A natural resource space planning management system based on territorial space element data

Abstract

The application discloses a natural resource space planning management system based on territorial space element data, relates to the technical field of space planning, and through unified structure conversion and standardized processing of original space data Draw, generates standard space element data set Fstd with consistent structure specifications, and provides high-quality data support for subsequent analysis; an attribute confrontation evaluation module constructs a functional attribute vector Ex based on the standard space element data set Fstd, and further calculates a tension matrix F, so that quantitative analysis of confrontation relationships among multiple space function types is realized from the source; and a space overlap extraction module constructs a conflict unit set Cunit and a space superposition index vector Mw through space geometry analysis, ensures automatic identification of potential conflict areas and quantitative representation of overlap intensity, realizes full-dimensional quantitative evaluation of space planning conflict intensity, and achieves an automatic space conflict identification and mediation process.

Description

Technical Field

[0001] This invention relates to the field of spatial planning technology, specifically to a natural resource spatial planning management system based on land spatial element data. Background Technology

[0002] In the modern context of harmonious coexistence between human social development and natural ecology, spatial planning and resource allocation management have gradually become core components of territorial spatial governance. As a technical tool for balancing resource allocation, ecological protection, and urban development, territorial spatial planning is no longer merely a plan on paper, but a complex system involving the collaborative processing, comprehensive analysis, and dynamic feedback of multiple types of spatial data. Within this system, territorial spatial data elements, such as land use types, ecological protection red lines, permanent basic farmland boundaries, and construction land expansion boundaries, constitute the underlying support for planning decisions and supervision.

[0003] Although many regions have established national spatial information platforms and integrated various types of spatial element data, in practical applications, the identification of potential spatial functional conflicts between different elements still largely relies on traditional methods such as manual comparison and layer overlay analysis. These methods are not only inefficient but also highly dependent on the subjective judgment of professional technicians, making them prone to omissions and misjudgments.

[0004] Furthermore, most existing platforms only provide static data layer display functions, lacking the ability to deeply analyze the spatial logical relationships and functional attribute conflicts between data. For example, urban expansion trends and permanent basic farmland protection areas are contradictory, but current systems cannot effectively model and quantify such antagonistic relationships, let alone provide automated suggestions for conflict mediation or planning adjustments. The identification and measurement mechanisms for spatial conflicts have not yet formed a systematic set of tools to support them. Therefore, the lack of a quantifiable, visualized, and interventionist spatial conflict index model has become a major bottleneck in improving the intelligent capabilities of current natural resource management platforms. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a natural resource spatial planning and management system based on land and space element data, which solves the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a natural resource spatial planning management system based on territorial spatial element data, comprising a multi-source spatial data integration module, an attribute conflict assessment module, a spatial overlap extraction module, a conflict index fusion module, and a conflict level assessment module;

[0007] The multi-source spatial data integration module is used to receive raw spatial data (Draw) transmitted from the natural resource platform and remote sensing monitoring system, and then preprocess the raw spatial data (Draw) to form a standard spatial element dataset (Fstd).

[0008] The attribute adversarial assessment module analyzes multiple types of spatial elements based on the acquired standard spatial element dataset Fstd, establishes a functional attribute vector Ex, calculates the adversarial tension Y between elements on the functional attribute vector Ex, and obtains the tension matrix F.

[0009] The spatial overlap extraction module performs spatial geometric intersection analysis on the standard spatial feature dataset Fstd to identify feature pairs with overlapping relationships. Each region with overlapping relationships is marked as a conflict unit C. All conflict units are integrated to obtain a set of conflict units Cunit. Then, the spatial overlay index W(C) is calculated for each conflict unit C to form a spatial overlay index vector Mw.

[0010] The contradiction index fusion module evaluates the adaptive gap of each conflict unit C based on the acquired set of conflict units Cunit, forms the mediation adaptive gap ΔC, and then fuses it with the tension matrix F and the spatially superimposed index vector Mw to obtain the contradiction score index SCI of the conflict unit C.

[0011] The conflict level assessment module evaluates the conflict level of each conflict unit C based on the obtained conflict score index (SCI) and generates a level notification based on the conflict level.

[0012] Preferably, the multi-source spatial data integration module includes a data processing unit and a data conversion unit;

[0013] The data processing unit is used to receive raw spatial data (Draw) transmitted from the natural resources platform and remote sensing monitoring system, including land use and planning vector data (Duec) and grid remote sensing data (Draster), and then perform vector data processing and remote sensing data processing on the raw spatial data (Draw).

[0014] The original spatial data Draw is specifically Draw={Duec∪Draster};

[0015] Vector data processing unifies coordinates and clips spatial extents between land use categories and planning vector data (Duec) in the original spatial data (Draw). Remote sensing data processing unifies resolution, removes clouds and fog, selects bands, and performs interpolation repair on void areas in the original spatial data (Draw). Void area interpolation repair is performed using spatial interpolation and time-series interpolation methods.

[0016] Among them, the land use and planning vector data Duec are specifically Due = {Duec(1), Duec(2)}, Duec(1) represents the current land use map, Duec(2) represents the planning boundary map. The current land use map is specifically used for identifying the basic functional attributes of land, including urban land, farmland and forest land. The planning boundary map is used to identify urban development boundaries, ecological red lines and permanent basic farmland.

[0017] The grid remote sensing data Draster is specifically Draster={Draster(1), Draster(2), Draster(3), Draster(4)}, where Draster(1) represents topographic elevation data, Draster(2) represents multi-period remote sensing images, Draster(3) represents NDVI vegetation index, and Draster(4) represents slope map and water system and wetland distribution map.

[0018] Preferably, the data conversion unit preprocesses the acquired raw spatial data Draw. The preprocessing includes establishing a unified data field structure and functional encoding for the raw spatial data Draw, and then uniformly converting the raw spatial data Draw into a standard spatial feature dataset Fstd.

[0019] The establishment of a unified data field structure involves identifying the data types and function types in the original spatial data Draw, performing standard field extraction and structured encapsulation operations on each type of data, generating the kth spatial feature unit f(k) with the same language format, and assigning a unified spatial function code to the kth spatial feature unit f(k) by mapping a preset function type dictionary mapping table, integrating all spatial feature units f to obtain the standard spatial feature dataset Fstd;

[0020] The standard spatial feature dataset Fstd = {Fstd(1), Fstd(2), ..., Fstd(k) | k∈n}, where n represents the length of the standard spatial feature dataset;

[0021] Spatial element unit f(k) = {ID, Geometry, Type, SourceTag, Area, Timestamp, tk};

[0022] In the spatial element unit f(k), ID represents a unique identifier, generated by UUID; Geometry represents geometric data, specifically used for spatial overlay and overlap calculations, obtained by extracting the original spatial data Draw after vector data processing and remote sensing data processing; Type represents the element type, obtained by extracting the land use status map from the original spatial data Draw; SourceTag represents the data source identifier; Area represents the spatial element area, obtained by calculating the geometric data Geometry; Timestamp represents the timestamp; and tk represents the spatial function code, obtained by matching a preset function type dictionary mapping table.

[0023] Preferably, the attribute adversarial assessment module includes a function vector construction unit and a feature calculation unit;

[0024] The functional vector construction unit extracts spatial feature units f with the same spatial functional code tk based on the obtained standard spatial feature dataset Fstd, and establishes a unique corresponding functional attribute vector E(tk) for each spatial functional code tk of the spatial feature unit f. By integrating the functional attribute vectors E(tk) of each spatial functional code tk, the attribute vector set M = {E(tk)|tk∈T} is obtained, where T represents the set of spatial functional codes tk.

[0025] The functional attribute vector E(tk) is specifically defined as E(tk) = {β(tk, 1), β(tk, 2), ..., β(tk, d)}; d represents the dimension of the functional attribute vector; β(tk, i) represents the rating value on the i-th functional attribute dimension.

[0026] β(tk, i) is obtained through the following formula:

[0027]

[0028] In the formula, N(tk) represents the total number of spatial feature units f with the same spatial function code tk, and P(j, i) represents the score of the j-th spatial feature unit f in the i-th functional attribute dimension.

[0029] Preferably, the element calculation unit extracts different spatial function codes tx and ty from set T, obtains the functional attribute vector E(tx) of spatial function code tx and the functional attribute vector E(ty) of spatial function code ty respectively, calculates the functional antagonistic tension Y(tx, ty) between different spatial function codes, reflects the differences between spatial function codes tx and ty in terms of spatial utilization and control objectives, and forms the tension matrix F of spatial function code tk;

[0030] The tension matrix F is specifically as follows The tension matrix F is a k*k square matrix, and each matrix element F(tx, ty) represents the functional resistance tension Y(tx, ty) between spatial function codes tx and ty.

[0031] The functional resistance tension Y(tx, ty) is obtained through the following formula:

[0032]

[0033] In the formula, λ represents the tension adjustment coefficient, e represents the exponential function, and ||E(tx)-E(ty)|| represents the Euclidean distance between the functional attribute vector E(tx) of spatial function code tx and the functional attribute vector E(ty) of spatial function code ty.

[0034] Preferably, the spatial overlap extraction module includes an identification unit and an overlay calculation unit;

[0035] The identification unit performs spatial geometric intersection analysis on the standard spatial feature dataset Fstd. Geometric intersection analysis calculates the spatial geometric area Geom between spatial feature units f(p) and f(q) in the standard spatial feature dataset Fstd, obtains the geometric shape G(p,q) of the intersection region, and compares it with the preset minimum identification area threshold Gth to determine the overlap relationship between spatial feature units f(p) and f(q). When an overlap relationship is identified, spatial feature units f(p) and f(q) are marked as overlapping spatial feature pairs, and each overlapping spatial feature pair region is marked as a conflict unit C. All conflict units C are integrated to obtain the conflict unit set Cunit.

[0036] The conflict unit C is specifically defined as C = {f(p), f(q), G(p, q), f(p, tk)), f(q, tk)}; where f(p, tk)) represents the spatial function code tk of the spatial element unit f(p), and f(q, tk)) represents the spatial function code tk of the spatial element unit f(q);

[0037] The geometric shape G(p, q) is obtained by the definition G(p, q) = Geom(f(p)) ∩ Geom(f(q)), where Geom(f(p)) represents the spatial geometric area Geom of the spatial element unit f(p), and Geom(f(q)) represents the spatial geometric area Geom of the spatial element unit f(q); ∩ represents the intersection symbol.

[0038] Preferably, the overlay calculation unit calculates the spatial overlay index W(C) of each conflict unit C based on the acquired conflict unit set Cunit, reflecting the overlap intensity in space, and integrates the spatial overlay index W(C) of each conflict unit C to obtain the spatial overlay index vector Mw;

[0039] The spatial overlay index W(C) is obtained through the following formula:

[0040]

[0041] In the formula, W(C(r)) represents the spatial overlay index W of the r-th conflict unit C, AG(f(p), f(q)) represents the intersection area of ​​spatial element unit f(p) and spatial element unit f(q), which is specifically calculated through the geometric shape G(p, q), A(f(p)) represents the initial area of ​​spatial element unit f(p), and A(f(q)) represents the initial area of ​​spatial element unit f(q).

[0042] The intersection area AG(f(p), f(q)) is obtained by the formula AG(f(p), f(q)) = Area(G(p,q)), where Area(G(p,q)) represents the area of ​​the spatial element, specifically the spatial geometric area between spatial element unit f(p) and spatial element unit f(q). Geom obtains the geometric shape G(p,q) of the intersection region.

[0043] Preferably, the contradiction index fusion module includes an adaptive assessment unit and an index fusion unit;

[0044] The adaptive assessment unit extracts the r-th conflict unit C(r) from the acquired conflict unit set Cunit to evaluate the adaptive gap ΔC(r) of the r-th conflict unit C(r), and obtains the mediation adaptive gap ΔC by integrating all conflict units in the conflict unit set Cunit.

[0045]

[0046] In the formula, d represents the dimension of the functional attribute vector, and the subscript 1 in |E(tx)-E(ty)|1 indicates the L1 norm used.

[0047] Preferably, the index fusion unit performs fusion processing based on the obtained mediation adaptability gap △C, tension matrix F, and spatially superimposed index vector Mw to obtain the contradiction score index SCI of the r-th conflict unit C(r);

[0048] The Conflict Scoring Index (SCI) is obtained using the following formula:

[0049] SCI(C(r))=F(tx,ty)*W(C(r))*(1-ΔC(r));

[0050] In the formula, SCI(C(r)) represents the contradiction score index of the r-th conflict unit C(r), and F(tx, ty) represents the functional antagonistic tension between spatial functional codes tx and ty.

[0051] Preferably, the conflict level assessment module includes a decision generation unit;

[0052] The decision generation unit evaluates the conflict level of each conflict unit C based on the obtained conflict score index SCI. The conflict level is evaluated by comparing the conflict score index SCI with the preset upper limit threshold Smax and lower limit threshold Xmin of the conflict level to obtain the conflict level L(C(r)) of the r-th conflict unit C(r), and generates a level notification based on the conflict level L(C(r)) of the r-th conflict unit C(r).

[0053] The contradiction level L(C(r)) is obtained through the following comparison method:

[0054] When the conflict level L(C(r)) of the r-th conflict unit C(r) is greater than or equal to the upper limit threshold Smax, the conflict level L(C(r)) of the r-th conflict unit C(r) is obtained as 3, which represents the third level. This indicates that there is a planning conflict with spatial function code tk, including expansion requirements and unoccupiable restrictions. A level 3 notification is generated. After extracting the conflicting spatial function code tk and the conflict level L(C(r)) of the r-th conflict unit C(r), the notification is filled into the preset spatial planning level 3 notification template and placed at the top of the task list to be handled by relevant departments.

[0055] When the lower limit threshold Xmin < the contradiction level L(C(r)) of the r-th conflict unit C(r) < the upper limit threshold Smax, the contradiction level L(C(r)) of the r-th conflict unit C(r) is obtained as 2, which represents the second level. This indicates that there is an inconsistency between the planning objectives of the spatial function code tk and the specific structural conflict, but there is room for mediation. A second-level notification is generated. After extracting the spatial function code tk of the conflict and the contradiction level L(C(r)) of the r-th conflict unit C(r), it is filled into the preset spatial planning second-level notification template and then sent to the relevant department's pending task list for processing.

[0056] When the contradiction level L(C(r)) of the r-th conflict unit C(r) is less than the lower limit threshold Xmin, the contradiction level L(C(r)) of the r-th conflict unit C(r) is obtained as 1, indicating the first level. This indicates that there is compatibility and superposition of spatial function code tk. It suggests that natural integration, superposition guidance, coexistence design of ecological construction, and flexible use empowerment should be carried out through planning optimization. After extracting the conflicting spatial function code tk and the contradiction level L(C(r)) of the r-th conflict unit C(r), they are filled into the preset spatial planning optimization template and a notification is sent to the relevant departments' list of tasks to be optimized for further optimization.

[0057] This invention provides a natural resource spatial planning and management system based on land spatial element data, which has the following beneficial effects:

[0058] (1) The multi-source spatial data integration module realizes the unified structural transformation and standardization of the original spatial data Draw, generating a standard spatial element dataset Fstd with consistent structure and specifications, providing high-quality data support for subsequent analysis; the attribute conflict assessment module constructs the functional attribute vector Ex based on the standard spatial element dataset Fstd, and further calculates the tension matrix F, realizing the quantitative analysis of the conflict relationship between multiple spatial function types from the source; the spatial overlap extraction module constructs the conflict unit set Cunit and its spatial overlay index vector Mw through spatial geometric analysis, ensuring the automatic identification of potential conflict areas and the quantitative characterization of overlap intensity; the contradiction index fusion module integrates the tension matrix F, the spatial overlay index vector Mw and the mediation adaptability gap △C to form the contradiction score index SCI of the conflict unit, realizing the full-dimensional quantitative assessment of the intensity of spatial planning conflicts; finally, the contradiction level assessment module realizes the hierarchical judgment of conflict units based on the contradiction score index SCI, and automatically generates level notifications, realizing responsive management and proactive mediation prompts for conflict situations, achieving automated execution of spatial conflict identification and mediation processes.

[0059] (2) By distinguishing between land use and planning vector data Duec and grid remote sensing data Draster, the consistency of various heterogeneous data such as land use status map, planning boundary map, remote sensing image, topographic elevation data, NDVI vegetation index, slope map and water system wetland distribution map in terms of coordinate system, spatial range, resolution and time series is ensured. Furthermore, through the unified data field structure reconstruction and functional coding standardization of the original spatial data Draw, the system ultimately forms a standardized spatial element dataset Fstd with unified structure, consistent semantics, and standardized format. Each spatial element unit f(k) in the dataset has complete data attribute fields, ensuring that the system has a clear and traceable data foundation and multi-dimensional spatial functional semantic support in subsequent stages such as attribute conflict, spatial overlay, and contradiction fusion. This not only opens up a systematic integration channel between remote sensing monitoring data and land use planning data, but also, through the standardized structure of spatial element unit f(k) and the construction of unified spatial functional coding tk, enables the system to have a high degree of adaptability to spatial data of any time series and any region. This significantly improves the data organization capability, semantic interpretation capability, and automatic processing efficiency of natural resource spatial planning, laying a key data foundation for building a comprehensive, dynamic, and scalable spatial planning management system.

[0060] (3) Based on the adjustment adaptability gap △C, tension matrix F and spatial overlay index vector Mw, index fusion processing is performed to automatically obtain the conflict score index SCI(C(r)) of each conflict unit C(r). The decision generation unit then dynamically compares this score index with the preset upper limit threshold Smax and lower limit threshold Xmin of the conflict level to obtain the corresponding conflict level L(C(r)). The system further extracts the spatial function code tk involved in the conflict according to the level classification rules of L(C(r)), fills tk and L(C(r)) into the spatial planning notification template of the corresponding level, automatically generates the notification content of multi-level response, and distributes it to the task list or task list to be optimized of relevant departments according to the level. This realizes the fully automatic connection between conflict level judgment and spatial planning response. Unlike the traditional method of relying on static reports and manual interpretation of conflicts, it can trigger planning-level response notifications with semantic recognition capabilities and departmental docking structure, thereby greatly improving the timeliness, automation and decision-making closed-loop capability of land and space planning management in the conflict handling process. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of a natural resource spatial planning and management system based on land spatial element data according to the present invention.

[0062] Figure 2 This is a schematic diagram of the tension matrix F in a natural resource spatial planning and management system based on land spatial element data according to the present invention. Detailed Implementation

[0063] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0064] Example 1

[0065] This invention provides a natural resource spatial planning management system based on land spatial element data. Please refer to [link / reference]. Figure 1 It includes a multi-source spatial data integration module, an attribute conflict assessment module, a spatial overlap extraction module, a conflict index fusion module, and a conflict level assessment module;

[0066] The multi-source spatial data integration module is used to receive raw spatial data (Draw) transmitted from the natural resource platform and remote sensing monitoring system, and then preprocess the raw spatial data (Draw) to form a standard spatial element dataset (Fstd).

[0067] The attribute adversarial assessment module analyzes multiple types of spatial elements based on the acquired standard spatial element dataset Fstd, establishes a functional attribute vector Ex, calculates the adversarial tension Y between elements on the functional attribute vector Ex, and obtains the tension matrix F.

[0068] The spatial overlap extraction module performs spatial geometric intersection analysis on the standard spatial feature dataset Fstd to identify feature pairs with overlapping relationships. Each region with overlapping relationships is marked as a conflict unit C. All conflict units are integrated to obtain a set of conflict units Cunit. Then, the spatial overlay index W(C) is calculated for each conflict unit C to form a spatial overlay index vector Mw.

[0069] The contradiction index fusion module evaluates the adaptive gap of each conflict unit C based on the acquired set of conflict units Cunit, forms the mediation adaptive gap ΔC, and then fuses it with the tension matrix F and the spatially superimposed index vector Mw to obtain the contradiction score index SCI of the conflict unit C.

[0070] The conflict level assessment module evaluates the conflict level of each conflict unit C based on the obtained conflict score index (SCI) and generates a level notification based on the conflict level.

[0071] In this embodiment, the multi-source spatial data integration module achieves unified structural transformation and standardization of the original spatial data Draw, generating a standard spatial element dataset Fstd with consistent structural specifications, providing high-quality data support for subsequent analysis; the attribute adversarial assessment module constructs a functional attribute vector Ex based on the standard spatial element dataset Fstd, and further calculates the tension matrix F, realizing the quantitative analysis of adversarial relationships between multiple spatial function types from the source; the spatial overlap extraction module constructs a conflict unit set Cunit and its spatial overlap index vector Mw through spatial geometric analysis, ensuring the automatic identification of potential conflict areas and the quantitative characterization of overlap intensity; the contradiction index fusion module fuses the tension matrix F. The spatial overlay index vector Mw and the adjustment adaptability gap △C form the conflict scoring index SCI of the conflict unit, realizing a full-dimensional quantitative assessment of the intensity of spatial planning conflicts. Finally, the conflict level assessment module uses the conflict scoring index SCI to classify the conflict units and automatically generate level notifications, realizing responsive management and proactive mediation prompts for conflict situations. This achieves an automated spatial conflict identification and mediation process. Compared with the existing conflict discovery methods that are mainly based on manual interpretation, this not only significantly improves the objectivity, accuracy and comprehensiveness of conflict identification, but also provides a unified quantitative assessment standard and disposal basis for the delineation of "three zones and three lines", the optimization of land use and spatial governance, which has practical value and promotion potential.

[0072] Example 2

[0073] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the multi-source spatial data integration module includes a data processing unit and a data conversion unit;

[0074] The data processing unit is used to receive raw spatial data (Draw) transmitted from the natural resources platform and remote sensing monitoring system, including land use and planning vector data (Duec) and grid remote sensing data (Draster), and then perform vector data processing and remote sensing data processing on the raw spatial data (Draw).

[0075] The original spatial data Draw is specifically Draw={Duec∪Draster};

[0076] Vector data processing unifies coordinates and clips spatial extents between land use categories and planning vector data (Duec) in the original spatial data (Draw). Remote sensing data processing unifies resolution, removes clouds and fog, selects bands, and performs interpolation repair on void areas in the original spatial data (Draw). Void area interpolation repair is performed using spatial interpolation and time-series interpolation methods.

[0077] Among them, the land use and planning vector data Duec are specifically Due = {Duec(1), Duec(2)}, Duec(1) represents the current land use map, Duec(2) represents the planning boundary map. The current land use map is specifically used for identifying the basic functional attributes of land, including urban land, farmland and forest land. The planning boundary map is used to identify urban development boundaries, ecological red lines and permanent basic farmland.

[0078] The grid remote sensing data Draster is specifically Draster={Draster(1), Draster(2), Draster(3), Draster(4)}, where Draster(1) represents topographic elevation data, Draster(2) represents multi-period remote sensing images, Draster(3) represents NDVI vegetation index, and Draster(4) represents slope map and water system and wetland distribution map.

[0079] The data conversion unit preprocesses the acquired raw spatial data Draw. The preprocessing includes establishing a unified data field structure and functional encoding for the raw spatial data Draw, and then converting the raw spatial data Draw into a unified standard spatial feature dataset Fstd.

[0080] The establishment of a unified data field structure involves identifying the data types and function types in the original spatial data Draw, performing standard field extraction and structured encapsulation operations on each type of data, generating the kth spatial feature unit f(k) with the same language format, and assigning a unified spatial function code to the kth spatial feature unit f(k) by mapping a preset function type dictionary mapping table, integrating all spatial feature units f to obtain the standard spatial feature dataset Fstd;

[0081] The standard spatial feature dataset Fstd = {Fstd(1), Fstd(2), ..., Fstd(k) | k∈n}, where n represents the length of the standard spatial feature dataset;

[0082] Spatial element unit f(k) = {ID, Geometry, Type, SourceTag, Area, Timestamp, tk};

[0083] In the spatial element unit f(k), ID represents a unique identifier used for data traceability and index management, generated via UUID; Geometry represents geometric data, specifically used for spatial overlay and overlap calculations, obtained by extracting the original spatial data Draw after vector data processing and remote sensing data processing, such as polygons (land parcels and protected area boundaries), polylines (water systems and boundary lines), points (monitoring points), and grids (remote sensing image patches and DEMs); Type represents the element type, used for functional classification identification and linkage with the standardized coding field tk, obtained by extracting the land use status map from the original spatial data Draw, such as urban construction land, permanent basic farmland, ecological protection red lines, and water... The data source is identified by its source tag, specifically the platform and batch from which the element data originates. Area represents the spatial element area, used for spatial conflict calculations, obtained through geometry calculations. Timestamp represents the timestamp. tk represents the spatial function code, obtained by matching a pre-defined function type dictionary mapping table. For example: tk=LC101 represents urban construction land, tk=LC201 represents permanent basic farmland, tk=EC301 represents ecological protection red line, tk=HY401 represents water body, and tk=FL501 represents forest land.

[0084] In this embodiment, by distinguishing between land use type and planning vector data Duec and grid remote sensing data Draster, the consistency of various heterogeneous data such as land use status map, planning boundary map, remote sensing image, topographic elevation data, NDVI vegetation index, slope map and water system wetland distribution map in terms of coordinate system, spatial range, resolution and time series is ensured. Furthermore, the data conversion unit completes the unified data field structure reconstruction and functional coding standardization of the original spatial data Draw. The system ultimately forms a standardized spatial element dataset Fstd with unified structure, consistent semantics, and standardized format. Each spatial element unit f(k) in the dataset has complete data attribute fields, ensuring that the system has a clear and traceable data foundation and multi-dimensional spatial functional semantic support in subsequent stages such as attribute conflict, spatial overlay, and contradiction fusion. This not only opens up a systematic integration channel between remote sensing monitoring data and land use planning data, but also, through the standardized structure of spatial element unit f(k) and the construction of unified spatial functional coding tk, enables the system to have a high degree of adaptability to spatial data of any time series and any region. This significantly improves the data organization capability, semantic interpretation capability, and automatic processing efficiency of natural resource spatial planning, laying a key data foundation for building a comprehensive, dynamic, and scalable spatial planning management system.

[0085] Example 3

[0086] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 and Figure 2 Specifically: the attribute adversarial assessment module includes a functional vector construction unit and a feature calculation unit;

[0087] The functional vector construction unit extracts spatial feature units f with the same spatial functional code tk based on the obtained standard spatial feature dataset Fstd, and establishes a unique corresponding functional attribute vector E(tk) for each spatial functional code tk of the spatial feature unit f. By integrating the functional attribute vectors E(tk) of each spatial functional code tk, the attribute vector set M = {E(tk)|tk∈T} is obtained, where T represents the set of spatial functional codes tk.

[0088] The functional attribute vector E(tk) is specifically defined as E(tk) = {β(tk, 1), β(tk, 2), ..., β(tk, d)}; d represents the dimension of the functional attribute vector; β(tk, i) represents the rating value on the i-th functional attribute dimension.

[0089] β(tk, i) is obtained through the following formula:

[0090]

[0091] In the formula, N(tk) represents the total number of spatial feature units f with the same spatial function code tk, and P(j, i) represents the score of the j-th spatial feature unit f in the i-th functional attribute dimension.

[0092] The element calculation unit extracts different spatial function codes tx and ty from set T, obtains the functional attribute vector E(tx) of spatial function code tx and the functional attribute vector E(ty) of spatial function code ty respectively, calculates the functional antagonistic tension Y(tx, ty) between different spatial function codes, reflects the differences between spatial function codes tx and ty in terms of spatial utilization and control objectives, and forms the tension matrix F of spatial function code tk;

[0093] The tension matrix F is specifically as follows The tension matrix F is a k*k square matrix, and each matrix element F(tx, ty) represents the functional resistance tension Y(tx, ty) between spatial function codes tx and ty.

[0094] The functional resistance tension Y(tx, ty) is obtained through the following formula:

[0095]

[0096] In the formula, λ represents the tension adjustment coefficient, e represents the exponential function, and ||E(tx)-E(ty)|| represents the Euclidean distance between the functional attribute vector E(tx) of spatial function code tx and the functional attribute vector E(ty) of spatial function code ty.

[0097] Specific data example of the tension matrix F of spatial function encoding tk:

[0098] Set the space function code tk:

[0099] LC101 = Urban construction land; AG201 = Permanent basic farmland; EC301 = Ecological protection red line; FL501 = Forest land; HY401 = Water body;

[0100] Based on the five defined spatial function codes tk, a functional attribute vector is generated, and the pairwise functional antagonistic tension values ​​are calculated to generate the tension matrix F of the spatial function codes tk.

[0101]

[0102]

[0103] In this embodiment, a functional attribute semantic modeling system centered on spatial function codes tk is constructed based on the standard spatial element dataset Fstd. This module first aggregates and classifies spatial element units f with the same spatial function code tk in Fstd. Based on the functional attribute dimension index system, it calculates the functional attribute vector E(tk) corresponding to each type of tk, and integrates all functional attribute vectors into an attribute vector set M = {E(tk) | tk ∈ T}. On this basis, the element calculation unit pairs spatial function codes tx and ty in set T, calculates the degree of difference between their functional attribute vectors E(tx) and E(ty), generates functional antagonistic tension Y(tx, ty), and further constructs a tension matrix F. This k×k matrix structure comprehensively characterizes the target conflict relationship between various spatial function codes tk, realizing the quantitative modeling of attributes between spatial functions and the mathematical expression of conflict antagonistic relationships. This allows the system to automatically identify the intensity of contradictions and antagonistic trends between different spatial uses in terms of control intensity, development suitability, ecological sensitivity, and other multi-dimensional objectives without relying on subjective experience judgment. Compared to traditional static conflict analysis methods based on land type classification, this module uses spatial function coding (tk) in Fstd to perform semantic-driven attribute adversarial assessment. It constructs a quantifiable, adjustable, and dynamically evolving spatial use relationship tension expression mechanism, providing interpretable and differentiated key parameter support for subsequent spatial conflict index calculation and mediation path identification.

[0104] Example 4

[0105] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically: the spatial overlap extraction module includes an identification unit and an overlay calculation unit;

[0106] The identification unit performs spatial geometric intersection analysis on the standard spatial feature dataset Fstd. Geometric intersection analysis calculates the spatial geometric area Geom between spatial feature units f(p) and f(q) in the standard spatial feature dataset Fstd, obtains the geometric shape G(p,q) of the intersection region, and compares it with the preset minimum identification area threshold Gth to determine the overlap relationship between spatial feature units f(p) and f(q). When an overlap relationship is identified, spatial feature units f(p) and f(q) are marked as overlapping spatial feature pairs, and each overlapping spatial feature pair region is marked as a conflict unit C. All conflict units C are integrated to obtain the conflict unit set Cunit.

[0107] The conflict unit C is specifically defined as C = {f(p), f(q), G(p, q), f(p, tk)), f(q, tk)}; where f(p, tk)) represents the spatial function code tk of the spatial element unit f(p), and f(q, tk)) represents the spatial function code tk of the spatial element unit f(q);

[0108] The geometric shape G(p, q) is obtained by the definition G(p, q) = Geom(f(p)) ∩ Geom(f(q)), where Geom(f(p)) represents the spatial geometric area Geom of the spatial element unit f(p), and Geom(f(q)) represents the spatial geometric area Geom of the spatial element unit f(q); ∩ represents the intersection symbol.

[0109] The overlay computation unit calculates the spatial overlay index W(C) of each conflict unit C based on the acquired conflict unit set Cunit, reflecting the overlap intensity in space, and integrates the spatial overlay index W(C) of each conflict unit C to obtain the spatial overlay index vector Mw;

[0110] The spatial overlay index W(C) is obtained through the following formula:

[0111]

[0112] In the formula, W(C(r)) represents the spatial overlay index W of the r-th conflict unit C, AG(f(p), f(q)) represents the intersection area of ​​spatial element unit f(p) and spatial element unit f(q), which is specifically calculated through the geometric shape G(p, q), A(f(p)) represents the initial area of ​​spatial element unit f(p), and A(f(q)) represents the initial area of ​​spatial element unit f(q).

[0113] The intersection area AG(f(p), f(q)) is obtained by the formula AG(f(p), f(q)) = Area(G(p,q)), where Area(G(p,q)) represents the area of ​​the spatial element, specifically the spatial geometric area between spatial element unit f(p) and spatial element unit f(q). Geom obtains the geometric shape G(p,q) of the intersection region.

[0114] In this embodiment, automatic identification and quantitative expression of spatial geometric relationships based on the standard spatial element dataset Fstd are realized. The identification unit performs spatial geometric intersection analysis on spatial element units f(p) and f(q) in Fstd, extracts the geometric shape G(p, q) of the intersection region, and compares it with the minimum identification area threshold Gth to automatically determine whether a valid overlap relationship exists. This results in the generation of conflict units C, which are then integrated into a set of conflict units Cunit. Furthermore, the system calculates the spatial overlap index W(C) for each conflict unit C using the overlay calculation unit. The intersection area AG(f(p), f(q)) is normalized with the original areas A(f(p)) and A(f(q)) to reflect the overlap intensity of conflicting elements in spatial distribution, ultimately forming a spatial overlap index vector Mw. This breaks through the previous method of relying on manual layer comparison for spatial planning conflict identification, constructing an automatic spatial overlap extraction mechanism based on spatial elements, geometric intersection as the logical foundation, and the spatial overlap index W(C) as the quantitative indicator. The system can quickly identify and quantify the geometric interaction relationships between spatial elements with arbitrary region and arbitrary functional code tk in a large-scale, multi-source data environment. This improves the geometric accuracy, processing efficiency, and automation of conflict identification, provides solid spatial location information support for subsequent conflict index fusion and level assessment, and provides clear geometric basis and indicator foundation for spatial utilization layout optimization and boundary adjustment.

[0115] Example 5

[0116] This embodiment is an explanation based on Embodiment 4. Please refer to it. Figure 1 Specifically: The index fusion unit performs fusion processing based on the obtained mediation adaptability gap △C, tension matrix F, and spatially superimposed index vector Mw to obtain the contradiction score index SCI of the r-th conflict unit C(r);

[0117] The Conflict Scoring Index (SCI) is obtained using the following formula:

[0118] SCI(C(r))=F(tx,ty)*W(C(r))*(1-ΔC(r));

[0119] In the formula, SCI(C(r)) represents the contradiction score index of the r-th conflict unit C(r), and F(tx, ty) represents the functional antagonistic tension between spatial functional codes tx and ty.

[0120] The conflict level assessment module includes a decision generation unit;

[0121] The decision generation unit evaluates the conflict level of each conflict unit C based on the obtained conflict score index SCI. The conflict level is evaluated by comparing the conflict score index SCI with the preset upper limit threshold Smax and lower limit threshold Xmin of the conflict level to obtain the conflict level L(C(r)) of the r-th conflict unit C(r), and generates a level notification based on the conflict level L(C(r)) of the r-th conflict unit C(r).

[0122] The contradiction level L(C(r)) is obtained through the following comparison method:

[0123] When the conflict level L(C(r)) of the r-th conflict unit C(r) is greater than or equal to the upper limit threshold Smax, the conflict level L(C(r)) of the r-th conflict unit C(r) is obtained as 3, which represents the third level. This indicates that there is a planning conflict with spatial function code tk, including expansion needs and non-occupancy restrictions. For example, the land use conflict between urban construction expansion needs and permanent basic farmland protection targets. A third-level notification is generated. After extracting the spatial function code tk of the conflict and the conflict level L(C(r)) of the r-th conflict unit C(r), it is filled into the preset spatial planning third-level notification template and notified to relevant departments to be placed at the top of the pending task list for handling.

[0124] When the lower limit threshold Xmin < the contradiction level L(C(r)) of the r-th conflict unit C(r) < the upper limit threshold Smax, the contradiction level L(C(r)) of the r-th conflict unit C(r) is obtained as 2, which represents the second level. This indicates that there is an inconsistency between the planning objectives of the spatial function code tk and the specific structural conflict, but there is room for mediation. For example, general cultivated land overlaps with the secondary supporting construction area of ​​the city and general forest land overlaps with industrial land. A secondary notification is generated. After extracting the spatial function code tk of the conflict and the contradiction level L(C(r)) of the r-th conflict unit C(r), it is filled into the preset spatial planning secondary notification template and then sent to the relevant department's pending task list for handling.

[0125] When the contradiction level L(C(r)) of the r-th conflict unit C(r) is less than the lower limit threshold Xmin, the contradiction level L(C(r)) of the r-th conflict unit C(r) is obtained as 1, indicating the first level. This indicates that there is compatibility and overlapping use of spatial function code tk. It suggests that natural integration, overlapping use guidance, coexistence design of ecological construction, and flexible use empowerment suggestions should be made through planning optimization. For example, the overlap of forest land and ecological restoration area and the intersection of water area and buffer green belt. After extracting the conflict spatial function code tk and the contradiction level L(C(r)) of the r-th conflict unit C(r), they are filled into the preset spatial planning optimization template and a notification is sent to the relevant department's list of tasks to be optimized for optimization.

[0126] In this embodiment, based on the adjustment adaptability gap △C, tension matrix F and spatially superimposed index vector Mw, index fusion processing is performed to automatically obtain the contradiction score index SCI(C(r)) of each conflict unit C(r). The decision generation unit then dynamically compares the score index with the preset upper limit threshold Smax and lower limit threshold Xmin of the contradiction level to obtain the corresponding contradiction level L(C(r)) of the conflict unit. The system further extracts the spatial function code tk involved in the conflict based on the classification rules of L(C(r)), fills tk and L(C(r)) into the spatial planning notification template of the corresponding level, automatically generates notification content for multi-level responses, and distributes them to the task list or task list to be optimized of relevant departments according to the level. This realizes the fully automatic connection between conflict level judgment and spatial planning response. Unlike the traditional method of relying on static reports and manual interpretation of conflicts one by one, this module not only realizes the accurate quantification of spatial conflict level, but also enables each conflict unit C(r) to trigger a planning-level response notification with semantic recognition capability and departmental docking structure based on the clear level L(C(r)). This greatly improves the timeliness, automation and decision-making closed-loop capability of land and space planning management in the conflict handling process.

[0127] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A natural resource spatial planning management system based on land spatial element data, characterized in that: It includes a multi-source spatial data integration module, an attribute conflict assessment module, a spatial overlap extraction module, a conflict index fusion module, and a conflict level assessment module; The multi-source spatial data integration module is used to receive raw spatial data (Draw) transmitted from the natural resource platform and remote sensing monitoring system, and then preprocess the raw spatial data (Draw) to form a standard spatial element dataset (Fstd). The attribute adversarial assessment module analyzes multiple types of spatial elements based on the acquired standard spatial element dataset Fstd, establishes a functional attribute vector Ex, calculates the adversarial tension Y between elements on the functional attribute vector Ex, and obtains the tension matrix F. The spatial overlap extraction module performs spatial geometric intersection analysis on the standard spatial feature dataset Fstd to identify feature pairs with overlapping relationships. Each region with overlapping relationships is marked as a conflict unit C. All conflict units are integrated to obtain a set of conflict units Cunit. Then, the spatial overlay index W(C) is calculated for each conflict unit C to form a spatial overlay index vector Mw. The contradiction index fusion module evaluates the adaptive gap of each conflict unit C based on the acquired set of conflict units Cunit, forms the mediation adaptive gap ΔC, and then fuses it with the tension matrix F and the spatially superimposed index vector Mw to obtain the contradiction score index SCI of the conflict unit C. The conflict level assessment module evaluates the conflict level of each conflict unit C based on the obtained conflict score index (SCI) and generates a level notification based on the conflict level.

2. A natural resource spatial planning management system based on territorial spatial element data according to claim 1, characterized in that: The multi-source spatial data integration module includes a data processing unit and a data conversion unit; The data processing unit is used to receive raw spatial data (Draw) transmitted from the natural resources platform and remote sensing monitoring system, including land use and planning vector data (Duec) and grid remote sensing data (Draster), and then perform vector data processing and remote sensing data processing on the raw spatial data (Draw). The original spatial data Draw is specifically Draw={Duec∪Draster}; Vector data processing unifies coordinates and clips spatial extents between land use categories and planning vector data (Duec) in the original spatial data (Draw). Remote sensing data processing unifies resolution, removes clouds and fog, selects bands, and performs interpolation repair on void areas in the original spatial data (Draw). Void area interpolation repair is performed using spatial interpolation and time-series interpolation methods. Among them, the land use and planning vector data Duec are specifically Due = {Duec(1), Duec(2)}, Duec(1) represents the current land use map, Duec(2) represents the planning boundary map. The current land use map is specifically used for identifying the basic functional attributes of land, including urban land, farmland and forest land. The planning boundary map is used to identify urban development boundaries, ecological red lines and permanent basic farmland. The grid remote sensing data Draster is specifically Draster={Draster(1), Draster(2), Draster(3), Draster(4)}, where Draster(1) represents topographic elevation data, Draster(2) represents multi-period remote sensing images, Draster(3) represents NDVI vegetation index, and Draster(4) represents slope map and water system and wetland distribution map.

3. A natural resource spatial planning management system based on territorial spatial element data according to claim 2, characterized in that: The data conversion unit preprocesses the acquired raw spatial data Draw. The preprocessing includes establishing a unified data field structure and functional encoding for the raw spatial data Draw, and then converting the raw spatial data Draw into a unified standard spatial feature dataset Fstd. The establishment of a unified data field structure involves identifying the data types and function types in the original spatial data Draw, performing standard field extraction and structured encapsulation operations on each type of data, generating the kth spatial feature unit f(k) with the same language format, and assigning a unified spatial function code to the kth spatial feature unit f(k) by mapping a preset function type dictionary mapping table, integrating all spatial feature units f to obtain the standard spatial feature dataset Fstd; The standard spatial feature dataset Fstd = {Fstd(1), Fstd(2), ..., Fstd(k) | k∈n}, where n represents the length of the standard spatial feature dataset; Spatial element unit f(k) = {ID, Geometry, Type, SourceTag, Area, Timestamp, tk}; In the spatial feature unit f(k), ID represents a unique identifier, generated by UUID; Geometry represents geometric data, specifically used for spatial overlay and overlap calculations, obtained by extracting the original spatial data Draw after vector data processing and remote sensing data processing; Type represents the feature type, obtained by extracting the land use status map from the original spatial data Draw; SourceTag represents the data source identifier; Area represents the spatial feature area, obtained by calculating the geometric data Geometry; Timestamp represents the timestamp; and tk represents the spatial function code, obtained by matching a preset function type dictionary mapping table.

4. A natural resource spatial planning management system based on territorial spatial element data according to claim 3, characterized in that: The attribute adversarial assessment module includes a function vector construction unit and a feature calculation unit; The functional vector construction unit extracts spatial feature units f with the same spatial functional code tk based on the obtained standard spatial feature dataset Fstd, and establishes a unique corresponding functional attribute vector E(tk) for each spatial functional code tk of the spatial feature unit f. By integrating the functional attribute vectors E(tk) of each spatial functional code tk, the attribute vector set M = {E(tk)|tk∈T} is obtained, where T represents the set of spatial functional codes tk. The functional attribute vector E(tk) is specifically defined as E(tk) = {β(tk, 1), β(tk, 2), ..., β(tk, d)}; d represents the dimension of the functional attribute vector; β(tk, i) represents the rating value on the i-th functional attribute dimension. β(tk, i) is obtained through the following formula: In the formula, N(tk) represents the total number of spatial feature units f with the same spatial function code tk, and P(j, i) represents the score of the j-th spatial feature unit f in the i-th functional attribute dimension.

5. A natural resource spatial planning management system based on territorial spatial element data according to claim 4, characterized in that: The element calculation unit extracts different spatial function codes tx and ty from set T, obtains the functional attribute vector E(tx) of spatial function code tx and the functional attribute vector E(ty) of spatial function code ty respectively, calculates the functional antagonistic tension Y(tx, ty) between different spatial function codes, reflects the differences between spatial function codes tx and ty in terms of spatial utilization and control objectives, and forms the tension matrix F of spatial function code tk; The tension matrix F is specifically as follows The tension matrix F is a k*k square matrix, and each matrix element F(tx, ty) represents the functional resistance tension Y(tx, ty) between spatial function codes tx and ty. The functional resistance tension Y(tx, ty) is obtained through the following formula: In the formula, λ represents the tension adjustment coefficient, e represents the exponential function, and ||E(tx)-E(ty)|| represents the Euclidean distance between the functional attribute vector E(tx) of spatial function code tx and the functional attribute vector E(ty) of spatial function code ty.

6. A natural resource spatial planning management system based on territorial spatial element data according to claim 1, characterized in that: The spatial overlap extraction module includes an identification unit and an overlay calculation unit; The identification unit performs spatial geometric intersection analysis on the standard spatial feature dataset Fstd. Geometric intersection analysis calculates the spatial geometric area Geom between spatial feature units f(p) and f(q) in the standard spatial feature dataset Fstd, obtains the geometric shape G(p,q) of the intersection region, and compares it with the preset minimum identification area threshold Gth to determine the overlap relationship between spatial feature units f(p) and f(q). When an overlap relationship is identified, spatial feature units f(p) and f(q) are marked as overlapping spatial feature pairs, and each overlapping spatial feature pair region is marked as a conflict unit C. All conflict units C are integrated to obtain the conflict unit set Cunit. The conflict unit C is specifically defined as C = {f(p), f(q), G(p, q), f(p, tk)), f(q, tk)}; where f(p, tk)) represents the spatial function code tk of the spatial element unit f(p), and f(q, tk)) represents the spatial function code tk of the spatial element unit f(q); The geometric shape G(p, q) is obtained by the definition G(p, q) = Geom(f(p)) ∩ Geom(f(q)), where Geom(f(p)) represents the spatial geometric area Geom of the spatial element unit f(p), and Geom(f(q)) represents the spatial geometric area Geom of the spatial element unit f(q); ∩ represents the intersection symbol.

7. A natural resource spatial planning management system based on territorial spatial element data according to claim 6, characterized in that: The overlay computation unit calculates the spatial overlay index W(C) of each conflict unit C based on the acquired conflict unit set Cunit, reflecting the overlap intensity in space, and integrates the spatial overlay index W(C) of each conflict unit C to obtain the spatial overlay index vector Mw; The spatial overlay index W(C) is obtained through the following formula: In the formula, W(C(r)) represents the spatial overlay index W of the r-th conflict unit C, AG(f(p), f(q)) represents the intersection area of ​​spatial element unit f(p) and spatial element unit f(q), which is specifically calculated through the geometric shape G(p, q), A(f(p)) represents the initial area of ​​spatial element unit f(p), and A(f(q)) represents the initial area of ​​spatial element unit f(q). The intersection area AG(f(p), f(q)) is obtained by the formula AG(f(p), f(q)) = Area(G(p,q)), where Area(G(p,q)) represents the area of ​​the spatial element, specifically the spatial geometric area between spatial element unit f(p) and spatial element unit f(q). Geom obtains the geometric shape G(p,q) of the intersection region.

8. A natural resource spatial planning management system based on territorial spatial element data according to claim 7, characterized in that: The contradiction index fusion module includes an adaptive assessment unit and an index fusion unit; The adaptive assessment unit extracts the r-th conflict unit C(r) from the acquired conflict unit set Cunit to evaluate the adaptive gap ΔC(r) of the r-th conflict unit C(r), and obtains the mediation adaptive gap ΔC by integrating all conflict units in the conflict unit set Cunit. In the formula, d represents the dimension of the functional attribute vector, and the subscript 1 in |E(tx)-E(ty)|1 indicates the L1 norm used.

9. A natural resource spatial planning management system based on territorial spatial element data according to claim 8, characterized in that: The index fusion unit performs fusion processing based on the obtained mediation adaptability gap △C, tension matrix F, and spatially superimposed index vector Mw to obtain the contradiction score index SCI of the r-th conflict unit C(r); The Conflict Scoring Index (SCI) is obtained using the following formula: SCI(C(r))=F(tx,ty)*W(C(r))*(1-ΔC(r)); In the formula, SCI(C(r)) represents the contradiction score index of the r-th conflict unit C(r), and F(tx, ty) represents the functional antagonistic tension between spatial functional codes tx and ty.

10. A natural resource spatial planning management system based on territorial spatial element data according to claim 1, characterized in that: The conflict level assessment module includes a decision generation unit; The decision generation unit evaluates the conflict level of each conflict unit C based on the obtained conflict score index SCI. The conflict level is evaluated by comparing the conflict score index SCI with the preset upper limit threshold Smax and lower limit threshold Xmin of the conflict level to obtain the conflict level L(C(r)) of the r-th conflict unit C(r), and generates a level notification based on the conflict level L(C(r)) of the r-th conflict unit C(r). The contradiction level L(C(r)) is obtained through the following comparison method: When the conflict level L(C(r)) of the r-th conflict unit C(r) is greater than or equal to the upper limit threshold Smax, the conflict level L(C(r)) of the r-th conflict unit C(r) is obtained as 3, which represents the third level. This indicates that there is a planning conflict with spatial function code tk, including expansion requirements and unoccupiable restrictions. A level 3 notification is generated. After extracting the conflicting spatial function code tk and the conflict level L(C(r)) of the r-th conflict unit C(r), the notification is filled into the preset spatial planning level 3 notification template and placed at the top of the task list to be handled by relevant departments. When the lower limit threshold Xmin < the contradiction level L(C(r)) of the r-th conflict unit C(r) < the upper limit threshold Smax, the contradiction level L(C(r)) of the r-th conflict unit C(r) is obtained as 2, which represents the second level. This indicates that there is an inconsistency between the planning objectives of the spatial function code tk and the specific structural conflict, but there is room for mediation. A second-level notification is generated. After extracting the spatial function code tk of the conflict and the contradiction level L(C(r)) of the r-th conflict unit C(r), it is filled into the preset spatial planning second-level notification template and then sent to the relevant department's pending task list for processing. When the contradiction level L(C(r)) of the r-th conflict unit C(r) is less than the lower limit threshold Xmin, the contradiction level L(C(r)) of the r-th conflict unit C(r) is obtained as 1, indicating the first level. This indicates that there is compatibility and superposition of spatial function code tk. It suggests that natural integration, superposition guidance, coexistence design of ecological construction, and flexible use empowerment should be carried out through planning optimization. After extracting the conflicting spatial function code tk and the contradiction level L(C(r)) of the r-th conflict unit C(r), they are filled into the preset spatial planning optimization template and a notification is sent to the relevant departments' list of tasks to be optimized for further optimization.

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