Land development right management method and system

Abstract

The application belongs to the field of land development management, and discloses a land development right management method and system. The method comprises the following steps: acquiring geographic information of original land blocks and absolute restriction areas, accurately identifying land fragments available for development and geometric properties thereof; identifying connection properties such as spatial distance, traffic accessibility and visual corridor between the land fragments according to the geometric properties of the land fragments, and constructing a correlation network; matching the constructed correlation network with a preset distributed function mode library to determine an applicable distributed function mode combination; generating a distributed function integration scheme according to the matching result, evaluating the overall value of the distributed function integration scheme, and finally outputting land development scheme suggestion information. Through the above technical scheme, the problem of land fragmentation caused by multiple restrictions can be effectively addressed, and a new and feasible development scheme is created for all parties in a complex land environment with fundamental interest conflicts, thereby significantly improving land utilization efficiency and project success rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of land development management, and in particular, to a land development right management method and system. BACKGROUND

[0002] In modern land development projects, coordinating multiple stakeholders such as land owners, developers, local communities, and environmental organizations is the key to success. Existing digital coordination platforms quantify the demands of each party (such as return on investment, public green space ratio, etc.), and use optimization algorithms to find the land use solution with the highest overall satisfaction. The core of this method is that it believes that most interest demands can be quantified, weighed, and adjusted through parameters to compensate for losses, thus finding a balance point in the continuous solution space.

[0003] However, this coordination mode based on quantitative weights and continuous optimization has limitations when faced with non-traditional or absolute interest demands. For example, cultural heritage protection groups may make absolute demands for the "cultural integrity" of a particular plot, meaning that the area must be preserved in its original state due to its unique historical or spiritual significance, and any form of development is not allowed. This demand cannot be simply converted into a numerical weight, because it is essentially a binary choice: complete preservation or complete destruction. In a digital coordination platform, this "absolute exclusion" Boolean constraint will directly form an insurmountable physical boundary, causing the system to be unable to "compromise" by adjusting weights, thus causing a conflict in the solution.

[0004] In addition, large-scale land development projects have a long coordination period, which may lead to new information and technological interventions. For example, an area initially marked as a "potential karst geology instability zone" may be re-evaluated due to the emergence of new, more accurate simulation methods for geological structure stability. If the simulation results show that the underground structure of this area is much more fragile than expected, any deep foundation construction may cause irreversible collapse, and this area will be transformed from a "risk zone" to an "absolute development exclusion zone" based on public safety regulations. This transformation turns an negotiable economic factor into an insurmountable physical barrier, directly negating the original high-density building plan.

[0005] More complicatedly, the combination of these non-overlapping but equally absolute exclusion zones (such as cultural protection zones, geological safety exclusion zones) can result in the remaining developable land being fragmented into disconnected, irregularly shaped pieces. For developers pursuing economies of scale and functional intensification, the core profit-making project often requires a minimum contiguous land area to achieve design efficiency and operational benefits. If any single piece of fragmented land cannot meet this requirement, even if the total amount of developable land is theoretically still available, the entire project may become unfeasible because it cannot support its core economic model. This means that the problem is no longer simply a matter of choosing between different zones, but the "connectivity" and "availability" of the entire land has been fundamentally destroyed.

[0006] Under the above-mentioned superposition of multiple, different nature and non-compensable constraints, the optimization model based on numerical weight compromise built into the existing digital coordination platform will fall into a logical deadlock. When the system tries to generate a new development plan, it will find that any plan that can meet the developer's minimum economic return weight will be judged as unfeasible because it cannot obtain a large enough contiguous land area. This is because the cultural protection zone, the geological safety exclusion zone and the possible temporary administrative freeze zone have cut the entire plot into pieces, so that any single contiguous space that can support the core project no longer exists. The core problem is no longer how to find a balance point between the weights of various parties, but how to create a new and feasible development plan for parties with fundamental interest conflicts on a land whose physical and economic properties have been completely changed by multiple, different nature and non-compensable constraints.

[0007] The existing technology needs to be improved in view of the above problems. SUMMARY

[0008] In order to solve the problems of the prior art, the present application provides a land development right management method and system, which aims to solve the problem that the optimization model based on numerical weight compromise built into the existing digital coordination platform falls into a logical deadlock under the superposition of multiple, different nature and non-compensable constraints, and cannot create a feasible development plan for parties with fundamental interest conflicts.

[0009] In a first aspect, the present application provides a land development right management method for generating land development plan suggestions when land is fragmented due to multiple restrictions. The steps of the method include:

[0010] A1. Obtain the geographical boundary information of the original plot and the geographical information of the absolute restriction area located in the original plot;

[0011] A2. identifying land parcels available for development and their geometric attributes according to 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 characteristics;

[0012] A3. identifying connection attributes between any two land parcels according to the geometric attributes of the land parcels, and establishing a connection network describing the mutual relationship between the land parcels; the connection attributes include spatial distance information, traffic accessibility information, and visual corridor information;

[0013] A4. matching the connection network with distributed functional patterns in a preset distributed functional pattern library to determine a suitable distributed functional pattern combination; the distributed functional pattern is a land development pattern in which multiple functional units are dispersedly arranged and cooperatively operated to achieve overall value;

[0014] A5. generating at least one distributed functional integration scheme according to the matching result, and evaluating the overall value of the distributed functional integration scheme; the distributed functional integration scheme includes functional unit allocation suggestions and connection and cooperation strategy suggestions between functional units;

[0015] A6. outputting land development scheme suggestion information according to the distributed functional integration scheme and its overall value.

[0016] In a second aspect, the present application provides a land development right management system for generating land development scheme suggestions when land is fragmented due to multiple restrictions, which includes:

[0017] a data acquisition module for acquiring geographical boundary information of an original land parcel and geographical information of an absolute restriction area located in the original land parcel;

[0018] a parcel identification module for identifying land parcels available for development and their geometric attributes according to 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 characteristics;

[0019] a network construction module for identifying connection attributes between any two land parcels according to the geometric attributes of the land parcels, and establishing a connection network describing the mutual relationship between the land parcels; the connection attributes include spatial distance information, traffic accessibility information, and visual corridor information;

[0020] a pattern matching module for matching the connection network with distributed functional patterns in a preset distributed functional pattern library to determine a suitable distributed functional pattern combination; the distributed functional pattern is a land development pattern in which multiple functional units are dispersedly arranged and cooperatively operated to achieve overall value;

[0021] a scheme generation module configured to generate at least one distributed function integration scheme according to the matching result, and evaluate the overall value of the distributed function integration scheme; the distributed function integration scheme comprises function unit allocation suggestions and connection matching strategy suggestions between function units;

[0022] a suggestion output module configured to output land development scheme suggestion information according to the distributed function integration scheme and the overall value thereof.

[0023] In summary, the land development right management method and system provided by the present application can accurately identify land fragments available for development and their geometric properties by obtaining geographic information of original land plots and absolute restriction areas, overcome the defects of insufficient handling of absolute exclusivity constraints in traditional methods, and ensure the accuracy of subsequent analysis; according to the geometric properties of the land fragments, the connection properties such as spatial distance, traffic accessibility and visual corridor between them are identified, and a correlation network is constructed, effectively solving the connectivity destruction problem caused by land fragmentation, providing a spatial foundation for subsequent function integration; the correlation network constructed is matched with a preset distributed function mode library to determine the applicable distributed function mode combination, which can fully utilize the potential of fragmented land, maximize the overall value through decentralized layout and collaborative operation, and break through the limitations of the traditional centralized development mode; a distributed function integration scheme is generated according to the matching result, and the overall value thereof is evaluated, and finally land development scheme suggestion information is output, which enables the provision of feasible development schemes for all parties under complex constraints, effectively coordinates the demands of different interest subjects, and avoids the logical dilemma of traditional optimization models when facing non-quantitative and absolute constraints; the above technical solutions can effectively deal with the problem of land fragmentation due to multiple restrictions, create new and feasible development schemes for all parties in a complex land environment with fundamental interest conflicts, and significantly improve land use efficiency and project success rate. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 A flowchart of a land development right management method provided by the present application.

[0025] Figure 2 A schematic diagram of a land development right management system provided by the present application.

[0026] In the figure: 1, data acquisition module; 2, fragment identification module; 3, network construction module; 4, mode matching module; 5, scheme generation module; 6, suggestion output module. DETAILED DESCRIPTION

[0027] The technical solutions in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0028] It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0029] Reference Figure 1 The present application provides a land development right management method for generating a land development scheme suggestion when land is fragmented due to multiple restrictions. The steps of the method include:

[0030] A1. Obtain geographical boundary information of an original land plot and geographical information of absolute restriction areas located within the original land plot;

[0031] A2. Identify land fragments available for development and their geometric properties according to the geographical boundary information of the original land plot and the geographical information of the absolute restriction areas; the geometric properties include geographical boundary information, area, and shape characteristics;

[0032] A3. Identify connection properties between any two land fragments according to the geometric properties of the land fragments, and establish a correlation network describing the mutual relationship between the land fragments; the connection properties include spatial distance information, traffic accessibility information, and visual corridor information;

[0033] A4. Match the correlation network with distributed functional mode in a preset distributed functional mode library to determine the applicable distributed functional mode combination; the distributed functional mode is a land development mode in which multiple functional units are dispersedly arranged and cooperatively operated to achieve overall value;

[0034] A5. Generate at least one distributed functional integration scheme according to the matching result, and evaluate the overall value of the distributed functional integration scheme; the distributed functional integration scheme includes functional unit allocation suggestions and connection and cooperation strategy suggestions between functional units;

[0035] A6. Output land development scheme suggestion information according to the distributed function integration scheme and its overall value.

[0036] The present application can effectively deal with the problem of land fragmentation under multiple restrictions by introducing the identification of land fragmentation characteristics, the construction of the correlation network, and the matching of the distributed function mode, and can provide more feasible and overall valuable scheme suggestions for land development.

[0037] Among them, "original land" refers to the initial land range to be planned for land development. "Absolute restricted area" refers to the area within the original land that is completely prohibited or strictly restricted from development due to legal regulations, planning requirements, natural conditions, or special interest demands, such as cultural heritage protection areas, geological disaster high-risk areas, and ecological sensitive areas. The geographic information of these areas is usually represented in the form of vector data (such as polygons).

[0038] Among them, "land fragment" refers to the scattered land block remaining in the original land after deducting the absolute restricted area, which can be used for development and has independent geographical boundaries. These land fragments may be irregular in shape, vary in size, and be discontinuous with each other. "Geometric attribute" is the data describing the spatial characteristics of the land fragment, including its accurate geographical boundary information (such as coordinate point sequence), area size, and shape characteristics (such as perimeter, compactness, aspect ratio, etc.).

[0039] Among them, "connection attribute" refers to an index describing the correlation degree between any two land fragments, including spatial distance information (such as the straight-line distance between the center points of the two fragments), traffic accessibility information (such as the time or cost required to pass between the two fragments through the existing transportation network), and visual corridor information (such as whether there is an unobstructed line of sight between the two fragments). These connection attributes are used to construct a "correlation network", which takes land fragments as nodes and connection attributes as edges, reflecting the mutual relationship between the fragments.

[0040] Among them, "distributed function mode" is a new type of land development mode, whose core idea is to decompose the traditionally centrally located functions (such as residence, business, public service, etc.) into multiple "functional units", and to scatter these functional units on different land fragments according to the distribution characteristics of the land fragments, and to realize the overall value through coordinated operation. For example, a "community life circle" distributed function mode may include "small commercial center", "community park", "cultural activity station" and other functional units. These functional units, although scattered, can serve the surrounding residents through reasonable connection and cooperation strategies.

[0041] Among them, the "distributed function integration scheme" is a specific land development scheme generated according to the matching results, which includes "function unit allocation suggestions" (that is, which function unit is allocated to which land fragment) and "connection and cooperation strategy suggestions between function units" (that is, how to make scattered function units work together through transportation, landscape, information, etc. Means). "Overall value" is the comprehensive evaluation of the distributed function integration scheme, which can cover economic, social, environmental and other benefits.

[0042] The method can be implemented in a geographic information system (GIS) platform or a special land planning software environment that can process geographic spatial data, perform spatial analysis, support network construction and algorithm operation, and finally output land development scheme suggestion information in the form of visualization or report.

[0043] The present application proposes a land development right management method, the core of which is to effectively deal with the problem of land fragmentation and generate a feasible land development scheme suggestion. This method is realized through a series of steps, and each main feature will be described in detail below.

[0044] First, in step A1, the geographical boundary information of the original land plot and the geographical information of the absolute restriction area located in the original land plot need to be obtained. The geographical boundary information can be obtained in many ways, for example, it can be imported from existing geographic information system databases, which usually contain detailed land ownership boundary data; or it can be interpreted and digitized through high-precision satellite remote sensing images, combined with field measurement data for calibration to ensure the accuracy of the boundary information. For the geographical information of the absolute restriction area, its acquisition method is similar to that of the geographical boundary information of the original land plot, which can be extracted from official data sources such as government-issued planning maps, ecological protection zone maps, geological disaster assessment reports, etc. and digitized into processable geographic spatial data. For example, an urban renewal project, its original land plot may be an old industrial area, and the absolute restriction area contained therein may be a protected historical building group or an underground pipeline dense area.

[0045] Secondly, in step A2, land parcels available for development and their geometric properties are identified according to the geographical boundary information of the original land parcel and the geographical information of the absolute restriction area. This step usually involves geospatial analysis operations. For example, the part of the original land parcel that overlaps with the absolute restriction area can be removed by the "erase" or "difference set" function in GIS software, resulting in the remaining developable area. These remaining areas are often irregular and separated from each other, i.e., land parcels. After identifying the land parcels, their geometric properties need to be further extracted, including geographical boundary information (i.e., the exact outline of each parcel), area (obtained by calculating the polygon area of the parcel), and shape features. The extraction of shape features can use various indicators, such as the ratio of the perimeter to the area to assess the compactness of the parcel, or the aspect ratio and directionality determined by the principal axis analysis. For example, an original land parcel divided by a river and a highway may result in multiple land parcels with different shapes and areas after processing, some of which may be long and narrow, and others may be approximately circular.

[0046] Then, in step A3, the connection properties between any two land parcels are identified according to their geometric properties, and a correlation network describing the mutual relationship between land parcels is established. The identification of connection properties is the basis for building the correlation network. Spatial distance information can be obtained by calculating the Euclidean distance between the geometric center points of any two land parcels. Traffic accessibility information is more complex and can be combined with existing transportation network data (such as roads, rail transit, etc.) to evaluate the convenience of reaching one parcel from another using network analysis algorithms (such as the shortest path algorithm, service area analysis, etc.), such as driving time, walking time, or the number of public transportation transfers. Visual corridor information can be evaluated by line-of-sight analysis, i.e., considering terrain, buildings, and other obstructions, to determine whether there is an unobstructed line-of-sight between two land parcels. For example, if there is a wide green belt between two land parcels, there may be good visual corridors. After obtaining these connection properties, a correlation network is constructed with land parcels as nodes and spatial distance information, traffic accessibility information, and visual corridor information as the properties of the connection edges between nodes. This network can be a weighted graph, with edge weights reflecting the strength of the connection properties.

[0047] Subsequently, in step A4, the associated network is matched with the distributed functional patterns in the preset library of distributed functional patterns to determine the applicable distributed functional pattern combination. The library of distributed functional patterns pre-stores a plurality of land development patterns, each of which defines its contained functional unit composition information, land use requirement information of the functional units (such as minimum area, shape preference, etc.), and coordination relationship information between the functional units (such as spatial proximity, traffic convenience, functional complementarity, etc.). The matching process aims to find a combination of land fragments in the associated network that can carry these distributed functional patterns. For example, an “ecological tourism pattern” may require “visitor centers”, “ecological trails”, and “bird watching spots” and require good traffic accessibility and visual connection between these functional units. The matching algorithm will find a combination of land fragment nodes in the associated network that meets the conditions according to these requirements.

[0048] Further, in step A5, at least one distributed functional integration scheme is generated according to the matching results, and the overall value of the distributed functional integration scheme is evaluated. Once the applicable distributed functional pattern combination is determined, specific functional unit allocation suggestions can be generated, i.e., specifying which land fragment each functional unit will be placed on. At the same time, according to the coordination relationship information between the functional units in the distributed functional pattern and the connection attributes between the corresponding land fragments, connection and coordination strategy suggestions between the functional units are generated, such as suggestions to build connecting trails, set up shared transportation sites, or plan landscape corridors. Each generated distributed functional integration scheme needs to be evaluated for overall value, which can involve economic benefits (such as return on investment), social benefits (such as public service coverage, community vitality), and environmental benefits (such as ecosystem service value, carbon emissions), etc. The evaluation method can use multi-criteria decision analysis to assign weights to different benefits and make comprehensive calculations.

[0049] Finally, in step A6, land development scheme suggestion information is output according to the distributed functional integration scheme and its overall value. This step is to present the evaluation results in a user-friendly manner. The output information can include: detailed description of each distributed functional integration scheme, such as layout diagram of functional units, schematic diagram of connection and coordination strategy; overall value evaluation report of each scheme, including quantitative indicators of each benefit and comprehensive score; and advantages and disadvantages analysis and recommendation of different schemes. These information can be presented in the form of visual charts, written reports or interactive digital models, etc. so that land owners, developers, government departments and the public, etc. stakeholders can review and make decisions.

[0050] In summary, the present application effectively converts fragmented land under multiple restrictions into available resources and finds the optimal distributed functional integration scheme for these resources through the logical chain of "identifying fragments - constructing network - matching pattern - generating scheme - evaluating value". This method breaks through the bottleneck of existing technologies in dealing with absolute exclusive constraints and land fragmentation, and provides a new solution for sustainable development in complex land environments.

[0051] In some embodiments, step A3 comprises:

[0052] A301. According to the geometric properties of the land fragments, the distance between the center points of any two land fragments is calculated as the spatial distance information;

[0053] A302. According to the geometric properties of the land fragments and the traffic network data, the traffic accessibility between any two land fragments is evaluated to obtain the traffic accessibility information;

[0054] A303. According to the geometric properties of the land fragments and the terrain data, the line-of-sight analysis method is used to evaluate whether there is an unobstructed visual corridor between any two land fragments to obtain the visual corridor information;

[0055] A304. The correlation network is constructed by taking the land fragments as nodes and taking the spatial distance information, the traffic accessibility information and the visual corridor information as the attributes of the connecting edges between the nodes.

[0056] Wherein, the center point of the land fragment can be understood as the geometric center or the center of mass of the land fragment. The distance calculation can use Euclidean distance, Manhattan distance or other applicable distance measurement methods. The purpose is to quantify the physical proximity between different land fragments.

[0057] Specifically, the traffic accessibility information can be obtained by analyzing existing road network, public transportation lines, walking paths and other data, calculating the time, distance, cost or their weighted comprehensive value required to reach one land fragment from another. For example, network analysis tools in geographic information systems (GIS) can be used to simulate traffic conditions under different transportation modes. The purpose is to measure the convenience of traffic links between land fragments.

[0058] In practical applications, the line-of-sight analysis method can use digital elevation model (DEM) or three-dimensional city model and other terrain data to simulate the line-of-sight path and determine whether the two land fragments are obstructed by buildings, terrain undulations or other obstacles. If there is no obstruction on the line-of-sight path, it is considered that there is a visual corridor. The purpose is to identify the potential visual connection and landscape permeability between land fragments.

[0059] Thus, the association network is constructed as a graph structure, where each land parcel is represented as an independent node, and the connection edges between nodes carry the spatial distance information, traffic accessibility information and visual corridor information calculated above. These information as the attributes of the connection edges can comprehensively describe the mutual relationship between land parcels.

[0060] The scheme of the present application can more comprehensively and accurately depict the mutual relationship between land parcels by refining the connection attributes between land parcels into spatial distance information, traffic accessibility information and visual corridor information, and respectively quantitatively evaluating them. Specifically, the spatial distance information provides a physical proximity measure; the traffic accessibility information reveals functional contact potential; and the visual corridor information reflects landscape and perceptual connectivity. By taking these multi-dimensional connection attributes as the attributes of the connection edges in the association network, the constructed association network can reflect the complex interactions between land parcels from multiple perspectives, providing a more rich and accurate data basis for subsequent distributed functional mode matching and scheme generation.

[0061] Through the above technical scheme, the constructed association network not only contains the geometric attributes of land parcels, but also reveals their inherent connections in space, traffic and vision. This multi-dimensional and refined connection attribute recognition and network construction significantly improves the accuracy and information richness of the association network, enabling the subsequent distributed functional mode matching to be based on more realistic and comprehensive relationships between land parcels, thereby helping to generate land development scheme suggestions that are more feasible and meet actual needs.

[0062] In some preferred embodiments, the distributed functional mode library contains a plurality of distributed functional modes and their functional unit composition information, land use requirement information of functional units and coordination relationship information between functional units;

[0063] Step A4 includes:

[0064] A401. Extract the functional unit composition information, land use requirement information of functional units and coordination relationship information between functional units of each distributed functional mode in the distributed functional mode library;

[0065] A402. For each distributed functional mode in the distributed functional mode library, according to the geometric attributes of the nodes and the attributes of the connection edges in the association network, and combining the functional unit composition information, land use requirement information of functional units and coordination relationship information between functional units of the distributed functional mode, identify a node group matching the distributed functional mode from the association network, and if the matching is successful, add the distributed functional mode to the candidate mode set;

[0066] A403. The node groups corresponding to the distributed function patterns in the set of alternative patterns are combined to obtain an initial distributed function pattern combination without useless conflicts; the useless conflicts refer to that there is no intersection between the node groups of the distributed function patterns in the initial distributed function pattern combination;

[0067] A404. For each initial distributed function pattern combination, the comprehensive value of the initial distributed function pattern combination is evaluated according to the function unit composition information of the distributed function patterns included in the initial distributed function pattern combination;

[0068] A405. At least one initial distributed function pattern combination is selected as an applicable distributed function pattern combination according to the comprehensive values.

[0069] In step A401, before performing the matching process, the system will first extract the internal structure information of each distributed function pattern in detail from the preset distributed function pattern library.

[0070] In step A402, for each distributed function pattern in the distributed function pattern library, the system will try to find a land fragment node group matching it in the constructed association network. This matching process is multidimensional: first, according to the function unit composition information of the distributed function pattern and the land use requirement information of the function unit, the land fragment nodes in the association network that meet these requirements in terms of geometric properties (such as area, shape) will be filtered out. For example, if a function unit requires at least a 500 square meter regular plot, only land fragments meeting this condition will be considered. Secondly, the synergistic relationship information between the function units of the distributed function pattern and the attributes of the connection edges between the land fragment nodes in the association network (such as spatial distance information, traffic accessibility information, visual corridor information) will be combined to evaluate whether the combination of these filtered land fragment nodes can meet the synergistic requirements between the function units. For example, if close spatial proximity is required between two function units, the system will check 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 mutual relationship can highly match the function unit composition, land use requirements and synergistic relationships of a certain distributed function pattern, it is considered that the distributed function pattern is matched successfully, and it is added to the set of alternative patterns together with the corresponding node group.

[0071] In Step A403, after identifying the matching relationship between individual distributed functional patterns and land parcel nodes, the next step is to explore the combination possibilities of these patterns on the same original land block. The system will perform permutation and combination on the matched distributed functional patterns in the candidate pattern set and their corresponding land parcel node groups. During the combination process, a core constraint condition is "land conflict", i.e. there should be no overlap or intersection between the land parcel node groups occupied by any two combined distributed functional patterns. This means that a land parcel cannot be assigned to two different distributed functional patterns at the same time. In this way, it can be ensured that the generated initial distributed functional pattern combination is feasible in space, avoiding resource contention and planning conflicts.

[0072] In Step A404, for each initial distributed functional pattern combination screened out by the land conflict, the system will conduct 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 "residential patterns" and "commercial patterns", and the system will calculate the potential value of the combination in terms of economy, society, environment, etc. according to the residential units, retail units, etc. included in these patterns, combined with the preset value weights (for example, residential units may bring residential value, commercial units may bring economic value). This step aims to quantify the pros and cons of different combination schemes, providing a basis for subsequent optimization.

[0073] In Step A405, after completing the comprehensive value assessment of all initial distributed functional pattern combinations without land conflict, the system will sort or screen according to the evaluation results. Usually, a comprehensive value threshold is set to select combinations with a comprehensive value exceeding the threshold, or select several combinations with the highest comprehensive value, and determine them as the final applicable distributed functional pattern combinations. These selected combinations represent the land development direction that can achieve high overall value and is feasible under the current land fragmentation conditions.

[0074] The scheme of the present application effectively solves the problems of insufficient precision and poor practicability in the matching process of the basic scheme through detailed definition of the distributed functional pattern library and refinement of step A4. Specifically, by explicitly including functional unit composition information, functional unit land requirement information, and coordination relationship information between functional units in the distributed functional pattern library, the matching process can more comprehensively consider the internal structure and external adaptability of the pattern. Step A401 ensures that all necessary pattern information is fully extracted before matching. Step A402 uses these detailed information to conduct fine matching in combination with the geometric properties and connection properties of the land fragments in the associated network, thereby identifying the land fragment node groups that truly match the distributed functional pattern requirements and avoiding mismatching or missing matching that may occur in simple matching. Further, step A403 ensures the spatial feasibility of multiple distributed functional pattern combinations by introducing the constraint of "waste land conflict", avoiding planning conflicts. Finally, steps A404 and A405 ensure that the finally selected distributed functional pattern combination is not only feasible but also maximizes the overall value of the land, thereby providing high-quality input for subsequent scheme generation.

[0075] Through the above technical scheme, the present application can significantly improve the precision and practicability of land development scheme 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 the land fragments and the internal requirements of the functional patterns, including land requirements and coordination relationships between functional units, when identifying applicable distributed functional pattern combinations. This not only improves the success rate and accuracy of matching, but also ensures that the generated distributed functional pattern combinations are conflict-free in space and have high overall value. Thus, the present application can provide more targeted, efficient, and economically, socially, and environmentally beneficial land development scheme recommendations for fragmented land, effectively promoting the optimal allocation and sustainable use of land resources.

[0076] Preferably, step A402 can include the following steps performed for each distributed functional pattern in the distributed functional pattern library:

[0077] B1. According to the functional unit composition information and the land requirement information of the functional units of the distributed functional pattern, in combination with the geometric properties of each node, filter out a node set that meets the land requirements of each functional unit from the associated network;

[0078] B2. According to the coordination relationship information between the functional units of the distributed functional pattern and the properties of the connection edges between the nodes, combine the nodes in the node set to form multiple candidate node groups that match the coordination relationship between the functional units;

[0079] B3. For each candidate node group, evaluate its matching degree with the distributed functional pattern;

[0080] B4. If there exists a candidate node group whose matching degree reaches a preset matching degree threshold, determine that the distributed functional pattern is matched successfully, take the corresponding candidate node group as the node group matched with the distributed functional pattern, and add the distributed functional pattern into the candidate pattern set.

[0081] In step B1, the purpose is to preliminarily filter out land patches that do not meet the basic land use conditions of functional units. The functional unit composition information describes the various functional units required by the distributed functional pattern, and the land use requirement information of the functional unit specifies the geometric properties of the land patch required by each functional unit in detail. By comparing these land use requirements with the geometric properties of the nodes of the associated network, land patches that meet the basic requirements of specific functional units in terms of area, shape, etc. can be efficiently screened out, thereby forming a preliminary node set that meets the land use conditions.

[0082] In step B2, the coordination relationship information describes the mutual dependence and promotion relationship between different functional units in terms of space, traffic, function, resources, etc. For example, the "residential area" and "commercial area" need convenient traffic accessibility, and the "public green space" and "residential area" need good visual corridor. The attributes of the connection edges include spatial distance information, traffic accessibility information and visual corridor information between land patches. This step intelligently combines the land patches in the preliminary screened node set by analyzing these coordination relationships and connection attributes. For example, if a distributed functional pattern contains three functional units A, B and C, and A and B need high traffic accessibility and B and C need close distance, the system will preferentially combine land patches that meet these connection attributes, thereby forming one or more candidate node groups that can achieve the coordination effect between functional units.

[0083] In step B3, the evaluation process is a quantitative analysis of each formed candidate node group to determine its degree of fit with the target distributed functional pattern. The matching degree evaluation comprehensively considers the degree of conformity of the geometric properties of the candidate node group with the land use requirements of the functional units, and the degree of consistency of the connection attributes between the nodes in the candidate node group with the coordination relationship between the functional units. Through this comprehensive evaluation, a numerical matching degree can be obtained to measure the pros and cons of the candidate node group.

[0084] In step B4, the preset matching degree threshold is a configurable parameter for setting the minimum standard for matching success. Only when the matching degree of the candidate node group reaches or exceeds the threshold, the distributed functional mode is considered to be successfully matched with the land fragment combination in the associated network. Once the matching is successful, the distributed functional mode and its corresponding matching successful candidate node group are recorded and added to the alternative mode set, providing a basis for subsequent generation of distributed functional integration scheme.

[0085] The scheme of the present application effectively solves the problems of insufficient accuracy and efficiency in the matching process of the distributed functional mode and the land fragment by introducing a phased and refined matching evaluation mechanism. First, by preliminarily screening according to the land use requirement information of the functional units and the geometric properties of the nodes, it is ensured that each functional unit can find at least one land fragment that meets its basic spatial demand in the associated network, thereby avoiding invalid matching. Second, by further considering the coordination relationship information between functional units and the properties of the connecting edges between nodes, the screened nodes are combined to form multiple candidate node groups, which makes the matching process not only focus on the independent demand of a single functional unit, but also focus on the mutual cooperation and overall efficiency of each functional unit in the entire distributed functional mode. Finally, by evaluating the matching degree of each candidate node group and setting a preset matching degree threshold, the quantitative control of the matching quality is realized, ensuring that only highly compatible distributed functional mode combinations are included in the alternative mode set, thereby significantly improving the reliability and practicality of the matching result.

[0086] Through the above technical scheme, the present application can significantly improve the accuracy and efficiency of the matching between the distributed functional mode and the land fragment. The scheme not only ensures that the identified node group can meet the land use demand of each functional unit, but more importantly, by considering the coordination relationship between functional units and the connection properties between land fragments, the candidate node group formed can better fit the overall operation logic of the distributed functional mode, thereby generating a land development scheme suggestion with better coordination effect and overall value. In addition, the introduction of matching degree evaluation and threshold determination mechanism makes the matching process more intelligent and automated, reduces manual intervention, improves the objectivity and reliability of the matching result, and lays a solid foundation for subsequent scheme generation and value evaluation.

[0087] Further, step B3 can include:

[0088] calculating a first matching degree according to the geometric properties of the nodes of the candidate node group and the land use requirement information of the functional units of the distributed functional mode;

[0089] According to the attribute of the connection edge between the nodes of the candidate node group and the coordination relationship information between the functional units of the distributed functional mode, a second matching degree is calculated;

[0090] The matching degree of the candidate node group and the distributed functional mode is calculated by synthesizing the first matching degree and the second matching degree.

[0091] Specifically, the first matching degree refers to the degree of fit between the physical characteristics of the land fragments and the land requirements of the functional units. The geometric properties of the nodes of the candidate node group are the geometric properties of the land fragments corresponding to the nodes, which directly determine whether the land fragments can meet the physical space requirements of the specific functional units. For example, for a functional unit with minimum area requirements and plot shape requirements, if the area of the land fragment is less than the minimum area requirements, the first matching degree is zero, and if the area of the land fragment is not less than the minimum area requirements, the matching degree (e.g. similarity) of the shape characteristics of the land fragment and the plot shape requirements is calculated as the first matching degree.

[0092] Further, the second matching degree refers to the degree of fit between the mutual relationship between the land fragments and the coordination requirements between the functional units. The attributes of the connection edges between the nodes of the candidate node group include spatial distance information, traffic accessibility information, and visual corridor information, which reflect the internal connection and interaction potential between the land fragments. The coordination relationship information between the functional units of the distributed functional mode describes the mutual dependence and promotion between different functional units in terms of space, traffic, and function. For example, if two functional units in a distributed functional mode need to work closely and have convenient transportation, the second matching degree will evaluate whether the traffic accessibility between the corresponding land fragments in the candidate node group meets the requirements.

[0093] The first matching degree and the second matching degree can be identified using corresponding pre-trained matching degree identification models.

[0094] Therefore, when calculating the matching degree of the candidate node group and the distributed functional mode, various methods can be used to synthesize the first matching degree and the second matching degree. For example, a weighted summation method can be used to assign different weights according to the importance of different matching degrees in the overall evaluation; or a multi-criteria decision analysis method such as the analytic hierarchy process or the TOPSIS method can be used to more comprehensively consider the influence 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.

[0095] The scheme of the present application effectively solves the one-sidedness problem that may be caused by a single evaluation standard by refining the matching degree evaluation into a first matching degree and a second matching degree and performing comprehensive calculation. Specifically, the first matching degree focuses on evaluating whether the geometric properties of the land fragments as independent bearing spaces meet the land use requirements of the functional units, which ensures that the functional units can be physically accommodated and supported. The second matching degree focuses on the mutual connection properties between the land fragments and the degree of fit of these connection properties with the coordination relationship information between the functional units in the distributed functional mode, which ensures that the functional units can still work efficiently after being scattered and laid out, realizing the overall value. It is due to this dual-dimensional evaluation that the matching degree evaluation of the candidate node group and the distributed functional mode is more comprehensive and in-depth, and the truly suitable land fragment combination can be more accurately identified.

[0096] Through the above technical scheme, the present application can provide a more fine and comprehensive matching degree evaluation method. Compared with only considering a single dimension of matching, the present application can simultaneously take into account the physical bearing capacity of the land fragments and the coordination potential between them, thereby significantly improving the accuracy and reliability of the matching result. Thus, the candidate node group that is highly matched with the distributed functional mode can be more effectively screened out, avoiding resource mismatching or inefficient schemes caused by insufficient evaluation, thereby laying a solid foundation for generating land development scheme suggestions with higher feasibility and optimization potential, and improving the overall efficiency and sustainability of land resource utilization.

[0097] Preferably, step A404 can include the following steps performed for each initial distributed functional mode combination:

[0098] Obtaining the functional unit composition information of each distributed functional mode in the initial distributed functional mode combination, and the preset value weight of each functional unit;

[0099] According to the functional unit composition information and the preset value weight, calculating the independent value of each functional unit in the initial distributed functional mode combination;

[0100] According to the coordination relationship information between the functional units in the initial distributed functional mode combination, evaluating the coordination gain value generated by the coordination of the functional units;

[0101] Comprehensively calculating the comprehensive value of the initial distributed functional mode combination by comprehensively calculating the independent value and the coordination gain value.

[0102] Specifically, the preset value weight of each functional unit can be pre-set according to its importance in land development, market value, social benefit or environmental benefit, etc. For example, the weight of a residential functional unit can be higher than that of an ordinary commercial functional unit, and the weight of a public green functional unit can focus on environmental and social value.

[0103] Further, in calculating the individual value of each functional unit in the initial distributed functional pattern combination, a weighted sum or product operation can be performed based on the obtained 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, its individual value can be calculated as W*S. The individual value here refers to the value that the functional unit can generate on its own without considering the synergistic effect with other functional units.

[0104] Among them, evaluating the synergistic gain value generated by the synergistic effect of each functional unit is the key of the scheme. The synergistic gain value refers to the additional value generated due to the interaction, complementarity or resource sharing between multiple functional units when they are integrated into a distributed functional pattern combination. Such synergistic relationship information can include spatial proximity, traffic accessibility, functional complementarity and resource sharing potential between functional units. For example, the proximity of residential areas to commercial areas can increase the flow of commercial areas, while providing convenience for residential area residents, thereby generating synergistic gain.

[0105] Finally, the comprehensive value of the initial distributed functional pattern combination is calculated by integrating the individual value and the synergistic gain value. This usually involves adding the sum of individual values to the synergistic gain value, or using a weighted model for comprehensive evaluation. The purpose is to provide a comprehensive and accurate value assessment to guide the subsequent scheme selection and optimization.

[0106] The scheme of the present application evaluates the comprehensive value of the initial distributed functional pattern combination by decomposing it into individual value and synergistic gain value, so as to more comprehensively and accurately reflect the real potential of the combination. Traditionally, the value assessment of land development schemes may focus on the individual contribution of each functional unit, while ignoring the additional value generated by their interaction. It is because of the introduction of the evaluation of synergistic gain value that the present scheme can identify and quantify the value-added effect brought by the complementarity of different functional units in space, function or resource. For example, when a residential functional unit and a commercial functional unit are reasonably laid out on land fragments that are accessible to each other, they not only generate individual values respectively, but also generate additional synergistic values due to mutual promotion, such as improving residential convenience and increasing commercial vitality. In this way, the present scheme can avoid underestimating land development patterns with high synergistic potential, thereby guiding the generation of more efficient and higher overall value land development scheme recommendations.

[0107] By the technical solution, the limitation of value evaluation based on only function unit composition information can be overcome, and the comprehensive value evaluation of the initial distributed function mode combination is more accurate and comprehensive. The scheme can effectively identify and quantify the gain generated by the synergy between different function units, thereby avoiding underestimating the land development mode with high integration potential. Thus, the generated land development scheme recommendation information not only considers the independent contribution of each function unit, but also fully taps the synergy potential between them, significantly improving the overall efficiency and sustainability of land use, and providing a more scientific and optimized decision basis for the management of land development rights.

[0108] Further, the synergy relationship information can include spatial proximity information, traffic accessibility information, function complementarity information, and resource sharing potential information between function units.

[0109] Thus, the step of evaluating the synergy gain value generated by the synergy between function units according to the synergy relationship information between function units in the initial distributed function mode combination can include:

[0110] extracting spatial proximity information, traffic accessibility information, function complementarity, and resource sharing potential information between function units from the synergy relationship information between function units in the initial distributed function mode combination;

[0111] evaluating the spatial synergy gain value generated between function units due to physical proximity according to the extracted spatial proximity information;

[0112] evaluating the traffic synergy gain value generated between function units due to convenient transportation according to the extracted traffic accessibility information;

[0113] evaluating the complementary synergy gain value generated between function units due to function complementarity according to the extracted function complementarity information;

[0114] evaluating the resource synergy gain value generated between function units due to resource sharing according to the extracted resource sharing potential information;

[0115] comprehensively integrating the spatial synergy gain value, the traffic synergy gain value, the complementary synergy gain value, and the resource synergy gain value to obtain the synergy gain value.

[0116] ​​​​​​​​​Specifically, the synergy information is refined into spatial proximity information, traffic accessibility information, functional complementarity information, and resource sharing potential information. Among them, the spatial proximity information refers to the closeness of different functional units in geographical space, for example, the straight-line distance or the walking accessibility distance between two functional units. The traffic accessibility information refers to the convenience of functional units through the traffic network (such as roads, public transportation lines), for example, the commuting time or the transportation cost. The functional complementarity information refers to the degree of mutual complementation and mutual promotion of different functional units in function, for example, the relationship between residential areas and commercial areas, schools and libraries. The resource sharing potential information refers to the potential of functional units to share infrastructure, public services or natural resources, for example, sharing parking lots, sharing green spaces or sharing energy systems.

[0117] In the evaluation of the synergy gain value, first, the above four types of specific information are extracted from the synergy information between the functional units in the initial distributed functional mode combination. Subsequently, for each type of information, the synergy gain value generated by it is evaluated respectively. For example, the spatial synergy gain value is evaluated as the convenience or efficiency improvement brought by the physical proximity of functional units; the traffic synergy gain value is evaluated as the mobility or connectivity enhancement brought by the traffic convenience of functional units; the complementary synergy gain value is evaluated as the overall efficiency improvement generated by the functional complementarity of functional units; the resource synergy gain value is evaluated as the cost saving or efficiency optimization brought by the resource sharing of functional units. Finally, by comprehensively considering these independent synergy gain values, a more comprehensive and accurate synergy gain value is obtained. Among them, the spatial synergy gain value, the traffic synergy gain value, the complementary synergy gain value, and the resource synergy gain value can be calculated through corresponding mapping rules, function models or deep learning models.

[0118] For example, the spatial synergy gain value can be calculated by the following distance decay model (which belongs to a 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 the walking accessibility distance between two functional units), Dmax is a preset distance threshold, and W1 is a preset maximum spatial synergy gain value.

[0119] The traffic synergy gain value can be calculated by the following time decay model (which belongs to a function model): if t≤Tmax, then the traffic synergy gain value=W2*(1-t / Tmax), if t>Tmax, then the traffic synergy gain value=0; where t is the traffic accessibility information (specifically, the commuting time), Tmax is a preset time threshold, and W2 is a preset maximum traffic synergy gain value.

[0120] The complementary synergy gain value can be calculated by the following mapping rule: the functional complementarity information is used to query the complementarity score from a pre-set functional complementarity matrix, the rows and columns of the matrix represent different functional unit types, and the elements in the matrix represent the complementarity score (e.g. 0-100) between the corresponding two types of functional units; a corresponding complementary synergy gain value is pre-allocated for different pre-set complementarity score ranges, and the corresponding complementary synergy gain value is determined according to the pre-set complementarity score range into which the actual query complementarity score falls.

[0121] The resource synergy gain value can be calculated by the following accumulation model (belonging to the function model): the corresponding benefit value is queried from a pre-set benefit value query table (recording the benefit values of various shared resource types) according to the shared resource type contained in the resource sharing potential information, and then the resource synergy gain value is calculated according to the following formula: resource synergy gain value = f1*k1*R1+f2*k2*R2+…+fn*kn*Rn, wherein f1 to fn are the benefit values of the first to nth shared resources contained in the resource sharing potential information, n is the total number of shared resource types contained in the resource sharing potential information, k1 to kn are the scales (such as quantity or area, etc.) of the first to nth shared resources contained in the resource sharing potential information, and R1 to Rn are the sharing potential values of the first to nth shared resources contained in the resource sharing potential information (the resource sharing potential information contains the sharing potential values corresponding to several shared resources, the sharing potential value is a value of 0-1, and the higher the value, the greater the potential).

[0122] For example, the corresponding spatial synergy gain value, traffic synergy gain value, complementary synergy gain value, and resource synergy gain value can be pre-evaluated by expert experience for different spatial proximity information, traffic accessibility information, functional complementarity, and resource sharing potential information, and the evaluation results of the experts are used as label values to form a training data set, and evaluation models (belonging to deep learning models) for evaluating spatial synergy gain value, traffic synergy gain value, complementary synergy gain value, and resource synergy gain value are trained respectively; when it is necessary to evaluate the spatial synergy gain value, traffic synergy gain value, complementary synergy gain value, and resource synergy gain value, the extracted spatial proximity information, traffic accessibility information, functional complementarity, and resource sharing potential information are respectively input into the corresponding evaluation model, and the evaluation results output by the evaluation model can be obtained.

[0123] The scheme of the present application solves the problem of insufficient comprehensive and accurate evaluation of the synergistic gain value in the prior art by refining the synergistic relationship information into spatial proximity information, traffic accessibility information, functional complementarity information, and resource sharing potential information, and evaluating the synergistic gain value generated by each type of information respectively. Due to the quantitative analysis of multiple dimensions of synergistic effect, the understanding of the synergistic effect between functional units is more in-depth, and the value deviation caused by single or ambiguous evaluation criteria is avoided. The multi-dimensional evaluation mechanism can more truly reflect the comprehensive benefits that different functional units may generate in actual layout and operation, and provides a solid data foundation for subsequent scheme optimization and value evaluation.

[0124] Through the above technical scheme, fine and multi-dimensional evaluation of the synergistic gain value of each functional unit in the land development scheme can be realized. This not only improves the accuracy and reliability of the value evaluation, but also enables the generated land development scheme suggestion to more fully reflect its potential comprehensive value, and helps to identify and tap deeper synergistic potential between different functional units, thereby providing more scientific and optimized decision support for intensive and efficient use and sustainable development of land.

[0125] In some embodiments, step A5 comprises:

[0126] A501. generating a functional unit allocation suggestion for each applicable distributed functional mode combination in the matching result according to the corresponding node group of the distributed functional mode combination;

[0127] A502. generating a connection and cooperation strategy suggestion between the functional units according to the synergistic relationship information between the functional units of each distributed functional mode in the applicable distributed functional mode combination, and the attribute of the connection edge between each node in the corresponding node group;

[0128] A503. combining the functional unit allocation suggestion and the connection and cooperation strategy suggestion of each applicable distributed functional mode combination to form a candidate distributed functional integration scheme;

[0129] A504. for each candidate distributed functional integration scheme, evaluating the overall value of the candidate distributed functional integration scheme according to the functional unit allocation suggestion and the connection and cooperation strategy suggestion of the candidate distributed functional integration scheme;

[0130] A505. determining at least one final distributed functional integration scheme from the candidate distributed functional integration schemes according to the overall value.

[0131] Specifically, in step A501, the functional unit allocation suggestion refers to the specific mapping of each functional unit (e.g., residential, commercial, public service, green space, etc.) contained in the applicable distributed functional pattern combination to the land parcel nodes in the associated network that match the pattern combination. In the process of step A4, the corresponding node group has been determined for the applicable distributed functional pattern combination, and the nodes in the node group are one-to-one corresponding to the functional units contained in the applicable distributed functional pattern, so the mapping can be directly performed according to the one-to-one correspondence.

[0132] In step A502, the connection coordination strategy suggestion between the functional units aims to clarify the spatial relationship and functional coordination mode of different functional units. The generation of this strategy is based on the coordination relationship information (e.g., spatial proximity, traffic accessibility, functional complementarity, resource sharing potential, etc.) between the functional units in the applicable distributed functional pattern combination and the attributes (such as spatial distance information, traffic accessibility information, visual corridor information) of the connection edges between the corresponding land parcel nodes. For example, if there is functional complementarity between the residential functional unit and the commercial functional unit, and the traffic accessibility between their corresponding land parcels is good, then a convenient pedestrian path or public transportation line can be suggested to promote interaction between the two. Specifically, the connection coordination strategy suggestion can be determined based on an expert system or a connection coordination strategy adaptation table.

[0133] In actual application, step A503 integrates the functional unit allocation suggestion and the connection coordination strategy suggestion generated above to form a complete candidate distributed functional integration scheme. Each applicable distributed functional pattern combination will correspond to one or more candidate schemes, and these schemes are specific and operational land development blueprints.

[0134] Further, step A504 evaluates the overall value of each candidate distributed functional integration scheme. This evaluation is based on the functional unit allocation suggestion and the connection coordination strategy suggestion of the scheme, aiming to quantify the comprehensive benefits of the scheme in economic, social, environmental, and other aspects. The evaluation can adopt a multi-index system, for example, economic benefits can include expected return on investment, land value-added potential; social benefits can involve the improvement of residents' quality of life, the creation of employment opportunities; environmental benefits can focus on green coverage rate, ecosystem service function, etc.

[0135] Thus, step A505 selects at least one final distributed functional integration scheme from all candidate distributed functional integration schemes according to the overall value obtained by evaluation. This selection process can be based on a pre-set value threshold, a ranking mechanism, or the preferences of decision-makers, to ensure that the selected scheme is optimal or most suitable for specific development goals.

[0136] The scheme of the present application effectively solves the problem of only identifying pattern combinations without specific implementation guidance and value measurement by converting abstract distributed function pattern combinations into specific and operable land development schemes and performing multi-dimensional evaluation. Specifically, step A501 makes the development scheme have spatial entity by precisely allocating function units to the identified land fragments; step A502 further considers the synergistic relationship between function units and the connection properties between land fragments, ensuring the rationality and interactivity of the scheme in function layout, thereby avoiding the problem of fragmented land being self-governed and function isolated. Thus, step A503 integrates these specific allocation and connection strategies into complete candidate schemes, laying a foundation for subsequent evaluation. Subsequently, step A504 comprehensively evaluates the overall value of each candidate scheme, which not only considers the intrinsic composition of the scheme, but also may include external factors such as economic, social and environmental benefits, so that the pros and cons of the scheme can be quantified and compared. Finally, step A505 can select the optimal or most suitable scheme from multiple alternative schemes based on the evaluation results, thereby providing practical guidance for land development.

[0137] Through the above technical scheme, the present application can convert theoretical distributed function pattern combinations into specific and operable land development schemes, significantly improving the practicality and implementability of the scheme. The scheme ensures that the function layout on fragmented land is more reasonable and synergistic by refining the allocation and connection cooperation strategies of function units, effectively avoiding disorder and inefficiency in land use. In addition, the comprehensive overall value evaluation of the candidate scheme enables decision-makers to select a land development scheme with good economic, social and environmental benefits based on quantitative data and multi-dimensional considerations, thereby improving the comprehensive benefits and sustainability of land resource utilization.

[0138] Preferably, step A504 can include:

[0139] obtaining preset value weights of each function unit in the candidate distributed function integration scheme, and attention degree information of each stakeholder to economic benefits, social benefits and environmental benefits;

[0140] According to the function unit allocation suggestion and the connection cooperation strategy suggestion, calculating the initial evaluation values of economic benefits, social benefits and environmental benefits of the candidate distributed function integration scheme;

[0141] According to the attention degree information of each stakeholder to economic benefits, social benefits and environmental benefits, weighting and adjusting the initial evaluation values to obtain adjusted evaluation values considering the demands of stakeholders;

[0142] According to the adjusted evaluation value, the overall value of the candidate distributed functional integration scheme is comprehensively calculated.

[0143] Specifically, when evaluating the overall value of the candidate distributed functional integration scheme, first, the preset value weights of the functional units in the scheme need to be obtained. These preset value weights can be understood as the relative importance or value contribution degree of different functional units (such as residence, business, public service, green land, etc.) under a specific land development background, and the purpose is to quantify the inherent value of each functional unit in the overall scheme. At the same time, the attention degree information of each stakeholder to economic benefit, social benefit and environmental benefit needs to be obtained. Among them, the stakeholders can include but are not limited to government departments, developers, residents, environmental protection organizations, etc., and their attention points and priorities for land development schemes may differ significantly. For example, developers may pay more attention to economic benefits, while residents may pay more attention to social benefits and environmental benefits. These attention degree information aims to reflect the influence or preference of different stakeholders in the decision-making process.

[0144] Further, according to the functional unit allocation suggestion and the connection matching strategy suggestion, the initial evaluation values of the economic benefit, social benefit and environmental benefit of the candidate distributed functional integration scheme can be calculated. These initial evaluation values are the preliminary quantification of the performance of the scheme in various dimensions, for example, the economic benefit can be calculated based on expected income, return on investment rate, etc. indicators; social benefit can be evaluated based on employment opportunities, accessibility of public services, etc. indicators; environmental benefit can be evaluated based on ecological load, carbon emissions, etc. indicators.

[0145] For example, for the initial evaluation value of economic benefit, first, the expected income and return on investment rate, etc. indicators can be estimated, and then the initial evaluation value of economic benefit can be obtained by normalizing and weighting these indicators. Among them, the expected income and return on investment rate, etc. indicators can be estimated by using existing technologies, or by the following way:

[0146] First, at least one of the residential sales or rental income (which can be estimated according to the planned residential building area in the scheme, the expected sales price or rental price), commercial sales or rental income (which can be estimated according to the planned commercial building area in the scheme, the expected sales price or rental price), office sales or rental income (which can be estimated according to the planned building area of the office functional unit in the scheme and the expected sales price or rental price), ancillary facility income (such as parking lot tolls, property management fees, advertising site rental and other operating income) is estimated, and the sum of them is calculated as the expected income; the return on investment rate ROI is calculated according to the following formula: ROI = (expected income - total project cost) / total project cost x 100%, wherein the total project cost can be obtained by calculating the sum of land acquisition cost, construction cost, financing cost, management fee and tax.

[0147] For the initial evaluation value of social benefits, the indicators such as employment opportunities and public service accessibility can be estimated first, and then the initial evaluation value of social benefits can be obtained by weighting and operating the normalized indicators. The indicators such as employment opportunities and public service accessibility can be estimated by using existing technologies, or by the following methods:

[0148] Estimate the number of positions required during the construction period (such as construction workers, engineers, etc.) and the operation period (such as business service personnel, property management personnel, office personnel, etc.) (this can be estimated by referring to the experience data of similar scale and type of projects, industry average labor intensity or unit building area labor intensity), as direct employment positions, estimate the number of employment positions generated in the upstream and downstream industrial chains (such as building material supply, logistics, catering services, etc.) due to the construction and operation of the project (this usually needs to use existing economic models or input-output analysis methods to estimate), as indirect employment positions, and then calculate the sum of direct employment positions and indirect employment positions as the employment opportunity indicator. Using the spatial analysis technology of geographic information system (GIS), evaluate the proportion of residents in the project area who can reach various types of public service facilities (such as education (schools), medical care (hospitals, clinics), culture (libraries, theaters), sports (gymnasiums, fitness centers), etc.) within a certain walking or driving time (for example, 15-minute walking circle, 30-minute driving circle), as the coverage rate, calculate the average time or distance for residents in the project area to reach the nearest various types of public service facilities as the average accessibility time or distance, and then normalize and operate the coverage rate and the average accessibility time or distance to obtain the public service accessibility indicator.

[0149] For the initial evaluation value of environmental benefits, the indicators such as ecological load and carbon emissions can be estimated first, and then the initial evaluation value of environmental benefits can be obtained by weighting and operating the normalized indicators. The indicators such as ecological load and carbon emissions can be estimated by using existing technologies, or by the following methods:

[0150] The biological productive land area (e.g. farmland, woodland, wetland, etc.) occupied by the project construction is calculated, the energy consumption during the project construction and operation is estimated, and the total amount of waste (e.g. solid waste, wastewater, exhaust gas, etc.) generated by the project is estimated. Then, the occupied biological productive land area, energy consumption, and total amount of waste (the calculation of these quantities can use existing technologies, which are not described here) are normalized and weighted and operated to obtain the ecological load index. The carbon dioxide equivalent emissions generated by building material production, transportation, construction machinery operation, etc. are estimated, the carbon dioxide equivalent emissions generated by building operation (e.g. heating, cooling, lighting), resident and employee traffic, etc. are estimated, the carbon absorption amount (negative emissions) of the ecological system in the project, such as greening and water body, through photosynthesis is estimated as the carbon sink amount, and the difference between the carbon dioxide equivalent emissions and the carbon sink amount (the carbon dioxide equivalent emissions and the carbon sink amount described above can be calculated using existing technologies, which are not described here) is calculated to obtain the carbon emissions index.

[0151] On this basis, in order to make the evaluation results more inclusive and representative, it is necessary to adjust the initial evaluation value according to the attention information of each stakeholder on economic benefits, social benefits and environmental benefits. This means that the attention of different stakeholders will be used as weights to modify the initial evaluation value, so as to obtain the adjusted evaluation value considering the demands of stakeholders. For example, if a certain stakeholder pays more attention to environmental benefits, the initial evaluation value of environmental benefits will be given a higher weight in the adjustment process.

[0152] Finally, according to the adjusted evaluation value, the overall value of the candidate distributed functional integration scheme is calculated comprehensively. The overall value is a comprehensive index, which not only reflects the performance of the scheme in economy, society and environment, but more importantly, it integrates the intrinsic value of different functional units and the multi-demand of stakeholders, thereby providing a more comprehensive, balanced and actual demand-oriented evaluation result.

[0153] The scheme of the present application effectively solves the one-sidedness problem that may exist in the traditional evaluation method by introducing multi-dimensional evaluation factors and weighting adjustment mechanism. Specifically, by obtaining the preset value weight of each functional unit, the overall value evaluation of the scheme can fully consider the inherent contribution and importance of different functional units in land development, avoiding the simple and one-sided treatment of all functional units. At the same time, by obtaining and using the attention information of each stakeholder on economic benefit, social benefit and environmental benefit, the evaluation process can actively absorb and balance the interests of different groups. After the initial evaluation value is weighted and adjusted by the attention of the stakeholders, it can more accurately reflect the importance of the scheme to each party in different dimensions, so that the final calculated overall value is not only a technical judgment of advantages and disadvantages, but also a comprehensive embodiment of social acceptance and sustainable development potential. It is precisely because of this multi-dimensional and multi-subject participation evaluation mechanism that the generated land development scheme suggestion can better adapt to the complex and changing actual needs, improve the landing and effectiveness of the scheme.

[0154] Through the above technical scheme, the present application can significantly improve the evaluation accuracy and practicality of the land development scheme suggestion. First, by introducing the preset value weight of the functional unit, the evaluation of the intrinsic value of the scheme is more precise and objective, avoiding the simple treatment of the value of different functional units. Second, by considering the attention information of each stakeholder on economic benefit, social benefit and environmental benefit, and weighting and adjusting the initial evaluation value, the evaluation result can fully reflect the diversified social needs and interest balance, thereby improving the social acceptance and sustainability of the scheme. Therefore, the generated land development scheme suggestion is not only technically feasible, but also achieves a better balance in the economic, social and environmental dimensions, providing a more comprehensive and reliable basis for decision-makers, effectively avoiding the deviation or implementation resistance of the scheme due to single evaluation dimension or insufficient interest consideration, and ultimately promoting the optimal allocation and sustainable development of land resources.

[0155] In some preferred embodiments, the following is described by a specific example. Suppose there is a candidate distributed functional integration scheme, whose preliminary evaluation result shows that the economic benefit is 80 points, the social benefit is 70 points, and the environmental benefit is 60 points. At the same time, the main stakeholders are identified, such as the government, the developer and the surrounding residents. It is assumed that the government pays more attention to social benefit and environmental benefit, the developer pays more attention to economic benefit, and the surrounding residents pay more attention to social benefit and environmental benefit. When weighting and adjusting, different weights can be given to economic benefit, social benefit and environmental benefit according to the attention of these stakeholders to different benefit dimensions. For example, if the attention of the government and the residents makes the comprehensive weight of social benefit and environmental benefit be increased, and the attention of the developer makes the comprehensive weight of economic benefit be increased.

[0156] Specifically, assuming that the attention is represented by attention values, the sum of the attention values of each stakeholder for each benefit item (economic benefit, social benefit, environmental benefit) can be calculated, and the total sum of the attention values of all stakeholders for all benefit items is calculated, and then the proportion of the sum of the attention values of each benefit item in the total sum is calculated as the adjustment weight of each benefit item; Assuming that after considering the attention of each stakeholder, the adjustment weight of economic benefit is 0.4, the adjustment weight of social benefit is 0.35, and the adjustment weight of environmental benefit is 0.25.

[0157] Then, the adjusted economic benefit evaluation value = 80 * 0.4 = 32;

[0158] The adjusted social benefit evaluation value = 70 * 0.35 = 24.5;

[0159] The adjusted environmental benefit evaluation value = 60 * 0.25 = 15.

[0160] Finally, these adjusted evaluation values are integrated, for example, simple summation or weighted summation again, 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 evaluation of the scheme not only considers its objective benefits, but also integrates the multi-dimensional demands of different stakeholders, making the evaluation result more representative and decision-making reference value.

[0161] Reference Figure 2 The application provides a land development right management system for generating land development scheme suggestions when land is fragmented due to multiple restrictions. The system comprises:

[0162] A data acquisition module 1 acquires geographical boundary information of an original plot and geographical information of absolute restriction areas located within the original plot (the specific process can refer to step A1 in the foregoing) ;

[0163] A fragment identification module 2 identifies land fragments available for development and their geometric properties according to the geographical boundary information of the original plot and the geographical information of the absolute restriction areas; the geometric properties include geographical boundary information, area, and shape characteristics (the specific process can refer to step A2 in the foregoing) ;

[0164] A network construction module 3 is used to identify the connection properties between any two land fragments according to the geometric properties of the land fragments, and to establish a correlation network describing the mutual relationship between the land fragments; the connection properties include spatial distance information, traffic accessibility information, and visual corridor information (the specific process can refer to step A3 in the foregoing) ;

[0165] The mode matching module 4 is configured to match the associated network with distributed functional modes in a preset distributed functional mode library to determine an applicable distributed functional mode combination; the distributed functional mode is a land development mode in which multiple functional units are distributed and cooperatively operated to achieve an overall value (for details, refer to the step A4 in the foregoing description) ;

[0166] The scheme generation module 5 is configured to generate at least one distributed functional integration scheme according to the matching result and evaluate an overall value of the distributed functional integration scheme; the distributed functional integration scheme includes a functional unit distribution suggestion and a connection and cooperation strategy suggestion between the functional units (for details, refer to the step A5 in the foregoing description) ;

[0167] The suggestion output module 6 is configured to output land development scheme suggestion information according to the distributed functional integration scheme and the overall value thereof (for details, refer to the step A6 in the foregoing description).

[0168] The above merely describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present 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 cooperation strategy suggestions between functional units; A6. Based on the aforementioned distributed functional integration scheme and its overall value, output land development plan recommendations; 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 properties 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; 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.

2. The land development rights management method according to claim 1, 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 there is a candidate node group whose matching degree reaches the 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.

3. The land development rights management method according to claim 2, 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.

4. The land development rights management method according to claim 1, 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.

5. A method for managing land development rights according to claim 4, 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.

6. A method for managing land development rights according to claim 1, 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.

7. A method for managing land development rights according to claim 6, 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.

8. 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 recommended to output land development plan recommendations based on the distributed functional integration scheme and its overall value. When the network construction module identifies the connection attributes between any two land fragments based on their geometric attributes and establishes an association network describing the relationships between the land fragments, it performs the following: 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 properties 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; 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. When the pattern matching module matches the association network with distributed function patterns in a preset distributed function pattern library to determine the applicable combination of distributed function patterns, it performs the following: 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.

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