Digital scenario empowerment method and system for land use decision of canal heritage

Abstract

The application relates to the technical field of land resource management, in particular to a digital scenario empowerment method and system for canal heritage land use decision. The method comprises the following steps: acquiring environmental information, policy information, cultural information and economic information of a target cultural heritage area, and constructing multi-source cultural heritage information; performing policy semantic analysis and spatialization on the policy information to obtain policy semantic information and spatial information, and constructing a policy rule engine based on the policy semantic information and the spatial information; analyzing the multi-source cultural heritage information to obtain spatialized value distribution data of the target cultural heritage area; generating a land use optimization decision set based on the spatialized value distribution data and the policy rule engine; and performing screening analysis on the land use optimization decision set to output a land use optimization report. The application balances the protection strength of cultural heritage, the improvement of ecological connectivity and the stimulation of economic vitality, and effectively solves the common value game problem in linear heritage areas.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of land resource management, in particular to a canal heritage land use decision-making method and system based on digitalization and scenario empowerment. BACKGROUND

[0002] In the existing land use decision-making practice, the method adopted for linear cultural heritage (such as the Beijing-Hangzhou Grand Canal) is mainly based on traditional spatial planning technology, which has formed several technical characteristics. First, the acquisition of cultural value usually adopts a point-like identification method, which only spatially locates tangible heritage such as cultural relics points, and lacks quantitative means for the dynamic range and intensity of living cultural elements (including folk activities, traditional production and living ways, etc.). Second, ecological assessment mostly relies on general ecological value assessment models (such as the equivalent factor method), which regard the ecological system as a homogeneous unit in calculation, failing to reflect the functional transmission, directionality and special value of key ecological nodes of the canal as a linear ecological corridor. Third, policy constraints usually appear as post-check items in the existing process, without semantic analysis and spatial mapping of policy texts in the planning stage, resulting in potential conflicts between the scheme and heritage protection policies after the scheme is generated, which further affects the feasibility of the scheme. The above technical characteristics collectively show that the quantification of cultural value is insufficient, the ecological assessment is homogenized, and the policy conflict is post-processed, which limits the realization of precise and forward-looking land use decision-making for the special nature of linear cultural heritage. SUMMARY

[0003] The application provides a canal heritage land use decision-making method and system based on digitalization and scenario empowerment to solve the above problems.

[0004] In a first aspect, the application provides a canal heritage land use decision-making method based on digitalization and scenario empowerment, which comprises: acquiring environmental information, policy information, cultural information and economic information of a target cultural heritage area, and constructing multi-source cultural heritage information; performing policy semantic analysis and spatialization on the policy information to obtain policy semantic information and spatial information, and constructing a policy rule engine according to the policy semantic information and spatial information; performing multi-dimensional dynamic value analysis on the multi-source cultural heritage information to obtain spatialized value distribution data of the target cultural heritage area; performing multi-objective collaborative optimization and conflict diagnosis based on the spatialized value distribution data and the policy rule engine to generate a set of land use optimization decisions; and performing screening analysis on the set of land use optimization decisions to output a land use optimization report.

[0005] By the technical scheme, through unified spatialization of multi-source information, synchronous quantification and visualization of culture, ecology and economic value are realized, and spatial precision and dynamic response capability of decision-making are significantly improved; the preposition construction and conflict diagnosis mechanism of the policy rule engine embeds compliance inspection into the optimization process, and reduces the scheme late rectification cost and implementation risk; the multi-objective collaborative optimization algorithm strictly follows the policy constraints, and guarantees the balance of cultural heritage protection strength, ecological connectivity improvement and economic vitality stimulation, effectively solves the value game problem commonly existing in linear heritage regions; the replicability and modular design of the overall framework support adaptive application of different scale canal heritage regions, and provide scientific basis for sustainable land governance.

[0006] Optionally, the policy semantic analysis and spatialization of the policy information are performed to obtain policy semantic information and spatial information, and a policy rule engine is constructed according to the policy semantic information and the spatial information, including: performing natural language analysis on the policy information, extracting control strength vocabulary and spatial range description in the policy information, establishing a multi-level classification system including prohibition type rules, restriction type rules and encouragement type rules to obtain the policy semantic information; according to the spatial range description, the abstract policy information is converted into specific machine executable spatial constraint rules, the spatial constraint rules are used to define the spatial action range, constraint strength parameters and priority relationship of various policies; according to the policy semantic information, the spatial constraint rules are associated with geographical coordinates corresponding to each rule in the multi-level classification system in the policy semantic information through a spatial matching algorithm to obtain the policy spatial information.

[0007] Optionally, the multi-dimensional dynamic value analysis of the multi-source cultural heritage information obtains the spatialized value distribution data of the target cultural heritage region, including: identifying the spatial distribution characteristics of cultural heritage elements according to the cultural information, and constructing a cultural influence propagation model based on distance attenuation and spatial accessibility; calculating the influence radiation range and intensity attenuation law of each cultural source in space through the cultural influence propagation model, and constructing a continuously distributed cultural value intensity distribution data; analyzing the ecological background characteristics and structural connectivity of the canal corridor according to the environmental information, and constructing a corridor service flow model based on ecological process simulation; identifying the key positions of ecological pinch points and ecological hubs through the corridor service flow model, evaluating the importance of each region in maintaining the overall ecological function of the corridor, and constructing an ecological flow connectivity atlas data representing the strength of ecological function connectivity; integrating multi-source economic vitality index data according to the environmental information and economic information, and constructing a value density estimation model based on spatial statistics and economic geography principles; analyzing the influence of industrial agglomeration effect and market size factors through the value density estimation model, and constructing economic value density distribution data; and constructing the spatialized value distribution data according to the cultural value intensity distribution data, the ecological flow connectivity atlas data and the economic value density distribution data.

[0008] Optionally, the construction process of the cultural value intensity distribution data includes: identifying the spatial distribution characteristics of various cultural heritage elements according to the cultural information, and unifying the material cultural heritage points, non-material cultural heritage activity sites, historical and cultural lines and traditional production and living areas as cultural influence sources; constructing a cultural value propagation model based on the field strength theory according to the cultural influence sources; comprehensively considering the influence of distance attenuation effect, traffic accessibility difference and social activity level through the cultural value propagation model, and introducing a nonlinear attenuation function to describe the propagation characteristics of cultural value, considering the superposition effect and mutual enhancement mechanism of influence in cultural heritage intensive areas, and quantifying the spatial diffusion law of cultural influence; integrating multi-source cultural data, including cultural activity trajectories recorded in historical documents, cultural practice heat reflected in social media and cultural space use intensity obtained through on-site investigation; based on the spatial diffusion law, converting the discrete cultural activity trajectories, cultural practice heat and cultural space use intensity into continuously distributed cultural value intensity surface data through spatial interpolation and field strength superposition method, to obtain the cultural value intensity distribution data.

[0009] Optionally, the construction process of the ecological connectivity map data comprises: analyzing the ecological conditions in the target cultural heritage area according to the environmental information, extracting the ecological background conditions of the target cultural heritage area, including hydrological information, vegetation information, and biological information; taking the hydrological information, the vegetation information, and the biological information as node information, constructing an ecological service flow simulation model based on a directed graph, and introducing resistance factors affecting key ecological processes into the ecological service flow simulation model to simulate the flow change information of three key ecological processes, i.e., species migration, water conservation, and nutrient cycling, in the corridor; the resistance factors include land use barriers, traffic facility barriers, and topographic and geomorphic restrictions; according to the flow change information, visualizing the map of the flow change information to construct the ecological connectivity map data reflecting the ecological value of different regions.

[0010] Optionally, the construction process of the economic value density distribution data comprises: analyzing the economic vitality of the target cultural heritage area under the dual influence of environment and economy according to the environmental information and the economic information, constructing multi-source economic vitality observation data including nighttime light intensity, commercial facility density, traffic flow statistics, land transaction price, and industrial input-output data; constructing a value density estimation model based on geographic weighted regression and spatial econometrics, introducing the influence of spatial heterogeneity and spatial dependence; in-depth analysis of the spatial agglomeration law of economic activities to identify the distribution characteristics of industrial cluster regions, commercial center areas, and innovation active areas; using spatial interpolation methods to convert point and area economic observation data into continuous distribution economic value density surface; spatially registering the economic value density data with the land use grid cells to construct the economic value density distribution data.

[0011] Optionally, based on the spatialized value distribution data and the policy rule engine, multi-objective collaborative optimization and conflict diagnosis are performed to generate a land use optimization decision set, comprising: based on the policy rule engine, analyzing the spatial regions corresponding to the prohibition type rules, the restriction type rules, and the encouragement type rules according to the spatialized value distribution data, taking the spatial constraint rules of the policy rule engine as the hard constraint conditions of the multi-objective optimization process, taking the maximization of cultural value intensity, corridor ecological function connectivity, and economic value density as the core optimization objectives, and introducing the structure rationality and spatial coordination of land use as secondary optimization objectives to construct a multi-objective optimization model; based on the multi-objective optimization model, a targeted multi-objective evolutionary strategy is used for optimization and solution, and through adaptive parameter adjustment and non-dominated sorting mechanism, the land use optimization decision set is constructed; in the multi-objective optimization process, the compliance of policy constraints is monitored in real time, and the policy compliance and constraint violation position of each potential scheme are accurately recorded.

[0012] Optionally, the targeted multi-objective evolutionary strategy comprises: constructing a chromosome coding scheme for land use spatial configuration, wherein each chromosome individual represents a complete set of land use scheme, a chromosome gene locus corresponds to a planning grid cell, and a gene value represents a land use type allocated in the cell; designing a multi-objective fitness function to evaluate the fitness of the cultural value intensity distribution data, the corridor ecological function flowability data and the economic value density distribution data through a weighted summation mechanism, quantifying the cultural value intensity distribution data, the corridor ecological function flowability data and the economic value density distribution data into corresponding fitness values for evaluating the advantages and disadvantages of each chromosome individual; setting the operation parameters and operations of the genetic algorithm, initializing a random population through the genetic algorithm; adopting a simulated binary crossover operation to exchange gene fragments between parent chromosomes in the random population to generate new individuals; implementing a polynomial mutation operation to randomly change part of the gene loci of the new individuals, introducing a small probability of random disturbance to maintain population diversity; performing an iterative optimization process, in each generation, non-dominant sorting the population according to the fitness function, obtaining a non-dominant sorting result, calculating the crowding degree of individuals in the same front layer, and selecting excellent individuals into the next generation according to the non-dominant sorting result and the crowding degree by using a tournament selection method; setting a convergence condition and outputting a solution set, when the number of iterations reaches a preset maximum value, terminating the algorithm, and outputting all non-dominant solutions in the final population as the land use optimization decision set.

[0013] Optionally, the filtering analysis on the land use optimization decision set is performed to output a land use optimization report, including: based on the multi-attribute utility theory, a scheme filtering mechanism is established, the standardized utility value of each scheme in the land use optimization decision set in the economic, ecological and cultural dimensions is calculated, all schemes are sorted and preliminarily selected through weighted comprehensive scoring to obtain a preliminary scheme set; according to the preliminary scheme set, spatial superposition and change detection analysis are performed on different preliminary schemes, the land use layout diagram of different preliminary schemes is spatially superposed with the current land use diagram, the change area where the land use type is converted is identified through a grid algorithm, and the spatial distribution, area and conversion direction are counted, and the land use scheme set corresponding to the preliminary scheme set is integrated; for each preliminary scheme, the land use scheme set is analyzed, the land use scheme corresponding to each preliminary scheme is compared with the current situation in the cultural, ecological and economic three dimensions, the percentage of improvement in cultural value, ecological value and economic value compared with the current situation is quantified, and secondary compliance verification is performed by using the policy rule engine to ensure that the preliminary selected scheme does not contain any land use configuration that violates the mandatory policy constraints, to obtain an optimized scheme set; the optimized scheme set is analyzed, the land space configuration parameters of each scheme are decoded and visualized, the planning diagram of the land use space layout corresponding to each optimized scheme, the land use structure configuration table of the area and spatial position of each land class, and the analysis report including the comprehensive benefit comparison, policy compliance conclusion and potential implementation risk are generated, and the land use optimization report is constructed and output.

[0014] In a second aspect, the present application provides a digital scenario empowerment canal heritage land use decision system, the system comprising: an information collection module for acquiring environmental information, policy information, cultural information and economic information of a target cultural heritage area, and constructing multi-source cultural heritage information; a policy analysis module for performing policy semantic analysis and spatialization on the policy information to obtain policy semantic information and spatial information, and constructing a policy rule engine according to the policy semantic information and spatial information; a value analysis module for performing multi-dimensional dynamic value analysis on the multi-source cultural heritage information to obtain spatialized value distribution data of the target cultural heritage area; a decision optimization module for performing multi-objective collaborative optimization and conflict diagnosis based on the spatialized value distribution data and the policy rule engine to generate a land use optimization decision set; and a report output module for filtering analysis on the land use optimization decision set to output a land use optimization report. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those of ordinary skill in the art without any creative effort based on these drawings.

[0016] Figure 1 An application scenario schematic diagram provided for an embodiment of the present application;

[0017] Figure 2 A flowchart of a digital scenario empowerment canal heritage land use decision method provided for an embodiment of the present application;

[0018] Figure 3 A structural schematic diagram of a digital scenario empowerment canal heritage land use decision system provided for an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort fall within the scope of protection of the present application.

[0020] In addition, the term "and / or" in this paper is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents an "or" relationship between the associated objects unless otherwise specified.

[0021] The embodiments of the present application will be further described in combination with the accompanying drawings of the specification.

[0022] In the existing land use decision-making practice, the methods adopted for linear cultural heritage (such as the Beijing-Hangzhou Grand Canal) are mainly based on traditional spatial planning techniques, forming several technical characteristics. First, the acquisition of cultural value usually adopts a point-like identification method, only positioning tangible heritage such as cultural relics points, lacking quantitative means for the dynamic range and intensity of living cultural elements (including folk activities, traditional production and living ways, etc.). Second, ecological assessment relies on general ecological value assessment models (such as the equivalent factor method), which treat the ecological system as a homogeneous unit in calculation, failing to reflect the functional transmission, directionality and special value of key ecological nodes of the canal as a linear ecological corridor. Third, policy constraints usually appear as post-check items in the existing process, without semantic analysis and spatial mapping of policy texts in the planning stage, leading to potential conflicts between the scheme and heritage protection policies after the scheme is generated, affecting the feasibility of the scheme. The above technical characteristics collectively represent the lack of quantitative cultural value, the homogenization of ecological assessment, and the post-positioning of policy conflicts, limiting the realization of precise and forward-looking land use decisions for the special nature of linear cultural heritage.

[0023] Based on this, the application provides a digital scenario empowerment method and system for canal heritage land use decision-making, which realizes the simultaneous quantification and visualization of cultural, ecological and economic values through the unified spatialization of multi-source information, significantly improving the spatial accuracy and dynamic response capability of decision-making; the pre-positioning construction of the policy rule engine and the conflict diagnosis mechanism embed compliance checks into the optimization process, reducing the late rectification cost and implementation risk of the scheme; the multi-objective collaborative optimization algorithm strictly follows the policy constraints while ensuring the balance of cultural heritage protection intensity, ecological connectivity improvement and economic vitality stimulation, effectively solving the common value game problem in linear heritage areas; the replicability and modular design of the overall framework support adaptive application in different scale canal heritage areas, providing a scientific basis for sustainable land governance.

[0024] Figure 1 An application scenario diagram provided by the application, when planning the land use of the canal heritage, the method provided by the application ensures the balance of cultural heritage protection intensity, ecological connectivity improvement and economic vitality stimulation, effectively solving the common value game problem in linear heritage areas.

[0025] Specifically, the method provided by the application is applied to any server, the server interacts with a multi-source data collection node, multi-source cultural heritage information provided by the multi-source data collection node is obtained through the server, through the unified spatialization of the multi-source cultural heritage information, the synchronization quantification and visualization of culture, ecology and economic value are realized, the spatial accuracy and dynamic response capability of decision-making are significantly improved; the preposition construction and conflict diagnosis mechanism of the policy rule engine embeds compliance checking into the optimization process, reduces the post-correction cost and implementation risk of the scheme; the multi-objective collaborative optimization algorithm strictly follows the policy constraints while ensuring the balance of cultural heritage protection strength, ecological connectivity improvement and economic vitality stimulation, effectively solving the common value game problem of linear heritage areas; the replicability and modular design of the overall framework support the adaptive application of different scale canal heritage areas, provide scientific basis for sustainable land governance, and provide reliable scheme reference for planning personnel. The specific implementation mode can refer to the following embodiments.

[0026] Figure 2 A flowchart of a digital scenario empowerment canal heritage land use decision-making method provided by an embodiment of the application, the method of the embodiment can be applied to the server in the above scenarios. As shown in the method includes: Figure 2

[0027] S201, obtaining environmental information, policy information, cultural information and economic information of a target cultural heritage area, and constructing multi-source cultural heritage information;

[0028] S202, performing policy semantic analysis and spatialization on the policy information to obtain policy semantic information and spatial information, and constructing a policy rule engine according to the policy semantic information and spatial information;

[0029] S203, performing multi-dimensional dynamic value analysis on the multi-source cultural heritage information to obtain spatialized value distribution data of the target cultural heritage area;

[0030] S204, based on the spatialized value distribution data and the policy rule engine, performing multi-objective collaborative optimization and conflict diagnosis to generate a set of land use optimization decisions;

[0031] S205, screening and analyzing the set of land use optimization decisions, and outputting a land use optimization report.

[0032] ​Technical background and working principle: Linear cultural heritage (LCH) such as the Grand Canal region has multiple attributes of culture, ecology and economy. Its land use decision-making faces complex challenges of multi-value conflicts and policy constraints. Traditional decision-making methods often rely on single objective optimization or static evaluation, which is difficult to integrate the spatial correlation of environmental, policy, cultural and economic information simultaneously, resulting in homogeneous schemes, poor compliance and low comprehensive benefits. Based on the concept of digital scenario empowerment, this example solves the dynamic coordination problem of multi-dimensional value quantification and policy constraints through multi-source information fusion and spatial expression. The working principle is as follows: First, four types of heterogeneous data including environment, policy, culture and economy are unified into multi-source cultural heritage information. Natural language processing technology is used to analyze policy texts and convert them into machine executable spatial constraint rules to build a policy rule engine. Second, through the cultural influence propagation model, the ecological service flow model and the economic value density estimation model, the spatial dynamic evaluation of cultural, ecological and economic values is realized to generate continuous distribution value data. Finally, combined with the policy rule engine and spatial value data, multi-objective evolutionary algorithm is used for collaborative optimization and conflict diagnosis to generate a set of land use schemes that balance cultural heritage, ecological protection and economic development. This process realizes the whole chain of digitalization from data perception to decision output, ensuring the sustainable balance of cultural heritage, ecological protection and economic development.

[0033] Technical solutions and component functions: The technical solutions of this embodiment include five core steps: information collection, policy analysis, value analysis, decision optimization and report output. The information collection module constructs a multi-source cultural heritage information database through the environmental information acquisition unit (integrating remote sensing images, meteorological station data, etc.), the policy information acquisition unit (accessing government public texts and regulations), the cultural information acquisition unit (collecting material / non-material cultural heritage points and social media activity trajectories), and the economic information acquisition unit (integrating night light, land transaction price, etc.). The policy analysis module is composed of a natural language analysis unit (using morphological analysis and entity recognition technology to extract control vocabulary), a rule classification unit (establishing a three-level rule system of prohibition, restriction and encouragement), a spatial constraint generation unit (mapping rules to geographic polygons and buffer zones), and a knowledge graph unit (supporting dynamic updating and version management), which realizes policy semantic analysis and spatialization and outputs a policy rule engine. The value analysis module includes a cultural value propagation model (based on field strength theory to calculate the spatial influence attenuation and superposition of cultural heritage points), an ecological service flow model (through a directed graph to simulate the flow of species migration, water conservation, etc.), and an economic value density estimation model (applying geographically weighted regression analysis of spatial heterogeneity), which generates spatialized value distribution data through a data fusion unit (using spatial interpolation and weighted superposition). The decision optimization module generates land use optimization decision sets under hard policy constraints through a constraint rule mapping unit (converting policy constraints to grid feasibility markers), a multi-objective fitness calculation unit (weighted integration of cultural, ecological and economic value targets), and a solution unit based on evolutionary strategy (using the NSGA-II algorithm for non-dominated sorting and congestion calculation). The report output module outputs a land use optimization report containing planning maps, land use structure tables and risk analysis using a multi-attribute utility calculation unit (standardizing three-dimensional values and weighted sorting), a spatial superposition analysis unit (grid operation to identify land use change areas), and a compliance verification unit (secondary verification of policy compliance).

[0034] Beneficial effects: This embodiment realizes the simultaneous quantification and visualization of cultural, ecological and economic values through the unified spatialization of multi-source information, significantly improving the spatial accuracy and dynamic response capability of decision-making; the pre-constructed policy rule engine and conflict diagnosis mechanism embed compliance checks into the optimization process, reducing the cost of post-implementation rectification and the risk of implementation; the multi-objective collaborative optimization algorithm strictly follows policy constraints while ensuring the balance between cultural heritage protection intensity, ecological connectivity improvement and economic vitality stimulation, effectively solving the value game problem commonly encountered in linear heritage areas; the replicability and modular design of the overall framework support adaptive application in different scale canal heritage areas, providing a scientific basis for sustainable land governance.

[0035] In some embodiments, the policy information is subjected to natural language parsing, the control strength vocabulary and spatial range description in the policy information are extracted, a multi-level classification system including prohibition type rules, restriction type rules and encouragement type rules is established, and the policy semantic information is obtained; according to the spatial range description, the abstract policy information is converted into specific machine executable spatial constraint rules, the spatial constraint rules are used to define the spatial action range, constraint strength parameters and priority relationship of various policies; according to the policy semantic information, the spatial constraint rules are associated with the geographic coordinates corresponding to each rule in the multi-level classification system in the policy semantic information through a spatial matching algorithm, and the policy spatial information is obtained.

[0036] Technical background and working principle: The construction of the policy rule engine is the core of solving the policy compliance challenges in linear cultural heritage (LCH) land use decision-making. Traditional policy texts are presented in natural language form, lacking structured expression that is machine readable, making it difficult to achieve automatic constraint mapping and conflict detection in spatial planning. Especially for LCH regions such as the Beijing-Hangzhou Grand Canal, policy provisions (such as the core protection zone control requirements in the Grand Canal Heritage Protection Plan) often involve complex spatial range descriptions and multi-level control strength, and manual parsing is prone to ambiguity and omissions. This embodiment converts abstract policy provisions into executable spatial constraint rules through the integration of natural language processing (NLP) and spatial information technology. The working principle is as follows: First, NLP technology is used to parse policy texts, extract control strength vocabulary (such as "prohibit" and "restrict") and spatial descriptions (such as "200-meter buffer zone along the river"), and build a multi-level classification system; second, rules are mapped to spatial objects (such as polygons and buffer zones) through geometric modeling, and constraint strength parameters and priority relationships are defined; then, based on graph theory algorithms, spatial topological conflicts between rules (such as control conflicts in overlapping areas) are detected, and a priority coordination mechanism is used to achieve consistency; finally, knowledge graphs are used to dynamically maintain rule versions and association relationships, and spatial matching algorithms are used to accurately bind rules to geographic coordinates. This process ensures that policy requirements are front-loaded in land use decision-making, and automated compliance checks significantly improve the accuracy and timeliness of decision-making.

[0037] Technical solutions and component functions: The technical solutions of this embodiment include five core steps: natural language analysis, rule classification and parameterization, conflict detection and coordination, knowledge graph construction, and spatial matching. The natural language analysis unit uses morphological analysis (such as word segmentation, entity recognition) and dependency syntax analysis technology to extract control intensity vocabulary (such as "strictly prohibit development" "give priority to protection") and spatial range description (such as "1000 meters core area along the canal") from policy texts, and output structured label data; the rule classification unit establishes a three-level classification system of prohibition rules (such as absolute prohibition of construction land expansion), restriction rules (such as restriction of development intensity), and encouragement rules (such as encouragement of ecological restoration) based on the extraction results, and assigns each rule a unique identifier and metadata; the spatial constraint generation unit converts the classified rules into machine executable spatial objects - the prohibition rules are mapped to the forbidden construction area polygon, the restriction rules are mapped to the buffer zone and attached with constraint intensity parameters (such as development density threshold), and the encouragement rules are mapped to the incentive area and set priority weight, while the spatial topology operation (such as overlay analysis) defines the coverage relationship between rules; the conflict detection unit uses a graph theory model to construct a rule interaction network, with nodes representing rule entities and edges representing spatial overlap relationships, and through traversal algorithms (such as depth-first search) to identify conflict areas (such as being prohibited and encouraged at the same location), and generate consistent constraint sets according to priority labels (such as prohibition class priority to restriction class) and reconciliation strategies (such as weighted average); the knowledge graph unit stores rule entities, attributes, version history and dependency relationships in RDF / OWL format, provides SPARQL query interface and dynamic update mechanism (such as incremental loading when policy is revised); the spatial matching algorithm unit uses spatial indexing technology (such as R-Tree) to quickly match constraint rules with land use grid cells (such as 100ha grid), and through geometric inclusion judgment and attribute mapping, realizes the direct association of policy requirements to specific geographic coordinates, and outputs a grid-based feasibility marking layer.

[0038] Beneficial effects: This embodiment realizes the precise landing of policy constraints through automated policy analysis and spatialization, avoiding the subjective bias and delay of traditional manual interpretation; the multi-level classification system and parameterized design enhance the flexibility and adaptability of rules, supporting dynamic adjustment of different granularity control requirements; the conflict detection and coordination mechanism eliminates internal contradictions in the early stage of decision-making, reducing legal risks and rectification costs in the implementation process; the dynamic maintenance capability of knowledge graph ensures the system's immediate response to policy updates, improving the sustainability of long-term decision-making; the efficiency of spatial matching algorithm greatly shortens the compliance checking time, providing technical support for large-scale land use optimization.

[0039] In some embodiments, according to the cultural information, the spatial distribution characteristics of cultural heritage elements are identified, a cultural influence propagation model based on distance attenuation and spatial accessibility is constructed, the influence radiation range and intensity attenuation law of each cultural source in space are calculated through the cultural influence propagation model, and continuous distribution of cultural value intensity distribution data is constructed; according to the environmental information, the ecological background characteristics and structural connectivity of the canal corridor are analyzed, a corridor service flow model based on ecological process simulation is constructed, key positions of ecological pinch points and ecological hubs are identified through the corridor service flow model, the importance of each region in maintaining the overall ecological function of the corridor is evaluated, and an ecological flow connectivity atlas data representing the strength of ecological function connectivity is constructed; according to the environmental information and economic information, multi-source economic vitality index data is integrated, a value density estimation model based on spatial statistics and economic geography principles is constructed, the influence of industrial agglomeration effect and market size factors is analyzed through the value density estimation model, and economic value density distribution data is constructed; according to the cultural value intensity distribution data, the ecological flow connectivity atlas data and the economic value density distribution data, the spatialized value distribution data is constructed.

[0040] Technical background and working principle: The land use decision of linear cultural heritage region needs to consider the multi-dimensional values of culture, ecology and economy. Traditional evaluation methods often analyze each dimension separately, which is difficult to reflect the spatial interaction and dynamic balance relationship. The Beijing-Hangzhou Grand Canal and other LCH regions have the functions of living cultural heritage, ecological corridor and economic corridor. Single value evaluation is easy to lead to decision bias. This embodiment solves the problems of spatial continuity and timeliness in value quantization by constructing a multi-dimensional dynamic value analysis framework. The core principle is as follows: based on the field strength theory, a cultural influence propagation model is constructed to convert discrete cultural heritage elements into continuous spatial radiation field, considering distance attenuation, traffic accessibility and social activity factors; based on ecological process simulation, a corridor service flow model is constructed to analyze the flow path and resistance of key ecological processes such as species migration and water conservation through directed graph; based on the principles of spatial statistics and economic geography, a value density estimation model is constructed to capture the spatial heterogeneity and agglomeration effect of economic activities. Finally, through multi-source data fusion and spatial interpolation technology, value distribution data in a unified coordinate system is generated, providing accurate quantitative input for multi-objective optimization.

[0041] Technical solutions and component functions: The technical solution of the embodiment contains four core links: the cultural value analysis collects material / non-material cultural heritage point data through a cultural element identification unit, inputs a cultural influence propagation model (calculates spatial attenuation using an exponential decay function f(d)=a·exp(-bd), combines road network accessibility matrix to modify radiation intensity, and introduces social media heat index g to dynamically adjust weight), generates cultural value intensity distribution data through a spatial interpolation unit; the ecological value analysis obtains hydrology, vegetation, and species distribution data through an ecological background extraction unit, inputs an ecological service flow model (constructs a directed graph network, nodes represent ecological key areas, edge weights are determined by land use barriers, traffic barriers, and other surface resistance factors, and the minimum path algorithm is used to simulate the flow efficiency of three types of ecological processes), and outputs ecological flowability atlas data; the economic value analysis integrates night light, commercial density, and other multi-source indicators through an economic observation collection unit, inputs a value density estimation model (applies geographic weighted regression GWR to analyze spatial heterogeneity, and combines hotspot detection Getis-Ord Gi* to identify industrial cluster areas), and generates economic value density distribution data through Kriging interpolation; finally, the data fusion unit normalizes the three types of data, integrates them into spatialized value distribution data through a weighted superposition algorithm (weights can be dynamically adjusted based on the analytic hierarchy process), and outputs a standardized value matrix with a 100 ha grid as a unit.

[0042] Beneficial effects: The embodiment realizes the spatial quantification of the "living state" attribute of cultural heritage, overcoming the limitations of traditional point-based cultural relic evaluation; the ecological service flow model replaces static indicators with process simulation to accurately identify key nodes and fragile areas of corridor ecological function; the economic value density model integrates multi-source real-time data, significantly improving the spatial and temporal resolution of regional economic vitality evaluation; the unified spatial framework of three-dimensional value data provides a directly computable basic layer for subsequent multi-objective optimization, effectively supporting the scientificity and fineness of land use decision-making.

[0043] In some embodiments, according to the cultural information, the spatial distribution characteristics of various types of cultural heritage elements are identified, and the material cultural heritage points, non-material cultural heritage activity sites, historical and cultural routes, and traditional production and living areas are unified as cultural influence sources; based on the field strength theory, a cultural value propagation model is constructed according to the cultural influence sources; the influence of distance attenuation effect, traffic accessibility difference and social activity level is comprehensively considered through the cultural value propagation model, and a nonlinear attenuation function is introduced to describe the propagation characteristics of cultural value, the superposition effect and mutual enhancement mechanism of influence are considered in the cultural heritage intensive area, and the spatial diffusion law of cultural influence is quantified; multi-source cultural data is integrated, including cultural activity tracks recorded in historical documents, cultural practice heat reflected in social media, and cultural space use intensity obtained through on-site investigation; based on the spatial diffusion law, through spatial interpolation and field strength superposition method, the discrete cultural activity tracks, cultural practice heat and cultural space use intensity are converted into continuous distribution of cultural value intensity surface data, and the cultural value intensity distribution data is obtained.

[0044] Technical background and working principle: The cultural value assessment of linear cultural heritage (LCH) region traditionally relies on discrete relic point records, lacking quantitative analysis of the spatial continuity and dynamic propagation characteristics of intangible heritage, historical routes and living cultural practices. This leads to the simplification or neglect of cultural value in land use decision-making, making it difficult to optimize with ecological and economic values. This embodiment is based on the field strength theory, which regards cultural value as a kind of "field" that radiates and attenuates in space, and constructs continuous distribution of cultural value intensity data by simulating the propagation law of cultural influence. The working principle is that cultural influence sources (such as heritage points and activity sites) act like source points in a physical field, their influence decays with distance, and is modulated by factors such as traffic accessibility and social activity level; the nonlinear attenuation function captures the rapid decline and local saturation characteristics of cultural value, while in heritage-intensive areas, the superposition effect of multi-source influence can more realistically reflect the accumulation of cultural value. By integrating multi-source data such as historical documents, social media and on-site investigation, this method realizes the transformation of cultural value from discrete observation to continuous surface, providing accurate spatial input of cultural value for multi-objective land use optimization.

[0045] Technical solutions and component functions: The technical solutions of the embodiment include the following steps: First, identify the material cultural heritage points, intangible cultural heritage activity sites, historical and cultural routes, and traditional production and living areas as cultural influence sources through the cultural heritage database and the geographic information system (GIS), and extract their spatial coordinates, types, and activity attributes; second, build a cultural value propagation model based on field strength theory, which uses a nonlinear decay function (such as an exponential or Sigmoid function) to calculate the radiation intensity of each influence source on the spatial grid, introduces traffic network data (such as road density and public transportation coverage) to calculate accessibility weights, and social activity data (such as social media heat and population flow) to adjust radiation intensity, forming an initial cultural value field; third, in areas with dense cultural heritage, use a field strength superposition algorithm (such as weighted accumulation) to handle the mutual enhancement effect of multiple sources of influence and avoid underestimating the value; fourth, integrate multi-source cultural data - historical literature data is extracted through text mining to extract cultural activity trajectories and spatialize, social media data is obtained through API interface to obtain cultural practice heat index and mapped to geographic coordinates, and field research data is recorded through mobile collection terminals to record cultural space usage intensity and interpolation - use spatial interpolation methods (such as Kriging interpolation or inverse distance weighting) to convert discrete observations into continuous cultural value intensity surfaces; finally, output the cultural value intensity distribution data in raster form, with each grid cell value representing the cultural value intensity at that location. Component functions include: the cultural influence source identification component is responsible for data collection and spatialization; the field strength model construction component implements decay function and accessibility calculation; the data integration component processes the formatting and fusion of multi-source cultural data; and the spatial interpolation and superposition component generates continuous surface data.

[0046] Beneficial effects: This embodiment uses field strength theory and multi-source data fusion to achieve continuous and dynamic quantification of cultural value in space, overcoming the limitations of traditional point-based evaluation; the nonlinear decay and superposition mechanism more accurately reflects the value accumulation in areas with dense cultural heritage, improving the precision and authenticity of cultural value assessment; integrating historical, real-time, and field data ensures the spatiotemporal integrity of cultural value analysis, providing reliable cultural dimension input for land use decision-making; in addition, this method supports the spatial coordination and optimization of cultural value with ecological and economic value, helping to achieve a balance between cultural heritage and sustainable development in canal heritage areas.

[0047] In some embodiments, according to the environmental information, the ecological conditions in the target cultural heritage area are analyzed, and the ecological background conditions of the target cultural heritage area are extracted, including hydrological information, vegetation information, and biological information; the hydrological information, the vegetation information, and the biological information are taken as node information, a directed graph-based ecological service flow simulation model is constructed, and resistance factors affecting key ecological processes are introduced into the ecological service flow simulation model to simulate the flow change information of three types of key ecological processes, i.e., species migration, water conservation, and nutrient circulation, in corridors; the resistance factors include land use barriers, traffic facility barriers, and topographic and geomorphic restrictions; according to the flow change information, the flow change information is processed by visual atlas, and the ecological flow atlas data reflecting the ecological values of different regions are constructed.

[0048] Technical background and working principle: Traditional ecological assessment methods mainly focus on the distribution of static ecological elements, such as vegetation coverage or species richness, and ignore the dynamic flow characteristics of ecological processes in space. In linear cultural heritage areas such as canal corridors, the core of ecological function is the spatial flow of key ecological processes such as species migration, water conservation, and nutrient circulation. These processes are affected by multiple resistance factors such as land use barriers, traffic facility barriers, and topographic and geomorphic restrictions, forming a complex ecological flow network. Based on landscape ecology and graph theory, this embodiment regards ecological flow as a directed network system, in which ecological nodes (such as wetlands and forest patches) are taken as network vertices, ecological flow processes are taken as directed edges, and resistance factors are quantified as edge weights. The working principle is as follows: by constructing a directed graph model, the spatial flow path and intensity of three types of key ecological processes (species migration, water conservation, and nutrient circulation) affected by resistance factors are simulated; network analysis algorithms are used to identify key nodes (ecological hubs) and blocking points (ecological pinch points) of ecological flow, so as to evaluate the importance of different regions in maintaining the overall ecological function of the corridor, and finally generate flow atlas data representing the strength of ecological function connectivity.

[0049] Technical solutions and component functions: The technical solutions of the embodiment include the following steps: first, obtain the ecological background conditions of the target area including hydrological information (such as river network, water level change), vegetation information (such as vegetation type, coverage) and biological information (such as species distribution, habitat range) through remote sensing image interpretation, field investigation and monitoring data; second, construct an ecological service flow simulation model based on directed graph: set the areas with significant ecological function as nodes, set the ecological flow process between nodes as directed edges, and calculate the weight value of each edge according to the resistance factors (land use barrier is assigned by land cover type, traffic barrier is calculated by road density, and topography restriction is derived by slope factor of digital elevation model), forming a complete ecological flow network; then, respectively implement simulation for three types of ecological processes: species migration, water conservation and nutrient cycling: species migration adopts path analysis based on random walk or circuit theory, water conservation applies hydrological model to calculate runoff path and catchment area, and nutrient cycling simulates the spatial transmission of nutrients through nutrient flow network; based on the simulation results, identify ecological hubs and pinch points through network centrality analysis (such as betweenness centrality), and calculate the flow intensity of each node; finally, convert the node flow intensity into continuous raster surface through spatial interpolation, and generate ecological flow connectivity map data. The component functions include: the ecological background extraction component is responsible for the collection and preprocessing of multi-source environmental data; the directed graph construction component realizes the generation of node-edge network topology and weight calculation; the ecological process simulation component respectively performs spatial analysis of three types of ecological flow; the atlas generation component completes network index calculation and spatial interpolation output.

[0050] Beneficial effects: The embodiment realizes the dynamic simulation of ecological process spatial flow through the directed graph model, overcoming the limitations of traditional static assessment; the introduction of multiple resistance factors quantifies the influence of human activities and natural conditions on ecological flow, improving the real relevance of the assessment; identifying ecological hubs and pinch points helps to accurately locate the key protection areas and repair priority areas, optimizing the ecological spatial pattern; the generated flow connectivity map provides an intuitive basis for ecological sensitivity for land use decision-making, supporting the overall maintenance and improvement of the ecological function of the canal corridor.

[0051] In some embodiments, according to the environmental information and the economic information, the economic vitality of the target cultural heritage area under the dual influence of environment and economy is analyzed, and multi-source economic vitality observation data are constructed, including nighttime light intensity, commercial facility density, traffic flow statistics, land transaction price, and industrial input-output data; according to the multi-source economic vitality observation data, a value density estimation model based on geographic weighted regression and spatial econometrics is constructed, and the influence of spatial heterogeneity and spatial dependence is introduced; the spatial agglomeration law of economic activities is analyzed in depth, and the distribution characteristics of industrial cluster areas, commercial center areas, and innovation active areas are identified; a spatial interpolation method is used to convert the point and area economic observation data into a continuously distributed economic value density surface; the economic value density data are spatially registered with the land use grid cells, and the economic value density distribution data are constructed.

[0052] Technical background and working principle: Traditional economic value assessment methods rely mainly on statistical data of administrative units, and it is difficult to reflect the spatial heterogeneity and local agglomeration characteristics of economic activities. In linear cultural heritage areas, economic activities present a complex spatial pattern of gradient distribution along the canal corridor and high agglomeration at specific nodes. Based on spatial econometrics and the first law of geography, this embodiment believes that economic value has spatial dependence and heterogeneity, and economic activities in adjacent areas influence each other, and the influence factor weights of different locations are different. The working principle is as follows: by integrating multi-source economic vitality observation data (such as nighttime light, commercial facility density, etc.), a geographic weighted regression (GWR) model is constructed, which allows the regression coefficients to vary with the spatial location, thereby capturing the spatial non-stationarity of economic value; at the same time, a spatial econometrics model is introduced to handle spatial dependence, avoiding estimation bias caused by ignoring spatial autocorrelation; combined with spatial interpolation technology, point and area observation data are converted into continuous value density surface, and finally economic value density distribution data accurately registered with land use grid are generated, providing fine economic dimension input for multi-objective land use optimization.

[0053] Technical solutions and component functions: The technical solutions of the embodiment include the following steps: first, economic vitality observation data is obtained through multi-source data acquisition, including satellite remote sensing night light data (reflecting economic activity intensity), commercial facility POI density data (representing commercial activity), traffic flow monitoring data (indicating logistics and people flow), land transaction price data (reflecting land market value), and industrial input-output data (measuring economic efficiency); second, a value density estimation model based on geographic weighted regression (GWR) and spatial econometrics is constructed; the GWR model calculates the local regression coefficient of each spatial unit by moving window weighted least squares estimation, captures the spatial heterogeneity effect of economic influence factors (such as traffic accessibility and industrial agglomeration degree), and the spatial econometric model (such as spatial lag model or spatial error model) processes the interaction and dependence between adjacent units by introducing a spatial weight matrix; then, spatial autocorrelation analysis (such as global / local Moran's I) and hot spot detection (Getis-Ord Gi*) are used to identify the spatial distribution characteristics of industrial cluster areas, commercial center areas and innovation active areas; then, spatial interpolation methods (such as Kriging interpolation or inverse distance weighting) are used to convert discrete economic observation data into continuous distribution economic value density surface; finally, the economic value density surface is aligned with the land use grid unit through spatial registration, and economic value density distribution data is generated. The component functions include: the economic observation data acquisition component is responsible for the acquisition and standardization preprocessing of multi-source data; the model building module realizes the GWR parameter estimation and spatial econometric model calibration; the cluster analysis component performs spatial statistics and hotspot detection; the spatial interpolation component completes the surface generation; and the registration component ensures the spatial consistency of economic data and land use grid.

[0054] Beneficial effects: The embodiment realizes the improvement of real-time and fineness of economic value spatial evaluation by fusing multi-source high-frequency economic data; the geographic weighted regression model effectively captures the local influence of economic driving factors, overcoming the homogenization assumption defects of global model; the introduction of spatial econometric method processes the spatial spillover effect of economic activity, improving the statistical reliability of value estimation; clear cluster and hotspot identification provides direct basis for economic growth pole positioning and industrial layout optimization in land use planning; the finally output of grid economic value density data can be seamlessly integrated with cultural and ecological value data, supporting multi-dimensional collaborative land use optimization decision-making.

[0055] In some embodiments, based on the policy rule engine, according to the spatialized value distribution data, the spatial regions corresponding to the prohibition type rules, the restriction type rules and the encouragement type rules are analyzed, the spatial constraint rules of the policy rule engine are taken as hard constraint conditions of a multi-objective optimization process, the core optimization objectives are to maximize the cultural value intensity, the corridor ecological function flowability and the economic value density, the secondary optimization objectives are the structural rationality and the spatial coordination of land use, a multi-objective optimization model is constructed; based on the multi-objective optimization model, a targeted multi-objective evolutionary strategy is used for optimization and solution, through adaptive parameter adjustment and non-dominated sorting mechanism, the land use optimization decision set is constructed; in the multi-objective optimization process, the compliance of policy constraints is monitored in real time, and the policy compliance and the constraint violation position of each potential scheme are accurately recorded.

[0056] Technical background and working principle: The land use decision of linear cultural heritage (LCH) region involves the complex game of multi-dimensional values of culture, ecology and economy, and the traditional method is difficult to realize the simultaneous optimization of multiple objectives under policy constraints. Policy rules usually exist in the form of natural language, lacking direct spatial expression, resulting in low efficiency of compliance check in the optimization process and easy to produce scheme conflict. The present embodiment converts the spatial constraint rules of the policy rule engine into hard constraint conditions of the optimization process, ensuring that all potential schemes comply with the policy requirements; at the same time, taking the maximization of cultural value intensity, corridor ecological function flowability and economic value density as the core optimization objectives, and introducing the structural rationality and spatial coordination of land use as the secondary objectives, a multi-objective optimization model is constructed. In terms of working principle, a targeted multi-objective evolutionary strategy (such as NSGA-II) is used for solution, the Pareto frontier is searched in the solution space through adaptive parameter adjustment and non-dominated sorting mechanism, and a diversified land use optimization decision set is generated; in the optimization process, the compliance of policy constraints is monitored in real time, the policy compliance and the constraint violation position of each scheme are accurately recorded, the pre-conflict diagnosis and dynamic compliance management are realized, so as to improve the comprehensive value on the basis of ensuring the policy compliance.

[0057] Technical solutions and component functions: The technical solutions of the embodiment include: based on the policy rule engine, mapping the space constraint rules (such as prohibited type and restricted type rules) to the hard constraint conditions of the optimization model, to ensure that the scheme does not violate the policy requirements at the grid cell level; constructing a multi-objective optimization model, in which the core objective function is the weighted maximization of cultural value intensity, corridor ecological function flowability and economic value density, and the secondary objective function is the structural rationality (such as land type proportion balance) and spatial coordination (such as landscape connectivity) of land use; using a multi-objective evolutionary strategy for optimization and solution, including constructing a chromosome coding scheme (each gene site corresponds to the land use type of a grid cell), designing a multi-objective fitness function (quantifying cultural, ecological and economic values by weighted summation), setting genetic algorithm operation parameters (such as crossover probability 0.7, mutation probability 0.3), and executing an iterative optimization process (including initialization of population, simulation of binary crossover, polynomial mutation, non-dominated sorting and calculation of crowding degree); in the optimization process, the real-time conflict diagnosis unit monitors the compliance of policy constraints, compares the land use configuration of the scheme with the spatial mapping of policy rules, and records the policy compliance degree (such as compliance proportion) and constraint violation position (such as violation cell coordinates) of each scheme. In terms of component functions, the constraint rule mapping unit converts policy spatial constraints into binary feasibility flags; the multi-objective optimization model integrates core and secondary objective functions; the evolutionary strategy module is responsible for population evolution and solution set generation; the conflict diagnosis unit realizes policy compliance evaluation and violation location.

[0058] Beneficial effects: The embodiment significantly reduces the post-implementation compliance modification cost and improves decision-making efficiency by treating policy rules as hard constraints; the multi-objective optimization model ensures policy compliance while balancing the improvement of cultural, ecological and economic values, avoiding the bias caused by single-target dominance; real-time conflict diagnosis provides immediate feedback, helping decision-makers quickly identify and adjust violation areas, enhancing the operability and transparency of the scheme; the evolutionary strategy based on non-dominated sorting generates a diversified Pareto optimal solution set, meeting the decision-making needs of different value focuses and supporting flexible land use planning.

[0059] In some embodiments, a chromosome coding scheme for land use spatial configuration is constructed, wherein each chromosome individual represents a complete set of land use scheme, and the chromosome gene locus corresponds to the planning grid cell, and the gene value represents the land use type allocated in the cell; a multi-objective fitness function is designed, and the cultural value intensity distribution data, the corridor ecological function flowability data and the economic value density distribution data are evaluated by the fitness function through a weighted summation mechanism, so as to quantify the cultural value intensity distribution data, the corridor ecological function flowability data and the economic value density distribution data into corresponding fitness values, and to evaluate the advantages and disadvantages of each chromosome individual; the operation parameters and operations of the genetic algorithm are set, and a random population is initialized by the genetic algorithm; a simulated binary crossover operation is adopted to exchange gene fragments between parent chromosomes in the random population to generate new individuals; a polynomial mutation operation is implemented to randomly change part of the gene loci of the new individuals, and a small probability of random disturbance is introduced to maintain population diversity; an iterative optimization process is performed, in each generation, the population is non-dominantly sorted according to the fitness function, the non-dominant sorting result is obtained, and the crowding degree of individuals in the same front layer is calculated, and the excellent individuals entering the next generation are selected by the tournament selection method according to the non-dominant sorting result and the crowding degree; the convergence condition is set and the solution set is output, when the iteration number reaches the preset maximum value, the algorithm is terminated, and all non-dominant solutions in the final population are output as the land use optimization decision set.

[0060] Technical background and working principle: The land use optimization of linear cultural heritage region involves the complex trade-off of multi-dimensional value objectives, and traditional optimization methods are difficult to effectively approach the Pareto front while ensuring the diversity of solution set. Multi-objective evolutionary algorithm can search multiple non-dominated solutions in high-dimensional solution space in parallel by simulating the natural evolution process, and is particularly suitable for handling discrete combinatorial optimization problems such as land use configuration. This embodiment is based on the NSGA-II algorithm framework, which discretizes land use spatial configuration into heritable gene sequences through chromosome coding, evaluates cultural, ecological and economic value performance simultaneously using multi-objective fitness function, maintains population diversity through genetic operation of crossover and mutation, and finally selects a uniformly distributed Pareto optimal solution set by non-dominant sorting and crowding degree calculation. The core working principle is to make the population gradually converge to the Pareto front in multi-dimensional objective space through iterative evolution mechanism, while maintaining the diversity and uniform distribution of the solution set, providing multiple value trade-off schemes for decision makers.

[0061] Technical solutions and component functions: The technical solution of the embodiment first constructs a chromosome coding scheme for land use spatial configuration, divides the planning area into uniform grids, and each chromosome individual represents a complete land use scheme. The chromosome gene site corresponds to the grid unit, and the gene value uses integer coding to represent the land use type allocated to the unit. A multi-objective fitness function is designed, and the cultural value intensity distribution data, ecological flowability data and economic value density distribution data are quantified into a unified fitness value through a weighted summation mechanism, wherein the weight coefficients can be dynamically adjusted according to the decision preference. The genetic algorithm operation parameters are set, including initialization of a random population (size 1000), adoption of a simulated binary crossover operation (probability 0.7) to exchange gene fragments between parent chromosomes, implementation of a polynomial mutation operation (probability 0.3) to randomly change part of the gene sites, and introduction of a small probability random disturbance to prevent premature convergence. An iterative optimization process is performed, in which the population is quickly non-dominantly sorted according to the fitness function in each generation, the crowding distance of individuals in the same front layer is calculated, and a tournament selection mechanism based on sorting and crowding is used to select excellent individuals into the next generation. The convergence condition is set as iteration reaching 1208 generations or the solution set improving by less than 1% in the continuous 50 generations, and the final output is all non-dominated solutions to form the land use optimization decision set. In terms of component functions, the chromosome coding unit is responsible for grid division and land use type mapping; the fitness calculation module integrates multi-dimensional value data and performs weighted evaluation; the genetic operation module realizes population updating and diversity maintenance; and the convergence judgment unit monitors the algorithm termination condition and outputs the final solution set.

[0062] Beneficial effects: The embodiment realizes efficient coding and search of complex land use schemes through discrete chromosome structure, significantly improving optimization efficiency; the weighted mechanism of the multi-objective fitness function flexibly accommodates different decision preferences, ensuring the comprehensiveness of value evaluation; the adaptive crossover and mutation parameters cooperate with the non-dominant sorting mechanism to effectively balance the exploration and development capabilities of the algorithm, avoiding falling into local optimum; the crowding distance calculation ensures the uniform distribution of the final solution set on the Pareto frontier, providing a diversified selection space for decision makers; the explicit convergence condition controls the calculation cost while ensuring the quality of the solution set, enhancing the practicality of the method.

[0063] In some embodiments, a multi-attribute utility theory is used to establish a scheme screening mechanism, to calculate the standardized utility value of each scheme in the land use optimization decision set in the economic, ecological and cultural dimensions, to sort and preliminarily select all schemes by weighted comprehensive score, and to obtain a preliminary scheme set; according to the preliminary scheme set, spatial superposition and change detection analysis are performed on different preliminary schemes, the land use layout of different preliminary schemes is spatially superimposed on the current land use map, the change area where the land use type is converted is identified by a grid algorithm, and the spatial distribution, area and conversion direction are counted, and the land use scheme set corresponding to the preliminary scheme set is integrated; for each preliminary scheme, the land use scheme set is analyzed, the land use scheme corresponding to each preliminary scheme is compared with the current situation in the cultural, ecological and economic three dimensions, the percentage of improvement in cultural value, ecological value and economic value compared with the current situation is quantified, and the policy rule engine is used for secondary compliance verification to ensure that the preliminary selected scheme does not contain any land use configuration that violates the mandatory policy constraints, and an optimal scheme set is obtained; the optimal scheme set is analyzed, the land space configuration parameters of each scheme are decoded and visualized, the planning map of the land use space layout corresponding to each optimal scheme, the land use structure configuration table of the area and spatial position of each land class, and the analysis report containing the comprehensive benefit comparison, policy compliance conclusion and potential implementation risk are generated, and the land use optimization report is constructed and output.

[0064] Technical background and working principle: The land use decision set generated by the multi-objective optimization process often contains a large number of Pareto non-dominated solutions. The traditional method relies on manual experience screening, which has strong subjectivity, low efficiency and difficulty in comprehensive evaluation of the overall benefits of each scheme. The present embodiment establishes an objective screening mechanism based on multi-attribute utility theory, realizes the quantitative sorting and preliminary selection of schemes by weighted comprehensive score of the standardized utility value of the economic, ecological and cultural three dimensions; combined with spatial superposition and change detection technology, the land use conversion area of the scheme relative to the current situation is accurately identified, and the spatial change characteristics are quantified; further, the policy rule engine is used for secondary compliance verification to ensure that the final optimal scheme strictly follows the mandatory policy constraints. The core working principle is to systematically identify the optimal scheme with the best overall benefit, reasonable spatial conversion and policy compliance from the massive optimization solutions through the three-level screening process of "quantitative evaluation-spatial analysis-compliance verification", and to provide reliable planning basis for decision makers.

[0065] Technical solutions and component functions: The technical solution of the embodiment first establishes a scheme screening mechanism based on multi-attribute utility theory. The utility calculation unit reads the original data of each scheme in the three dimensions of economic value density, ecological flowability and cultural value intensity. After eliminating the dimension influence by using the range standardization method, the weighted comprehensive utility value is calculated according to the preset weight coefficient (such as economy 0.4, ecology 0.3, culture 0.3). According to this, all schemes are sorted and the top 20% are selected as the preliminary selected schemes. Then, the spatial overlay analysis unit performs pixel-level overlay operation on the land use layout grid of the preliminary selected schemes and the current land use grid. The change detection algorithm is used to identify the areas where the land use type is converted. The change area, spatial distribution pattern and main conversion direction (such as farmland to construction land, water area to ecological land) are counted. Then, the compliance verification unit maps the land use configuration of each scheme to the spatial constraint rules of the policy rule engine, and detects whether there is a situation of violating the prohibited rules by Boolean operation, to ensure that the optimal scheme set is completely compliant. Finally, the report generation unit integrates all analysis results to generate a complete report for each optimal scheme, including land use planning map, land use structure configuration table, comprehensive benefit comparison chart, policy compliance statement and implementation risk assessment. In terms of component functions, the multi-attribute utility calculation module realizes dimension standardization and weighted synthesis; the spatial overlay unit performs grid operation and change statistics; the compliance verification module performs rule matching and conflict detection; and the report generation module integrates text, charts and spatial visualization output.

[0066] Beneficial effects: The embodiment realizes objective quantitative sorting of schemes through multi-attribute utility theory, significantly reduces the interference of subjective factors, and improves the scientific nature of screening. Spatial overlay and change detection provide intuitive land use conversion information to help decision-makers accurately assess the spatial impact range of scheme implementation. Secondary compliance verification eliminates the risk of policy violations and avoids the cost loss caused by later rectification. Structured report output integrates spatial layout, statistical data and risk assessment to provide comprehensive technical support for scheme approval and implementation. The three-level screening process ensures that the final scheme achieves the optimal balance in the three dimensions of benefit, space and policy.

[0067] Figure 3 The structural diagram of a digital scenario empowerment canal heritage land use decision system provided by an embodiment of the present application is shown in Figure 3 As shown in the structural diagram of a digital scenario empowerment canal heritage land use decision system 300 of the embodiment, the digital scenario empowerment canal heritage land use decision system 300 includes an information collection module 301, a policy analysis module 302, a value analysis module 303, a decision optimization module 304 and a report output module 305.

[0068] The information collection module 301 is configured to acquire environmental information, policy information, cultural information and economic information of a target cultural heritage area, and construct multi-source cultural heritage information; the policy analysis module 302 is configured to perform policy semantic analysis and spatialization on the policy information, to obtain policy semantic information and spatial information, and to construct a policy rule engine according to the policy semantic information and the spatial information; the value analysis module 303 is configured to perform multi-dimensional dynamic value analysis on the multi-source cultural heritage information, to obtain spatialized value distribution data of the target cultural heritage area; the decision optimization module 304 is configured to perform multi-objective collaborative optimization and conflict diagnosis based on the spatialized value distribution data and the policy rule engine, and to generate a set of land use optimization decisions; and the report output module 305 is configured to perform screening analysis on the set of land use optimization decisions, and to output a land use optimization report.

[0069] Optionally, the policy analysis module 302 is specifically configured to perform natural language analysis on the policy information, to extract control intensity vocabulary and spatial range description in the policy information, to establish a multi-level classification system including prohibition rules, restriction rules and encouragement rules, and to obtain the policy semantic information; to convert the abstract policy information into specific machine-executable spatial constraint rules according to the spatial range description, the spatial constraint rules being used to define the spatial action range, constraint intensity parameters and priority relationship of various policies; and to associate the spatial constraint rules with geographical coordinates corresponding to each rule in the multi-level classification system in the policy semantic information by a spatial matching algorithm, to obtain the policy spatial information.

[0070] Optionally, the value analysis module 303 is specifically configured to identify spatial distribution characteristics of cultural heritage elements according to the cultural information, to construct a cultural influence propagation model based on distance attenuation and spatial accessibility; to calculate the influence radiation range and intensity attenuation law of each cultural source in space through the cultural influence propagation model, to construct continuous distribution cultural value intensity distribution data; to analyze the ecological background characteristics and structural connectivity of the canal corridor according to the environmental information, to construct a corridor service flow model based on ecological process simulation; to identify key positions of ecological pinch points and ecological hubs through the corridor service flow model, to evaluate the importance of each region in maintaining the overall ecological function of the corridor, to construct an ecological flow connectivity atlas data representing the strength of ecological function connectivity; to integrate multi-source economic vitality index data according to the environmental information and the economic information, to construct a value density estimation model based on spatial statistics and economic geography principles; to analyze the influence of industrial agglomeration effect and market size factors through the value density estimation model, to construct economic value density distribution data; and to construct the spatialized value distribution data according to the cultural value intensity distribution data, the ecological flow connectivity atlas data and the economic value density distribution data.

[0071] Optionally, in the value analysis module 303, the process of constructing the cultural value intensity distribution data is specifically used for: identifying the spatial distribution characteristics of various cultural heritage elements based on the cultural information, and unifying tangible cultural heritage sites, intangible cultural heritage activity sites, historical and cultural routes, and traditional production and living areas as sources of cultural influence; constructing a cultural value dissemination model based on field strength theory and the sources of cultural influence; comprehensively considering the influence of distance attenuation effect, differences in accessibility, and social activity factors through the cultural value dissemination model, and introducing a nonlinear attenuation function to describe the dissemination characteristics of cultural value, considering the superposition effect and mutual reinforcement mechanism of influence in areas with dense cultural heritage, and quantifying the spatial diffusion law of cultural influence; integrating multi-source cultural data, including the trajectory of cultural activities recorded in historical documents, the popularity of cultural practices reflected in social media, and the intensity of cultural space use obtained from on-site surveys; and, based on the spatial diffusion law, transforming the discrete cultural activity trajectories, the popularity of cultural practices, and the intensity of cultural space use into continuously distributed cultural value intensity surface data through spatial interpolation and field strength superposition methods, thereby obtaining the cultural value intensity distribution data.

[0072] Optionally, in the value analysis module 303, the process of constructing the ecological circulation map data is specifically used for: analyzing the ecological status within the target cultural heritage area based on the environmental information, extracting the ecological baseline conditions of the target cultural heritage area, including hydrological information, vegetation information, and biological information; using the hydrological information, vegetation information, and biological information as node information, constructing an ecological service flow simulation model based on a directed graph, and introducing resistance factors affecting key ecological processes into the ecological service flow simulation model to simulate the circulation and change information of the three key ecological processes—species migration, water conservation, and nutrient cycling—in the corridor; the resistance factors include land use barriers, transportation facility barriers, and topographic limitations; and performing visualization map processing on the circulation change information to construct the ecological circulation map data reflecting the ecological value of different regions.

[0073] Optionally, in the value analysis module 303, the construction process of the economic value density distribution data is specifically used for: analyzing economic vitality of the target cultural heritage area under the double influence of environment and economy according to the environment information and the economic information, and constructing multi-source economic vitality observation data including night light intensity, commercial facility density, traffic flow statistics, land transaction price and industrial input-output data; constructing a value density estimation model based on geographic weighted regression and spatial econometrics according to the multi-source economic vitality observation data, and introducing the influence of spatial heterogeneity and spatial dependence; in-depth analysis of the spatial agglomeration law of economic activities, and identification of the distribution characteristics of industrial cluster areas, commercial center areas and innovation active areas; using a spatial interpolation method, converting point and surface economic observation data into continuous distribution economic value density surface; spatially registering the economic value density data and the land use grid unit, and constructing the economic value density distribution data.

[0074] Optionally, the decision optimization module 304 is specifically used for: based on the policy rule engine, analyzing spatial regions corresponding to the prohibition type rule, the restriction type rule and the encouragement type rule according to the spatialized value distribution data, taking the spatial constraint rule of the policy rule engine as a hard constraint condition of a multi-objective optimization process, and taking maximizing cultural value intensity, corridor ecological function flowability and economic value density as core optimization targets, while introducing structure rationality and spatial coordination of land use as secondary optimization targets, to construct a multi-objective optimization model; based on the multi-objective optimization model, using a targeted multi-objective evolutionary strategy for optimization and solution, and through an adaptive parameter adjustment and a non-dominated sorting mechanism, constructing the land use optimization decision set; in the multi-objective optimization process, real-time monitoring of compliance of policy constraints, and accurate recording of policy compliance and constraint violation positions of each potential scheme.

[0075] Optionally, the decision optimization module 304 is specifically configured to: construct a chromosome coding scheme for land use spatial configuration, wherein each chromosome individual represents a complete set of land use scheme, the chromosome gene position corresponds to the planning grid cell, and the gene value represents the land use type allocated in the cell; design a multi-objective fitness function, and perform fitness evaluation on the cultural value intensity distribution data, the corridor ecological function flowability data and the economic value density distribution data through a weighted summation mechanism, so as to quantify the cultural value intensity distribution data, the corridor ecological function flowability data and the economic value density distribution data into corresponding fitness values, and evaluate the advantages and disadvantages of each chromosome individual; set the operation parameters and operations of the genetic algorithm, and initialize a random population through the genetic algorithm; adopt a simulated binary crossover operation to exchange gene fragments between parent chromosomes in the random population to generate new individuals; implement a polynomial mutation operation to randomly change part of the genes of the new individuals, introduce a small probability of random disturbance to maintain population diversity; perform an iterative optimization process, in each generation, perform non-dominated sorting on the population according to the fitness function, obtain a non-dominated sorting result, calculate the crowding degree of individuals in the same front layer, and select excellent individuals into the next generation according to the non-dominated sorting result and the crowding degree by using a tournament selection method; set a convergence condition and output a solution set, when the number of iterations reaches a preset maximum value, terminate the algorithm, and output all non-dominated solutions in the final population as the land use optimization decision set.

[0076] Optionally, the report output module 305 is specifically configured to: based on the multi-attribute utility theory, establish a scheme screening mechanism, calculate the standardized utility value of each scheme in the land use optimization decision set in the economic, ecological and cultural dimensions, sort and preliminarily select all schemes through weighted comprehensive scoring to obtain a preliminary scheme set; according to the preliminary scheme set, perform spatial superposition and change detection analysis on different preliminary schemes, perform spatial superposition on the land use layout diagram of different preliminary schemes and the current land use diagram, identify the change area of the land use type conversion through a grid algorithm, and count the spatial distribution, area and conversion direction, and integrate to obtain the land use scheme set corresponding to the preliminary scheme set; for each preliminary scheme, analyze the land use scheme set, compare the land use scheme corresponding to each preliminary scheme with the current situation in the cultural, ecological and economic three-dimensional value, quantify the percentage of improvement in cultural value, ecological value and economic value compared with the current situation, and perform secondary compliance verification by using the policy rule engine to ensure that the preliminary selected scheme does not contain any land use configuration that violates the mandatory policy constraints, and obtain an optimized scheme set; analyze the optimized scheme set, decode and visualize the land space configuration parameters of each scheme, generate the planning diagram of the land use space layout corresponding to each optimized scheme, the land use structure configuration table of the area and spatial position of each land class, and the analysis report containing the comprehensive benefit comparison, policy compliance conclusion and potential implementation risk, and output the land use optimization report.

[0077] The system of the embodiment can be used to execute the method of any of the above embodiments, and has similar implementation principles and technical effects, which will not be described here again.

Claims

1. A digitally-enabled, scenario-based land use decision-making method for canal heritage sites, characterized in that, include: Acquire environmental, policy, cultural, and economic information about the target cultural heritage area to construct multi-source cultural heritage information; The policy information is subjected to policy semantic parsing and spatialization to obtain policy semantic information and spatial information, and a policy rule engine is constructed based on the policy semantic information and spatial information; Multidimensional dynamic value analysis is performed on the multi-source cultural heritage information to obtain spatial value distribution data of the target cultural heritage area; Based on the spatialized value distribution data and the policy rule engine, multi-objective collaborative optimization and conflict diagnosis are performed to generate a land use optimization decision set; The land use optimization decision set is screened and analyzed to output a land use optimization report; The process of performing multidimensional dynamic value analysis on the multi-source cultural heritage information to obtain spatial value distribution data of the target cultural heritage area includes: Based on the cultural information, identify the spatial distribution characteristics of cultural heritage elements and construct a cultural influence dissemination model based on distance attenuation and spatial accessibility; The cultural influence dissemination model is used to calculate the spatial influence radiation range and intensity decay law of each cultural source, and to construct a continuously distributed cultural value intensity distribution data. Based on the environmental information, the ecological background characteristics and structural connectivity of the canal corridor are analyzed, and a corridor service flow model based on ecological process simulation is constructed. The corridor service flow model identifies key locations of ecological pinch points and ecological hubs, assesses the importance of each region in maintaining the overall ecological function of the corridor, and constructs ecological flow map data that characterizes the strength of ecological function connectivity. Based on the aforementioned environmental and economic information, multi-source economic vitality indicator data are integrated to construct a value density estimation model based on spatial statistics and economic geography principles. The impact of industrial agglomeration effect and market size factor is analyzed through the value density estimation model to construct economic value density distribution data. Based on the cultural value intensity distribution data, the ecological circulation map data, and the economic value density distribution data, the spatialized value distribution data is constructed.

2. The method according to claim 1, characterized in that, The process of performing policy semantic parsing and spatialization on the policy information to obtain policy semantic information and spatial information, and constructing a policy rule engine based on the policy semantic information and spatial information, includes: The policy information is parsed using natural language processing to extract control intensity terms and spatial scope descriptions. A multi-level classification system including prohibition rules, restriction rules, and encouragement rules is established to obtain the policy semantic information. Based on the spatial scope description, the abstract policy information is transformed into specific machine-executable spatial constraint rules, which are used to define the spatial scope of action, constraint strength parameters, and priority relationships of various policies. Based on the policy semantic information, the spatial constraint rules are associated with the geographic coordinates corresponding to each rule under the multi-level classification system in the policy semantic information through a spatial matching algorithm to obtain policy spatial information.

3. The method according to claim 2, characterized in that, The process of constructing the cultural value intensity distribution data includes: Based on the cultural information, the spatial distribution characteristics of various cultural heritage elements are identified, and tangible cultural heritage sites, intangible cultural heritage activity sites, historical and cultural routes, and traditional production and living areas are uniformly regarded as sources of cultural influence. Based on field strength theory, a cultural value dissemination model is constructed according to the sources of cultural influence. The cultural value dissemination model comprehensively considers the effects of distance attenuation, differences in accessibility, and social activity, and introduces a nonlinear attenuation function to describe the dissemination characteristics of cultural values. In areas with dense cultural heritage, the superposition effect and mutual reinforcement mechanism of influence are considered to quantify the spatial diffusion law of cultural influence. Integrate multi-source cultural data, including the trajectory of cultural activities recorded in historical documents, the popularity of cultural practices reflected on social media, and the intensity of cultural space use obtained from on-site surveys; Based on the aforementioned spatial diffusion pattern, the discrete cultural activity trajectories, cultural practice popularity, and cultural space usage intensity are transformed into continuously distributed cultural value intensity surface data through spatial interpolation and field strength superposition methods, thereby obtaining the cultural value intensity distribution data.

4. The method according to claim 3, characterized in that, The process of constructing the ecological circulation map data includes: Based on the environmental information, the ecological status of the target cultural heritage area is analyzed, and the ecological baseline conditions of the target cultural heritage area are extracted, including hydrological information, vegetation information, and biological information. Using the hydrological information, vegetation information, and biological information as node information, an ecoservice flow simulation model based on a directed graph is constructed. The resistance factors affecting key ecological processes are introduced into the ecoservice flow simulation model to simulate the flow and change information of the three key ecological processes—species migration, water conservation, and nutrient cycling—in the corridor. The resistance factors include land use barriers, transportation infrastructure barriers, and topographical limitations; Based on the circulation change information, the circulation change information is visualized and processed into a map to construct the ecological circulation map data that reflects the ecological value of different regions.

5. The method according to claim 4, characterized in that, The process of constructing the economic value density distribution data includes: Based on the environmental and economic information, the economic vitality of the target cultural heritage area under the dual verification of environment and economy is analyzed, and multi-source economic vitality observation data is constructed, including nighttime light intensity, commercial facility density, traffic flow statistics, land transaction prices, and industrial input-output data. Based on the multi-source economic vitality observation data, a value density estimation model based on geographically weighted regression and spatial econometrics is constructed, incorporating the effects of spatial heterogeneity and spatial dependence. In-depth analysis of the spatial agglomeration patterns of economic activities, identifying the distribution characteristics of industrial clusters, commercial centers, and innovation hubs; Spatial interpolation methods are used to transform point-like and area-like economic observation data into continuously distributed economic value density surfaces; The economic value density data is spatially registered with land use grid cells to construct the economic value density distribution data.

6. The method according to claim 5, characterized in that, The process of generating a land use optimization decision set based on the spatialized value distribution data and the policy rule engine, involving multi-objective collaborative optimization and conflict diagnosis, includes: Based on the policy rule engine, and according to the spatialized value distribution data, the spatial regions corresponding to the prohibited rules, restricted rules, and encouraged rules are analyzed. The spatial constraint rules of the policy rule engine are used as hard constraints in the multi-objective optimization process. The core optimization objectives are to maximize the intensity of cultural value, the circulation of corridor ecological functions, and the density of economic value. At the same time, the structural rationality and spatial coordination of land use are introduced as secondary optimization objectives to construct a multi-objective optimization model. Based on the multi-objective optimization model, a targeted multi-objective evolutionary strategy is adopted for optimization solution. Through adaptive parameter adjustment and non-dominated ranking mechanism, the land use optimization decision set is constructed. During the multi-objective optimization process, the compliance with policy constraints is monitored in real time, and the policy compliance and constraint violation locations of each potential solution are accurately recorded.

7. The method according to claim 6, characterized in that, The targeted multi-objective evolutionary strategy includes: Construct a chromosome coding scheme for spatial configuration of land use, where each chromosome represents a complete land use scheme, chromosome gene loci correspond to planning grid units, and gene values ​​characterize the land use types allocated within the unit; A multi-objective fitness function is designed, and a weighted summation mechanism is used to evaluate the fitness of the cultural value intensity distribution data, corridor ecological function circulation data, and economic value density distribution data. The cultural value intensity distribution data, corridor ecological function circulation data, and economic value density distribution data are quantified into corresponding fitness values ​​to evaluate the merits of each chromosome individual. Set the running parameters and operations of the genetic algorithm, and initialize the random population through the genetic algorithm; New individuals are generated by exchanging gene segments between parent chromosomes in the random population using a simulated binary crossover operation. Polynomial mutation operations are performed to randomly change some gene loci of the new individuals, introducing a small probability of random perturbation to maintain population diversity. An iterative optimization process is performed. In each generation, the population is non-dominated and sorted according to the fitness function to obtain the non-dominated sorting results. The crowding degree of individuals in the same frontier layer is calculated. Based on the non-dominated sorting results and the crowding degree, a tournament selection method is used to select superior individuals to enter the next generation. Set convergence conditions and output the solution set. When the number of iterations reaches the preset maximum value, terminate the algorithm and output all non-dominated solutions in the final population as the land use optimization decision set.

8. The method according to claim 7, characterized in that, The process of screening and analyzing the land use optimization decision set and outputting a land use optimization report includes: A scheme selection mechanism is established based on the multi-attribute utility theory. The standardized utility values ​​of each scheme in the economic, ecological and cultural dimensions are calculated for each scheme in the land use optimization decision set. All schemes are ranked and initially selected through weighted comprehensive scoring to obtain a preliminary scheme set. Based on the preliminary selection scheme set, spatial overlay and change detection analysis are performed on different preliminary selection schemes. The land use layout maps of different preliminary selection schemes are spatially overlaid with the current land use maps. The change areas where land use types have changed are identified through the raster algorithm, and their spatial distribution, area and change direction are statistically analyzed. The land use scheme set corresponding to the preliminary selection scheme set is then integrated. For each preliminary selection scheme, the land use scheme set is analyzed. The land use scheme corresponding to each preliminary selection scheme is compared with the current situation in terms of cultural, ecological and economic value. The percentage increase in cultural value, ecological value and economic value compared with the current situation is quantified. The policy rule engine is used to perform a second compliance verification to ensure that the preliminary selection schemes do not contain any land use configurations that violate mandatory policy constraints, thus obtaining the optimal scheme set. The optimal solution set is analyzed, and the land space configuration parameters of each solution are decoded and visualized to generate a planning map of land use spatial layout, a land use structure configuration table of land use area and spatial location for each optimal solution, and an analysis report including comprehensive benefit comparison, policy compliance conclusions and potential implementation risks. The land use optimization report is then constructed and output.

9. A digitally-enabled, scenario-driven land use decision-making system for canal heritage sites, characterized in that: The method applied to any one of claims 1-8 includes: The information collection module is used to acquire environmental, policy, cultural, and economic information about the target cultural heritage area, and to construct multi-source cultural heritage information. The policy analysis module is used to perform policy semantic parsing and spatialization based on the policy information, and to build a policy rule engine; The value analysis module is used to perform multi-dimensional dynamic value analysis based on the multi-source cultural heritage information to obtain spatial value distribution data of the target cultural heritage area; The decision optimization module is used to perform multi-objective collaborative optimization and conflict diagnosis based on the spatialized value distribution data and the policy rule engine, and generate a land use optimization decision set. The report output module is used to filter and analyze the land use optimization decision set and output a land use optimization report.

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