Land development right management method and system

Abstract

The invention belongs to the field of land development management, and discloses a land development right management method and system, and the method comprises the steps: obtaining the geographic information of an original land parcel and an absolute limitation region, and precisely recognizing land fragments which can be used for development and geometric attributes of the land fragments; according to geometric attributes of the land debris, identifying connection attributes such as spatial distance, traffic accessibility and visual vestibule among the land debris, and constructing an association network; matching the constructed associated network with a preset distributed function mode library, and determining an applicable distributed function mode combination; generating a distributed function integration scheme according to a matching result, evaluating the overall value of the distributed function integration scheme, and finally outputting land development scheme suggestion information; through the technical scheme, the problem that the land is fragmented due to multiple restrictions can be effectively solved, a brand-new and feasible development scheme is created for all parties in a complex land environment with fundamental benefit conflicts, and the land utilization efficiency and the project success rate are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of land development management, and more specifically, to a method and system for managing land development rights. Background Technology

[0002] In modern land development projects, coordinating multiple stakeholders, such as landowners, developers, local communities, and environmental organizations, is crucial for project success. Existing digital coordination platforms quantify the demands of each party (such as return on investment and the proportion of public green space) and use optimization algorithms to find the land use plan with the highest overall satisfaction. The core of this approach is that it assumes most interests can be quantified, weighed, and losses can be mitigated through parameter adjustments, thereby finding a balance point in a continuous solution space.

[0003] However, this coordination model based on quantitative weights and continuous optimization reveals its limitations when faced with non-traditional or absolute interests. For example, cultural heritage preservation groups may make absolute demands for the "cultural integrity" of a specific site, meaning that the area, due to its unique historical or spiritual significance, must remain unchanged and no form of development is permitted. This demand cannot be simply translated into numerical weights because it is essentially a binary choice: complete preservation or complete destruction. In a digital coordination platform, this "absolutely exclusive" Boolean constraint directly forms an insurmountable physical boundary, preventing the system from "compromising" by adjusting weights, thus leading to conflicting proposals.

[0004] Furthermore, the long coordination cycles of large-scale land development projects may lead to the intervention of new information and technologies. For example, an area initially marked as a "potentially unstable karst geological zone" may be reassessed due to the emergence of new, more accurate geological structural stability simulation methods. If the simulation results show that the underground structure of the area is far more fragile than expected, and any deep foundation construction could lead to irreversible collapse, then the area will transform from a "risk zone" into an "absolute no-development zone" based on public safety regulations. This transformation turns a negotiable economic factor into an insurmountable physical barrier, directly negating the original high-density building plan.

[0005] More complexly, the combined effect of these non-overlapping but equally exclusive zones (such as cultural conservation areas and geological safety exclusion zones) can lead to the fragmentation of remaining developable land into disconnected, irregularly shaped pieces. For developers pursuing economies of scale and functional integration, their core profit-generating projects often require a minimum contiguous land area to achieve design efficiency and operational effectiveness. If any single fragmented plot fails to meet this requirement, even if there is still a theoretically remaining total amount of developable land, the entire project may become unfeasible because it cannot support its core economic model. This means that the problem is no longer simply choosing between different zones, but rather that the "connectivity" and "availability" of the entire land are fundamentally disrupted.

[0006] Under the superposition of the aforementioned multiple, different, and non-compensating constraints, the optimization model built into the existing digital coordination platform, based on numerical weight compromise, will fall into a logical deadlock. When the system attempts to generate new development plans, it will find that any plan that can meet the developer's minimum economic return weight is deemed infeasible because it cannot obtain a sufficiently large contiguous land area. This is because cultural protection zones, geological safety restrictions, and possible temporary administrative freeze zones collectively fragment the entire plot, making any single, continuous space sufficient to support the core project non-existent. The core issue is no longer how to find a balance between the weights of various parties, but how to create a completely new and feasible development plan for parties with fundamentally conflicting interests on land whose physical and economic attributes have been drastically altered by multi-source, different, and non-compensating constraints.

[0007] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this application provides a land development rights management method and system, aiming to solve the problem that the optimization model built into existing digital coordination platforms, which is based on numerical weight compromise, gets stuck in a logical deadlock under multiple, different and non-compensable constraints, and is unable to create feasible development solutions for parties with fundamental conflicts of interest.

[0009] Firstly, this application provides a land development rights management method for generating land development plan recommendations when land is fragmented due to multiple restrictions. The steps of this method include: A1. Obtain the geographic boundary information of the original land parcel and the geographic information of the absolute restricted area located within the original land parcel; A2. Based on the geographical boundary information of the original land parcel and the geographical information of the absolute restriction area, identify land fragments available for development and their geometric attributes; the geometric attributes include geographical boundary information, area, and shape features; A3. Based on the geometric attributes of the land fragments, identify the connection attributes between any two land fragments and establish an association network describing the relationships between the land fragments; the connection attributes include spatial distance information, traffic accessibility information, and visual corridor information; A4. Match the associated network with the distributed functional patterns in the preset distributed functional pattern library to determine the applicable combination of distributed functional patterns; the distributed functional pattern is a land development model in which multiple functional units achieve overall value through decentralized layout and collaborative operation. A5. Based on the matching results, generate at least one distributed function integration scheme and evaluate the overall value of the distributed function integration scheme; the distributed function integration scheme includes functional unit allocation suggestions and connection and coordination strategy suggestions between functional units; A6. Based on the aforementioned distributed functional integration scheme and its overall value, output land development plan recommendations.

[0010] Secondly, this application provides a land development rights management system for generating land development plan suggestions when land is fragmented due to multiple restrictions. The system includes: The data acquisition module acquires the geographical boundary information of the original land parcel and the geographical information of the absolute restricted area located within the original land parcel; The fragment identification module identifies land fragments that can be developed and their geometric attributes based on the geographical boundary information of the original land parcel and the geographical information of the absolute restriction area; the geometric attributes include geographical boundary information, area, and shape features. The network construction module is used to identify the connection attributes between any two land fragments based on the geometric attributes of the land fragments, and to establish an association network describing the relationships between the land fragments; the connection attributes include spatial distance information, traffic accessibility information, and visual corridor information; The pattern matching module is used to match the associated network with the distributed functional patterns in the preset distributed functional pattern library to determine the applicable combination of distributed functional patterns; the distributed functional pattern is a land development model in which multiple functional units achieve overall value through decentralized layout and collaborative operation. The solution generation module is used to generate at least one distributed function integration solution based on the matching results, and to evaluate the overall value of the distributed function integration solution; the distributed function integration solution includes functional unit allocation suggestions and connection and cooperation strategy suggestions between functional units. The output module is used to output land development plan suggestions based on the distributed functional integration scheme and its overall value.

[0011] In summary, the land development rights management method and system provided in this application, by acquiring the geographical information of the original land parcels and absolute restriction areas, accurately identifies land fragments available for development and their geometric attributes, overcoming the shortcomings of traditional methods in handling absolute exclusivity constraints and ensuring the accuracy of subsequent analysis. Based on the geometric attributes of the land fragments, it identifies connectivity attributes such as spatial distance, accessibility, and visual corridors between them, and constructs an association network, effectively solving the connectivity disruption problem caused by land fragmentation and providing a spatial foundation for subsequent functional integration. Matching the constructed association network with a pre-set distributed functional pattern library determines the applicable combination of distributed functional patterns, fully utilizing the potential of fragmented land. By maximizing overall value through decentralized layout and collaborative operation, this approach breaks through the limitations of traditional centralized development models. It generates distributed functional integration schemes based on matching results, evaluates their overall value, and ultimately outputs land development plan recommendations. This allows for feasible development solutions for all parties even under complex constraints, effectively coordinating the demands of different stakeholders and avoiding the logical dilemmas of traditional optimization models when facing non-quantifiable, absolute constraints. Through the aforementioned technical solution, this application effectively addresses the problem of land fragmentation due to multiple restrictions. In complex land environments with fundamental conflicts of interest, it creates entirely new and feasible development plans for all parties, significantly improving land use efficiency and project success rates. Attached Figure Description

[0012] Figure 1 A flowchart of a land development rights management method provided for this application.

[0013] Figure 2 This is a schematic diagram of a land development rights management system provided for this application.

[0014] In the diagram: 1. Data acquisition module; 2. Fragment identification module; 3. Network construction module; 4. Pattern matching module; 5. Scheme generation module; 6. Suggestion output module. Detailed Implementation

[0015] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0016] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0017] refer to Figure 1 This application provides a land development rights management method for generating land development plan suggestions when land is fragmented due to multiple restrictions. The method includes the following steps: A1. Obtain the geographic boundary information of the original land parcel and the geographic information of the absolute restricted area located within the original land parcel; A2. Based on the geographical boundary information of the original land parcel and the geographical information of the absolute restriction area, identify land fragments available for development and their geometric attributes; the geometric attributes include geographical boundary information, area, and shape features; A3. Based on the geometric attributes of the land fragments, identify the connection attributes between any two land fragments and establish an association network describing the relationships between the land fragments; the connection attributes include spatial distance information, traffic accessibility information, and visual corridor information; A4. Match the associated network with the distributed functional patterns in the preset distributed functional pattern library to determine the applicable combination of distributed functional patterns; the distributed functional pattern is a land development model in which multiple functional units achieve overall value through decentralized layout and collaborative operation. A5. Based on the matching results, generate at least one distributed function integration scheme and evaluate the overall value of the distributed function integration scheme; the distributed function integration scheme includes functional unit allocation suggestions and connection and coordination strategy suggestions between functional units; A6. Based on the aforementioned distributed functional integration scheme and its overall value, output land development plan recommendations.

[0018] This application, by introducing the identification of land fragmentation characteristics, the construction of related networks, and the matching of distributed functional patterns, can effectively address the problem of land fragmentation under multiple constraints, and provide more feasible and holistic solutions for land development.

[0019] The "original plot" refers to the initial land area for which land development planning is to be carried out. The "absolutely restricted area" refers to an area within the original plot that is completely prohibited or strictly restricted from development due to laws and regulations, planning requirements, natural conditions, or special interests, such as cultural heritage protection areas, high-risk geological disaster areas, and ecologically sensitive areas. The geographic information of these areas is usually represented in the form of vector data (such as polygons).

[0020] "Land fragments" refer to scattered plots of land with independent geographical boundaries that remain within the original land parcels after deducting the absolute restriction zone and are available for development. These land fragments may be irregular in shape, vary in size, and are discontinuous with each other. "Geometric attributes" are data describing the spatial characteristics of land fragments, including their precise geographical boundary information (such as a sequence of coordinate points), area size, and shape characteristics (such as perimeter, compactness, aspect ratio, etc.).

[0021] "Connectivity attributes" refer to indicators describing the degree of interrelation between any two land fragments, including spatial distance information (such as the straight-line distance between the center points of the two fragments), transportation accessibility information (such as the time or cost required to connect the two fragments via the existing transportation network), and visual corridor information (such as whether there is an unobstructed line of sight between the two fragments). These connectivity attributes are used to construct an "association network," which uses land fragments as nodes and connectivity attributes as edges, reflecting the interrelationships between the fragments.

[0022] Among them, the "distributed functional model" is a new type of land development model. Its core idea is to break down traditionally centralized functions (such as residential, commercial, and public services) into multiple "functional units," and then distribute these functional units across different land fragments based on their distribution characteristics. These units achieve overall value through collaborative operation. For example, a distributed functional model for a "community living circle" might include functional units such as a "small commercial center," a "community park," and a "cultural activity center." Although these functional units are dispersed, through reasonable connection and coordination strategies, they can collectively serve the surrounding residents.

[0023] The "distributed functional integration plan" is a specific land development plan generated based on the matching results. It includes "functional unit allocation suggestions" (i.e., which functional unit is allocated to which land fragment) and "connection and coordination strategy suggestions between functional units" (i.e., how to enable the dispersed functional units to operate collaboratively through means such as transportation, landscape, and information). "Overall value" is a comprehensive evaluation of the distributed functional integration plan, which can cover economic, social, and environmental benefits.

[0024] This method can be implemented in a Geographic Information System (GIS) platform or a dedicated land planning software environment that can process geospatial data, perform spatial analysis, support network construction and algorithm operation, and ultimately output land development plan recommendations in the form of visualizations or reports.

[0025] This application proposes a land development rights management method, the core of which lies in effectively addressing the problem of land fragmentation and generating feasible land development plan recommendations. This method is implemented through a series of steps, each key feature of which will be elaborated in detail below.

[0026] First, in step A1, it is necessary to obtain the geographic boundary information of the original land parcel and the geographic information of the absolute restriction zone within the original land parcel. Geographic boundary information can be obtained in various ways. For example, it can be imported from existing geographic information system databases, which typically contain detailed land ownership boundary data; or it can be interpreted and digitized using high-precision satellite remote sensing imagery, combined with field measurement data for calibration to ensure the accuracy of the boundary information. The geographic information of the absolute restriction zone is obtained in a similar manner to that of the original land parcel's geographic boundary information. It can be extracted from official data sources such as government-issued planning maps, ecological protection zone maps, and geological hazard assessment reports, and digitized into processable geospatial data. For example, in an urban renewal project, the original land parcel might be an old industrial area, while the absolute restriction zone within it might be a protected historical building complex or an area with dense underground pipelines.

[0027] Secondly, in step A2, based on the geographic boundary information of the original plot and the geographic information of the absolute restriction area, developable land fragments and their geometric attributes are identified. This step typically involves geospatial analysis operations; for example, the "erasure" or "difference" function in GIS software can be used to remove the parts of the original plot that overlap with the absolute restriction area, thus obtaining the remaining developable areas. These remaining areas are often irregular and separated from each other, i.e., land fragments. After identifying the land fragments, their geometric attributes need to be further extracted, including geographic boundary information (i.e., the precise outline of each fragment), area (obtained by calculating the polygon area of ​​the fragment), and shape features. Shape features can be extracted using various indicators; for example, the compactness can be assessed by calculating the ratio of the fragment's perimeter to its area, or its aspect ratio and orientation can be determined through principal axis analysis. For example, an original plot divided by a river and a highway may, after processing, yield multiple land fragments of varying shapes and areas, some of which may be elongated, while others may be approximately circular.

[0028] Next, in step A3, based on the geometric attributes of the land fragments, the connection attributes between any two land fragments are identified, and an association network describing the relationships between the land fragments is established. Identifying connection attributes is the foundation for constructing the association network. Spatial distance information can be obtained by calculating the Euclidean distance between the geometric centers of any two land fragments. Traffic accessibility information is more complex; it can be combined with existing traffic network data (such as roads, rail transit, etc.) and network analysis algorithms (such as shortest path algorithms, service area analysis, etc.) to assess the ease of reaching one fragment from another. For example, driving time, walking time, or the number of public transport transfers can be calculated. Visual corridor information can be evaluated using line-of-sight analysis, that is, considering obstructions such as terrain and buildings, determining whether there is an unobstructed line-of-sight connection between two land fragments. For example, if there is a wide green belt between two land fragments, there may be a good visual corridor. After obtaining these connection attributes, an association network is constructed using land fragments as nodes and spatial distance information, traffic accessibility information, and visual corridor information as attributes of the connecting edges between nodes. This network can be a weighted graph, where the weights of the edges reflect the strength of the connectivity properties.

[0029] Subsequently, in step A4, the association network is matched with distributed functional patterns in a pre-defined distributed functional pattern library to determine suitable combinations of distributed functional patterns. The distributed functional pattern library pre-stores various land development patterns, each defining its constituent functional units, land use requirements (such as minimum area and shape preferences), and collaborative relationships between functional units (such as spatial proximity, accessibility, and functional complementarity). The matching process aims to find combinations of land fragments from the association network that can support these distributed functional patterns. For example, an "ecotourism model" might require functional units such as a "visitor center," "eco-tourism trails," and "birdwatching spots," and requires good accessibility and visual connectivity between these units. The matching algorithm searches for groups of land fragment nodes in the association network that meet these requirements.

[0030] Further, in step A5, based on the matching results, at least one distributed functional integration scheme is generated, and the overall value of the distributed functional integration scheme is evaluated. Once the applicable combination of distributed functional patterns is determined, specific functional unit allocation suggestions can be generated, that is, specifying which land fragment each functional unit will be placed on. Simultaneously, based on the synergistic relationship information between functional units in the distributed functional pattern and the connection attributes between corresponding land fragments, connection and coordination strategy suggestions between functional units are generated, such as suggesting the construction of connecting walkways, the establishment of shared transportation stations, or the planning of landscape corridors. Each generated distributed functional integration scheme requires an overall value assessment, which can involve multiple dimensions such as economic benefits (e.g., return on investment), social benefits (e.g., public service coverage, community vitality), and environmental benefits (e.g., ecosystem service value, carbon emissions). The assessment method can employ multi-criteria decision analysis, assigning weights to different benefits and performing comprehensive calculations.

[0031] Finally, in step A6, based on the distributed functional integration schemes and their overall value, land development plan recommendations are output. This step presents the evaluation results in a user-friendly manner. The output information may include: a detailed description of each distributed functional integration scheme, such as a layout diagram of functional units and a schematic diagram of connection and coordination strategies; an overall value assessment report for each scheme, including quantitative indicators of various benefits and a comprehensive score; and an analysis of the advantages and disadvantages of different schemes and recommendations. This information can be presented in the form of visual charts, text reports, or interactive digital models for review and decision-making by stakeholders such as landowners, developers, government departments, and the public.

[0032] In summary, this application effectively transforms fragmented land under multiple constraints into usable resources through a logical chain of "identifying fragments - building a network - matching patterns - generating solutions - evaluating value," and finds the optimal distributed functional integration scheme for these resources. This method overcomes the bottlenecks of existing technologies in dealing with absolute exclusivity constraints and land fragmentation problems, providing a novel solution for sustainable development in complex land environments.

[0033] In some implementations, step A3 includes: A301. Based on the geometric properties of the land fragments, calculate the distance between the center points of any two land fragments, and use it as the spatial distance information; A302. Based on the geometric attributes and traffic network data of the land fragments, assess the traffic accessibility between any two land fragments to obtain the traffic accessibility information; A303. Based on the geometric attributes and topographic data of the land fragments, use the line-of-sight analysis method to assess whether there is an unobstructed visual corridor between any two land fragments, and obtain the visual corridor information; A304. Using the land fragments as nodes, and the spatial distance information, the traffic accessibility information, and the visual corridor information as attributes of the connecting edges between nodes, construct the association network.

[0034] The center point of the land fragment can be understood as its geometric center or centroid. The distance can be calculated using Euclidean distance, Manhattan distance, or other applicable distance metrics. The purpose is to quantify the physical proximity between different land fragments.

[0035] Specifically, the accessibility information can be obtained by analyzing existing road networks, public transportation routes, walking paths, and other data to calculate the time, distance, cost, or a weighted average of these factors required to travel from one land fragment to another. For example, network analysis tools in a Geographic Information System (GIS) can be used to simulate traffic conditions under different modes of transportation. The aim is to measure the ease of transportation connections between land fragments.

[0036] In practical applications, the aforementioned line-of-sight analysis method can utilize topographic data such as digital elevation models (DEMs) or 3D city models to simulate line-of-sight paths and determine whether there are obstructions between two land fragments caused by buildings, terrain undulations, or other obstacles. If there are no obstructions along the line-of-sight path, a visual corridor is considered to exist. Its purpose is to identify potential visual connections and landscape permeability between land fragments.

[0037] Therefore, the associated network is constructed as a graph structure, where each land fragment is represented as an independent node, and the connecting edges between nodes carry the spatial distance information, traffic accessibility information, and visual corridor information calculated above. These information, as attributes of the connecting edges, comprehensively describe the relationships between the land fragments.

[0038] This application's approach refines the connectivity attributes between land fragments into spatial distance information, transportation accessibility information, and visual corridor information, and quantifies and evaluates each separately, thereby enabling a more comprehensive and accurate characterization of the relationships between land fragments. Specifically, spatial distance information provides a measure of physical proximity; transportation accessibility information reveals functional connectivity potential; and visual corridor information reflects landscape and perceptual connectivity. By using these multi-dimensional connectivity attributes as properties of the connecting edges in the association network, the constructed association network can reflect the complex interactions between land fragments from multiple perspectives, providing a richer and more accurate data foundation for subsequent distributed functional pattern matching and scheme generation.

[0039] Through the aforementioned technical solutions, the constructed network not only incorporates the geometric attributes of land fragments but also reveals their intrinsic connections at the spatial, transportation, and visual levels. This multi-dimensional and refined identification of connectivity attributes and network construction significantly improves the accuracy and information richness of the network, enabling subsequent distributed functional pattern matching to be based on more realistic and comprehensive relationships between land fragments. This, in turn, helps generate more feasible and practically relevant land development solutions.

[0040] In some preferred embodiments, the distributed functional pattern library includes multiple distributed functional patterns and their functional unit composition information, land use requirement information of the functional units, and collaborative relationship information between the functional units; Step A4 includes: A401. Extract the functional unit composition information, land use requirement information, and collaborative relationship information between functional units of each distributed functional mode in the distributed functional mode library; A402. For each distributed functional pattern in the distributed functional pattern library, based on the geometric attributes of each node and the attributes of the connecting edges in the associated network, combined with the functional unit composition information, land use requirement information of the functional units and collaborative relationship information between the functional units, identify the node group that matches the distributed functional pattern from the associated network. If the match is successful, add the distributed functional pattern to the candidate pattern set. A403. Combine the node groups corresponding to the distributed functional modes in the candidate mode set to obtain an initial distributed functional mode combination without land conflicts; the absence of land conflicts means that there is no overlap between the node groups of the distributed functional modes in the initial distributed functional mode combination. A404. For each initial distributed functional pattern combination, evaluate the overall value of the initial distributed functional pattern combination based on the functional unit composition information of the distributed functional patterns contained in the initial distributed functional pattern combination; A405. Select at least one initial combination of distributed functional patterns as the applicable combination of distributed functional patterns based on the comprehensive value.

[0041] In step A401, before executing the matching process, the system first extracts the internal structure information of each distributed functional pattern from the preset distributed functional pattern library.

[0042] In step A402, for each distributed functional pattern in the distributed functional pattern library, the system attempts to find matching land fragment node groups in the constructed association network. This matching process is multi-dimensional: First, based on the functional unit composition information and land use requirements of the distributed functional pattern, the system filters out land fragment nodes in the association network that meet these requirements in terms of geometric attributes (such as area and shape). For example, if a functional unit requires a regular plot of land of at least 500 square meters, only land fragments that meet this condition will be considered. Second, the system combines the collaborative relationship information between functional units of the distributed functional pattern with the attributes of the connecting edges between land fragment nodes in the association network (such as spatial distance information, traffic accessibility information, and visual corridor information) to evaluate whether these filtered combinations of land fragment nodes can meet the collaborative requirements between functional units. For example, if two functional units require close spatial proximity, the system checks whether the spatial distance between the corresponding land fragment nodes is small enough. If one or more land fragment node groups are found whose internal structure and interrelationships are highly consistent with the functional unit composition, land use requirements and collaborative relationships of a certain distributed functional mode, then the distributed functional mode is considered to be a successful match, and it is added to the candidate mode set along with the corresponding node groups.

[0043] In step A403, after identifying the matching relationships between individual distributed functional patterns and land fragment nodes, the next step is to explore the possibility of combining these patterns on the same original land parcel. The system will arrange and combine the matched distributed functional patterns and their corresponding land fragment node groups in the candidate pattern set. A core constraint during the combination process is "no land conflict," meaning that there can be no overlap or intersection between the land fragment node groups occupied by any two combined distributed functional patterns. This implies that a land fragment cannot be simultaneously allocated to two different distributed functional patterns. In this way, it can be ensured that the generated initial combinations of distributed functional patterns are spatially feasible, avoiding resource contention and planning conflicts.

[0044] In step A404, for each initial distributed functional pattern combination selected through the waste land conflict screening, the system performs a comprehensive value assessment. This assessment is based on the functional unit composition information of each distributed functional pattern included in the combination. For example, a combination may include a "residential pattern" and a "commercial pattern." The system calculates the potential value of the combination in economic, social, and environmental aspects based on the residential units, retail units, etc., contained in these patterns, combined with preset value weights (e.g., residential units may bring residential value, and commercial units bring economic value). This step aims to quantify the advantages and disadvantages of different combination schemes, providing a basis for subsequent optimization.

[0045] In step A405, after completing the comprehensive value assessment of all initial distributed functional pattern combinations without land use conflicts, the system will sort or filter these assessment results. Typically, a comprehensive value threshold is set to select combinations with a comprehensive value exceeding the threshold, or several combinations with the highest comprehensive values ​​are selected and determined as the ultimately applicable distributed functional pattern combinations. These selected combinations represent feasible land development directions that can achieve high overall value under current land fragmentation conditions.

[0046] This application's solution effectively addresses the potential issues of insufficient accuracy and poor practicality in the matching process of basic schemes by defining the distributed functional pattern library in detail and refining step A4. Specifically, by explicitly including information on functional unit composition, land use requirements, and collaborative relationships between functional units in the distributed functional pattern library, the matching process can more comprehensively consider the internal structure and external adaptability of the patterns. Step A401 ensures that all necessary pattern information is fully extracted before matching. Step A402 then utilizes this detailed information, combined with the geometric and connectivity attributes of land fragments in the associated network, to perform refined matching, thereby identifying land fragment node groups that truly match the requirements of the distributed functional patterns and avoiding mismatches or omissions that may result from simple matching. Furthermore, step A403, by introducing the constraint of "no land use conflict," ensures the spatial feasibility of combining multiple distributed functional patterns and avoids planning contradictions. Finally, steps A404 and A405, through comprehensive value assessment and optimization of the combined schemes, ensure that the final selected distributed functional pattern combination is not only feasible but also maximizes the overall value of the land, thus providing high-quality input for subsequent scheme generation.

[0047] Through the aforementioned technical solutions, this application significantly improves the accuracy and practicality of land development plan recommendations. By enriching the content of the distributed functional pattern library and refining the matching process, the system can more fully consider the actual conditions of land fragments and the inherent needs of functional patterns when identifying applicable combinations of distributed functional patterns, including land use requirements and the synergistic relationships between functional units. This not only improves the success rate and accuracy of matching but also ensures that the generated combinations of distributed functional patterns are spatially conflict-free and have high comprehensive value. Therefore, this application can provide more targeted, efficient, and economically, socially, and environmentally beneficial land development plan recommendations for fragmented land, effectively promoting the optimal allocation and sustainable use of land resources.

[0048] Preferably, step A402 may include the steps performed for each distributed functional pattern in the distributed functional pattern library: B1. Based on the functional unit composition information and land use requirement information of the functional units in the distributed functional mode, and combined with the geometric attributes of each node, select a set of nodes from the associated network that meet the land use requirements of each functional unit. B2. Based on the collaborative relationship information between the functional units of the distributed functional mode and the attributes of the connection edges between each node, the nodes in the node set are combined to form multiple candidate node groups that match the collaborative relationship between the functional units. B3. For each candidate node group, evaluate its matching degree with the distributed functional pattern; B4. If there is a candidate node group whose matching degree reaches the preset matching degree threshold, then the distributed function mode is determined to be successfully matched, and the corresponding candidate node group is taken as the node group that matches the distributed function mode, and the distributed function mode is added to the candidate mode set.

[0049] Step B1 aims to initially filter out land fragments that do not meet the basic land use conditions of the functional units. The functional unit composition information describes the various functional units required by the distributed functional model, while the land use requirement information details the geometric attributes of the land parcels required for each functional unit. By comparing these land use requirements with the geometric attributes of each land fragment node in the associated network, land fragments that meet the basic requirements of specific functional units in terms of area, shape, etc., can be efficiently screened out, thus forming a preliminary set of nodes that meet the land use conditions.

[0050] In step B2, the collaborative relationship information describes the interdependence and facilitation relationships between different functional units in terms of space, transportation, function, and resources. For example, convenient transportation accessibility is required between "residential areas" and "commercial areas," and good visual corridors are required between "public green spaces" and "residential areas." The attributes of the connecting edges include spatial distance information, transportation accessibility information, and visual corridor information between land fragments. This step intelligently combines land fragments in the initially selected node set by analyzing these collaborative relationships and connection attributes. For example, if a distributed functional pattern includes three functional units A, B, and C, and high transportation accessibility is required between A and B, and proximity is required between B and C, the system will prioritize combining land fragments that meet these connection attributes, thereby forming one or more potential candidate node groups that can achieve synergistic effects between functional units.

[0051] The evaluation process in step B3 involves quantitative analysis of each formed candidate node group to determine its degree of fit with the target distributed functional pattern. The matching degree assessment comprehensively considers the degree to which the geometric attributes of the candidate node group conform to the land use requirements of the functional units, as well as the degree of conformity between the connection attributes of the nodes within the candidate node group and the collaborative relationships between the functional units. Through this comprehensive evaluation, a numerical matching degree can be obtained to measure the quality of the candidate node group.

[0052] In step B4, the preset matching threshold is a configurable parameter used to set the minimum standard for successful matching. Only when the matching degree of the candidate node group reaches or exceeds this threshold is the distributed functional mode considered to have successfully matched with the land fragment combination in the associated network. Once a match is successful, the distributed functional mode and its corresponding successfully matched candidate node group will be recorded and added to the candidate mode set, providing a basis for the subsequent generation of distributed functional integration schemes.

[0053] This application's solution effectively addresses the aforementioned issues of insufficient accuracy and efficiency in matching distributed functional patterns with land fragments by introducing a phased and refined matching evaluation mechanism. First, preliminary screening based on the land use requirements of functional units and the geometric attributes of each node ensures that each functional unit can find at least one land fragment in the associated network that meets its basic spatial needs, thus avoiding invalid matching. Second, by further considering the collaborative relationships between functional units and the attributes of the connecting edges between nodes, the selected nodes are combined to form multiple candidate node groups. This makes the matching process focus not only on the independent needs of individual functional units but also on the mutual cooperation and overall efficiency among functional units within the entire distributed functional pattern. Finally, by evaluating the matching degree of each candidate node group and setting a preset matching degree threshold, quantitative control of matching quality is achieved, ensuring that only highly compatible distributed functional pattern combinations are included in the candidate pattern set, thereby significantly improving the reliability and practicality of the matching results.

[0054] Through the above technical solution, this application can significantly improve the accuracy and efficiency of matching distributed functional patterns with land fragments. This solution not only ensures that the identified node groups can meet the land use needs of each functional unit, but more importantly, by considering the synergistic relationships between functional units and the connectivity attributes between land fragments, the resulting candidate node groups can better fit the overall operational logic of the distributed functional pattern, thereby generating land development plan suggestions with greater synergistic effects and overall value. Furthermore, the introduction of matching degree evaluation and threshold determination mechanisms makes the matching process more intelligent and automated, reduces human intervention, and improves the objectivity and reliability of the matching results, laying a solid foundation for subsequent plan generation and value assessment.

[0055] Furthermore, step B3 may include: The first matching degree is calculated based on the geometric attributes of the nodes in the candidate node group and the land use requirements of the functional units of the distributed functional mode. The second matching degree is calculated based on the attributes of the connection edges between the nodes in the candidate node group and the collaborative relationship information between the functional units of the distributed functional mode. The matching degree between the candidate node group and the distributed functional mode is calculated by combining the first matching degree and the second matching degree.

[0056] Specifically, the first matching degree refers to the degree of fit between the physical characteristics of land fragments and the land use requirements of functional units. The geometric attributes of the nodes in the candidate node group are the geometric attributes of the land fragments corresponding to those nodes. These attributes directly determine whether the land fragments can meet the physical space requirements of a specific functional unit. For example, for functional units with minimum area requirements and plot shape requirements, if the area of ​​the land fragment is less than the minimum area requirement, the first matching degree is zero. If the area of ​​the land fragment is not less than the minimum area requirement, the matching degree (e.g., similarity) between the shape characteristics of the land fragment and the plot shape requirement is calculated as the first matching degree.

[0057] Furthermore, the second matching degree refers to the degree of fit between the interrelationships between land fragments and the collaborative needs between functional units. The attributes of the connecting edges between nodes in the candidate node group include spatial distance information, transportation accessibility information, and visual corridor information. These attributes reflect the inherent connections and interaction potential between land fragments. The collaborative relationship information between functional units in the distributed functional pattern describes the interdependence and promoting effects between different functional units in terms of space, transportation, and function. For example, if two functional units in a distributed functional pattern require close collaboration and convenient transportation, the second matching degree will assess whether the transportation accessibility between corresponding land fragments in the candidate node group meets the requirements.

[0058] The first and second matching degrees can be obtained using the corresponding pre-trained matching degree recognition model.

[0059] Therefore, when calculating the matching degree between the candidate node group and the distributed functional mode, various methods can be used to combine the first matching degree and the second matching degree. For example, a weighted summation method can be used, assigning different weights according to the importance of different matching degrees in the overall evaluation; alternatively, multi-criteria decision analysis methods, such as the analytic hierarchy process (AHP) or the TOPSIS method, can be used to more comprehensively consider the impact of various factors on the matching degree. The purpose is to ensure the comprehensiveness and accuracy of the matching degree evaluation, thereby providing a reliable basis for subsequent mode selection.

[0060] This application's solution effectively addresses the potential bias of a single evaluation standard by refining the matching degree assessment into a first matching degree and a second matching degree and performing a comprehensive calculation. Specifically, the first matching degree focuses on evaluating whether the geometric attributes of land fragments as independent carrying spaces meet the land use requirements of functional units, ensuring that functional units can be physically accommodated and supported. The second matching degree, on the other hand, focuses on the interconnectivity between land fragments and the degree of fit between these interconnectivity attributes and the collaborative relationship information between functional units in a distributed functional pattern. This ensures that functional units can still operate efficiently and collaboratively after being distributed, realizing overall value. It is precisely because of this dual-dimensional assessment that the matching degree evaluation of candidate node groups and distributed functional patterns is more comprehensive and in-depth, enabling a more accurate identification of truly suitable land fragment combinations.

[0061] Through the above technical solution, this application provides a more refined and comprehensive matching evaluation method. Compared to matching that only considers a single dimension, this solution can simultaneously take into account the physical carrying capacity of land fragments and their synergistic potential, thereby significantly improving the accuracy and reliability of the matching results. This allows for more effective screening of candidate node groups that are highly compatible with the distributed functional model, avoiding resource mismatch or inefficient solutions due to insufficient evaluation. Consequently, it lays a solid foundation for generating more feasible and optimized land development schemes, improving the overall efficiency and sustainability of land resource utilization.

[0062] Preferably, step A404 may include steps performed for each initial combination of distributed functional modes: Obtain the functional unit composition information of each distributed functional mode in the initial distributed functional mode combination, as well as the preset value weight of each functional unit; Based on the functional unit composition information and the preset value weight, calculate the independent value of each functional unit in the initial distributed functional mode combination; Based on the information on the collaborative relationships between the functional units in the initial distributed functional pattern combination, evaluate the value of the collaborative gain generated by the collaborative effect of each functional unit. The combined value of the initial distributed functional pattern combination is calculated by integrating the independent value and the synergistic gain value.

[0063] Specifically, the preset value weight of each functional unit can be pre-set based on factors such as its importance in land development, market value, social benefits, or environmental benefits. For example, the weight of residential functional units may be higher than that of ordinary commercial functional units, while the weight of public green space functional units may focus on environmental and social value.

[0064] Furthermore, when calculating the independent value of each functional unit in the initial distributed functional pattern combination, a weighted summation or product operation can be performed based on the acquired functional unit composition information and preset value weights. For example, if the preset value weight of a functional unit is W, and its quantity or scale in the combination is S, then its independent value can be calculated as W*S. Here, independent value refers to the value that the functional unit can generate on its own without considering the synergistic effect with other functional units.

[0065] Assessing the synergistic gain value generated by the collaborative effects of each functional unit is crucial to this scheme. Synergistic gain value refers to the additional value generated when multiple functional units are integrated into a distributed functional pattern combination due to their interactions, complementarity, or resource sharing. This synergistic relationship information can include spatial proximity, transportation accessibility, functional complementarity, and resource sharing potential between functional units. For example, the adjacency of residential and commercial areas can increase foot traffic in the commercial area while providing convenience for residents of the residential area, thereby generating synergistic gains.

[0066] Ultimately, the overall value of the initial combination of distributed functional patterns is calculated by integrating the independent values ​​and the synergistic gain values. This typically involves summing the independent values ​​and the synergistic gain values, or using a weighted model for comprehensive evaluation. The aim is to provide a comprehensive and accurate value assessment to guide subsequent scheme selection and optimization.

[0067] This application's approach assesses the overall value of an initial distributed functional pattern combination by decomposing it into independent value and synergistic gain value, thereby providing a more comprehensive and accurate reflection of the combination's true potential. Traditionally, land development scheme valuations may focus on the independent contributions of each functional unit, neglecting the additional value generated by their interactions. By introducing the assessment of synergistic gain value, this approach can identify and quantify the added-value effects resulting from the complementarity of different functional units in terms of space, function, or resources. For example, when a residential functional unit and a commercial functional unit are rationally located on mutually accessible land fragments, they not only generate independent value but also create additional synergistic value through mutual promotion, such as improved residential convenience and increased commercial vitality. In this way, this approach avoids underestimating land development patterns with high synergistic potential, thereby guiding the generation of more efficient and higher overall value land development scheme recommendations.

[0068] The aforementioned technical solution overcomes the limitations of value assessment based solely on functional unit composition information, enabling a more accurate and comprehensive evaluation of the overall value of initial distributed functional pattern combinations. This solution effectively identifies and quantifies the gains generated by the synergistic effects between different functional units, thereby avoiding underestimation of land development patterns with high integration potential. Consequently, the generated land development plan recommendations not only consider the independent contributions of each functional unit but also fully explore their synergistic potential, significantly improving the overall efficiency and sustainability of land use and providing a more scientific and optimized decision-making basis for land development rights management.

[0069] Furthermore, the collaborative relationship information may include spatial proximity information, traffic accessibility information, functional complementarity information, and resource sharing potential information between functional units; Therefore, the step of evaluating the synergistic gain value generated by the synergistic effect of each functional unit based on the synergistic relationship information between each functional unit in the initial distributed functional pattern combination may include: From the collaborative relationship information between functional units in the initial distributed functional mode combination, extract spatial proximity information, traffic accessibility information, functional complementarity information, and resource sharing potential information between functional units; Based on the extracted spatial proximity information, the spatial synergistic gain value between functional units due to physical proximity is evaluated; Based on the extracted traffic accessibility information, evaluate the value of traffic synergy gains between functional units due to convenient transportation. Based on the extracted functional complementarity information, evaluate the complementary synergistic gain value between functional units due to functional complementarity; Based on the extracted resource sharing potential information, evaluate the value of resource synergy gains generated between functional units due to resource sharing; The synergistic gain value is obtained by combining the spatial synergistic gain value, the traffic synergistic gain value, the complementary synergistic gain value, and the resource synergistic gain value.

[0070] Specifically, collaborative relationship information is further refined into spatial proximity information, transportation accessibility information, functional complementarity information, and resource sharing potential information. Spatial proximity information refers to the degree of proximity between different functional units in geographic space, such as the straight-line distance or walking distance between two functional units. Transportation accessibility information refers to the ease with which functional units can connect via transportation networks (such as roads and public transportation lines), such as commuting time or transportation costs. Functional complementarity information refers to the degree to which different functional units complement and promote each other functionally, such as the relationship between residential areas and commercial areas, or schools and libraries. Resource sharing potential information refers to the potential for functional units to share infrastructure, public services, or natural resources, such as shared parking lots, shared green spaces, or shared energy systems.

[0071] When evaluating the value of synergistic gains, the four specific information categories mentioned above are first extracted from the synergistic relationship information between the functional units in the initial distributed functional pattern combination. Then, for each type of information, the resulting synergistic gain value is evaluated separately. For example, spatial synergistic gain value is evaluated as the convenience or efficiency improvement brought about by the physical proximity of functional units; transportation synergistic gain value is evaluated as the enhanced mobility or connectivity brought about by convenient transportation; complementary synergistic gain value is evaluated as the overall efficiency improvement brought about by functional units' functional complementarity; and resource synergistic gain value is evaluated as the cost savings or efficiency optimization brought about by resource sharing. Finally, by comprehensively considering these independent synergistic gain values, a more comprehensive and accurate synergistic gain value is obtained. The spatial synergistic gain value, transportation synergistic gain value, complementary synergistic gain value, and resource synergistic gain value can be calculated using corresponding mapping rules, function models, or deep learning models.

[0072] For example, the spatial synergy gain value can be calculated using the following distance decay model (which belongs to the function model): if d≤Dmax, then the spatial synergy gain value = W1*(1-d / Dmax); if d>Dmax, then the spatial synergy gain value = 0; where d is the spatial proximity information (specifically the straight-line distance or walking distance between two functional units), Dmax is the preset distance threshold, and W1 is the preset maximum spatial synergy gain value.

[0073] The traffic cooperation gain value can be calculated using the following time decay model (which belongs to the function model): if t≤Tmax, then the traffic cooperation gain value = W2*(1-t / Tmax); if t>Tmax, then the traffic cooperation gain value = 0; where t is the traffic accessibility information (specifically, commuting time), Tmax is the preset time threshold, and W2 is the preset maximum traffic cooperation gain value.

[0074] The complementary synergistic gain value can be calculated using the following mapping rules: Functional complementarity information is obtained by querying the complementarity score from a pre-set functional complementarity matrix based on the type of functional unit. The rows and columns of this functional complementarity matrix represent different functional unit types, and the elements in the matrix represent the complementarity score between the corresponding two types of functional units (e.g., 0-100 points). Corresponding complementary synergistic gain values ​​are pre-assigned to different preset complementary score ranges. The corresponding complementary synergistic gain value is determined based on the preset complementary score range into which the actual queried complementary score falls.

[0075] The resource synergy gain value can be calculated using the following cumulative model (a function model): Using a pre-defined benefit value lookup table (which records the benefit values ​​of various shared resource types), the corresponding benefit value is retrieved based on the shared resource type included in the resource sharing potential information. Then, the resource synergy gain value is calculated using the following formula: Resource Synergy Gain Value = f1*k1*R1 + f2*k2*R2 + ... + fn*kn*Rn, where f1 to fn are the benefit values ​​of the first to nth shared resources included in the resource sharing potential information, n is the total number of shared resource types included in the resource sharing potential information, k1 to kn are the scale (e.g., quantity or area) of the first to nth shared resources included in the resource sharing potential information, and R1 to Rn are the sharing potential values ​​of the first to nth shared resources in the resource sharing potential information (the resource sharing potential information contains several sharing potential values ​​corresponding to shared resources; the sharing potential value is a value between 0 and 1, with higher values ​​indicating greater potential).

[0076] Alternatively, for example, different spatial proximity information, traffic accessibility information, functional complementarity information, and resource sharing potential information can be evaluated in advance by experts to obtain corresponding spatial synergy gain values, traffic synergy gain values, complementary synergy gain values, and resource synergy gain values. The evaluation results of the experts are used as label values ​​to form a training dataset, and evaluation models (belonging to deep learning models) are trained to evaluate the spatial synergy gain values, traffic synergy gain values, complementary synergy gain values, and resource synergy gain values, respectively. When it is necessary to evaluate the spatial synergy gain values, traffic synergy gain values, complementary synergy gain values, and resource synergy gain values, the extracted spatial proximity information, traffic accessibility information, functional complementarity information, and resource sharing potential information are input into the corresponding evaluation models, and the evaluation results output by the evaluation models can be obtained.

[0077] This application's solution addresses the shortcomings of existing technologies in comprehensively and accurately assessing synergistic gain value by refining synergistic relationship information into spatial proximity, accessibility, functional complementarity, and resource sharing potential, and then evaluating the synergistic gain value generated by each. The quantitative analysis of multiple dimensions of synergistic effects allows for a deeper understanding of the synergistic interactions between functional units, avoiding value biases caused by single or vague evaluation criteria. This multi-dimensional evaluation mechanism more realistically reflects the comprehensive benefits that different functional units may generate in actual layout and operation, providing a solid data foundation for subsequent scheme optimization and value assessment.

[0078] The aforementioned technical solutions enable a refined and multi-dimensional assessment of the synergistic value gains of various functional units within a land development plan. This not only improves the accuracy and reliability of the value assessment, allowing the generated land development plan recommendations to more fully reflect their potential comprehensive value, but also helps identify and explore deeper synergistic potential among different functional units, thereby providing more scientific and optimized decision support for the intensive and efficient use of land and sustainable development.

[0079] In some implementations, step A5 includes: A501. Generate the functional unit allocation suggestion based on the node group corresponding to each applicable distributed functional mode combination in the matching results; A502. Based on the collaborative relationship information between the functional units of each distributed functional mode in the applicable distributed functional mode combination, and the attributes of the connection edges between the nodes in the corresponding node group, generate a connection and cooperation strategy suggestion between the functional units. A503. The functional unit allocation suggestions and connection coordination strategy suggestions of each applicable distributed functional mode combination are combined to form a candidate distributed functional integration scheme; A504. For each candidate distributed function integration scheme, evaluate the overall value of the candidate distributed function integration scheme based on the functional unit allocation suggestion and the connection coordination strategy suggestion of the candidate distributed function integration scheme; A505. Determine at least one final distributed function integration scheme from the candidate distributed function integration schemes based on the overall value.

[0080] Specifically, in step A501, the functional unit allocation suggestion refers to mapping each functional unit (e.g., residential, commercial, public service, green space, etc.) included in the applicable distributed functional pattern combination to land fragment nodes in the associated network that match the pattern combination. In step A4, during the process of determining the applicable distributed functional pattern combination, a corresponding node group has already been determined for the applicable distributed functional pattern combination. The nodes in this node group correspond one-to-one with the functional units included in the applicable distributed functional pattern; therefore, mapping can be performed directly based on this one-to-one correspondence.

[0081] In step A502, the proposed connection and coordination strategy between functional units aims to clarify the spatial relationships and functional collaborative operation modes of different functional units. This strategy is generated based on the collaborative relationship information between functional units in the applicable distributed functional pattern combination (e.g., spatial proximity, transportation accessibility, functional complementarity, resource sharing potential, etc.) and the attributes of the connection edges between corresponding land fragment nodes (e.g., spatial distance information, transportation accessibility information, visual corridor information). For example, if there is functional complementarity between residential and commercial functional units, and the corresponding land fragments have good transportation accessibility, it is possible to propose planning convenient pedestrian paths or public transportation routes to promote interaction between the two. Specifically, the connection and coordination strategy proposal can be determined based on an expert system or a connection and coordination strategy adaptation table.

[0082] In practical applications, step A503 integrates the generated functional unit allocation suggestions and connection coordination strategy suggestions to form a complete candidate distributed functional integration scheme. Each applicable combination of distributed functional modes will generate one or more candidate schemes, which are specific and actionable land development blueprints.

[0083] Further, step A504 assesses the overall value of each candidate distributed functional integration scheme. This assessment is based on the scheme's functional unit allocation recommendations and connectivity strategies, aiming to quantify the scheme's comprehensive benefits in economic, social, and environmental aspects. The assessment can employ a multi-indicator system; for example, economic benefits may include the projected rate of return on investment and land appreciation potential; social benefits may involve improvements in residents' quality of life and job creation; and environmental benefits may focus on green coverage and ecosystem service functions.

[0084] Therefore, step A505 selects at least one final distributed function integration scheme from all candidate distributed function integration schemes based on the overall value obtained from the evaluation. This selection process can be based on a preset value threshold, ranking mechanism, or decision-maker preferences to ensure that the selected scheme is optimal or best suited to specific development goals.

[0085] This application's solution effectively addresses the problem of merely identifying pattern combinations without concrete implementation guidance and value assessment by transforming abstract distributed functional pattern combinations into concrete and operable land development plans and conducting multi-dimensional evaluations. Specifically, step A501 precisely allocates functional units to identified land fragments, giving the development plan spatial entityhood. Step A502 further considers the synergistic relationships between functional units and the connectivity attributes between land fragments, ensuring the rationality and interactivity of the plan's functional layout, thus avoiding the problem of fragmented land operating independently and functionally isolated. Therefore, step A503 integrates these specific allocation and connection strategies into complete candidate plans, laying the foundation for subsequent evaluation. Subsequently, step A504 comprehensively evaluates the overall value of each candidate plan, considering not only the plan's internal structure but also potentially incorporating external factors such as economic, social, and environmental benefits, allowing for the quantification and comparison of plan merits. Finally, step A505, based on these evaluation results, can select the optimal or most suitable plan from multiple alternatives, thereby providing practically guiding suggestions for land development.

[0086] Through the aforementioned technical solution, this application transforms theoretically distributed functional model combinations into concrete and operable land development plans, significantly enhancing the practicality and feasibility of the plans. By refining the allocation and connection strategies of functional units, this plan ensures a more rational and coordinated functional layout on fragmented land, effectively avoiding disordered and inefficient land use. Furthermore, a comprehensive overall value assessment of candidate plans enables decision-makers to select land development plans that offer the best economic, social, and environmental benefits based on quantitative data and multi-dimensional considerations, thereby improving the comprehensive benefits and sustainability of land resource utilization.

[0087] Preferably, step A504 may include: Obtain the preset value weight of each functional unit in the candidate distributed functional integration scheme, as well as the attention information of each stakeholder to economic, social and environmental benefits; Based on the functional unit allocation suggestions and the connection and coordination strategy suggestions, calculate the initial evaluation values ​​of the economic, social, and environmental benefits of the candidate distributed functional integration scheme; Based on the information on the concerns of the stakeholders regarding economic, social, and environmental benefits, the initial assessment value is weighted and adjusted to obtain an adjusted assessment value that takes into account the demands of the stakeholders. Based on the adjusted evaluation values, the overall value of the candidate distributed function integration schemes is calculated.

[0088] Specifically, when evaluating the overall value of candidate distributed functional integration schemes, it is first necessary to obtain the pre-defined value weights of each functional unit within the scheme. These pre-defined value weights can be understood as the relative importance or value contribution assigned to different functional units (e.g., residential, commercial, public service, green space, etc.) under a specific land development context, aiming to quantify the inherent value of each functional unit in the overall scheme. Simultaneously, it is also necessary to obtain information on the attention given to economic, social, and environmental benefits by various stakeholders. These stakeholders may include, but are not limited to, government departments, developers, residents, and environmental organizations, whose priorities and focus on the land development scheme may differ significantly. For example, developers may prioritize economic benefits, while residents may prioritize social and environmental benefits. This attention information aims to reflect the influence or preferences of different stakeholders in the decision-making process.

[0089] Furthermore, based on the functional unit allocation suggestions and connection and coordination strategy suggestions, initial assessment values ​​of the economic, social, and environmental benefits of the candidate distributed functional integration schemes can be calculated. These initial assessment values ​​are preliminary quantifications of the schemes' performance in various dimensions. For example, economic benefits can be calculated based on indicators such as expected returns and rate of return on investment; social benefits can be assessed based on indicators such as employment opportunities and accessibility to public services; and environmental benefits can be assessed based on indicators such as ecological load and carbon emissions.

[0090] For example, to obtain an initial assessment of economic benefits, indicators such as expected returns and rate of return on investment can be estimated first. Then, these indicators are normalized and weighted summed to obtain the initial assessment of economic benefits. Specifically, existing technologies can be used to estimate expected returns and rate of return on investment, or the estimation can be performed using the following methods: First, estimate at least one of the following: residential sales or rental revenue (which can be estimated based on the planned residential building area, expected sales price, or rental price per unit in the plan); commercial sales or rental revenue (which can be estimated based on the planned commercial building area, expected sales price, or rental price per unit in the plan); office sales or rental revenue (which can be estimated based on the planned office functional unit building area and expected sales price or rental price per unit in the plan); and revenue from ancillary facilities (such as parking fees, property management fees, advertising space rentals, and other operating revenue). Calculate their sum as the expected return. Then, calculate the return on investment (ROI) using the following formula: ROI = (Expected Return - Total Project Cost) / Total Project Cost × 100%, where the total project cost can be obtained by calculating the sum of land acquisition costs, construction costs, financing costs, management fees, and taxes.

[0091] For the initial assessment of social benefits, indicators such as employment opportunities and accessibility of public services can be estimated first. Then, these indicators are normalized and weighted to obtain the initial assessment value of social benefits. Specifically, existing technologies can be used to estimate indicators such as employment opportunities and accessibility of public services, or the following methods can be employed for estimation: Estimate the number of jobs required during the project's construction phase (e.g., construction workers, engineers, etc.) and operation phase (e.g., commercial service personnel, property management personnel, office staff, etc.) (this can be estimated by referring to experience data of similar-sized and type projects, industry average employment, or labor index per unit building area) as the number of direct jobs. Estimate the number of jobs generated by the upstream and downstream industrial chains driven by the project's construction and operation (e.g., building materials supply, logistics, catering services, etc.) as the number of indirect jobs. Then calculate the sum of the number of direct and indirect jobs as the employment opportunity indicator. Using spatial analysis technology based on Geographic Information System (GIS), the proportion of various public service facilities such as education (schools), medical care (hospitals, clinics), culture (libraries, theaters), and sports (gymnasiums, fitness centers) that residents within the project area can reach within a specific walking or driving time (e.g., a 15-minute walking circle, a 30-minute driving circle) is assessed as the coverage rate. The average time or distance for residents within the project area to reach the nearest public service facility is calculated as the average accessibility time or distance. After normalizing the coverage rate and the average accessibility time or distance, a weighted sum is calculated to obtain the public service accessibility index.

[0092] For the initial assessment of environmental benefits, indicators such as ecological load and carbon emissions can be estimated first. Then, these indicators are normalized and weighted summed to obtain the initial assessment value of environmental benefits. Specifically, existing technologies can be used to estimate indicators such as ecological load and carbon emissions, or they can be estimated using the following methods: The ecological load index is obtained by calculating the area of ​​bioproductive land occupied by the project (such as farmland, forest land, wetlands, etc.), estimating the energy consumption during the construction and operation of the project, and estimating the total amount of waste generated by the project (such as solid waste, wastewater, exhaust gas, etc.). Then, the area of ​​bioproductive land occupied, energy consumption, and total amount of waste (the calculation of these quantities can use existing technology, which will not be detailed here) are normalized and weighted and calculated. The carbon dioxide equivalent emissions generated by building material production, transportation, and construction machinery operation are estimated. The carbon dioxide equivalent emissions generated by building operation (such as heating, cooling, and lighting), and resident and employee transportation are estimated. The amount of carbon absorbed by the ecosystem within the project, such as green areas and water bodies, through photosynthesis (which is a negative emission) is estimated as the carbon sink. The difference between the carbon dioxide equivalent emissions and the carbon sink (the carbon dioxide equivalent emissions and carbon sink mentioned above can be calculated using existing technology, which will not be detailed here) is calculated to obtain the carbon emission index.

[0093] Building upon this, to make the assessment results more inclusive and representative, the initial assessment values ​​need to be weighted and adjusted based on the stakeholders' levels of concern regarding economic, social, and environmental benefits. This means that the level of concern from different stakeholders will be used as weights to revise the initial assessment values, resulting in adjusted assessment values ​​that take into account the stakeholders' demands. For example, if a stakeholder has a higher level of concern regarding environmental benefits, then the initial assessment value for environmental benefits will be given a higher weight during the adjustment process.

[0094] Finally, based on the adjusted evaluation values, the overall value of the candidate distributed functional integration schemes is calculated. This overall value is a comprehensive indicator that not only reflects the scheme's performance in economic, social, and environmental aspects, but more importantly, it incorporates the intrinsic value of different functional units and the diverse demands of various stakeholders, thus providing a more comprehensive, balanced, and practically relevant evaluation result.

[0095] This application's solution effectively addresses the potential biases of traditional assessment methods by introducing multi-dimensional evaluation factors and a weighted adjustment mechanism. Specifically, by acquiring pre-defined value weights for each functional unit, the overall value assessment of the solution fully considers the inherent contributions and importance of different functional units in land development, avoiding the simplistic approach of treating all functional units the same. Simultaneously, by acquiring and utilizing information on the concerns of various stakeholders regarding economic, social, and environmental benefits, the assessment process proactively incorporates and balances the interests of different groups. After weighted adjustment based on stakeholder concerns, the initial assessment value more accurately reflects the importance of the solution to all parties across different dimensions. This ensures that the final calculated overall value is not only a judgment of technical merit but also a comprehensive reflection of social acceptance and sustainable development potential. It is precisely this multi-dimensional, multi-stakeholder-participatory assessment mechanism that enables the generated land development plan recommendations to better adapt to complex and ever-changing practical needs, improving the plan's feasibility and effectiveness.

[0096] Through the aforementioned technical solutions, this application significantly improves the accuracy and practicality of land development plan recommendations. Firstly, by introducing pre-defined value weights for functional units, the assessment of the intrinsic value of the plan becomes more refined and objective, avoiding simplistic treatment of the value of different functional units. Secondly, by considering the concerns of various stakeholders regarding economic, social, and environmental benefits, and by weighting the initial assessment values, the assessment results fully reflect diverse social needs and a balance of interests, thereby improving the social acceptance and sustainability of the plan. Consequently, the generated land development plan recommendations are not only technically feasible but also achieve a better balance in economic, social, and environmental dimensions, providing decision-makers with a more comprehensive and reliable basis. This effectively avoids plan deviations or implementation obstacles caused by a single assessment dimension or insufficient consideration of interests, ultimately promoting the optimal allocation of land resources and sustainable development.

[0097] In some preferred embodiments, a specific example is given below. Suppose there is a candidate distributed functional integration scheme whose preliminary evaluation results show an economic benefit score of 80, a social benefit score of 70, and an environmental benefit score of 60. Meanwhile, key stakeholders are identified, such as the government, developers, and surrounding residents. Assume the government is more concerned with social and environmental benefits, developers are more concerned with economic benefits, and surrounding residents have a higher concern for social and environmental benefits. When making weighted adjustments, different weights can be assigned to economic, social, and environmental benefits based on the stakeholders' level of concern for different benefit dimensions. For example, if the government and residents' concern increases the overall weight of social and environmental benefits, while the developers' concern increases the overall weight of economic benefits.

[0098] Specifically, assuming that attention is represented by attention values, the sum of attention values ​​of each stakeholder for each benefit item (economic benefits, social benefits, and environmental benefits) can be calculated, and the total attention values ​​of all stakeholders for all benefit items can be calculated. Then, the proportion of the sum of attention values ​​of each benefit item in the total can be calculated as the adjustment weight of each benefit item. Assuming that after comprehensively considering the attention of each stakeholder, the adjustment weight of economic benefits is 0.4, the adjustment weight of social benefits is 0.35, and the adjustment weight of environmental benefits is 0.25.

[0099] Therefore, the adjusted economic benefit assessment value = 80 * 0.4 = 32; The adjusted social benefit assessment value = 70 * 0.35 = 24.5; The adjusted environmental benefit assessment value = 60 * 0.25 = 15.

[0100] Finally, these adjusted assessment values ​​are combined and calculated, for example, by simple summation or weighted summation, to obtain the overall value of the candidate distributed function integration scheme. For example, the overall value = 32 + 24.5 + 15 = 71.5. In this way, the overall value assessment of the scheme not only considers its objective benefits but also incorporates the diverse demands of different stakeholders, making the assessment results more representative and valuable for decision-making.

[0101] refer to Figure 2 This application provides a land development rights management system for generating land development plan suggestions when land is fragmented due to multiple restrictions. The system includes: Data acquisition module 1 acquires the geographical boundary information of the original land parcel and the geographical information of the absolute restricted area located within the original land parcel (for details, please refer to step A1 above). Fragment identification module 2 identifies land fragments that can be developed and their geometric attributes based on the geographical boundary information of the original land parcel and the geographical information of the absolute restriction area; the geometric attributes include geographical boundary information, area and shape features (the specific process can be referred to step A2 above). Network construction module 3 is used to identify the connection attributes between any two land fragments based on the geometric attributes of the land fragments, and to establish an association network describing the relationships between the land fragments; the connection attributes include spatial distance information, traffic accessibility information, and visual corridor information (for details, please refer to step A3 above). Pattern matching module 4 is used to match the associated network with the distributed functional patterns in the preset distributed functional pattern library to determine the applicable distributed functional pattern combination; the distributed functional pattern is a land development model in which multiple functional units achieve overall value through decentralized layout and collaborative operation (the specific process can be referred to step A4 above). The solution generation module 5 is used to generate at least one distributed function integration solution based on the matching results, and to evaluate the overall value of the distributed function integration solution; the distributed function integration solution includes functional unit allocation suggestions and connection and cooperation strategy suggestions between functional units (the specific process can be referred to step A5 above). It is suggested that output module 6 be used to output land development plan suggestion information based on the distributed functional integration scheme and its overall value (the specific process can be referred to step A6 above).

[0102] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for managing land development rights, used to generate land development plan suggestions when land is fragmented due to multiple restrictions, characterized in that, The steps of this method include: A1. Obtain the geographic boundary information of the original land parcel and the geographic information of the absolute restricted area located within the original land parcel; A2. Based on the geographical boundary information of the original land parcel and the geographical information of the absolute restriction area, identify land fragments available for development and their geometric attributes; the geometric attributes include geographical boundary information, area, and shape features; A3. Based on the geometric attributes of the land fragments, identify the connection attributes between any two land fragments and establish an association network describing the relationships between the land fragments; the connection attributes include spatial distance information, traffic accessibility information, and visual corridor information; A4. Match the associated network with the distributed functional patterns in the preset distributed functional pattern library to determine the applicable combination of distributed functional patterns; the distributed functional pattern is a land development model in which multiple functional units achieve overall value through decentralized layout and collaborative operation. A5. Based on the matching results, generate at least one distributed function integration scheme and evaluate the overall value of the distributed function integration scheme; the distributed function integration scheme includes functional unit allocation suggestions and connection and coordination strategy suggestions between functional units; A6. Based on the aforementioned distributed functional integration scheme and its overall value, output land development plan recommendations.

2. The land development rights management method according to claim 1, characterized in that, Step A3 includes: A301. Based on the geometric properties of the land fragments, calculate the distance between the center points of any two land fragments, and use it as the spatial distance information; A302. Based on the geometric attributes and traffic network data of the land fragments, assess the traffic accessibility between any two land fragments to obtain the traffic accessibility information; A303. Based on the geometric attributes and topographic data of the land fragments, use the line-of-sight analysis method to assess whether there is an unobstructed visual corridor between any two land fragments, and obtain the visual corridor information; A304. Using the land fragments as nodes, and the spatial distance information, the traffic accessibility information, and the visual corridor information as attributes of the connecting edges between nodes, construct the association network.

3. The land development rights management method according to claim 2, characterized in that, The distributed functional pattern library contains multiple distributed functional patterns and their functional unit composition information, land use requirements information of functional units, and collaborative relationship information between functional units. Step A4 includes: A401. Extract the functional unit composition information, land use requirement information, and collaborative relationship information between functional units of each distributed functional mode in the distributed functional mode library; A402. For each distributed functional pattern in the distributed functional pattern library, based on the geometric attributes of each node and the attributes of the connecting edges in the associated network, combined with the functional unit composition information, land use requirement information of the functional units and collaborative relationship information between the functional units, identify the node group that matches the distributed functional pattern from the associated network. If the match is successful, add the distributed functional pattern to the candidate pattern set. A403. Combine the node groups corresponding to the distributed functional modes in the candidate mode set to obtain an initial distributed functional mode combination without land conflicts; the absence of land conflicts means that there is no overlap between the node groups of the distributed functional modes in the initial distributed functional mode combination. A404. For each initial distributed functional pattern combination, evaluate the overall value of the initial distributed functional pattern combination based on the functional unit composition information of the distributed functional patterns contained in the initial distributed functional pattern combination; A405. Select at least one initial combination of distributed functional patterns as the applicable combination of distributed functional patterns based on the comprehensive value.

4. A method for managing land development rights according to claim 3, characterized in that, Step A402 includes the steps performed for each distributed functional pattern in the distributed functional pattern library: Based on the functional unit composition information and land use requirements of the functional units in the distributed functional mode, and combined with the geometric attributes of each node, a set of nodes that meet the land use requirements of each functional unit is selected from the associated network. Based on the collaborative relationship information between the functional units of the distributed functional mode and the attributes of the connection edges between each node, the nodes in the node set are combined to form multiple candidate node groups that match the collaborative relationship between the functional units. For each candidate node group, evaluate its matching degree with the distributed functional pattern; If a candidate node group has a matching degree that reaches a preset matching degree threshold, the distributed function mode is determined to be successfully matched, and the corresponding candidate node group is taken as the node group that matches the distributed function mode, and the distributed function mode is added to the candidate mode set.

5. A method for managing land development rights according to claim 4, characterized in that, The step of evaluating the matching degree between each candidate node group and the distributed functional pattern includes: The first matching degree is calculated based on the geometric attributes of the nodes in the candidate node group and the land use requirements of the functional units of the distributed functional mode. The second matching degree is calculated based on the attributes of the connection edges between the nodes in the candidate node group and the collaborative relationship information between the functional units of the distributed functional mode. The matching degree between the candidate node group and the distributed functional mode is calculated by combining the first matching degree and the second matching degree.

6. A method for managing land development rights according to claim 3, characterized in that, Step A404 includes the steps performed for each initial combination of distributed functional patterns: Obtain the functional unit composition information of each distributed functional mode in the initial distributed functional mode combination, as well as the preset value weight of each functional unit; Based on the functional unit composition information and the preset value weight, calculate the independent value of each functional unit in the initial distributed functional mode combination; Based on the information on the collaborative relationships between the functional units in the initial distributed functional pattern combination, evaluate the value of the collaborative gain generated by the collaborative effect of each functional unit. The combined value of the initial distributed functional pattern combination is calculated by integrating the independent value and the synergistic gain value.

7. A method for managing land development rights according to claim 6, characterized in that, The collaborative relationship information includes spatial proximity information, traffic accessibility information, functional complementarity information, and resource sharing potential information between functional units; The step of evaluating the synergistic gain value generated by the synergistic effect of each functional unit based on the synergistic relationship information between each functional unit in the initial distributed functional pattern combination includes: From the collaborative relationship information between functional units in the initial distributed functional mode combination, extract spatial proximity information, traffic accessibility information, functional complementarity information, and resource sharing potential information between functional units; Based on the extracted spatial proximity information, the spatial synergistic gain value between functional units due to physical proximity is evaluated; Based on the extracted traffic accessibility information, evaluate the value of traffic synergy gains between functional units due to convenient transportation. Based on the extracted functional complementarity information, evaluate the complementary synergistic gain value between functional units due to functional complementarity; Based on the extracted resource sharing potential information, evaluate the value of resource synergy gains generated between functional units due to resource sharing; The synergistic gain value is obtained by combining the spatial synergistic gain value, the traffic synergistic gain value, the complementary synergistic gain value, and the resource synergistic gain value.

8. A method for managing land development rights according to claim 3, characterized in that, Step A5 includes: A501. Generate the functional unit allocation suggestion based on the node group corresponding to each applicable distributed functional mode combination in the matching results; A502. Based on the collaborative relationship information between the functional units of each distributed functional mode in the applicable distributed functional mode combination, and the attributes of the connection edges between the nodes in the corresponding node group, generate a connection and cooperation strategy suggestion between the functional units. A503. The functional unit allocation suggestions and connection coordination strategy suggestions of each applicable distributed functional mode combination are combined to form a candidate distributed functional integration scheme; A504. For each candidate distributed function integration scheme, evaluate the overall value of the candidate distributed function integration scheme based on the functional unit allocation suggestion and the connection coordination strategy suggestion of the candidate distributed function integration scheme; A505. Determine at least one final distributed function integration scheme from the candidate distributed function integration schemes based on the overall value.

9. A method for managing land development rights according to claim 8, characterized in that, Step A504 includes: Obtain the preset value weight of each functional unit in the candidate distributed functional integration scheme, as well as the attention information of each stakeholder to economic, social and environmental benefits; Based on the functional unit allocation suggestions and the connection and coordination strategy suggestions, calculate the initial evaluation values ​​of the economic, social, and environmental benefits of the candidate distributed functional integration scheme; Based on the information on the concerns of the stakeholders regarding economic, social, and environmental benefits, the initial assessment value is weighted and adjusted to obtain an adjusted assessment value that takes into account the demands of the stakeholders. Based on the adjusted evaluation values, the overall value of the candidate distributed function integration schemes is calculated.

10. A land development rights management system, used to generate land development plan suggestions when land is fragmented due to multiple restrictions, characterized in that, The system includes: The data acquisition module acquires the geographical boundary information of the original land parcel and the geographical information of the absolute restricted area located within the original land parcel; The fragment identification module identifies land fragments that can be developed and their geometric attributes based on the geographical boundary information of the original land parcel and the geographical information of the absolute restriction area; the geometric attributes include geographical boundary information, area, and shape features. The network construction module is used to identify the connection attributes between any two land fragments based on the geometric attributes of the land fragments, and to establish an association network describing the relationships between the land fragments; the connection attributes include spatial distance information, traffic accessibility information, and visual corridor information; The pattern matching module is used to match the associated network with the distributed functional patterns in the preset distributed functional pattern library to determine the applicable combination of distributed functional patterns; the distributed functional pattern is a land development model in which multiple functional units achieve overall value through decentralized layout and collaborative operation. The solution generation module is used to generate at least one distributed function integration solution based on the matching results, and to evaluate the overall value of the distributed function integration solution; the distributed function integration solution includes functional unit allocation suggestions and connection and cooperation strategy suggestions between functional units. The output module is used to output land development plan suggestions based on the distributed functional integration scheme and its overall value.

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