A resource space optimization configuration method and system for land planning

Abstract

The application discloses a resource space optimization configuration method and system for land planning, and relates to the technical field of land planning and resource space configuration; the application collects and pre-processes space data of a planning area, constructs complete topological adjacency relations, establishes a neighborhood correlation constraint matrix and a constraint processing priority sequence, filters candidate land use types through priority, completes initial configuration based on comprehensive configuration scores, detects and resolves neighborhood conflicts, adjusts land use structure deviation, and outputs a configuration scheme after constraint compliance verification, effectively solving the problem that neighborhood correlation constraints are difficult to be effectively coupled and processed in the optimization solving process, significantly improving the scientificity and rationality of land resource space configuration, and improving the constraint compliance rate of the configuration scheme and resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of land planning and resource spatial allocation technology, specifically to a method and system for optimizing resource spatial allocation in land planning. Background Technology

[0002] As a scarce resource with inherent spatial attributes, the optimal spatial allocation of land resources is the core of national land spatial planning, directly determining the efficiency of land use, the rationality of spatial layout, and the practical implementation of planning schemes. With the development of geographic information science, operations research, and intelligent optimization algorithms, traditional land planning methods are gradually being replaced by quantitative and model-based technical means, enabling quantitative solutions for land resource allocation and improving the scientificity and efficiency of planning to a certain extent. Neighborhood constraints between land parcels are the core spatial constraints for land spatial allocation, encompassing multiple dimensions such as land use adjacency adaptation, ecological space connectivity, and public service coverage. They directly determine the spatial feasibility and implementability of planning schemes, but existing technologies have consistently struggled to effectively couple them to the core optimization solution process. The shortcomings of existing technologies are specifically reflected in the following aspects: Neighborhood association constraints are only used as post-hoc verification conditions and are not integrated into the entire algorithm solution process; constraints are mostly qualitative descriptions and lack quantitative expressions and evaluation systems that can be identified by the algorithm; GIS and algorithms only achieve data-level integration, and neighborhood spatial information cannot be transmitted to the solution process in real time; a multi-dimensional constraint coupling system has not been built and there is no dynamic adaptation mechanism, which cannot meet the personalized planning needs of different regions. This often leads to problems such as land use adjacency conflicts and ecological space fragmentation in optimization schemes, resulting in the contradiction of "numerical optimal but spatially infeasible". Correction costs are high and the numerical optimization effect of the original scheme is easily destroyed. It is difficult to adapt to the refined requirements of current land spatial planning. There is an urgent need to develop a land resource spatial optimization allocation method that can deeply and accurately couple the neighborhood association constraints to the entire optimization solution process.

[0003] To address the aforementioned shortcomings, a technical solution is provided. Summary of the Invention

[0004] The purpose of this invention is to solve the problem that neighborhood association constraints are difficult to be effectively coupled and processed in the optimization solution process, and to propose a resource space optimization allocation method and system for land planning.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for optimizing the spatial allocation of resources in land planning includes: S1. Spatial Data Acquisition and Preprocessing: By integrating remote sensing image interpretation, geographic information database retrieval, and field survey data entry into the data acquisition unit, spatial unit vector boundaries, current land use type codes, land parcel attribute parameters, and topological adjacency relationship data are collected. Through quality verification, completion, repair, standardization processing, and topological adjacency relationship reconstruction, a basic spatial dataset for the planning area is constructed. S2. Neighborhood association constraint construction: Based on the boundary contact coefficient, direction weight coefficient and type compatibility coefficient of spatial unit pairs, the neighborhood association coefficient is obtained, the neighborhood association constraint matrix is ​​constructed, the constraint sensitivity level is divided and the spatial units are classified to form a constraint processing priority sequence. S3. Constraint-coupled optimization solution: The neighborhood correlation constraint matrix is ​​embedded in the iterative optimization solution process, and the coordinated optimization of land use type configuration of each spatial unit is realized through the constraint transfer mechanism; S4. Configuration scheme generation: Construct a configuration result data table, adjust the structure based on the land use structure deviation, and generate spatial configuration scheme vector layer and statistical reports; S5. Configuration Scheme Verification and Output: Verify the compliance of the configuration scheme with neighborhood constraints, determine the validity of the configuration scheme, and output the final configuration result.

[0006] As a further improvement of the present invention, the specific operation steps of S2 include: Based on the reconstructed topological adjacency table and standardized vector boundaries, geographic feature parameters are extracted from spatial cell pairs with adjacency identifiers, and the boundary contact coefficient is obtained by the shared boundary length and cell perimeter of the spatial cell pairs. By combining the geometric coordinates of spatial units, a two-dimensional plane vector is constructed, and the direction weight coefficient is obtained based on the angle between the vector and the true north direction and the direction sensitivity factor. Land use type compatibility rules are extracted from land planning rules, and then graded, scored, and normalized to obtain type compatibility coefficients. Based on the coupling of boundary contact coefficient, direction weight coefficient and type compatibility coefficient, the neighborhood correlation coefficient is obtained, and a detailed table of correlation coefficients is formed. An N×N empty matrix is ​​constructed based on the total number of spatial units. The neighborhood association constraint matrix is ​​constructed based on the adjacency and non-adjacency relationships between units and the assignment rules for the same unit.

[0007] As a further improvement of the present invention, the specific operation steps of S2 also include: By combining the neighborhood correlation coefficient and the area ratio of adjacent spatial units, the neighborhood constraint strength of each spatial unit is obtained; The neighborhood constraint strength of all spatial units is normalized and classified to generate three levels of constraint sensitivity: high, medium and low. A three-dimensional association table containing spatial unit number, neighborhood constraint strength and constraint sensitivity level is constructed. Based on the three-dimensional association table, high constraint sensitivity level is selected as key constraint unit and corresponding adjacent medium constraint sensitivity level is selected as secondary constraint unit, and the rest are ordinary spatial units. A constraint unit linked list is constructed with key constraint units as core nodes and secondary nodes as subordinate nodes. Core nodes are arranged in descending order based on the neighborhood constraint strength of key constraint units, and subordinate nodes are arranged in association to form an overall constraint processing priority sequence, which is stored in association with the constraint matrix.

[0008] As a further improvement of the present invention, the specific operation steps of S3 include: Based on the constraint processing priority sequence, we integrate the neighborhood association constraint matrix, constraint sensitivity level, adjacency unit set and basic spatial dataset to construct a dedicated dataset for constraint coupling optimization solution; Combining regional land planning objectives, land use type compatibility rules, and spatial unit topography and ownership, selectable land use types from the corresponding classification standards to form a candidate type set; Based on the neighborhood association coefficient with adjacent units when configuring each candidate land use type, the spatial unit configuration determinism is initialized; The neighborhood fit score is obtained based on the neighborhood correlation coefficient and configuration certainty; the resource benefit score is obtained based on the resource benefit evaluation index; and the neighborhood fit weight is obtained by retrieving the constraint sensitivity level. The normalized values ​​of neighborhood adaptation score, resource benefit score and neighborhood adaptation weight are used to obtain the comprehensive configuration score of candidate land use types. The one with the highest comprehensive configuration score is selected as the temporary configuration type and the configuration certainty is set to 1. Based on priority, the temporary configuration of all spatial units is completed in sequence to form the initial temporary configuration scheme.

[0009] As a further improvement of the present invention, the specific operation steps of S3 also include: Retrieve the temporary configuration type of each spatial unit's adjacent units from the initial temporary configuration scheme, and match the type compatibility coefficient based on the land use type compatibility lookup table; If the type compatibility coefficient is less than the preset threshold, it is marked as a conflict unit pair and recorded in the conflict list; the comprehensive configuration score, constraint sensitivity level and area of ​​the conflict unit pair are retrieved to calculate the configuration stability index; Spatial units with low stability indices in conflicting units are marked as units to be adjusted; the current temporary configuration types of units to be adjusted are removed, and the replacement configuration type with the highest comprehensive configuration score is re-selected from the remaining candidate types, and the neighborhood association constraint matrix and association coefficient details table are updated simultaneously. Traverse the adjacent spatial units of the unit to be adjusted. If the change in constraint sensitivity level exceeds the preset threshold, it is included in the secondary detection queue. Repeat the conflict determination, marking, configuration stability index calculation, unit to be adjusted screening and configuration replacement, and adjacent unit constraint sensitivity level update process until the conflict list is empty or the preset maximum number of iterations is reached. If the iteration limit is exceeded, reduce the type compatibility coefficient preset threshold and start constraint relaxation processing.

[0010] As a further improvement of the present invention, the specific operation steps of S4 are as follows: Based on the final provisional configuration scheme, the final configuration type and comprehensive configuration score of all spatial units are extracted, and a configuration result data table is constructed using the spatial unit number as the unique identifier. Based on the final land use type coding and grouping, and based on the total area ratio of each land use type, the deviation of land use structure is obtained by comparing it with the regional land planning target ratio. Land use types with deviations exceeding a preset threshold are sorted in descending order of deviation, with priority given to adjusting the type with the largest deviation, and the spatial unit with the lowest corresponding comprehensive configuration score is selected as the adjustable unit. Among the remaining candidate types, select alternative types that meet the neighborhood association constraints and have a decrease in overall deviation. After verifying that the type compatibility coefficients of the alternative types with all adjacent units meet the standards, update the configuration. After completing the structural adjustments for all land use types that exceed the preset threshold, the final land use type code is written into the spatial unit vector boundary attribute field constructed by S1, generating a spatial configuration scheme vector layer, and simultaneously generating a configuration scheme statistical report based on land use type classification and summary.

[0011] As a further improvement of the present invention, the specific operation steps of S5 are as follows: Based on the generated spatial configuration scheme vector layer and configuration result data table, a neighborhood constraint compliance check is performed on each spatial unit. The final land use type code of the adjacent units of the spatial unit is retrieved, and the type compatibility coefficient is matched based on the land use type compatibility lookup table to obtain the unit-level constraint compliance. The scheme-level constraint compliance rate is obtained by weighting and summing the data using the area of ​​the spatial units as the weight; then, a hierarchical processing is performed based on the scheme-level constraint compliance rate. If the compliance rate of the scheme-level constraints is greater than the maximum value of the preset interval, the scheme is deemed valid and the current configuration result is locked. If the scheme-level constraint compliance rate is within the preset range, then select spatial cells with a unit-level compliance rate lower than the threshold and return to S3 for optimization solution; If the compliance rate of the scheme-level constraint is less than the minimum value of the preset interval, record the reason for failure, reset the configuration status and increase the benchmark value of the neighborhood adaptation weight, and re-execute S3-S5. After successful verification, the integrated spatial configuration scheme vector layer, configuration scheme statistical report, and verification result record are output to the planning results database, and a configuration scheme description document is generated simultaneously.

[0012] A second aspect of the present invention provides a resource space optimization allocation system for land planning, comprising: Spatial data acquisition and preprocessing module: integrates remote sensing image interpretation, geographic information database retrieval, and field survey data entry functions, collects four types of core basic data, and reconstructs topological adjacency relationships through quality verification, completion, topology repair and standardization processing to build a basic spatial dataset for the planning area; Neighborhood association constraint construction module: Based on boundary contact, direction weight and type compatibility coefficient, it generates neighborhood association coefficient and constraint matrix, obtains constraint sensitivity level by level, classifies spatial units and forms constraint processing priority sequence; Constraint-coupled optimization solution module: It completes the initial configuration based on the priority sequence, detects and resolves neighborhood conflicts, iterates until the conflicts are cleared, and outputs the final temporary configuration scheme. Configuration scheme generation module: Constructs a configuration result data table, performs structural adjustments based on land use structure deviation, and generates spatial configuration scheme vector layers and statistical reports; Configuration scheme verification and output module: Based on the compliance rate of unit-level and scheme-level constraints, hierarchical judgment and processing are performed. After verification, the results are integrated and output to the database, and standardized explanatory documents are generated simultaneously.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention constructs accurate topological adjacency relationships by integrating multi-source data acquisition and full-dimensional preprocessing; it establishes a multi-dimensional neighborhood association constraint matrix and processing priority sequence that includes boundary contact, direction weight, and type compatibility; and completes the initial configuration by combining neighborhood adaptation and resource benefit calculation comprehensive scores. After full-domain conflict detection and resolution, land use structure deviation adjustment, and hierarchical verification and optimization, standardized results are output. This invention significantly improves the scientificity and accuracy of land resource spatial allocation, increases the scheme-level constraint compliance rate and neighborhood compatibility, effectively reduces the conflict occurrence rate, and enhances the feasibility of the configuration scheme. Attached Figure Description

[0014] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example: like Figure 1 As shown, a resource spatial optimization allocation method for land planning includes spatial data acquisition and preprocessing, neighborhood association constraint construction, constraint coupling optimization solution, allocation scheme generation, and allocation scheme verification and output.

[0017] S1. Spatial Data Acquisition and Preprocessing: By integrating data acquisition units that combine remote sensing image interpretation, geographic information database retrieval, and on-site survey data entry, comprehensive basic spatial data collection is conducted for the planning area, simultaneously acquiring four types of core basic data, including: The spatial unit vector boundary is the planar vector outline and boundary geographic coordinate string of each land spatial unit within the planning area; The current land use type code is a standardized code corresponding to the national or industry land use classification standard for each spatial unit; The land parcel attribute parameters are the geographic characteristic parameters and basic attribute parameters of each spatial unit, including the spatial unit area, perimeter, geometric centroid coordinates, weighted attribute properties, and terrain slope; The topological adjacency relationship data between spatial units consists of the initial adjacency identifier data of each spatial unit and the preliminary correspondence of adjacent spatial unit numbers stored in the geographic information database; The raw basic spatial data acquired by the data acquisition unit undergoes full-dimensional quality verification and cleaning to remove duplicate and invalid spatial unit data; for spatial units with missing land parcel attribute parameters, spatial interpolation is used to complete the parameters, and missing data that cannot be completed is specially marked and stored separately for verification; geometric repair algorithms are used to correct topological errors in the vector boundaries of spatial units; and the current land use type coding is checked for consistency, and non-standard and erroneous coding data is deleted. To address the issues of missing adjacency identifiers and incorrect correspondences of adjacent spatial units in the initial topological adjacency relationship data, based on the cleaned spatial unit vector boundaries, a surface feature spatial topology analysis algorithm is invoked to re-extract the adjacency relationships of all spatial units within the planning area. This process identifies the actual geographical adjacency relationship between any two spatial units, clarifies the spatial unit adjacency correspondence, records the number pairs of all adjacent spatial units, and generates a complete and accurate topological adjacency relationship table for spatial units in the planning area, replacing the original topological adjacency relationship data. The cleaned spatial unit vector boundaries, existing land use type codes, land parcel attribute parameters, and reconstructed topological adjacency tables are subjected to unified standardization processing, including coordinate system one and parameter dimension normalization; at the same time, all data are uniformly converted into a standardized vector data format that can support subsequent matrix operations and spatial analysis. The standardized spatial unit vector boundaries, the verified existing land use type codes, the normalized land parcel attribute parameters, and the reconstructed topological adjacency table are integrated to construct a basic spatial dataset for the planning area, using the spatial unit number as the unique association identifier. This ensures that each spatial unit corresponds to a complete attribute record containing four types of core data.

[0018] S2, Neighborhood Association Constraint Construction: Based on the spatial unit topological adjacency table and standardized vector boundary data reconstructed by S1, geographic feature parameters are extracted for all spatial unit pairs with adjacency markers within the planning area. Spatial units are obtained through surface feature boundary overlay analysis. and The shared boundary length between them, while retrieving the normalized spatial units in S1. The perimeter parameter; Through formula Calculations are performed to obtain spatial unit pairs The boundary contact coefficient, where, Representing spatial units and Shared boundary length between Representing spatial units circumference, Describes the minimum value function; Based on the coordinate data of the spatial unit vector boundary, the coordinates of the geometric center point of the shared boundary and the spatial unit are obtained through the geometric center algorithm. The geometric centroid coordinates; In terms of spatial units Using the geometric centroid coordinates as the vector origin and the geometric center point coordinates of the shared boundary as the vector midpoint, a two-dimensional planar vector is constructed; using the formula... Calculations are performed to obtain spatial unit pairs The directional weighting coefficients, where, This represents the angle between a two-dimensional plane vector and the direction of true north. , representing the direction sensitivity factor; Compatibility rules between land use types are extracted from existing land planning technical specifications and regional land use planning rules. Based on the degree of compatibility, different levels are divided and corresponding scores are assigned. A standardized land use type compatibility lookup table is constructed, which clearly defines the compatibility level score corresponding to any two land use types. At the same time, the highest compatibility level score in the land use type compatibility lookup table is extracted as the normalization benchmark value. The compatibility level score is normalized and calculated to obtain the type compatibility coefficient of any pair of land use types. Based on the boundary contact coefficient, direction weight coefficient, and type compatibility coefficient, through the formula Calculations are performed to obtain spatial unit pairs The neighborhood correlation coefficient, where, This indicates the spatial unit pairs matched from the land use type compatibility lookup table. Current land use type coding Corresponding type compatibility coefficient; The neighborhood correlation coefficient characterizes the strength of the correlation constraints between two spatial units under the dual dimensions of geographical adjacency and planning rules. For all adjacent spatial units within the planning area, complete the calculation of the full neighborhood correlation coefficient and establish a one-to-one correspondence with the spatial unit number pairs to form a detailed table of neighborhood correlation coefficients. The total number of spatial units within the statistical planning area Using spatial unit numbers as row and column indices, construct a dimension of The empty matrix is ​​used as the neighborhood association constraint matrix; Matrix elements are set based on planning requirements. The rules for assigning values ​​to elements of an empty matrix are as follows: When spatial unit With spatial units When adjacency exists, The value is consistent with the neighborhood correlation coefficient; When spatial unit With spatial units When there is no adjacency, The value is assigned to zero; When the row and column indices correspond to the same spatial unit. The value is assigned to 1; Values ​​are assigned to all elements of the empty matrix based on the assignment rules to construct a neighborhood correlation constraint matrix; Through formula The neighborhood constraint strength of the spatial cell is calculated, where, Representation and spatial unit The set of all adjacent spatial units that share a boundary. Indicates the first The area of ​​each adjacent spatial unit. Represents the sum of the areas of all adjacent spatial units of a spatial unit , where is the summation index, representing an adjacent spatial unit in the set of adjacent spatial units; The neighborhood constraint strength represents the degree of the comprehensive constraint influence of a spatial unit by all its adjacent spatial units. The greater the neighborhood constraint strength, the stronger the restriction of the spatial unit by the surrounding plots during the optimization configuration process; Perform a full calculation of the neighborhood constraint strength for all spatial units in the planning area to form a corresponding table of spatial units and neighborhood constraint strength; Based on all the neighborhood constraint strength values in the corresponding table of spatial units and neighborhood constraint strength, perform normalization grading to generate three levels of constraint sensitivity levels: high, medium, and low; Add a constraint sensitivity level mark to each spatial unit to form a three-dimensional association table containing the spatial unit number, neighborhood constraint strength, and constraint sensitivity level; Based on the three-dimensional association table, classify all spatial units by constraint type: Screen out the spatial units with high constraint sensitivity levels, mark them as key constraint units, and extract the spatial unit numbers, neighborhood constraint strengths, constraint sensitivity levels, and the corresponding sets of adjacent spatial units of all key constraint units; For the set of adjacent spatial units of each key constraint unit, match the constraint sensitivity levels of each adjacent spatial unit from the three-dimensional association table, screen out the adjacent spatial units with medium constraint sensitivity levels as secondary constraint units, and at the same time extract the core attribute parameters of the secondary constraint units, including core basic data, constraint sensitivity level, neighborhood constraint strength, and the corresponding neighborhood correlation coefficient; Unify the remaining spatial units that are not marked as key constraint units or secondary constraint units and mark them as ordinary spatial units; Based on the classification marking results of constraint types, construct a constraint unit linked list; the constraint unit linked list takes the key constraint unit as the core node and the secondary constraint unit as the attached node, and each node includes the spatial unit number, constraint type mark, neighborhood constraint strength, constraint sensitivity level, set of adjacent units, and the corresponding neighborhood correlation coefficient; Taking the neighborhood constraint strength of the key constraint unit as the core sorting basis, perform a descending order arrangement on all core nodes in the constraint unit linked list, and the secondary constraint units are arranged in association attached to the corresponding core nodes to form an overall constraint processing priority sequence; Standardize and store the constructed constraint unit linked list and the constraint processing priority sequence, and store them in association with the neighborhood association constraint matrix.

[0019] S3. Constraint coupling optimization solution: S301. Initial allocation: Based on the constraint processing priority sequence, using the sorting result of the constraint processing priority sequence as the iterative optimization processing order of each spatial unit, synchronously obtain the neighborhood association constraint matrix, the constraint sensitivity level of each spatial unit, the set of adjacent units, and the basic spatial data set, and construct a dedicated data set for constraint coupling optimization solution; For each to-be-processed spatial unit sorted by priority, combined with the regional land use planning objectives, the compatibility rules of land use types, and the terrain and ownership of the spatial unit, select the applicable land use types from the national or industry land use classification standards that are suitable for the current spatial unit to form a candidate type set; For each candidate land use type in each candidate type set, calculate the neighborhood adaptation score: (a) Recalculate the spatial unit configured land use type when, with each adjacent spatial unit the neighborhood association coefficient ; (b) Initialize the configuration determination degree of each spatial unit , the configuration determination degree of the spatial unit that has not been configured ; the configuration determination degree of the spatial unit that has completed configuration ;<**********>(c) Calculate through the formula to obtain the neighborhood adaptation score of the candidate land use type ; Calculate through the formula to obtain the resource benefit score of the candidate land use type , where represents the total number of resource benefit evaluation indicators, represents the spatial unit configured land use type when the th resource benefit evaluation indicator's normalized value, represents the th resource benefit evaluation indicator's weight coefficient; Retrieve the constraint sensitivity level of the to-be-processed spatial unit from the three-dimensional association table, and calculate through the formula to obtain the neighborhood adaptation weight of the current spatial unit; where represents the normalized value of the constraint sensitivity level of the th spatial unit; Normalize the neighborhood adaptation score, the resource benefit score, and the neighborhood adaptation weight, and substitute them into the formula to calculate the Candidate land use types The comprehensive configuration score; Select the land use type with the highest comprehensive configuration score in the candidate type set as the temporary configuration type of the spatial unit, and set the configuration certainty of the current spatial unit to 1; Based on the processing priority, complete the temporary configuration of all spatial units in the planning area in sequence to form an initial temporary configuration plan; S302. After completing the temporary configuration of all spatial units, start the global neighborhood conflict detection and resolution process: For each spatial unit, retrieve the temporary configuration types of all units in the corresponding adjacent unit set from the initial temporary configuration plan; based on the land use type compatibility lookup table, match the type compatibility coefficients between the spatial unit and each adjacent spatial unit; If the type compatibility coefficient corresponding to any adjacent spatial unit is less than the preset threshold, it is determined that the spatial unit and the current adjacent spatial unit have a neighborhood conflict, and are marked as a conflict unit pair , and record the unit numbers, temporary configuration types, and corresponding type compatibility coefficients of the conflict units in the conflict list; For each conflict unit pair in the conflict list , retrieve the comprehensive configuration score and constraint sensitivity level corresponding to the current temporary configuration type of the spatial unit; obtain the configuration stability indices of the two spatial units in the conflict unit pair through the following formula: : : Among them, respectively represent the configuration stability indices of the spatial units , represents the area of the th spatial unit, represents the th candidate land use type The comprehensive configuration score of, represents the normalized value of the constraint sensitivity level of the th spatial unit; Compare the configuration stability index of the conflict unit pair with , and mark the spatial unit with the lower configuration stability index as the unit to be adjusted; For each unit to be adjusted, exclude the current temporary configuration type from the corresponding set of candidate types. Among the remaining types, re - execute the initial allocation process and select the land use type with the highest new comprehensive configuration score as the replacement configuration type. At the same time, based on the new land use type, update the values of the corresponding elements in the neighborhood association constraint matrix and the neighborhood association coefficients in the neighborhood association coefficient明细表; When the replacement configuration of the unit to be adjusted is completed, traverse all adjacent spatial units of the unit to be adjusted. Based on the updated neighborhood association coefficients, obtain the updated constraint sensitivity level. If the normalized value of the updated constraint sensitivity level is compared with the normalized value of the constraint sensitivity level before the update, and if the difference between the normalized values of the constraint sensitivity levels before and after the update is greater than the preset threshold, record the number of the current adjacent spatial unit in the secondary detection queue for secondary conflict detection; After processing all conflict unit pairs in the conflict list, clear the original conflict list, and perform the conflict detection and resolution process on the spatial units in the secondary detection queue and the global spatial units until the conflict list is empty or the preset maximum number of iterations is reached; If the number of iterations reaches the preset maximum number of iterations and there are still unresolved conflict unit pairs in the conflict list, start the constraint relaxation processing flow: Reduce the preset threshold corresponding to the original type compatibility coefficient by 10% to obtain a new preset threshold, and re - determine the neighborhood conflicts for the remaining conflict unit pairs based on the new threshold. If the conflict list is empty after re - determination, determine the current temporary configuration plan as the final temporary configuration plan. If there are still conflicts, repeat the constraint relaxation and re - determination process until the conflict list is empty.

[0020] S4. Generation of the configuration plan: Based on the final temporary configuration plan output by S3, extract the final configuration types and the corresponding comprehensive configuration scores of all spatial units in the planning area; Construct a configuration result data table with the spatial unit number as the unique identifier. Each record in the data table includes the spatial unit number, the spatial unit area, the final land use type code, the comprehensive configuration score, and the constraint sensitivity level; Based on the configuration result data table, group and summarize all spatial units in the planning area according to the final land use type code;[[ID=1,8]] Through the formula Calculate the total area proportion of each type of land use , where represents the set of spatial units with the configuration land use type code ; Retrieve the planned target proportion of each type of land use from the regional land planning target file ; Through the formula Calculate the land use structure deviation degree of each type of land use; Traverse the land use structure deviation degrees of all land use types. If there is any land use type whose land use structure deviation degree is greater than the preset threshold, start the structure adjustment program: For the land use types whose land use structure deviation degrees are greater than the preset threshold, arrange them in descending order based on the land use structure deviation degrees, and preferentially adjust the land use type with the largest land use structure deviation degree; For the land use type with the largest land use structure deviation degree, retrieve the set of spatial units corresponding to the current land use type from the configuration result data table, arrange them in ascending order based on the comprehensive configuration scores, and screen out several spatial units with the lowest comprehensive configuration scores as the adjustable units; For each adjustable unit, remove the current final land use type code from the candidate type set, and among the remaining candidate types, screen out the alternative land use types that meet the neighborhood association constraints and reduce the overall land use structure deviation degree; Based on the alternative land use types, recalculate the type compatibility coefficients between the adjustable units and the corresponding adjacent spatial units. If the type compatibility coefficients corresponding to all adjacent spatial units are greater than the preset threshold, then use the alternative land use types as the new configuration types of the adjustable units, and synchronously update the final land use type code and the comprehensive configuration score in the corresponding records in the configuration result data table; [[ID=,10]] After completing the type change of a single adjustable unit, recalculate the land use structure deviation degree of the land use type. If the land use structure deviation degree has dropped below the preset threshold, stop the adjustment process of the current land use type; if the land use structure deviation degree is still higher than the preset threshold, continue to process the next adjustable unit until all adjustable units are processed; Complete the structure adjustment of all land use types exceeding the preset threshold in sequence to form an adjusted configuration result data table; Based on the adjusted configuration result data table, with the spatial unit number as the association identifier, write the final land use type code into the attribute field corresponding to the spatial unit vector boundary constructed by S1 to generate a spatial configuration plan vector layer; Based on the adjusted configuration result data table, classify and summarize by land use type code to generate a configuration plan statistical report. The report content includes the total area, area proportion, description of spatial distribution characteristics, average comprehensive configuration score of each type of land use, and the preliminary statistical value of the compliance rate of the plan-level constraints.

[0021] S5. Configuration plan verification and output: Based on the generated spatial configuration plan vector layer and the configuration result data table, perform neighborhood constraint compliance inspection on each spatial unit in the planning area: For each spatial unit, retrieve the corresponding adjacent unit set and the final land use type codes of each adjacent spatial unit from the configuration result data table; based on the land use type compatibility lookup table, match the type compatibility coefficients between the current spatial unit and each adjacent spatial unit; Calculate the compliance degree of unit-level constraints for the spatial unit through the formula ; After calculating the compliance degree of unit-level constraints for all spatial units within the planned area, perform weighted aggregation with the spatial unit area as the weight to obtain the compliance rate of the plan-level constraints for the configuration plan; Based on the compliance rate of the plan-level constraints, perform hierarchical determination and differential processing: When the compliance rate of the plan-level constraints is greater than the maximum value of the preset interval, determine that the configuration plan passes the verification, mark the configuration plan as a valid plan, and lock the current configuration result data table and the spatial configuration plan vector layer; When the compliance rate of the plan-level constraints is within the preset interval, it is determined as a local correction; screen out the spatial units with a unit-level constraint compliance degree lower than the preset threshold from the configuration result data table, and output the corresponding spatial unit numbers, current final land use type codes, unit-level constraint compliance degrees, and adjacent unit sets to the list of units to be corrected; For the spatial units in the list of units to be corrected, return to the S3 constraint coupling optimization solution again, perform directional optimization only on the units to be corrected, and keep the configuration types of the remaining spatial units unchanged; after the directional optimization is completed, re-execute the S4 configuration plan generation and S5 result verification processes; When the compliance rate of the plan-level constraints is less than the minimum value of the preset interval, it is determined that the configuration fails; record the reasons for failure, including the value of the compliance rate of the plan-level constraints, the number and proportion of spatial units with a unit-level constraint compliance degree lower than the preset threshold; Reset the configuration status of all spatial units, clear the final land use type codes and comprehensive configuration scores in the configuration result data table; increase the reference value of the neighborhood adaptation weight calculation formula in S3 from 0.3 to 0.4, and re-execute the S3 constraint coupling optimization solution and the subsequent complete configuration process; After the verification passes, integrate and package the spatial configuration plan vector layer, the configuration plan statistical report, and the verification result record, and synchronously output them to the planning result database; at the same time, generate a standardized configuration plan description document based on the configuration result data table and the verification result, and the document content includes the basic information of the planned area, the core indicators of the configuration plan, the conclusion of the neighborhood constraint compliance verification, and the implementation suggestions for the configuration plan.

[0022] A resource space optimization configuration system for land planning, including: Spatial data acquisition and preprocessing module: Obtain the basic spatial data of the planned area through the data acquisition unit, including the vector boundary of spatial units, the encoding of current land use types, the plot attribute parameters, and the topological adjacency relationship data between spatial units; perform quality verification and cleaning processing on the original basic spatial data, re-extract the adjacency relationship based on the vector boundary of spatial units, and generate a topological adjacency relationship table; perform standardization processing on the cleaned data, and construct a basic spatial data set of the planned area with the spatial unit number as the unique association identifier. Neighborhood association constraint construction module: Based on the topological adjacency relationship table and the standardized vector boundary data, calculate the boundary contact coefficient, direction weight coefficient, and type compatibility coefficient of spatial unit pairs to obtain the neighborhood association coefficient; construct a neighborhood association constraint matrix, calculate the neighborhood constraint intensity and constraint sensitivity level of each spatial unit; classify the constraint types of spatial units based on the constraint sensitivity level, and construct a constraint unit linked list and a constraint processing priority sequence. Constraint coupling optimization and solution module: Determine the iterative optimization processing order based on the constraint processing priority sequence. For each spatial unit to be processed, calculate the neighborhood adaptation score, resource benefit score, and comprehensive configuration score of candidate land use types, and select the land use type with the highest comprehensive configuration score as the temporary configuration type; execute the global neighborhood conflict detection and resolution process, determine the units to be adjusted through the comparison of configuration stability indexes and perform replacement configuration until the conflict list is empty or the preset maximum number of iterations is reached, and output the final temporary configuration plan. Configuration plan generation module: Construct a configuration result data table based on the final temporary configuration plan, calculate the total area ratio of various land uses and the deviation degree of the land use structure; execute the structure adjustment program when the deviation degree of the land use structure exceeds the preset threshold; generate a spatial configuration plan vector layer and a configuration plan statistical report based on the adjusted configuration result data table. Configuration plan verification and output module: Perform neighborhood constraint compliance inspection on each spatial unit, calculate the unit-level constraint compliance degree and the plan-level constraint compliance rate; perform hierarchical determination based on the plan-level constraint compliance rate. When it is determined to be an effective plan, lock the configuration result. When it is determined to be a local correction, output the list of units to be corrected and trigger directional optimization. When it is determined to be a configuration failure, reset the configuration status, adjust the parameters, and then re-execute the configuration process; after verification, output the configuration plan vector layer, statistical report, and verification result to the planning result database.

[0023] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

Claims

1. A method for optimizing the spatial allocation of resources in land planning, characterized in that, include: S1. Spatial Data Acquisition and Preprocessing: By integrating remote sensing image interpretation, geographic information database retrieval, and field survey data entry into the data acquisition unit, spatial unit vector boundaries, current land use type codes, land parcel attribute parameters, and topological adjacency relationship data are collected. Through quality verification, completion, repair, standardization processing, and topological adjacency relationship reconstruction, a basic spatial dataset for the planning area is constructed. S2. Neighborhood association constraint construction: Based on the boundary contact coefficient, direction weight coefficient and type compatibility coefficient of spatial unit pairs, the neighborhood association coefficient is obtained, the neighborhood association constraint matrix is ​​constructed, the constraint sensitivity level is divided and the spatial units are classified to form a constraint processing priority sequence. S3. Constraint-coupled optimization solution: The neighborhood correlation constraint matrix is ​​embedded in the iterative optimization solution process, and the coordinated optimization of land use type configuration of each spatial unit is realized through the constraint transfer mechanism; S4. Configuration scheme generation: Construct a configuration result data table, adjust the structure based on the land use structure deviation, and generate spatial configuration scheme vector layer and statistical reports; S5. Configuration scheme verification and output: Verify the compliance of the configuration scheme with neighborhood constraints, determine the validity of the configuration scheme, and output the final configuration result. The specific operation steps of S2 include: Based on the reconstructed topological adjacency table and standardized vector boundaries, geographic feature parameters are extracted from spatial cell pairs with adjacency identifiers, and the boundary contact coefficient is obtained by the shared boundary length and cell perimeter of the spatial cell pairs. By combining the geometric coordinates of spatial units, a two-dimensional plane vector is constructed, and the direction weight coefficient is obtained based on the angle between the vector and the true north direction and the direction sensitivity factor. Land use type compatibility rules are extracted from land planning rules, and then graded, scored, and normalized to obtain type compatibility coefficients. Based on the coupling of boundary contact coefficient, direction weight coefficient and type compatibility coefficient, the neighborhood correlation coefficient is obtained, and a detailed table of correlation coefficients is formed. An N×N empty matrix is ​​constructed based on the total number of spatial units. The neighborhood association constraint matrix is ​​constructed based on the adjacency and non-adjacency relationships between units and the assignment rules for the same unit.

2. The method for optimizing the spatial allocation of resources for land planning according to claim 1, characterized in that, The specific operation steps of S2 also include: By combining the neighborhood correlation coefficient and the area ratio of adjacent spatial units, the neighborhood constraint strength of each spatial unit is obtained; The neighborhood constraint strength of all spatial units is normalized and classified to generate three levels of constraint sensitivity: high, medium and low. A three-dimensional association table containing spatial unit number, neighborhood constraint strength and constraint sensitivity level is constructed. Based on the three-dimensional association table, high constraint sensitivity level is selected as key constraint unit and corresponding adjacent medium constraint sensitivity level is selected as secondary constraint unit, and the rest are ordinary spatial units. A constraint unit linked list is constructed with key constraint units as core nodes and secondary nodes as subordinate nodes. Core nodes are arranged in descending order based on the neighborhood constraint strength of key constraint units, and subordinate nodes are arranged in association to form an overall constraint processing priority sequence, which is stored in association with the constraint matrix.

3. The method for optimizing the spatial allocation of resources for land planning according to claim 1, characterized in that, The specific operation steps of S3 include: Based on the constraint processing priority sequence, we integrate the neighborhood association constraint matrix, constraint sensitivity level, adjacency unit set and basic spatial dataset to construct a dedicated dataset for constraint coupling optimization solution; Combining regional land planning objectives, land use type compatibility rules, and spatial unit topography and ownership, selectable land use types from the corresponding classification standards to form a candidate type set; Based on the neighborhood association coefficient with adjacent units when configuring each candidate land use type, the spatial unit configuration determinism is initialized; The neighborhood fit score is obtained based on the neighborhood correlation coefficient and configuration certainty; the resource benefit score is obtained based on the resource benefit evaluation index; and the neighborhood fit weight is obtained by retrieving the constraint sensitivity level. The normalized values ​​of neighborhood adaptation score, resource benefit score and neighborhood adaptation weight are used to obtain the comprehensive configuration score of candidate land use types. The one with the highest comprehensive configuration score is selected as the temporary configuration type and the configuration certainty is set to 1. Based on priority, the temporary configuration of all spatial units is completed in sequence to form the initial temporary configuration scheme.

4. The method for optimizing the spatial allocation of resources for land planning according to claim 3, characterized in that, The specific operation steps of S3 also include: Retrieve the temporary configuration type of each spatial unit's adjacent units from the initial temporary configuration scheme, and match the type compatibility coefficient based on the land use type compatibility lookup table; If the type compatibility coefficient is less than the preset threshold, it is marked as a conflict unit pair and recorded in the conflict list; the comprehensive configuration score, constraint sensitivity level and area of ​​the conflict unit pair are retrieved to calculate the configuration stability index; Spatial units with low stability indices in conflicting units are marked as units to be adjusted; the current temporary configuration types of units to be adjusted are removed, and the replacement configuration type with the highest comprehensive configuration score is re-selected from the remaining candidate types, and the neighborhood association constraint matrix and association coefficient details table are updated simultaneously. Traverse the adjacent spatial units of the unit to be adjusted. If the change in constraint sensitivity level exceeds the preset threshold, it is included in the secondary detection queue. Repeat the conflict judgment, marking, configuration stability index calculation, unit to be adjusted screening and configuration replacement, and adjacent unit constraint sensitivity level update process until the conflict list is empty or the preset maximum number of iterations is reached. If the iteration limit is exceeded, reduce the type compatibility coefficient preset threshold and start constraint relaxation processing.

5. The method for optimizing the spatial allocation of land planning resources according to claim 1, characterized in that, The specific operation steps of S4 are as follows: Based on the final provisional configuration scheme, the final configuration type and comprehensive configuration score of all spatial units are extracted, and a configuration result data table is constructed using the spatial unit number as the unique identifier. Based on the final land use type coding and grouping, and based on the total area ratio of each land use type, the deviation of land use structure is obtained by comparing it with the regional land planning target ratio. Land use types with deviations exceeding a preset threshold are sorted in descending order of deviation, with priority given to adjusting the type with the largest deviation, and the spatial unit with the lowest corresponding comprehensive configuration score is selected as the adjustable unit. Among the remaining candidate types, select alternative types that meet the neighborhood association constraints and have a decrease in overall deviation. After verifying that the type compatibility coefficients of the alternative types with all adjacent units meet the standards, update the configuration. After completing the structural adjustments for all land use types that exceed the preset threshold, the final land use type code is written into the spatial unit vector boundary attribute field constructed by S1, generating a spatial configuration scheme vector layer, and simultaneously generating a configuration scheme statistical report based on the land use type classification summary.

6. The method for optimizing the spatial allocation of resources for land planning according to claim 1, characterized in that, The specific operation steps of S5 are as follows: Based on the generated spatial configuration scheme vector layer and configuration result data table, a neighborhood constraint compliance check is performed on each spatial unit. The final land use type code of the adjacent units of the spatial unit is retrieved, and the type compatibility coefficient is matched based on the land use type compatibility lookup table to obtain the unit-level constraint compliance. The scheme-level constraint compliance rate is obtained by weighting and summing the data using the area of ​​the spatial units as the weight; then, a hierarchical processing is performed based on the scheme-level constraint compliance rate. If the compliance rate of the scheme-level constraints is greater than the maximum value of the preset interval, the scheme is deemed valid and the current configuration result is locked. If the scheme-level constraint compliance rate is within the preset range, then select spatial cells with a unit-level compliance rate lower than the threshold and return to S3 for optimization solution; If the compliance rate of the scheme-level constraint is less than the minimum value of the preset interval, record the reason for failure, reset the configuration status and increase the benchmark value of the neighborhood adaptation weight, and re-execute S3-S5. After successful verification, the integrated spatial configuration scheme vector layer, configuration scheme statistical report, and verification result record are output to the planning results database, and a configuration scheme description document is generated simultaneously.

7. A system applied to the resource space optimization allocation method for land planning as described in any one of claims 1-6, comprising: Spatial data acquisition and preprocessing module: integrates remote sensing image interpretation, geographic information database retrieval, and field survey data entry functions, collects four types of core basic data, and reconstructs topological adjacency relationships through quality verification, completion, topology repair and standardization processing to build a basic spatial dataset for the planning area; Neighborhood association constraint construction module: Based on boundary contact, direction weight and type compatibility coefficient, it generates neighborhood association coefficient and constraint matrix, obtains constraint sensitivity level by level, classifies spatial units and forms constraint processing priority sequence; Constraint-coupled optimization solution module: It completes the initial configuration based on the priority sequence, detects and resolves neighborhood conflicts, iterates until the conflicts are cleared, and outputs the final temporary configuration scheme. Configuration scheme generation module: Constructs a configuration result data table, performs structural adjustments based on land use structure deviation, and generates spatial configuration scheme vector layers and statistical reports; Configuration scheme verification and output module: Based on the compliance rate of unit-level and scheme-level constraints, hierarchical judgment and processing are performed. After verification, the results are integrated and output to the database, and standardized explanatory documents are generated simultaneously.

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