Reconstruction method of land space planning system in highly urbanized area

Abstract

The application discloses a highly urbanized area land space planning system reconstruction method, and relates to the technical field of land space planning.The method comprises the following steps: based on the reconstruction object multi-dimensional granulation splitting of the stock land space, a multi-dimensional space particle is obtained; each particle attribute vector is obtained, and a space particle attribute database is constructed; a particle recombination constraint rule set is constructed; the database is analyzed under the rule set, an optimal planning scheme corresponding to a preset threshold is screened, and the stock land space particle level planning result is output according to the scheme.The technical problem that the existing planning system is difficult to realize fine and collaborative management when facing the stock space update in the highly urbanized area is solved, the technical effect that the multi-dimensional element fine splitting, collaborative optimization and particle level reconstruction of the stock land space are realized under multiple constraint conditions, and the scientificity, adaptability and implementation efficiency of the land space planning are improved is achieved.

Description

Technical Field

[0001] This invention relates to the field of land spatial planning technology, specifically to a method for reconstructing the land spatial planning system in highly urbanized areas. Background Technology

[0002] Traditional land spatial planning often focuses on macro-level structure and layout, lacking a detailed understanding of the multi-dimensional attributes within plots or even smaller units. This makes it difficult to effectively coordinate the interests of different ownership entities and to anticipate and resolve potential temporal conflicts in future development. Furthermore, traditional planning methods tend to examine spatial elements in isolation, failing to fully explore the potential synergistic effects between different spatial elements. This limits the comprehensiveness, scientific rigor, and feasibility of planning schemes, hindering the maximization of the overall value of land space. Summary of the Invention

[0003] This application provides a method for reconstructing the territorial spatial planning system in highly urbanized areas, which solves the technical problems of existing planning systems in highly urbanized areas when facing the renewal of existing space, such as coordination difficulties, insufficient flexibility, and transmission losses, making it difficult to achieve refined and collaborative governance.

[0004] The first aspect of this application provides a method for reconstructing the territorial spatial planning system in highly urbanized areas, the method comprising:

[0005] The existing land space is decomposed into multidimensional granular units based on the reconstructed objects to obtain multidimensional spatial units, including structural spatial units, functional spatial units, ownership spatial units, and temporal spatial units. The attribute vector of each spatial unit is obtained to generate a spatial unit attribute database. A set of spatial unit reorganization constraint rules is constructed, including rigid constraint rules, operational constraint rules, and legal constraint rules. Under the spatial unit reorganization constraint rule set, a synergistic gain analysis is performed on the spatial unit attribute database to obtain the optimal planning scheme corresponding to the multidimensional spatial units that meets a preset synergistic gain threshold. Based on the optimal planning scheme corresponding to the multidimensional spatial units, the granular-level land space planning results for the existing land space are output.

[0006] A second aspect of this application provides a system for reconstructing the territorial spatial planning system for highly urbanized areas, the system comprising:

[0007] The system comprises the following modules: a granular decomposition module, an attribute vector acquisition module, and a granular spatial planning module. The granular decomposition module performs multi-dimensional granular decomposition on existing land space based on reconstructed objects, obtaining multi-dimensional spatial particles, including structural, functional, ownership, and temporal spatial particles. An attribute vector acquisition module acquires the attribute vectors of each spatial particle in the multi-dimensional spatial particles, generating a spatial particle attribute database. A constraint construction module constructs a set of spatial particle reorganization constraint rules, including rigid constraint rules, operational constraint rules, and legal constraint rules. A synergistic gain analysis module performs synergistic gain analysis on the spatial particle attribute database under the set of spatial particle reorganization constraint rules, obtaining the optimal planning scheme corresponding to the multi-dimensional spatial particles that meets a preset synergistic gain threshold. A planning result output module outputs the granular-level land space planning results for the existing land space according to the optimal planning scheme corresponding to the multi-dimensional spatial particles.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, guided by the objects of planning restructuring, existing land space is meticulously decomposed from multiple dimensions such as structure, function, ownership, and time, forming spatial particles that can be independently identified and combined. Then, the key attributes of each type of spatial particle are quantified, constructing a unified spatial particle attribute database. Next, a spatial particle reorganization rule system covering baseline controls, operational requirements, and legal constraints is established. Then, under constraints, the synergistic relationships between spatial particles are comprehensively analyzed and their benefits evaluated, selecting planning combination schemes that achieve the set standards for synergistic benefits. Finally, based on the optimized schemes, granular-level land spatial planning results are formed for implementation, achieving refined optimization and synergistic allocation of existing space. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of the process for reconstructing the territorial spatial planning system in highly urbanized areas, provided in an embodiment of this application.

[0012] Figure 2 A schematic diagram of the synergistic gain analysis process for the method of reconstructing the territorial spatial planning system in highly urbanized areas provided in this application embodiment.

[0013] Figure 3A schematic diagram of the system structure for reconstructing the territorial spatial planning system in highly urbanized areas, provided in an embodiment of this application.

[0014] Figure labeling: 11 Granular splitting module, 12 Attribute vector acquisition module, 13 Constraint construction module, 14 Synergistic gain analysis module, 15 Planning result output module. Detailed Implementation

[0015] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0016] Example 1, as Figure 1 As shown, this application provides a method for reconstructing the territorial spatial planning system in highly urbanized areas, wherein the method includes:

[0017] The existing land space is divided into multi-dimensional granular parts based on the reconstruction object to obtain multi-dimensional spatial particles, which include structural spatial particles, functional spatial particles, ownership spatial particles and temporal spatial particles.

[0018] In this embodiment, the existing land space to be reconstructed is first used as the input object. The basic units and scales for granular division are determined based on the planning reconstruction objectives, and a unified spatial benchmark is established, including a unified coordinate benchmark, a unified boundary benchmark, and a unified time benchmark. Subsequently, the existing land space is divided into multi-dimensional granular units. Specifically, in the structural dimension, based on the current spatial form and spatial organization elements, the area is divided into structural spatial units. Each structural spatial unit is represented by a clear spatial geometric boundary, which can be defined by any element such as road framework, street boundary, plot boundary, building base outline, or green / blue space boundary, forming a calculable planar or volumetric spatial unit. In the functional dimension, based on the identifiable use characteristics of the current situation and planning, the space is divided into functional spatial units. Functional spatial units can correspond to single-function or multi-function units. The functional boundaries are determined by merging and identifying the current functional use of each object. When the functional boundary is inconsistent with the structural boundary, a new functional unit is generated through overlay cutting. In terms of ownership, the space is divided into ownership space particles based on registration or management boundary information. These particles are bounded by ownership boundaries, management boundaries, or usage boundaries, recording the consistency of ownership or management entities within the same boundary. When ownership boundaries intersect with structural or functional boundaries, spatial overlay and cutting are used to generate ownership particle units. In terms of temporal dimension, the space is divided into temporal space particles based on the temporal attributes and update times of space use. These particles express the differences in usage or development / update rhythm of the same space at different time periods. Their temporal boundaries can be determined by daily usage periods, weekly usage periods, phased utilization cycles, construction and update plan windows, etc., and are spatially-temporally bound to structural, functional, and ownership particles to form traceable temporal units. After the above decomposition, the four types of particles are numbered and indexed using the same spatial benchmark, outputting a multidimensional spatial particle set containing geometric boundaries, dimensional categories, and relationships. Each multidimensional spatial particle possesses the characteristics of being locatable, divisible, combinable, and traceable, providing basic data for subsequent attribute vector extraction, constraint construction, and particle recombination calculations.

[0019] Obtain the attribute vector of each spatial particle in the multidimensional spatial particles to generate a spatial particle attribute database.

[0020] In one embodiment, after the multi-dimensional spatial particles are decomposed, a unique identifier is assigned to each spatial particle for consistent referencing across different data tables and processing flows. Subsequently, based on the type of the spatial particle, a corresponding set of attribute indicators is preset. The attribute indicators for structural spatial particles include at least spatial area, morphological characteristic parameters, development intensity indicators, and facility configuration indicators; the attribute indicators for functional spatial particles include at least the dominant functional type, functional compatibility level, and service radius; the attribute indicators for ownership spatial particles include at least the ownership subject category, right type, ownership stability level, and ownership boundary clarity; and the attribute indicators for temporal spatial particles include at least the activation time, usage period, and update cycle. Then, based on the standardized basic data, the corresponding attribute indicators for each spatial particle are extracted, assigned values, or categorized and coded to form an attribute vector composed of multi-dimensional attribute indicators. Finally, the attribute vector is associated with and stored with the corresponding spatial particle identifier, and written into the spatial particle attribute database according to a unified data structure. This constructs a searchable, analyzable, and retrieval-enabled spatial particle attribute database, providing data support for subsequent collaborative analysis and planning decisions.

[0021] A set of spatial particle reorganization constraint rules is constructed, which includes rigid constraint rules, operational constraint rules, and legal constraint rules.

[0022] In one embodiment, after completing the construction of the spatial particle attribute database, the first step is to identify the bottom-line conditions that must be followed during the planning implementation process, forming rigid constraint rules. These rigid constraint rules include at least the ecological protection bottom line, spatial safety control requirements, resource carrying capacity limits, and conditions prohibiting or restricting development. The corresponding constraint parameters are then solidified into the rule base in the form of thresholds or Boolean conditions. For example, if a region has a wetland ecosystem, a wetland protection zone needs to be set as a rigid constraint; if a region has limited water resources, water resource use restrictions need to be set as a rigid constraint; if a region has historical and cultural sites, a prohibited development area needs to be set as a rigid constraint. To address the adaptability requirements of spatial particles in actual operation and use, operational constraint rules are constructed. These operational constraint rules include at least functional compatibility requirements, facility service capacity matching relationships, traffic accessibility conditions, and temporal use coordination requirements. These rules are used to constrain the operational efficiency and usage conflicts of different spatial particles after combination. For example, when residential areas and commercial areas are adjacent, noise control requirements are set; if a region is a shopping center, it needs to be ensured that it is coordinated with surrounding transportation facilities during reorganization. Furthermore, based on existing spatial planning and management systems, control requirements with legal or quasi-legal force are formalized into legally binding rules. These rules include at least land use control requirements, inviolable ownership boundaries, mandatory planning indicators, and approval constraints. For example, agricultural land can only be used for agricultural production and cannot be converted into construction land; the land use right of a certain plot belongs to a specific development company, and during the reorganization process, the boundaries of that plot cannot encroach on adjacent land or change the ownership division. Finally, the rigid constraint rules, operational constraint rules, and legally binding rules are encoded according to a unified logical structure to establish a spatial particle reorganization constraint rule set. The set of rules clarifies the applicable objects, triggering conditions, and constraint priorities of each type of constraint, ensuring that the subsequent spatial particle reorganization process is executable, verifiable, and traceable under multiple constraints.

[0023] Under the set of spatial particle recombination constraint rules, a collaborative gain analysis is performed on the spatial particle attribute database to obtain the optimal planning scheme corresponding to the multidimensional spatial particles that meets the preset collaborative gain threshold.

[0024] In one embodiment, after constructing the spatial particle reorganization constraint rule set, the attribute vector of each spatial particle is first analyzed based on the spatial particle attribute database. Then, according to the rigid constraints, operational constraints, and legal constraints in the spatial particle reorganization constraint rule set, all potential particle combinations are screened, excluding combinations that do not meet the constraints. Subsequently, a synergistic gain index system is constructed, which includes multiple dimensions of synergistic gain indicators, such as functional synergistic gain and spatial efficiency gain. For each pair of spatial particles, its synergistic gain value is calculated. This synergistic gain value is the result of a weighted calculation based on multiple aspects such as the functional complementarity between spatial particles, the degree of resource sharing, and the improvement of space utilization. Afterward, the calculated synergistic gain value is compared with a preset synergistic gain threshold. If the synergistic gain value of a combination is higher than the set threshold, then that combination is considered the preferred planning scheme. In this way, the optimal spatial particle combination scheme can be automatically selected, that is, the planning scheme that brings the greatest synergistic benefits while satisfying all constraints. Ultimately, the optimal planning scheme that meets the synergistic gain threshold is output, and data support is provided for the next step of spatial particle reorganization and specific planning implementation. This ensures that the reorganization of spatial particles not only complies with the prescribed legal and operational requirements, but also maximizes the optimization of configuration and efficiency improvement in terms of functionality.

[0025] Furthermore, such as Figure 2 As shown, a collaborative gain analysis of the spatial particle attribute database is performed under the set of spatial particle recombination constraint rules. The method includes:

[0026] The spatial particle attribute database is analyzed to filter the target spatial particle set for collaborative analysis; a candidate set of collaborative relationships based on the target spatial particle set is constructed, wherein the candidate set of collaborative relationships consists of at least two spatial particle combinations; a collaborative gain index system is constructed, and the collaborative gain of the candidate set of collaborative relationships is quantified based on the collaborative gain index system to output a set of collaborative gain values; the optimal planning scheme corresponding to the multidimensional spatial particles that meets the preset collaborative gain threshold is obtained according to the set of collaborative gain values.

[0027] Preferably, when analyzing the spatial particle attribute database, the first step is to extract the attribute vector for each spatial particle from the database. These attribute vectors contain all the quantitative and qualitative characteristics of the spatial particle. Then, based on the core objectives of the current planning task, a subset closely related to the objectives is selected from all multidimensional spatial particles according to the attribute vectors. This subset serves as the target spatial particle set for collaborative analysis. Selection criteria typically include spatial location, dominant function, ownership status, and temporal stage. For example, under the ecological protection objective, structural spatial particles located at the intersection of ecologically sensitive areas and development boundaries are prioritized; under the traffic optimization objective, functional spatial particles adjacent to traffic nodes are prioritized. After selecting the target spatial particle set, the spatial particles in the target spatial particle set are paired based on factors such as geographical proximity and functional compatibility to generate a candidate set of collaborative relationships. This candidate set consists of at least two spatial particles. These combinations of spatial particles represent potential sources of collaborative gains, potentially optimizing the allocation of existing resources through mutual complementarity, shared facilities, and improved space utilization efficiency. Subsequently, to evaluate the synergistic effect of each candidate set of synergistic relationships, a synergistic gain index system is constructed. This system includes gain indicators across multiple dimensions, such as functional synergistic gain, spatial efficiency gain, operational efficiency gain, and facility sharing gain. These gain indicators quantify the synergistic benefits of each pair of spatial particles. By weighted summing of these indicators, the total synergistic gain value for each candidate set of synergistic relationships is obtained, forming a set of synergistic gain values. Then, the calculated synergistic gain values ​​are compared with a preset synergistic gain threshold. Spatial particle combinations with synergistic gain values ​​exceeding this threshold are retained as the preferred planning schemes for the multi-dimensional spatial particles. These preferred planning schemes not only meet planning objectives such as space utilization and functional optimization but also maximize synergistic benefits under certain resource and environmental constraints. This provides a scientific basis for the next step of spatial particle reorganization and implementation, ensuring that resource and functional allocation during the planning process aligns with optimal efficiency.

[0028] Furthermore, the spatial particle attribute database is analyzed, and the target spatial particle set is analyzed according to the planning target type to obtain a candidate set of collaborative relationships; wherein, the collaborative relationships include location collaborative relationships located in the same planning area, connection collaborative relationships with direct or indirect adjacency, and functional collaborative relationships with complementary functional attributes.

[0029] Optionally, after analyzing the spatial particle attribute database and constructing the target spatial particle set, a collaborative relationship analysis will be performed on this target spatial particle set according to the planning target type. Specifically, spatial association analysis will be conducted on the target particles based on their spatial location and adjacency relationships. The locational collaborative relationship between spatial particles is determined by calculating the degree of overlap of their geographical locations. For example, if two spatial particles are located in the same planning area and their functions can complement each other or share public facilities, then a locational collaborative relationship will be formed between them. In this case, by querying the geographic coordinate information in the spatial data table, the spatial distance between the particles is calculated, and it is determined whether the conditions for spatial aggregation are met, thereby determining whether a locational collaborative relationship exists. Subsequently, based on the adjacency situation between spatial particles, the connection collaborative relationship is analyzed. This connection collaborative relationship includes two cases: direct adjacency and indirect adjacency. Direct adjacency means that two spatial particles share a boundary or that two spatial particles are in contact with each other in geographic space; indirect adjacency means that two spatial particles are connected through other spatial particles, public facilities, etc. By utilizing the geometric boundary information of spatial particles, it is determined whether adjacent particles meet the adjacency condition, thereby identifying which pairs of spatial particles can form a connecting and synergistic relationship. Next, the functional synergy analysis stage is entered, where the functional complementarity between spatial particles is identified. For example, certain commercial functional areas and surrounding residential areas have strong functional synergies because the services provided by the commercial area can meet the needs of the residents in the residential area. By analyzing the functional attributes of each spatial particle and using preset functional matching rules, such as the functional complementarity between residential and commercial areas, and between educational and cultural areas, the functional suitability of particle combinations is determined. Finally, spatial particles with arbitrary synergistic relationships are aggregated to form a candidate set of synergistic relationships. This candidate set contains at least two spatial particles. In subsequent processing, the candidate set of synergistic relationships will be screened to ensure that all selected spatial particle combinations can generate synergistic benefits in actual planning, providing data support for the next step of spatial planning scheme optimization.

[0030] Furthermore, the method for quantifying the collaborative gain of the candidate set of collaborative relationships based on the aforementioned collaborative gain index system includes:

[0031] The candidate set of collaborative relationships includes binary collaborative candidate pairs consisting of two spatial particles and multi-dimensional collaborative candidate groups consisting of more than two spatial particles. The binary collaborative candidate pairs and the multi-dimensional collaborative candidate groups satisfy one of the following conditions: functional complementarity feasibility, staggered use during peak periods, facility sharing, and composite space utilization. Based on the collaborative gain index system, the candidate set of collaborative relationships is quantified for collaborative gain, outputting multiple collaborative gain indices. These multiple collaborative gain indices are then standardized, and a weighted calculation is performed on the standardized collaborative gain indices to output the set of collaborative gain values ​​corresponding to the candidate set of collaborative relationships.

[0032] Optionally, after constructing the candidate set of cooperative relationships, when performing cooperative gain quantization on the candidate set, the type of each cooperative combination in the candidate set is first identified. Combinations containing only two spatial particles are marked as binary cooperative candidate pairs, and combinations containing three or more spatial particles are marked as multivariate cooperative candidate groups. For combinations containing three or more spatial particles, they are formed by merging multiple combinations containing two spatial particles. For example, if particle A is complementary to particle B, particle B is complementary to particle C, and particle C is complementary to particle A, then particle A, B, and C form a ternary cooperative candidate pair. Subsequently, the attribute vectors of each spatial particle in the collaborative combination are read one by one, and they are initially screened according to the preset collaborative feasibility judgment logic. This judgment logic includes at least one of the following: functional complementarity feasibility condition, time-sequential peak use condition, facility sharing condition, and space composite utilization condition. Among them, the functional complementarity feasibility condition is determined by comparing the functional type code and functional matching rule table of each spatial particle; the time-sequential peak use condition is determined by analyzing the usage time period, activation cycle, and peak period attributes of each spatial particle; the facility sharing condition is determined by comparing the facility type, service radius, and remaining service capacity attributes; and the space composite utilization condition is determined by analyzing the spatial form, vertical or horizontal superposition possibility, and structural bearing parameters. Only when the collaborative combination meets any of the above conditions is it retained as a candidate for a valid collaborative relationship.

[0033] For candidate collaborative relationships that pass the feasibility assessment, their collaborative gains are quantitatively calculated based on a collaborative gain index system. This system predefines multiple quantifiable indicators, such as functional collaborative gain, spatial efficiency improvement, operational efficiency improvement, facility sharing degree, and other preset gain indicators. By extracting attribute values ​​corresponding to each indicator from a spatial granular attribute database and calculating them item by item according to preset indicator calculation rules, multiple corresponding collaborative gain index values ​​can be formed. After completing the initial calculation of each indicator, the collaborative gain indicators with different dimensions and value ranges are standardized. This standardization process uses linear normalization, extreme value normalization, or interval mapping to uniformly convert each collaborative gain indicator into a dimensionless value within a preset range. Then, according to the indicator weight parameters pre-set for the planning objectives, weights are assigned to each standardized collaborative gain indicator, and a weighted summation method is used to calculate the comprehensive collaborative gain value of each candidate collaborative relationship. Finally, the comprehensive collaborative gain values ​​corresponding to all candidate collaborative relationships are summarized to form a collaborative gain value set, providing a directly callable quantitative basis for subsequent threshold-based selection of optimal planning schemes.

[0034] Furthermore, based on the aforementioned collaborative gain index system, the collaborative gain of the candidate set of collaborative relationships is quantified, and multiple collaborative gain indices are output. These multiple collaborative gain indices include functional collaborative gain indices, spatial efficiency gain indices, operational efficiency gain indices, facility sharing gain indices, and energy consumption improvement gain indices.

[0035] Optionally, when quantifying the synergistic gain of the candidate set of synergistic relationships based on the synergistic gain index system, firstly, for each synergistic combination in the candidate set, all spatial particles contained therein are read, and the attribute vectors corresponding to each spatial particle in the spatial particle attribute database are retrieved respectively. Based on this, according to the pre-set synergistic gain index calculation rules, different types of synergistic gain indices are quantified sequentially. For the functional synergistic gain index, the functional type code and functional strength parameter of each spatial particle in the synergistic combination are read, the complementarity coefficient between different functions is determined through the functional complementarity relationship matrix, and the conflict coefficient of the functional overlap is calculated. Based on this, the sum of the products of each functional complementarity coefficient and the corresponding functional scale is subtracted from the sum of the products of the functional conflict coefficient and the conflict scale to obtain this index value, reflecting the net gain level of the synergistic combination in functional configuration. For the spatial efficiency gain index, the spatial utilization efficiency values ​​of each spatial particle before and after collaborative use are calculated. This spatial utilization efficiency value is calculated from the ratio of building area to land area and the carrying capacity per unit space. The index value is obtained by dividing the difference between the reconstructed and pre-reconstruction spatial utilization efficiency values ​​by the original value. This index value is used to measure the degree of efficiency improvement brought about by the composite use of space. For the operational efficiency gain index, based on the operational paths, service flows, and usage time sequence data of each spatial particle in the collaborative combination, the average operational path length, average service response time, and number of peak conflicts before and after collaboration are calculated. The difference between the average operational path length, average service response time, and number of peak conflicts before and after collaboration is calculated and then summed with weights to obtain the index value, which quantifies the improvement effect of collaborative reconstruction on operational efficiency. For the facility sharing gain index, the types of facilities that can be shared in the collaborative combination are identified. The number of facilities when each spatial particle is independently configured before collaboration and the facility demand under the shared configuration state after collaboration are calculated. The index value is obtained by weighting the reduction in facility duplication and the increase in facility utilization. This index value is used to reflect the resource saving effect brought about by facility collaboration. For the energy consumption improvement gain index, the energy consumption parameters per unit area or per unit function of each spatial particle in the collaborative combination are read. The total energy consumption value under the independent operation state before collaboration and the total energy consumption value under the conditions of functional integration, facility sharing, and peak-shifting after collaboration are calculated respectively. The proportion of energy consumption reduction before and after collaboration is used as the index value to quantify the degree of improvement of the overall energy consumption level by collaborative reconfiguration. All of the above-mentioned collaborative gain indices are independently quantified according to unified calculation rules and data sources. Finally, a set of corresponding collaborative gain indices is output for each candidate collaborative relationship object for subsequent standardization processing and comprehensive collaborative gain value calculation.

[0036] Furthermore, the method for obtaining the preferred planning scheme corresponding to the multidimensional spatial particles that satisfy the preset collaborative gain threshold according to the set of collaborative gain values ​​includes:

[0037] According to the set of cooperative gain values, obtain the preferred set of cooperative relationships that meet the preset cooperative gain threshold; construct a set of candidate planning schemes based on the preferred set of cooperative relationships; perform consistency verification on the set of candidate planning schemes, and when the consistency verification fails, optimize and adjust the set of candidate planning schemes according to the cooperative relationships with consistency verification conflicts until the preferred planning scheme that passes the consistency verification is output; wherein, the consistency verification includes space occupancy consistency, functional configuration consistency, ownership boundary consistency, and timing usage consistency.

[0038] Optionally, after obtaining the set of collaborative gain values, the set is first traversed and analyzed. The collaborative gain value corresponding to each candidate collaborative relationship is compared with a pre-set collaborative gain threshold. If the collaborative gain value is greater than or equal to the collaborative gain threshold, the collaborative relationship is determined to meet the optimization requirements and added to the preferred set of collaborative relationships. Subsequently, based on the preferred set of collaborative relationships, each collaborative relationship in the set is combined and mapped according to the spatial granularity involved. Then, multiple collaborative relationships that can be implemented simultaneously within the same planning framework and do not have explicit conflicts are combined to generate corresponding candidate planning schemes. Each candidate planning scheme clearly includes the set of spatial granularities involved in the reorganization and its collaborative reconstruction method. After forming the set of candidate planning schemes, consistency verification is performed on each candidate planning scheme in turn from four dimensions: spatial occupancy, functional configuration, ownership boundaries, and temporal usage.

[0039] When performing space occupancy consistency verification, the spatial particle reorganization results corresponding to each collaborative relationship in the candidate planning scheme are first read, and the reorganized spatial range is expressed in the form of geometric boundaries. Subsequently, spatial overlay analysis is performed on the boundaries of all reorganized spatial particles to detect whether there are cases where the same spatial range is repeatedly occupied by multiple spatial particles, exceeds the original spatial boundaries, or exceeds the upper limit of available space capacity. At the same time, combined with the structural attribute parameters of the spatial particles, indicators such as building strength and spatial carrying capacity are verified to determine whether the planning scheme has any unimplementable occupancy conflicts at the physical space level. If any spatial particle is found to have overlapping occupancy, exceeding the volume limit, or encroaching on unusable space after reorganization, the planning scheme is determined to have failed the space occupancy consistency verification.

[0040] During the functional configuration consistency verification, based on the functional configuration results after the spatial particles in the candidate planning scheme are reorganized, the planning function type and function combination method corresponding to each spatial particle are extracted one by one, and compared and analyzed with the pre-set functional control rules and functional compatibility rules. The functional matching matrix is ​​used to determine whether the composite functional configuration within the same spatial particle meets the functional compatibility requirements. Simultaneously, it is determined whether there are conflicts or unreasonable overlaps in the functional configurations between adjacent or related spatial particles. For example, whether there are functional combinations that are not allowed to be arranged adjacently, or whether the functional configuration exceeds the scope allowed by the planning objectives. If any spatial particle or combination of spatial particles is found to violate the functional compatibility rules or planning functional control requirements in its functional configuration, the candidate planning scheme is determined to have failed the functional configuration consistency verification.

[0041] During the ownership boundary consistency verification, the ownership spatial particle boundary information recorded in the spatial particle attribute database is retrieved and compared with the spatial scope after reorganization in the candidate planning scheme. Spatial boundary alignment analysis is used to determine whether the reorganization result crosses the established ownership boundary, and whether spatial particles of different ownership entities are merged, overlapped, or adjusted without meeting ownership constraints. Simultaneously, considering the right type and ownership stability parameters in the ownership attributes, the planning scheme is checked to verify whether it involves spatial adjustments that affect the rights and interests of ownership entities. If any spatial reorganization result in the planning scheme is found to have exceeded ownership boundaries, changed ownership relationships, or violated existing ownership constraints, the candidate planning scheme is deemed to have failed the ownership boundary consistency verification.

[0042] During the time-series usage consistency verification, the activation time, usage cycle, update stage, and time constraints of each spatial particle in the candidate planning scheme are uniformly compared and analyzed based on their corresponding time-series spatial particle information. By overlaying timelines, it is checked whether there are time conflicts in the usage arrangements of different spatial particles within the same time period. For example, the same space may be planned for multiple incompatible uses during the same time period, or a spatial particle may be scheduled to enter the next usage stage before meeting the prerequisite conditions. Simultaneously, the time-series staggered usage conditions in the collaborative relationship are considered to determine whether the planning scheme truly achieves time-level collaborative utilization. If any spatial particle is found to have an unexecutable or conflicting time arrangement, the candidate planning scheme is deemed to have failed the time-series usage consistency verification.

[0043] When a candidate planning scheme fails any of the above consistency checks, the specific collaborative relationship causing the failure is identified. This collaborative relationship is then used as the adjustment target to optimize the candidate planning scheme. This optimization includes replacing conflicting collaborative relationships, adjusting the spatial particle combination method, or reconstructing the planning scheme combination. After the adjustment is completed, the updated candidate planning scheme undergoes a consistency check again, and the above process is repeated until a planning scheme that passes the consistency checks in terms of space occupancy, functional configuration, ownership boundaries, and timing of use is obtained. This final planning scheme is then determined as the preferred planning scheme and output. In summary, the core of the above process is to ensure the feasibility of the planning scheme through consistency checks, avoiding inconsistencies and conflicts during actual implementation, and ensuring the smooth implementation of the plan. Simultaneously, through optimization, the final preferred planning scheme can efficiently utilize spatial resources, meet various functional requirements, and resolve potential planning conflicts.

[0044] Furthermore, the method for constructing a set of candidate planning schemes based on the preferred set of collaborative relationships includes:

[0045] Construct a set of planning directions and establish an influence mapping relationship between the collaborative relationships and the set of planning directions; mark the priority identifier of the planning direction corresponding to each collaborative relationship according to the influence mapping relationship; generate a set of candidate planning schemes for the preferred set of collaborative relationships according to the priority identifier of the planning direction.

[0046] Optionally, when generating candidate planning schemes, a set of planning directions is first extracted and constructed based on the planning objective system. This set of planning directions consists of several executable planning orientations, each corresponding to a clear spatial optimization orientation, functional guidance requirements, and implementation focus, and is encoded and stored with a unique direction identifier. Subsequently, each collaborative relationship in the preferred set of collaborative relationships is analyzed one by one, reading the spatial particle attribute vectors involved in the collaborative relationship and their collaborative gain index results. Based on a preset influence judgment rule, an influence mapping relationship between the collaborative relationship and the set of planning directions is established. This influence judgment rule determines the degree of positive influence of the collaborative relationship on one or more planning directions by comparing the functional changes, spatial efficiency improvement directions, facility sharing characteristics, and energy consumption improvement characteristics generated by the collaborative relationship with the target characteristic parameters of each planning direction, and records the corresponding relationship in the form of a mapping matrix or association table. After establishing the influence mapping relationship, the planning direction priority of each collaborative relationship is determined based on the mapping results. Specifically, the influence intensity of the collaborative relationship in different planning directions is quantified and ranked. This involves obtaining a pre-defined directional feature parameter vector for each planning direction in the set of planning directions. This directional feature parameter vector characterizes the core focus of that planning direction, and its parameters include at least functional optimization weight, spatial efficiency improvement weight, operational efficiency improvement weight, facility sharing emphasis weight, and energy consumption improvement emphasis weight, with each parameter assigned a directional weight coefficient. Next, the calculated collaborative gain index results for each collaborative relationship are retrieved, and the corresponding functional collaborative gain index, spatial efficiency gain index, operational efficiency gain index, facility sharing gain index, and energy consumption improvement gain index are combined to form a collaborative gain vector. Based on this, a vector matching calculation method is used to perform operations on the collaborative gain vector and the feature parameter vectors of each planning direction one by one. The influence intensity value of the collaborative relationship under the corresponding planning direction is calculated by weighted product summation. This influence intensity value reflects the degree to which the collaborative relationship supports the goal of that planning direction.

[0047] After calculating the influence intensity values ​​for all planning directions, the influence intensity values ​​of the same collaborative relationship under different planning directions are sorted. The planning direction with the highest influence intensity value is determined as the dominant planning direction of the collaborative relationship, and its corresponding priority is the highest priority. The remaining planning directions are assigned secondary priority labels in descending order of influence intensity value. This quantifies and sorts the influence intensity of collaborative relationships in different planning directions, providing a directly executable priority basis for the subsequent generation of candidate planning schemes. Finally, using the planning direction priority labels as constraints, the preferred set of collaborative relationships is combined to generate a set of candidate planning schemes. During the generation process, collaborative relationships with the same or compatible planning direction priority labels are prioritized for combination, while combinations of collaborative relationships with obvious conflicts in planning directions are excluded. This forms a set of candidate planning schemes that conform to planning direction consistency and have implementation feasibility, which is used for subsequent consistency verification and selection of preferred schemes.

[0048] Furthermore, the candidate planning scheme set is optimized and adjusted based on the collaborative relationships where consistency verification conflicts exist. The method includes:

[0049] Extract collaborative relationships with consistency verification conflicts; assess the conflict severity of the collaborative relationships and output a conflict severity value, including the scope of conflict impact, the impact of conflict constraints, and the key weight of conflict; filter alternative collaborative relationships from the candidate collaborative relationship set; evaluate the conflict severity value of the alternative collaborative relationships; if the conflict severity value of the alternative collaborative relationships is less than the conflict severity value of the collaborative relationship, optimize and adjust the candidate planning scheme set according to the alternative collaborative relationships.

[0050] Optionally, when a candidate planning scheme fails the consistency verification, the verification results are first backtracked to extract the collaborative relationships that cause conflicts in space occupancy, functional configuration, ownership boundaries, or temporal use from the corresponding candidate planning schemes. These relationships are then marked as target collaborative relationships with consistency verification conflicts. Subsequently, a conflict severity assessment is performed on these target collaborative relationships. During the assessment, the degree of conflict is quantified from multiple dimensions. The scope of conflict impact is quantified by statistically analyzing the number of spatial particles involved in the collaborative relationship, the scale of the affected space, and the number of associated planning units. The impact of conflict constraints is quantified by analyzing the type and number of rigid constraints, operational constraints, or legal constraints triggered by the collaborative relationship. The conflict key weight is assigned a comprehensive weight based on the impact of the above-mentioned types of conflicts on the achievement of planning objectives. Finally, the assessment results are weighted and the corresponding conflict severity value is output. After obtaining the conflict severity value, other collaborative relationships with similar spatial particle ranges, similar functional objectives, or consistent planning directions are retrieved from the original candidate collaborative relationship set, using the target collaborative relationship as a reference, forming a set of alternative collaborative relationships that can be used for replacement. Subsequently, using the same conflict severity assessment method as the target collaborative relationship, the conflict severity value of each alternative collaborative relationship is calculated and compared with the conflict severity value of the target collaborative relationship. When the conflict severity value of an alternative collaborative relationship is less than that of the target collaborative relationship, it is determined that the alternative collaborative relationship is superior in terms of consistency. Based on this, the original target collaborative relationship in the candidate planning scheme is replaced with the alternative collaborative relationship, thereby optimizing and adjusting the set of candidate planning schemes. This provides an adjustment basis for subsequent consistency verification and gradual convergence to the feasible optimal planning scheme, ensuring that the final optimal planning scheme will not encounter irreconcilable contradictions during implementation, optimizing spatial layout and resource allocation, and improving the overall efficiency of the planning.

[0051] Furthermore, the method of filtering alternative collaborative relationships from the candidate collaborative relationship set also includes:

[0052] If the alternative collaboration relationship returns empty, the collaboration relationship is removed from the candidate planning scheme set, and the candidate planning scheme set is updated; the consistency check is re-executed on the updated candidate planning scheme set.

[0053] Optionally, after filtering for alternative collaborative relationships with consistency verification conflicts, if no alternative collaborative relationship with the conflicting relationship is found in the candidate set (i.e., the alternative collaborative relationship returns empty), it will be determined that the collaborative relationship cannot be effectively replaced under the current planning constraints. In this case, the candidate planning scheme set will be directly updated. Specifically, collaborative relationships with consistency verification conflicts and no alternatives will be removed from the corresponding candidate planning schemes, and the spatial granular combination relationships involved in the candidate planning scheme will be updated simultaneously, so that the remaining collaborative relationships can reconstruct a valid planning scheme structure. After completing the removal of collaborative relationships and updating the candidate planning scheme set, the consistency verification process will be automatically re-executed on the updated candidate planning scheme set, sequentially verifying the consistency of spatial occupancy, functional configuration, ownership boundaries, and temporal usage to verify whether the planning scheme meets all constraints after removing conflicting collaborative relationships. If the consistency check still fails, the conflict identification, alternative screening or elimination process continues until the consistency check is passed or the set of candidate planning schemes is empty, thereby ensuring that the final output planning scheme is feasible and logically consistent under the constraints.

[0054] Based on the optimal planning scheme corresponding to the multidimensional spatial particles, the granular-level land space planning results of the existing land space are output.

[0055] In one embodiment, after obtaining the optimal planning scheme corresponding to the multi-dimensional spatial particles, the process enters the planning results generation and output stage. First, based on the spatial particle combination relationships, functional configuration results, and reorganization methods determined in the optimal planning scheme, the basic spatial units in the existing land space are mapped and reconstructed. The structural spatial particles, functional spatial particles, ownership spatial particles, and temporal spatial particles in the planning scheme are mapped back to their original spatial locations and management units one by one. Subsequently, based on the configuration results of each spatial particle in the optimal planning scheme, granular planning control information is generated. This control information includes at least the planning purpose, functional combination method, implementation phase arrangement, and ownership constraint description of the spatial particles. This information is then bound to the corresponding spatial particle identifier in structured data form. After the control information generation is completed, the granular planning results are uniformly coded and standardized to form data results that can be directly called by the planning management system. Simultaneously, corresponding spatial expression results are generated. By overlaying the planning control information with spatial geometric information, planning unit data is formed. Finally, the granular-level territorial spatial planning results are exported according to the preset data format and output specifications. This output includes database results for management decision-making, spatial data results for implementation and control, and planning result documents for presentation. This enables the output of planning results at the granular scale for existing territorial space, ensuring that the planning results have precise expression, are transferable, and have implementable application value.

[0056] In summary, the embodiments of this application have at least the following technical effects:

[0057] First, the existing land space is decomposed into multidimensional granular components based on the reconstructed objects, obtaining multidimensional spatial particles, including structural spatial particles, functional spatial particles, ownership spatial particles, and temporal spatial particles. Next, the attribute vector of each spatial particle is obtained, generating a spatial particle attribute database. Then, a set of spatial particle reorganization constraint rules is constructed, including rigid constraint rules, operational constraint rules, and legal constraint rules. Then, under the spatial particle reorganization constraint rule set, a synergistic gain analysis is performed on the spatial particle attribute database to obtain the optimal planning scheme corresponding to the multidimensional spatial particles that meets a preset synergistic gain threshold. Finally, according to the optimal planning scheme corresponding to the multidimensional spatial particles, the granular-level land space planning results for the existing land space are output. This solves the technical problems of existing planning systems in highly urbanized areas facing the renewal of existing space, such as coordination difficulties, insufficient flexibility, and transmission losses, making it difficult to achieve refined and collaborative governance. It achieves the technical effect of refined decomposition, synergistic optimization, and granular-level reconstruction of multidimensional elements of existing land space under multiple constraints, improving the scientific nature, adaptability, and implementation effectiveness of land space planning.

[0058] Example 2, based on the same inventive concept as the method for reconstructing the land spatial planning system in highly urbanized areas in the foregoing examples, such as... Figure 3 As shown, this application provides a system for reconstructing the territorial spatial planning system for highly urbanized areas, wherein the system includes:

[0059] Granular Decomposition Module 11: Performs multi-dimensional granular decomposition of existing land space based on the reconstructed object to obtain multi-dimensional spatial particles, including structural spatial particles, functional spatial particles, ownership spatial particles, and temporal spatial particles; Attribute Vector Acquisition Module 12: Acquires the attribute vector of each spatial particle in the multi-dimensional spatial particles and generates a spatial particle attribute database; Constraint Construction Module 13: Constructs a spatial particle reorganization constraint rule set, including rigid constraint rules, operational constraint rules, and legal constraint rules; Synergistic Gain Analysis Module 14: Performs synergistic gain analysis on the spatial particle attribute database under the spatial particle reorganization constraint rule set to obtain the optimal planning scheme corresponding to the multi-dimensional spatial particles that meets the preset synergistic gain threshold; Planning Result Output Module 15: Outputs the granular land space planning results of the existing land space according to the optimal planning scheme corresponding to the multi-dimensional spatial particles.

[0060] Furthermore, the cooperative gain analysis module 14 is used to perform the following method:

[0061] The spatial particle attribute database is analyzed to filter the target spatial particle set for collaborative analysis; a candidate set of collaborative relationships based on the target spatial particle set is constructed, wherein the candidate set of collaborative relationships consists of at least two spatial particle combinations; a collaborative gain index system is constructed, and the collaborative gain of the candidate set of collaborative relationships is quantified based on the collaborative gain index system to output a set of collaborative gain values; the optimal planning scheme corresponding to the multidimensional spatial particles that meets the preset collaborative gain threshold is obtained according to the set of collaborative gain values.

[0062] Furthermore, the cooperative gain analysis module 14 is used to perform the following method:

[0063] The spatial particle attribute database is analyzed, and the target spatial particle set is analyzed according to the planning target type to obtain a candidate set of collaborative relationships. The collaborative relationships include location collaborative relationships located in the same planning area, connection collaborative relationships with direct or indirect adjacency, and functional collaborative relationships with complementary functional attributes.

[0064] Furthermore, the cooperative gain analysis module 14 is used to perform the following method:

[0065] The candidate set of collaborative relationships includes binary collaborative candidate pairs consisting of two spatial particles and multi-dimensional collaborative candidate groups consisting of more than two spatial particles. The binary collaborative candidate pairs and the multi-dimensional collaborative candidate groups satisfy one of the following conditions: functional complementarity feasibility, staggered use during peak periods, facility sharing, and composite space utilization. Based on the collaborative gain index system, the candidate set of collaborative relationships is quantified for collaborative gain, outputting multiple collaborative gain indices. These multiple collaborative gain indices are then standardized, and a weighted calculation is performed on the standardized collaborative gain indices to output the set of collaborative gain values ​​corresponding to the candidate set of collaborative relationships.

[0066] Furthermore, the cooperative gain analysis module 14 is used to perform the following method:

[0067] According to the set of cooperative gain values, obtain the preferred set of cooperative relationships that meet the preset cooperative gain threshold; construct a set of candidate planning schemes based on the preferred set of cooperative relationships; perform consistency verification on the set of candidate planning schemes, and when the consistency verification fails, optimize and adjust the set of candidate planning schemes according to the cooperative relationships with consistency verification conflicts until the preferred planning scheme that passes the consistency verification is output; wherein, the consistency verification includes space occupancy consistency, functional configuration consistency, ownership boundary consistency, and timing usage consistency.

[0068] Furthermore, the cooperative gain analysis module 14 is used to perform the following method:

[0069] Based on the aforementioned collaborative gain index system, the collaborative gain of the candidate set of collaborative relationships is quantified, and multiple collaborative gain indices are output. These multiple collaborative gain indices include functional collaborative gain indices, spatial efficiency gain indices, operational efficiency gain indices, facility sharing gain indices, and energy consumption improvement gain indices.

[0070] Furthermore, the cooperative gain analysis module 14 is used to perform the following method:

[0071] Extract collaborative relationships with consistency verification conflicts; assess the conflict severity of the collaborative relationships and output a conflict severity value, including the scope of conflict impact, the impact of conflict constraints, and the key weight of conflict; filter alternative collaborative relationships from the candidate collaborative relationship set; evaluate the conflict severity value of the alternative collaborative relationships; if the conflict severity value of the alternative collaborative relationships is less than the conflict severity value of the collaborative relationship, optimize and adjust the candidate planning scheme set according to the alternative collaborative relationships.

[0072] Furthermore, the cooperative gain analysis module 14 is used to perform the following method:

[0073] If the alternative collaboration relationship returns empty, the collaboration relationship is removed from the candidate planning scheme set, and the candidate planning scheme set is updated; the consistency check is re-executed on the updated candidate planning scheme set.

[0074] Furthermore, the cooperative gain analysis module 14 is used to perform the following method:

[0075] Construct a set of planning directions and establish an influence mapping relationship between the collaborative relationships and the set of planning directions; mark the priority identifier of the planning direction corresponding to each collaborative relationship according to the influence mapping relationship; generate a set of candidate planning schemes for the preferred set of collaborative relationships according to the priority identifier of the planning direction.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for reconstructing the territorial spatial planning system in highly urbanized areas, characterized in that: The method includes: The existing land space is divided into multi-dimensional granular parts based on the reconstruction object to obtain multi-dimensional spatial particles, which include structural spatial particles, functional spatial particles, ownership spatial particles and temporal spatial particles. Obtain the attribute vector of each spatial particle in the multidimensional spatial particles and generate a spatial particle attribute database; Construct a set of spatial particle reorganization constraint rules, which includes rigid constraint rules, operational constraint rules, and legal constraint rules. Under the set of spatial particle recombination constraint rules, a synergistic gain analysis is performed on the spatial particle attribute database to obtain the optimal planning scheme corresponding to the multidimensional spatial particles that meets the preset synergistic gain threshold. According to the preferred planning scheme corresponding to the multi-dimensional spatial particles, the granular-level land space planning results of the existing land space are output. The method for performing synergistic gain analysis on the spatial particle attribute database under the set of spatial particle recombination constraint rules includes: The spatial particle attribute database is analyzed to filter the target spatial particle set for collaborative analysis; Construct a candidate set of cooperative relationships based on the target set of spatial particles, wherein the candidate set of cooperative relationships is a combination of at least two spatial particles; Construct a collaborative gain index system, quantify the collaborative gain of the candidate set of collaborative relationships based on the collaborative gain index system, and output a set of collaborative gain values. According to the set of synergistic gain values, obtain the preferred planning scheme corresponding to the multidimensional spatial particles that satisfy the preset synergistic gain threshold; The method for obtaining the preferred planning scheme corresponding to the multidimensional spatial particles that satisfy a preset collaborative gain threshold according to the set of collaborative gain values ​​includes: According to the set of cooperative gain values, obtain the preferred set of cooperative relationships that satisfy the preset cooperative gain threshold; Construct a set of candidate planning schemes based on the preferred set of collaborative relationships; Perform a consistency check on the candidate planning scheme set. If the consistency check fails, optimize and adjust the candidate planning scheme set according to the collaborative relationship of the consistency check conflict until the preferred planning scheme that passes the consistency check is output. Consistency checks include consistency in space usage, consistency in functional configuration, consistency in ownership boundaries, and consistency in timing usage. The synergistic gain value is the result of a weighted calculation based on the functional complementarity between spatial particles, the degree of resource sharing, and the improvement of space utilization.

2. The method for reconstructing the territorial spatial planning system in highly urbanized areas as described in claim 1, characterized in that, The spatial particle attribute database is analyzed, and the collaborative relationship analysis of the target spatial particle set is performed according to the planning target type to obtain a candidate set of collaborative relationships; The collaborative relationships include location collaborative relationships within the same planning area, connection collaborative relationships with direct or indirect adjacency, and functional collaborative relationships with complementary functional attributes.

3. The method for reconstructing the territorial spatial planning system in highly urbanized areas as described in claim 1, characterized in that, The method for quantifying the collaborative gain of the candidate set of collaborative relationships based on the aforementioned collaborative gain index system includes: The candidate set of collaborative relationships includes binary collaborative candidate pairs consisting of two spatial particles and multi-dimensional collaborative candidate groups consisting of more than two spatial particles. The binary collaborative candidate pairs and the multi-dimensional collaborative candidate groups satisfy one of the following conditions: functional complementarity feasibility, time-series staggered use, facility sharing, and space composite utilization. Based on the aforementioned collaborative gain index system, the collaborative gain quantification is performed on the candidate set of collaborative relationships, and multiple collaborative gain indices are output. The multiple collaborative gain indices are standardized, and the standardized collaborative gain indices are weighted and calculated to output the collaborative gain value set corresponding to the collaborative relationship candidate set.

4. The method for reconstructing the territorial spatial planning system in highly urbanized areas as described in claim 3, characterized in that, Based on the aforementioned collaborative gain index system, the collaborative gain of the candidate set of collaborative relationships is quantified, and multiple collaborative gain indices are output. These multiple collaborative gain indices include functional collaborative gain indices, spatial efficiency gain indices, operational efficiency gain indices, facility sharing gain indices, and energy consumption improvement gain indices.

5. The method for reconstructing the territorial spatial planning system in highly urbanized areas as described in claim 1, characterized in that, The candidate planning scheme set is optimized and adjusted based on the collaborative relationships where consistency verification conflicts exist. The method includes: Extract collaboration relationships that have consistency check conflicts; The collaboration relationship is evaluated for conflict severity, and the conflict severity value is output, including the scope of conflict impact, the impact of conflict constraints, and the key weight of conflict. Filter alternative collaborative relationships from the candidate collaborative relationship set; The conflict severity value of the alternative collaboration relationship is evaluated. If the conflict severity value of the alternative collaboration relationship is less than the conflict severity value of the collaboration relationship, the candidate planning scheme set is optimized and adjusted according to the alternative collaboration relationship.

6. The method for reconstructing the territorial spatial planning system in highly urbanized areas as described in claim 5, characterized in that, The method further includes filtering alternative collaborative relationships from the candidate collaborative relationship set. If the alternative collaboration relationship of the collaboration relationship returns empty, the collaboration relationship is removed from the candidate planning scheme set, and the candidate planning scheme set is updated; Re-perform consistency checks on the updated set of candidate plans.

7. The method for reconstructing the territorial spatial planning system in highly urbanized areas as described in claim 1, characterized in that, The method for constructing a set of candidate planning schemes based on the preferred set of collaborative relationships includes: Construct a set of planning directions and establish a collaborative relationship—the influence mapping relationship of the set of planning directions; Mark the planning direction priority identifier corresponding to each collaborative relationship according to the influence mapping relationship; A set of candidate planning schemes for the preferred set of collaborative relationships is generated based on the priority identifier of the planning direction.

8. A system for reconstructing the territorial spatial planning system for highly urbanized areas, characterized in that: A system for implementing the method for reconstructing the territorial spatial planning system of highly urbanized areas as described in any one of claims 1-7, the system comprising: Granular Decomposition Module: Performs multi-dimensional granular decomposition of existing land space based on reconstruction objects to obtain multi-dimensional spatial particles, including structural spatial particles, functional spatial particles, ownership spatial particles and temporal spatial particles. Attribute vector acquisition module: acquires the attribute vector of each spatial particle in the multidimensional spatial particles and generates a spatial particle attribute database; Constraint Construction Module: Constructs a set of spatial particle reorganization constraint rules, which includes rigid constraint rules, operational constraint rules, and legal constraint rules; Synergistic gain analysis module: Performs synergistic gain analysis on the spatial particle attribute database under the spatial particle recombination constraint rule set to obtain the optimal planning scheme corresponding to the multidimensional spatial particles that meets the preset synergistic gain threshold; Planning Results Output Module: Based on the optimal planning scheme corresponding to the multi-dimensional spatial particles, output the granular-level land space planning results of the existing land space.

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