tensors. Each dimension is subdivided into a number of indicators and constraint conditions, forming a complex solution space containing at least 1000 planning variables and 500 constraint conditions.

[0109] Further, the solution space topology analysis adopts complex network theory, calculates network characteristic indicators including betweenness centrality, eigenvector centrality and community structure, identifies key nodes and bottleneck links in the solution space, and presents low-dimensional mapping representation of high-dimensional solution space through visualization method.

[0110] Further, the hierarchical classification of constraint conditions adopts a three-level system: the first level is legal regulations and red line control hard constraints, the second level is technical standards and specification constraints, and the third level is policy guidance and development goal soft constraints. The weights of each level of constraints are assigned by AHP method, with the weight of first-level constraints not less than 0.5, the weight of second-level constraints not less than 0.3, and the weight of third-level constraints not more than 0.2.

[0111] Further, the target-oriented evaluation system adopts balanced scorecard method, and constructs an index system from four dimensions of economic development, social fairness, ecological protection and spatial efficiency. Four to six key performance indicators are set under each dimension, and the standard values and weight coefficients of each indicator are determined based on historical data and expert judgment.

[0112] Further, step 3 includes the following steps:

[0113] Deploying regional exploration agents, specifically including: according to the macro planning target and spatial strategy orientation, performing global search and partition exploration tasks in the land space planning solution space, using spatial clustering and partition differentiation algorithm to identify functional blocks with similar geographical characteristics and development potential, generating a preliminary framework scheme of spatial layout, and establishing the development orientation and spatial structure form of each region;

[0114] Constructing constraint verification agents, specifically including: introducing and loading statutory constraint conditions and technical specifications, performing constraint consistency checking on the preliminary scheme generated by regional exploration agents, identifying spatial units that do not meet rigid constraints, marking conflict areas and outputting correction suggestions, to ensure that the generated scheme meets the compliance requirements of legal regulations and policy constraints;

[0115] Configure a refined optimization agent, specifically: after the preliminary plan passes the constraint verification, make local fine-tuning of the land use structure, industrial function layout and infrastructure network, improve the efficiency of spatial resource allocation and the rationality of organizational form based on micro-scale optimization algorithms, and achieve refined adjustment and optimization of the planning scheme;

[0116] Establish a multi-agent communication and collaboration mechanism, specifically: building a multi-level communication network based on a message-passing mechanism to enable information exchange and feedback loops between regional exploration, constraint verification, and fine-tuning agents. Reconciling decision-making differences among agents through iterative negotiation strategies, building a closed-loop collaborative process of "macro-exploration-constraint verification-micro-optimization" to ensure the logical consistency and integrity of the planning scheme.

[0117] Develop an adaptive learning module, specifically: building a case library and rule library based on historical planning cases, extracting typical planning patterns and strategies through deep learning and pattern mining, and empowering various intelligent agents with experience transfer and strategy update capabilities to continuously optimize planning search paths and agent behavior rules;

[0118] The evolutionary generation mechanism of planning schemes is realized, specifically including: introducing an evolutionary algorithm based on a population diversity maintenance mechanism to generate multiple alternative planning schemes with differences and diversity, and using the Pareto optimal principle to screen and combine non-dominated solutions to form an optimal solution cluster that meets multi-objective constraints and planning requirements.

[0119] In summary, the introduction of intelligent agents and multi-agent collaboration significantly enhances the automation, refinement, and optimization capabilities of national land spatial planning. First, a regional exploration agent is deployed to perform global search and zoning exploration tasks based on macro-planning objectives and spatial strategic guidance. Spatial clustering and zoning differentiation algorithms are used to identify functional areas with similar geographic characteristics and development potential, thereby generating a preliminary framework for spatial layout and determining the positioning and structural form of each region. Next, a constraint verification agent is introduced to verify the preliminary plan against legal constraints and technical specifications, ensuring compliance. Areas that do not meet rigid constraints are identified and corrective suggestions are provided to ensure compliance. Finally, after the preliminary plan passes constraint verification, a refined optimization agent is deployed to fine-tune land use, industrial functions, and infrastructure. Micro-optimization algorithms are used to improve the rationality of spatial resource allocation and organizational form, achieving refined optimization of the plan. Furthermore, multi-agent communication and collaboration mechanisms enable information exchange and feedback loops between agents, ensuring the logical consistency and integrity of the planning plan throughout the "macro-exploration-constraint verification-micro-optimization" process. Furthermore, an adaptive learning module was developed, enabling the agent to extract planning patterns and strategies through deep learning based on a historical case library and rule base. This enhanced the agent's ability to transfer experience and update strategies, continuously optimizing planning paths and decision-making rules. Finally, an evolutionary generation mechanism was employed to generate multiple differentiated alternative solutions through an evolutionary algorithm that maintains population diversity. The optimal solution set was selected based on the Pareto optimality principle, ensuring that the planning solution achieves the optimal configuration under multi-objective constraints.

[0120] Furthermore, the regional exploration agent adopts a hierarchical reinforcement learning architecture, including a three-level decision-making mechanism: strategic, tactical, and operational. The strategic layer is responsible for macro-spatial structure and functional positioning, the tactical layer is responsible for meso-regional division and functional organization, and the operational layer is responsible for micro-land use unit configuration. The three layers of agents coordinate with each other through hierarchical reward signals, forming a planning and decision-making process that combines top-down and bottom-up approaches.

[0121] The constraint verification agent uses a reasoning engine based on a knowledge graph, loaded with no less than 2,000 planning regulations and technical standards, and supports both forward chain reasoning and backward chain reasoning. The verification efficiency reaches 10,000 spatial units per second.

[0122] The refined optimization agent uses a suite of multi-objective optimization algorithms, including the Pareto frontier-based NSGA-II algorithm, the multi-objective particle swarm optimization algorithm, and the multi-objective differential evolution algorithm. It adaptively selects the most suitable algorithm for local optimization problems of varying complexity, achieving fine-tuning and optimization of planning schemes.

[0123] The multi-agent communication and coordination mechanism adopts a distributed architecture based on federated learning, including a point-to-point communication protocol and a centralized coordination node, to realize knowledge sharing and behavior coordination among different types of agents, and to ensure continuous operation capability in the case of single point failure;

[0124] The adaptive learning module contains a structured knowledge base of at least 5000 historical planning cases, adopts a hybrid learning strategy combining case-based reasoning and deep reinforcement learning, and realizes analogical reasoning and experience transfer of the agent when encountering new problems, with the learning efficiency increasing logarithmically with the expansion of the case base size.

[0125] Further, step 4 includes the following steps:

[0126] A hierarchical planning constraint system is constructed, specifically including: defining statutory mandatory requirements as first-level hard constraints, defining physical and environmental limitations as second-level hard constraints, and defining development-oriented goals as soft constraints, thereby forming a multi-level constraint system with clear priority and hierarchical structure, for unified specification of constraint classification and processing logic in the planning scheme generation process;

[0127] Quantitative modeling and unified expression of constraints are realized, specifically including: converting qualitative description of planning constraints into calculable quantitative parameters through spatial threshold setting, mathematical inequality construction and logical expression analysis, constructing a unified constraint violation metric function, and realizing expression, calculation and comparison of different types of constraints in a unified mathematical framework;

[0128] Soft constraints are converted into optimization objective functions, specifically including: on the basis of meeting first-level and second-level hard constraints, designing corresponding objective functions according to the development goals represented by soft constraints, constructing a multi-objective solution evaluation mechanism based on the Pareto optimality principle, and seeking the optimal balanced solution set among the objectives;

[0129] A constraint sensitivity analysis model is established, specifically including: through perturbation experiments on various constraint parameters, evaluating their influence on the shape of the planning solution space and the optimization results, and identifying the key constraint factors most sensitive to the change of the solution space boundary, thereby providing a basis for subsequent analysis of the adjustability and robustness of the planning strategy;

[0130] A negotiation and reconciliation mechanism for constraint conflicts is constructed, specifically including: for constraint conflict areas that appear in the planning process, designing a negotiation strategy based on benefit trade-off and overall system goal orientation, allowing controlled relaxation of some local soft constraints or secondary hard constraints, to improve the overall feasibility and executability of the scheme;

[0131] The optimization solution method that matches the scale of the problem specifically includes: selecting an adaptive global optimization algorithm based on the scale of the planning problem and the complexity of the constraints, achieving efficient solution under the constraints, and ultimately generating an optimization planning scheme that meets multi-level constraints and has multi-objective balance characteristics.

[0132] In summary, by constructing a hierarchical planning constraint system and optimization algorithm, we achieved the precise generation and multi-objective optimization of national land space planning schemes under complex constraints. First, by constructing a multi-level constraint system with clear priorities and a hierarchical structure, different types of planning constraints (such as statutory requirements, physical environmental constraints, and development-oriented goals) were classified. Corresponding processing logic was defined for each constraint, ensuring the standardization and consistency of constraints during the planning process. Next, through quantitative modeling and unified expression, qualitative planning constraints were converted into computable quantitative parameters. A unified constraint measurement function was established using mathematical inequalities and logical expressions, ensuring the comparability and computability of different types of constraints within a unified mathematical framework. For soft constraints, objective functions were designed based on the development goals they represent. Incorporating the Pareto optimality principle, a mechanism for evaluating the quality of multi-objective solutions was established to explore the optimal balance between objectives. Furthermore, a constraint sensitivity analysis model was used to evaluate the impact of various constraints on the planning solution space and optimization results, identifying the key constraint factors most sensitive to changes in the solution space boundaries, providing a basis for subsequent strategy adjustments. For areas of conflicting constraints, a negotiation and reconciliation mechanism was designed. By balancing interests and prioritizing overall goals, it allows for the appropriate relaxation of certain soft or secondary hard constraints, thereby improving the overall feasibility and enforceability of the planning scheme. Finally, based on the scale and complexity of the planning problem, an appropriate global optimization algorithm was selected for efficient solution, generating an optimized planning scheme that meets multi-level constraints and possesses multi-objective balance characteristics.

[0133] Furthermore, the hierarchical planning constraint system further subdivides the first-level hard constraints into five subcategories: ecological protection red line, permanent basic farmland protection, historical and cultural protection, disaster risk prevention and control, and national defense security. Each subcategory has independent constraint processing rules and verification methods.

[0134] The negotiation and reconciliation mechanism for constraint conflicts adopts a multi-party negotiation model based on game theory, establishes a negotiation framework involving at least three participating parties, determines the constraint relaxation plan through Nash equilibrium solution or Pareto optimal solution, and sets the boundary conditions for constraint relaxation to ensure the realization of the overall system goals.

[0135] Furthermore, step 5 includes the following steps:

[0136] Conduct compliance verification and analysis of planning schemes, specifically including: based on the legal knowledge base and the national land and space planning standard system, conduct a comprehensive compliance check on the generated planning schemes, evaluate their coordination and consistency with the superior plan, identify potential land use conflicts and policy violations, and automatically generate a structured compliance assessment report;

[0137] Conducting plan stability and resilience analysis, specifically by conducting parameter perturbation experiments on key variables, analyzing the impact of external uncertainties on planning results, identifying highly sensitive nodes and vulnerable structural links in the plan, building a plan resilience assessment model, and quantifying the output of a resilience index;

[0138] Conduct multi-scenario simulations and adaptability verification, specifically including: constructing a set of typical scenarios covering different development trends, policy orientations, and changes in external conditions; simulating the dynamic implementation process of the planning scheme under each scenario; evaluating its adaptability, robustness, and sustainability; and verifying the effectiveness and feasibility of the scheme under various future paths;

[0139] Construct a multi-dimensional comprehensive evaluation model, specifically including: establishing a comprehensive evaluation index system for planning schemes from four dimensions: technical feasibility, economic rationality, social acceptance, and ecological and environmental compatibility; constructing a scheme confidence distribution model based on Bayesian reasoning or fuzzy mathematics methods, identifying spatial units with high uncertainty, and generating a spatial confidence distribution map reflecting the credibility of the decision;

[0140] Establishing a virtual simulation test environment includes: building a digital land space simulation platform, importing the planning scheme into the virtual operating environment for multiple rounds of dynamic simulation, tracking the system response, feedback mechanism and evolution path during the implementation of the scheme, and identifying potential systemic risks through a combination of three-dimensional visualization and quantitative indicator evaluation, so as to verify the long-term implementation effect and system stability of the planning scheme.

[0141] In summary, through a series of systematic analyses and verifications, the planning scheme's comprehensive reliability across multiple dimensions, including compliance, stability, adaptability, and feasibility, was ensured. First, through compliance verification analysis based on a regulatory knowledge base and a planning standards system, the planning scheme was ensured to comply with higher-level planning requirements, avoiding land use conflicts and policy violations. A structured compliance report was automatically generated. Second, through scheme stability and resilience analysis, the impact of external uncertainties on planning outcomes was assessed, sensitive nodes and vulnerable links were identified, and a resilience index was quantified to ensure the long-term implementation of the plan. Furthermore, through multi-scenario simulations and adaptability verification, the planning scheme's implementation effects under different development trends, policy changes, and external conditions were examined to ensure its adaptability and sustainability under various future scenarios. Next, a comprehensive evaluation model was established, encompassing four dimensions: technical, economic, social, and ecological. Using Bayesian reasoning or fuzzy mathematics, the credibility of the planning scheme was assessed, and spatial confidence distribution maps were generated to identify areas of high uncertainty. Finally, by building a virtual simulation testing environment, the planning scheme was imported into a digital land space simulation platform for dynamic simulation. Combining 3D visualization with quantitative indicator evaluation, potential risks were identified and the implementation effectiveness and stability of the planning scheme were verified. Through this comprehensive analysis and verification, the comprehensive feasibility and long-term effectiveness of the planning scheme were ensured across multiple dimensions.

[0142] Furthermore, the multi-scenario simulation and adaptability verification constructs at least five typical future scenarios, including conventional development scenarios, high-speed development scenarios, low-speed development scenarios, technological breakthrough scenarios and extreme climate scenarios. In each scenario, a range of variation of 10-15 key parameters is set, and 1,000 random scenario samples are generated through the Monte Carlo simulation method to evaluate the adaptability and effectiveness of the planning scheme under various future paths.

[0143] Furthermore, the calculation step of the toughness index includes:

[0144] Determine all key variables x in the planning results g , set its baseline value and the disturbance amplitude δ, and through the formula Generate multiple perturbation scenarios for each variable to form an experimental dataset, where represents the value of the g-th key variable in the r-th perturbation experiment;

[0145] Using response functions Calculate the planning results under each disturbance condition and determine the benchmark results As evaluation criteria, among which: y (r) It represents the value of the planning result output under the rth disturbance experiment condition; represents the value of the g-th key variable in the r-th perturbation experiment; M represents the total number of key variables; ybase represents the output result under the benchmark conditions, which serves as the comparison standard; f represents the response function of the planning result; represents the baseline value of the g-th variable;

[0146] For each key variable, the formula Calculate the sensitivity of its impact on the planning results and quantify the average contribution of variable changes to the system results, where S g represents the average sensitivity of the g-th variable; N represents the total number of perturbation experiments;

[0147] Calculate the standard deviation σ of the sensitivity of each variable g This allows identification of highly sensitive nodes and vulnerable structural links;

[0148] Combining the sensitivity and volatility of key variables, an overall planning resilience assessment model is constructed;

[0149] Using the formula Calculate the toughness index R index , where: ∈ is a very small constant that prevents division by zero and ensures robust evaluation.

[0150] Furthermore, step 6 includes the following steps:

[0151] Construct a phased implementation path model, specifically by dividing the spatial planning plan into three implementation phases: short-term, medium-term, and long-term. For each phase, set phased goals and key node indicators, establish a logical dependency network between projects, and generate a planning implementation roadmap with clear time nodes, execution sequence, and priority levels.

[0152] Carry out coordinated allocation of resources in time and space, specifically including: based on a multi-stage dynamic decision-making model, coordinate the allocation of various key resources in time and space, build a hierarchical and cross-departmental organizational execution guarantee system, and improve the efficiency and coordination of the implementation of planning schemes at different stages;

[0153] Establish a real-time monitoring and early warning mechanism, specifically including: establishing a multi-dimensional indicator system covering progress execution, goal achievement, and implementation deviations; designing early warning rules based on threshold triggers; achieving multi-scale, full-process, high-frequency real-time monitoring of the plan execution process; promptly identifying execution anomalies and providing feedback and control;

[0154] Establish a dynamic planning adjustment mechanism, specifically including: regularly conducting evaluations of planning implementation effectiveness, analyzing the degree of achievement of planning goals, spatial structure evolution trends, and causes of implementation deviations, generating structured evaluation reports and optimization recommendations, and building a three-level response mechanism including routine fine-tuning, special revisions, and overall reconstruction to ensure the dynamic adjustment and adaptive evolution capabilities of the planning scheme;

[0155] Establish a scenario response and path switching mechanism, specifically including: presetting typical external change scenarios and corresponding response strategies, forming a response strategy library based on scenario identification, and designing a triggerable path switching mechanism to achieve flexible adjustment and adaptation of the planning implementation path in the face of major external disturbances;

[0156] Establish a closed-loop optimization system based on practical feedback, specifically including: converting monitoring data and decision feedback during the implementation process into reusable knowledge assets, extracting planning optimization rules and implementation evolution models, and building a closed-loop learning mechanism of "planning-implementation-monitoring-feedback-replanning" to achieve continuous self-improvement and iterative evolution of the planning system.

[0157] In summary, systematic implementation path planning and dynamic adjustment mechanisms ensure the efficient execution and adaptive evolution of planning solutions. First, by constructing a phased implementation path model, the planning solution is divided into different phases, with clear goals and key milestone indicators set, forming a clear implementation roadmap and ensuring the orderly progress of the plan. Second, the coordinated spatial and temporal allocation of resources optimizes the allocation of key resources through a multi-phase dynamic decision-making model, improving the efficiency and synergy of implementation at each phase. The real-time monitoring and early warning mechanism implements high-frequency monitoring throughout the entire process through a multi-dimensional indicator system, promptly identifying anomalies and implementing adjustments to ensure the effectiveness of plan implementation. Regular evaluations and a dynamic adjustment mechanism analyze implementation results and provide optimization recommendations, ensuring that the plan can be flexibly adjusted to respond to changes. The scenario response and path switching mechanism enables flexible adjustment of the planning path in the face of external disturbances through pre-set scenarios and response strategies. Finally, a closed-loop optimization system based on practical feedback transforms data and feedback from the implementation process into knowledge assets, promoting continuous optimization and iterative development of the plan, ensuring that the plan adapts to the ever-changing environment and needs.

[0158] Furthermore, the scenario response and path switching mechanism presets at least 8 typical external change scenarios, including major industrial policy adjustments, public health emergencies, severe natural disasters, major infrastructure construction, administrative division adjustments, etc. Corresponding response strategies and path switching plans are configured for each scenario to ensure the adaptability and flexibility of the plan implementation.

[0159] Furthermore, the closed-loop optimization system based on practical feedback constructs a closed-loop architecture including a perception layer, an analysis layer, a decision-making layer and an execution layer. The frequency of data collection is no less than quarterly, and the feedback data is converted into knowledge rules after noise reduction, clustering and pattern recognition processing. The update frequency is no less than half a year, forming an intelligent planning system for self-learning and continuous optimization.

[0160] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art will appreciate that modifications may be made to the technical solutions described in the above embodiments, or that some of the technical features may be replaced with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A land space planning method based on big data, characterized in that: The following steps are involved: Step 1: Build a national land space knowledge map; Step 2: Construct a multi-dimensional national land space planning solution space, identify key constraints and determine the optimization path; Step 3: Search and generate the optimal planning solution in the solution space; Step 4: Quantify and classify the hard and soft constraints in national land space planning to achieve comprehensive optimization of multiple objectives; Step 5: Conduct multi-dimensional confidence evaluation and effectiveness verification on the optimized planning scheme; Step 6: Realize continuous optimization and adaptive evolution of national land space planning.

2. The method for land space planning based on big data according to claim 1, characterized in that: The construction of the national land space knowledge map includes: Standardize multi-source heterogeneous national land spatial data, including: adopting unified metadata specifications, converting the format and coordinating the scale of spatial data, attribute data and unstructured data to achieve data consistency and interoperability; Constructing a national land space domain ontology, specifically including: defining basic concepts, attributes, and their relationships in the national land space domain based on ontology engineering and semantic web technology, and building a hierarchical ontology system to support the semantic expression and integration of multi-source data; Conduct knowledge extraction and semantic enhancement processing, specifically including: semantic mining of unstructured text data related to land space, identifying and extracting entities, relationships, and events, and converting implicit knowledge into structured information to enhance the semantic expression capability of the knowledge graph; Constructing a semantic relationship network between data elements, specifically including: automatically identifying semantic associations between different data sources through semantic alignment and entity linking technology, establishing logical connection relationships between data elements, and forming an association map that reflects the interaction between national land space objects; Expanding the static knowledge graph into a dynamic spatiotemporal graph includes: introducing temporal and spatial attributes, dynamically modeling knowledge nodes and their relationships, and supporting the expression and reasoning of the evolution laws of national spatial structure and processes; Construct an interactive application interface for knowledge services, specifically including: designing a graph interaction platform that supports complex semantic retrieval, spatial reasoning and knowledge recommendation, to achieve efficient query, visual expression and intelligent push of national land space knowledge.

3. The method for land space planning based on big data according to claim 1, characterized in that: Step 2 includes the following steps: Constructing a multi-dimensional national land space planning solution space, specifically including: Based on the entity relationships and attribute constraints in the national land space knowledge map, constructing a high-dimensional solution space covering land use, ecological environment, economic development, population distribution and infrastructure dimensions, where each dimension is defined by a corresponding indicator system and constraint conditions to determine the feasible domain of the planning scheme; Conduct topological structure analysis of the solution space, specifically including: topological structure modeling and analysis of the constructed high-dimensional solution space, calculating the centrality index, clustering coefficient, and path connectivity of network nodes, identifying core nodes and key links with key influence, and revealing the coupling relationship and system structure characteristics between different planning dimensions; Conduct hierarchical classification and weight assignment of constraints, specifically including: classifying and weighting various hard and soft constraints that affect the planning solution space, analyzing their scope and impact, identifying key constraint factors that have a decisive impact on the solution space boundary and structure, and clarifying the key control variables in the planning process; Constructing a solution space goal-oriented evaluation system, specifically including: setting global optimization goals and local optimization goals based on the national land space strategic orientation and development needs, forming a hierarchical goal system to guide the optimization process of planning paths in the solution space; Determine the optimal path in the solution space, specifically including: under the constraints, calculate the set of feasible paths under multiple constraint combinations, evaluate the execution cost, resource allocation efficiency and expected benefits of each path, and then screen out the optimal path plan that meets the multi-objective optimization requirements, ensuring that the generated planning plan achieves the optimal solution for overall resource allocation within the constraints.

4. The method for land space planning based on big data according to claim 3, characterized in that: The hierarchical classification of the constraints adopts a three-level system: the first level is hard constraints such as laws, regulations and red line control, the second level is constraints such as technical standards and specifications, and the third level is soft constraints such as policy orientation and development goals. The weight coefficients of constraints at each level are assigned through the hierarchical analysis method. The weight of the first-level constraint is not less than 0.5, the weight of the second-level constraint is not less than 0.3, and the weight of the third-level constraint is not higher than 0.

2.

5. The method for land space planning based on big data according to claim 1, characterized in that: Step 3 includes the following steps: Deployment of regional exploration agents, specifically including: based on macro-planning goals and spatial strategic orientation, performing global search and zoning exploration tasks in the national land space planning solution space, using spatial clustering and zoning differentiation algorithms to identify functional blocks with similar geographical characteristics and development potential, generating a preliminary framework plan for spatial layout, and establishing the development positioning and spatial structure of each region; Construct a constraint-verifying agent, specifically including: introducing and loading legal constraints and technical specifications, performing constraint consistency checks on the preliminary solutions generated by the regional exploration agent, identifying spatial units that do not meet rigid constraints, marking conflicting areas, and outputting correction suggestions to ensure that the generated solutions meet the compliance requirements of laws, regulations, and policies; Configure a refined optimization agent, specifically: after the preliminary plan passes the constraint verification, make local fine-tuning of the land use structure, industrial function layout and infrastructure network, improve the efficiency of spatial resource allocation and the rationality of organizational form based on micro-scale optimization algorithms, and achieve refined adjustment and optimization of the planning scheme; Establish a multi-agent communication and collaboration mechanism. Specifically, this includes: building a multi-level communication network based on a message-passing mechanism to enable information exchange and feedback loops between regional exploration, constraint verification, and fine-tuning agents. Iterative negotiation strategies are used to coordinate decision-making differences among agents, creating a closed-loop collaborative process of "macro-exploration-constraint verification-micro-optimization" to ensure the logical consistency and integrity of the planning scheme. Develop an adaptive learning module, specifically: building a case library and rule library based on historical planning cases, extracting typical planning patterns and strategies through deep learning and pattern mining, and empowering various intelligent agents with experience transfer and strategy update capabilities to continuously optimize planning search paths and agent behavior rules; The evolutionary generation mechanism of planning schemes is realized, specifically including: introducing an evolutionary algorithm based on a population diversity maintenance mechanism to generate multiple alternative planning schemes with differences and diversity, and using the Pareto optimal principle to screen and combine non-dominated solutions to form an optimal solution cluster that meets multi-objective constraints and planning requirements.

6. The method for land space planning based on big data according to claim 5, characterized in that: The regional exploration agent adopts a hierarchical reinforcement learning architecture, including a three-level decision-making mechanism at the strategic, tactical, and operational levels. The strategic level is responsible for macro-spatial structure and functional positioning, the tactical level is responsible for meso-regional division and functional organization, and the operational level is responsible for micro-land use unit configuration. The three levels of agents coordinate with each other through hierarchical reward signals, forming a planning and decision-making process that combines top-down and bottom-up approaches. The constraint verification agent uses a reasoning engine based on a knowledge graph, loaded with no less than 2,000 planning regulations and technical standards, and supports both forward chain reasoning and backward chain reasoning. The verification efficiency reaches 10,000 spatial units per second. The refined optimization agent uses a suite of multi-objective optimization algorithms, including the Pareto frontier-based NSGA-II algorithm, the multi-objective particle swarm optimization algorithm, and the multi-objective differential evolution algorithm. It adaptively selects the most suitable algorithm for local optimization problems of varying complexity, achieving fine-tuning and optimization of planning schemes. The multi-agent communication and collaboration mechanism adopts a distributed architecture based on federated learning, including a point-to-point communication protocol and a centralized coordination node, to achieve knowledge sharing and behavior coordination among different types of agents, and ensure continuous operation in the event of a single point of failure; The adaptive learning module contains a structured knowledge base of at least 5,000 historical planning cases. It adopts a hybrid learning strategy that combines case reasoning and deep reinforcement learning to enable the intelligent agent to use analogical reasoning and experience transfer when encountering new problems. The learning efficiency grows logarithmically with the expansion of the case library.

7. The method for land space planning based on big data according to claim 1, characterized in that: Step 4 includes the following steps: Construct a hierarchical planning constraint system, specifically defining legally mandatory requirements as first-level hard constraints, physical and environmental restrictions as second-level hard constraints, and development-oriented goals as soft constraints. This will form a multi-level constraint system with clear priorities and a hierarchical structure, which will be used to standardize the constraint classification and processing logic during the planning scheme generation process. Achieve quantitative modeling and unified expression of constraints, specifically by converting qualitatively described planning constraints into computable quantitative parameters through spatial threshold setting, mathematical inequality construction, and logical expression parsing, constructing a unified constraint violation metric function, and enabling the expression, calculation, and comparison of different types of constraints within a unified mathematical framework. Transforming soft constraints into optimization objective functions involves: on the basis of satisfying the first- and second-level hard constraints, designing corresponding objective functions according to the development goals represented by the soft constraints, and building a multi-objective solution judgment mechanism based on the Pareto optimality principle to seek the optimal balance solution set among the objectives; Establish a constraint sensitivity analysis model, specifically by conducting perturbation experiments on various constraint parameters to evaluate their impact on the planning solution space morphology and optimization results, and identify the key constraint factors that are most sensitive to changes in the solution space boundary, thereby providing a basis for the subsequent adjustability and robustness analysis of the planning strategy; Establish a negotiation and reconciliation mechanism for constraint conflicts. Specifically, for conflicting constraint areas that arise during the planning process, design a negotiation strategy based on interest trade-offs and the overall goal of the system, allowing for controlled relaxation of some local soft constraints or secondary hard constraints to improve the overall feasibility and implementability of the plan. The optimization solution method that matches the scale of the problem specifically includes: selecting an adaptive global optimization algorithm based on the scale of the planning problem and the complexity of the constraints, achieving efficient solution under the constraints, and ultimately generating an optimization planning scheme that meets multi-level constraints and has multi-objective balance characteristics.

8. The method for land space planning based on big data according to claim 7, characterized in that: The hierarchical planning constraint system further subdivides the first-level hard constraints into five subcategories: ecological protection red line, permanent basic farmland protection, historical and cultural protection, disaster risk prevention and control, and national defense security. Each subcategory has independent constraint processing rules and verification methods. The negotiation and reconciliation mechanism for constraint conflicts adopts a multi-party negotiation model based on game theory, establishes a negotiation framework involving at least three participating parties, determines the constraint relaxation plan through Nash equilibrium solution or Pareto optimal solution, and sets the boundary conditions for constraint relaxation to ensure the realization of the overall system goals.

9. The method for land space planning based on big data according to claim 1, characterized in that: Step 5 includes the following steps: Conduct compliance verification and analysis of planning schemes, specifically including: based on the legal knowledge base and the national land and space planning standard system, conduct a comprehensive compliance check on the generated planning schemes, evaluate their coordination and consistency with the superior plan, identify potential land use conflicts and policy violations, and automatically generate a structured compliance assessment report; Conducting plan stability and resilience analysis, specifically by conducting parameter perturbation experiments on key variables, analyzing the impact of external uncertainties on planning results, identifying highly sensitive nodes and vulnerable structural links in the plan, building a plan resilience assessment model, and quantifying the output of a resilience index; Conduct multi-scenario simulations and adaptability verification, specifically including: constructing a set of typical scenarios covering different development trends, policy orientations, and changes in external conditions; simulating the dynamic implementation process of the planning scheme under each scenario; evaluating its adaptability, robustness, and sustainability; and verifying the effectiveness and feasibility of the scheme under various future paths; Construct a multi-dimensional comprehensive evaluation model, specifically including: establishing a comprehensive evaluation index system for planning schemes from four dimensions: technical feasibility, economic rationality, social acceptance, and ecological and environmental compatibility; constructing a scheme confidence distribution model based on Bayesian reasoning or fuzzy mathematics methods, identifying spatial units with high uncertainty, and generating a spatial confidence distribution map reflecting the credibility of the decision; Establishing a virtual simulation test environment includes: building a digital land space simulation platform, importing the planning scheme into the virtual operating environment for multiple rounds of dynamic simulation, tracking the system response, feedback mechanism and evolution path during the implementation of the scheme, and identifying potential systemic risks through a combination of three-dimensional visualization and quantitative indicator evaluation, so as to verify the long-term implementation effect and system stability of the planning scheme.

10. The method for land space planning based on big data according to claim 1, characterized in that: Step 6 includes the following steps: Construct a phased implementation path model, specifically by dividing the spatial planning plan into three implementation phases: short-term, medium-term, and long-term. For each phase, set phased goals and key node indicators, establish a logical dependency network between projects, and generate a planning implementation roadmap with clear time nodes, execution sequence, and priority levels. Carry out coordinated allocation of resources in time and space, specifically including: based on a multi-stage dynamic decision-making model, coordinate the allocation of various key resources in time and space, build a hierarchical and cross-departmental organizational execution guarantee system, and improve the efficiency and coordination of the implementation of planning schemes at different stages; Establish a real-time monitoring and early warning mechanism, specifically including: establishing a multi-dimensional indicator system covering progress execution, goal achievement, and implementation deviations; designing early warning rules based on threshold triggers; achieving multi-scale, full-process, high-frequency real-time monitoring of the plan execution process; promptly identifying execution anomalies and providing feedback and control; Establish a dynamic planning adjustment mechanism, specifically including: regularly conducting evaluations of planning implementation effectiveness, analyzing the degree of achievement of planning goals, spatial structure evolution trends, and causes of implementation deviations, generating structured evaluation reports and optimization recommendations, and building a three-level response mechanism including routine fine-tuning, special revisions, and overall reconstruction to ensure the dynamic adjustment and adaptive evolution capabilities of the planning scheme; Establish a scenario response and path switching mechanism, specifically including: presetting typical external change scenarios and corresponding response strategies, forming a response strategy library based on scenario identification, and designing a triggerable path switching mechanism to achieve flexible adjustment and adaptation of the planning implementation path in the face of major external disturbances; Establish a closed-loop optimization system based on practical feedback, specifically including: converting monitoring data and decision feedback during the implementation process into reusable knowledge assets, extracting planning optimization rules and implementation evolution models, and building a closed-loop learning mechanism of "planning-implementation-monitoring-feedback-replanning" to achieve continuous self-improvement and iterative evolution of the planning system. Furthermore, the scenario response and path switching mechanism presets at least 8 typical external change scenarios, including major industrial policy adjustments, public health emergencies, severe natural disasters, major infrastructure construction, administrative division adjustments, etc. Corresponding response strategies and path switching plans are configured for each scenario to ensure the adaptability and flexibility of the plan implementation. Furthermore, the closed-loop optimization system based on practical feedback constructs a closed-loop architecture including a perception layer, an analysis layer, a decision-making layer and an execution layer. The frequency of data collection is no less than quarterly, and the feedback data is converted into knowledge rules after noise reduction, clustering and pattern recognition processing. The update frequency is no less than half a year, forming an intelligent planning system for self-learning and continuous optimization.

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