Land space planning analysis system based on big data

Abstract

The invention relates to the technical field of space planning analysis, in particular to a land space planning analysis system based on big data, which comprises a boundary discrimination module, a purpose offset module, a coordination analysis module, a change identification module and an ownership selection module. According to the method, an efficient spatial information circulation and feedback chain is constructed by combining boundary space structure extraction, use weight distribution mapping, population use collaborative feature analysis, resource configuration change trend tracking and an ownership affiliation unit optimization mechanism; global linkage judgment of core elements such as spatial forms, purposes, population and resources in a complex territorial structure is achieved, the whole-course quantitative perception ability of planning and management is enhanced, land purpose optimization, resource allocation dynamic balance and accurate orientation of ownership relations are promoted, data transparency of a space governance link and continuity of a decision closed loop are improved, and the space governance efficiency is improved. Flexible adjustment and fine management of a spatial pattern in a multi-dimensional scene are supported, and the datamation and intelligence level of a territorial space treatment system is enhanced.

Description

Technical Field

[0001] This invention relates to the field of spatial planning and analysis technology, and in particular to a land spatial planning and analysis system based on big data. Background Technology

[0002] Spatial planning analysis primarily involves a comprehensive assessment and scientific decision-making process regarding the current status and future development trends of land resources distribution and utilization. This includes spatial data collection and integration, geographic information processing and analysis, optimized spatial configuration, and adjustments to land use structure. It is widely applied in land management, urban planning, and ecological protection. Traditional land spatial planning analysis involves acquiring spatial information through manual surveys, analysis of paper maps, and statistical data compilation. Based on topographic maps and land use status tables, it conducts spatial pattern analysis and planning decisions, typically employing methods such as topographic mapping, remote sensing image interpretation, on-site site surveys, and statistical data tables to complete the research and evaluation of land spatial patterns.

[0003] Existing technologies rely on traditional survey methods and static maps and tables. In scenarios where the frequency of changes in multi-source spatial elements is high, it is difficult to reflect the complex interactions between spatial patterns, uses, population, and resources in a synchronized manner. The manual data processing stage is easily affected by subjectivity, and untimely response leads to insufficient identification of spatial anomalies. Misjudgments or delays in regional boundaries and ownership are prone to occur. Regional planning adjustments cannot take into account dynamic changes and element coordination, resulting in problems such as imbalances in land use adjustments, misallocation of spatial resources, and one-sided management decisions. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a land spatial planning and analysis system based on big data. The technical solution is as follows: On the one hand, it provides a land spatial planning analysis system based on big data, including: The boundary discrimination module is based on the ecological protection red line area. It analyzes the coordinates of the boundary nodes of the functional area, calculates the spatial distance between adjacent nodes, summarizes the spatial difference characteristics, and compares the area and perimeter with the planning standards to judge the spatial structure and obtain key morphological features. Based on the key morphological features, the application offset module optimizes the application type of spatial units, analyzes the application type encoding involved in abnormal boundary segments, compares the current application weight with the application weight of neighboring units, and determines the average distribution through the spatial neighborhood radius to obtain the application distribution offset. Based on the aforementioned usage distribution offset, the coordination analysis module determines the synergistic relationship between the spatial unit usage and the regional population density, compares the usage weight with the average level of similar usages, and combines population density analysis to determine the coordination between usage and population distribution, thereby obtaining the usage-population coupling degree. Based on the population coupling degree of the use, the change identification module analyzes the spatial distribution of boundary change points, determines the correspondence between functional area numbers and use type codes before and after the change, compares standardized resource reserve data, identifies changes in use and resources, and obtains use-resource change indicators.

[0005] On the other hand, the key morphological features include coherence parameters, compactness parameters, and boundary integrity indicators; the use distribution offset includes offset intensity, distribution heterogeneity, and spatial consistency; the use population coupling degree includes matching parameters, synergy coefficients, and balance indicators; and the use resource change indicators include change magnitude, structural change parameters, and distribution anomaly coefficients.

[0006] On the other hand, the boundary discrimination module includes: The node distance calculation submodule is based on the ecological protection red line area. It analyzes the coordinate data of the functional area boundary nodes, uses spatial straight-line distance measurement for each group of adjacent nodes, gradually sorts out the changes in the length of the boundary segment, calculates the spatial interval distribution, and obtains the spatial interval sequence between nodes. The spatial difference summarization submodule analyzes the fluctuation segments of the boundary segment in the spatial distribution process based on the spatial interval sequence between the nodes. Through the joint determination of frequency statistics and fluctuation amplitude, it filters out the key areas of spatial change and obtains the spatial fluctuation parameters. Based on the spatial fluctuation parameters, combined with the functional area measurement results and the boundary perimeter measurement results, the boundary morphology analysis submodule determines the degree of boundary closure and enclosure compactness, analyzes the tortuousness of the path trend, and compares various morphological parameters with the planning standard parameters to obtain key morphological features.

[0007] On the other hand, the purpose offset module includes: Based on the key morphological features, the purpose optimization submodule analyzes the purpose type of spatial units, optimizes the purpose type coding of spatial units corresponding to abnormal boundary segments within the functional area, and classifies the purpose type coding of spatial units after adjustment to obtain the spatial unit purpose coding sequence. The usage weight comparison submodule compares the current usage weight of each spatial unit with the usage weight of neighboring spatial units based on the spatial unit usage encoding sequence, defines the set of neighboring units by the neighborhood range, and statistically analyzes the distribution difference between the current unit usage weight and the neighboring usage weight to obtain the usage neighborhood difference quantity. The purpose offset calculation submodule calculates the average distribution of purpose weights for each spatial unit based on the purpose neighborhood difference, filters spatial units where purpose distribution changes, judges the distribution balance and purpose concentration, and obtains the purpose distribution offset.

[0008] On the other hand, the determination of distribution balance and usage concentration is made using the following formula: ; The calculation is used to balance the offset parameters, where, Representative spatial unit For the type of use The purpose of the equalization offset parameter is to balance the offset parameter. Representative spatial unit Neighborhood usage types Average usage weight, Representative spatial unit In terms of usage type The weight of the use on, Representative spatial unit The number of neighborhood spatial units, Representative spatial unit The Neighborhood units in terms of usage type The weight of usage on.

[0009] On the other hand, the coordination analysis module includes: The collaborative relationship judgment submodule determines the correspondence between the spatial unit's use type and the population density of the area based on the use distribution offset. It matches the use type of each spatial unit with the population data, calculates the correlation between the use and population distribution of each spatial unit, and obtains the use-population matching coefficient. The use weight comparison submodule compares the use weight of the current spatial unit with the average level of the same type of use in the same region based on the use population matching coefficient, and analyzes the distribution difference between the use weight of each spatial unit and the use weight of the same type of use to obtain the weight balance parameter. The coordination analysis submodule analyzes the degree of matching between the use weight distribution and the population distribution based on the weight balance parameters and the population density of the spatial unit, judges the consistency of the two distributions, and obtains the use-population coupling degree.

[0010] On the other hand, the analysis of the matching degree between the use weight distribution and the population distribution, which combines the population density of spatial units, uses the following formula: ; Determine the consistency between the two distributions to obtain the degree of coupling between population and usage, where... Representing the The population coupling degree of the use of each spatial unit Representing the In the _ spatial unit, the _ Use weight of class purpose Representing the In the _ spatial unit, the _ Population density corresponding to the type of use Representing the The average population density across all uses of a spatial unit. Representing the The total number of space unit usage types.

[0011] On the other hand, the change identification module includes: The boundary change analysis submodule determines the location of boundary change points in spatial distribution based on the aforementioned population coupling degree, identifies the distribution of change points in the overall spatial structure, and determines the distribution characteristics of boundary changes by combining geographic coordinate data, thereby obtaining the distribution characteristics of change points. The functional area relationship judgment submodule compares the functional area numbers and usage type codes before and after the change based on the distribution characteristics of the change points, analyzes the correspondence between the change unit number and the usage code, filters out the units whose usage codes have changed, and obtains the usage code change parameters. Based on the usage code change parameters, the resource change calculation submodule compares the standardized resource reserve data of the change unit, analyzes the distribution changes of the resource reserves of the change unit, calculates the change range of usage type code and resource distribution, and obtains the usage resource change index.

[0012] On the other hand, the system also includes: Based on the resource change index, the ownership selection module identifies the ownership unit and use type code of the change unit, analyzes the distribution of standardized resource reserve data, judges the clarity of ownership unit ownership, selects the configuration with single use and optimal resource distribution change range, and obtains the optimal ownership item. The optimal ownership criteria include uniqueness criteria and configuration optimization criteria.

[0013] On the other hand, the ownership selection module includes: The ownership identification submodule determines the ownership unit and usage type code corresponding to each change unit based on the usage resource change index, identifies the correspondence between the ownership unit and the usage code, organizes the ownership status of the change unit, and obtains ownership unit ownership data. Based on the ownership unit attribution data, the resource distribution analysis submodule analyzes the distribution of standardized resource reserve data in each ownership unit, compares the distribution characteristics of resource reserves under each ownership unit, identifies key configurations for resource distribution changes, and obtains the resource ownership distribution factor. The attribution optimization submodule determines the clarity of the attribution of each ownership unit based on the resource attribution distribution factor, compares the configuration with the single use type and the optimal range of resource distribution variation, and obtains the optimal attribution item.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By integrating multi-parameter real-time collaborative analysis and dynamic spatial data, and combining boundary spatial structure extraction, use weight distribution mapping, population use collaborative feature analysis, resource allocation change trend tracking, and ownership unit optimization mechanism, an efficient spatial information flow and feedback chain is constructed. This enables global linkage and identification of core elements such as spatial form, use, population, and resources in complex land structures, enhances the full-process quantitative perception capability of planning and management, promotes land use optimization, dynamic balance of resource allocation, and precise targeting of ownership relationships, improves data transparency and decision-making continuity in spatial governance, supports flexible adjustment and refined management of spatial patterns in multi-dimensional scenarios, and strengthens the datafication and intelligence level of the national land spatial governance system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the system of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the boundary discrimination module of the present invention; Figure 4 A flowchart illustrating the offset module used in this invention; Figure 5 This is a flowchart of the coordination analysis module of the present invention; Figure 6 This is a flowchart of the change identification module of the present invention; Figure 7 This is a flowchart of the ownership selection module of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a land spatial planning and analysis system based on big data, such as... Figure 1 As shown, the system includes: The boundary discrimination module is based on the ecological protection red line area. It analyzes the coordinates of the boundary nodes of the functional area, calculates the actual spatial distance between each pair of adjacent nodes, summarizes the spatial differences between nodes, and then compares the calculation results of the functional area area and perimeter with the planning requirements standards to judge the spatial form and structural characteristics of the boundary and obtain the key morphological characteristics. The usage offset module optimizes the usage type of spatial units based on key morphological features, analyzes the usage type encoding of spatial units corresponding to abnormal boundary segments, compares the current usage weight with the usage weight of neighboring spatial units, calculates the average distribution of each usage weight using the spatial neighborhood radius as the neighborhood delineation standard, and obtains the usage distribution offset. The coordination analysis module determines the synergistic relationship between the use of spatial units and the regional population density based on the use distribution offset. It compares the current use weight of spatial units with the average level of the same type of use in the same region, and analyzes the coordination between use weight and population distribution in combination with the population density of spatial units to obtain the use-population coupling degree. The change identification module analyzes the spatial distribution of boundary change points based on the usage-population coupling degree, determines the correspondence between functional area numbers and usage type codes before and after the change, compares the resource reserve data after the change unit standardization process, identifies the change range of usage type codes and resource distribution, and obtains usage resource change indicators. The ownership selection module identifies the ownership unit and use type code corresponding to the change unit based on the resource change index, analyzes the distribution of standardized resource reserve data, judges the clarity of ownership unit attribution, selects the configuration with a single use type and the optimal resource distribution change range, and obtains the optimal ownership attribution option.

[0023] Key morphological features include coherence parameters, compactness parameters, and boundary integrity indicators; usage distribution offset includes offset intensity, distribution heterogeneity, and spatial consistency; usage-population coupling degree includes allocation parameters, synergy coefficients, and balance indicators; usage-resource change indicators include change magnitude, structural change parameters, and distribution anomaly coefficients; and optimal ownership items include uniqueness criteria and preferred configuration items.

[0024] In the boundary discrimination module, the ecological protection red line area refers to the priority ecological protection area designated in the national land space planning, which usually has strict land use and ecological protection restrictions; the functional area boundary node represents the key coordinate point on the boundary line of a certain block in the national land space (such as ecological zone, urban area, etc.), which is the basic data for describing the scope and shape of the area; the actual spatial distance refers to the real straight-line distance between two adjacent nodes on the boundary in geographic space, which is the basic quantity used to measure the length and shape of the boundary; the spatial difference between nodes refers to the analysis of the deviation or change characteristics by comparing the distance between adjacent boundary nodes with the target data of the planning; the planning requirement standard refers to the standard or benchmark of quantitative indicators such as area and perimeter set in advance in the national land space planning, which serves as the basis for judging and comparing actual spatial data; the spatial morphology refers to the geometric attributes such as shape, outline, continuity, and compactness of the regional boundary and the spatial distribution of plots within it; the structural characteristics refer to the distribution pattern and structural characteristics of the regional space at the overall and local levels, including the tortuosity and integrity of the boundary.

[0025] In the land use offset module, the spatial unit land use type refers to the land use attribute of the basic division unit in the national land space, such as different types like residential, industrial, ecological, and agricultural; abnormal boundary segment refers to the boundary part that does not conform to the planning form standard or has significant differences identified in the boundary identification process; land use type code refers to the unique identification code assigned to different land uses, which facilitates data management and computer processing; current land use weight refers to the relative importance or distribution ratio of the current land use of the spatial unit in the overall land use structure; the land use weight of the spatial unit emphasizes the quantification of the importance of the use within a specific spatial unit; neighborhood delineation standard refers to the discrimination rule for determining which spatial units belong to the "neighborhood" of the current unit (such as units within a certain radius); average distribution refers to averaging all land use weights within the neighborhood to obtain the relative level of the current unit in the land use distribution of the region.

[0026] In the coordination analysis module, the synergy relationship refers to the degree of mutual coordination between the use of spatial units and population density in terms of spatial distribution and functional realization; the same type of use in the same region refers to the set of all spatial units with the same use type code in the same analysis area, which is used for comparative analysis; coordination refers to the degree of consistency and matching between use weight and population distribution in terms of spatial pattern.

[0027] In the change identification module, the spatial distribution of boundary change points refers to the location distribution of boundary changes within the spatial range, including the geographic coordinates or spatial distribution characteristics of the change points; the functional area number is a unique identifier assigned to each spatial functional area for data management and tracking; the land use type code refers to the land use code of the change unit before and after the change; the change unit refers to the spatial unit that has been re-divided or reassigned due to boundary adjustment, and is the object of analysis of the impact of the change; the magnitude of change refers to the quantitative difference of a certain data (such as resource reserves, land use distribution, etc.) before and after the boundary change.

[0028] In the ownership selection module, the ownership unit refers to the entity that has the right to use, manage, or dispose of resources in a specific spatial unit; standardization processing refers to normalizing or standardizing data such as resource reserves according to a unified method to facilitate comparison and analysis; the distribution of resource reserve data refers to the distribution pattern of resource reserves (such as minerals, land area, etc.) in different units within a certain spatial range; the clarity of ownership refers to whether the ownership relationship of the ownership unit is clear, unique, and without overlap or dispute; the optimal configuration refers to the most efficient, singular, and reasonable scheme in terms of resource use and ownership allocation method selected after calculation and comparison.

[0029] like Figure 2 and Figure 3 As shown, the boundary discrimination module includes: The node distance calculation submodule is based on the ecological protection red line area. It analyzes the coordinate data of the functional area boundary nodes, uses spatial straight-line distance measurement for each group of adjacent nodes, gradually sorts out the changes in the length of the boundary segment, calculates the spatial interval distribution, and obtains the spatial interval sequence between nodes. The boundary coordinates of each functional area are obtained from the boundary data. After converting the original geographic coordinates into a two-dimensional point sequence in a planar projected coordinate system, each pair of adjacent coordinate points is extracted sequentially according to the boundary connection order. The straight-line connection length between each pair of nodes is calculated using the planar straight-line distance between two points. The calculation is performed sequentially from the first node to the last pair of nodes. After each set of distance values ​​is calculated, the result is immediately appended to the boundary segment length list according to the original node arrangement order. This process is performed separately for all functional area boundaries and categorized by functional area number. Subsequently, each The list of boundary segment lengths for each functional area is statistically processed to calculate basic statistics such as minimum, maximum, average, and standard deviation. Based on this, each boundary segment length is compared with the average. Segments significantly smaller than the average are marked as "contracted segments," segments larger than the average are marked as "stretched segments," and the rest are marked as "stable segments." For example, if the average distance of a functional area's boundary segment is 30 meters and the standard deviation is 6 meters, then segments less than 18 meters are considered contracted segments, and segments greater than 42 meters are considered stretched segments. After this process is completed, all segments are sequentially arranged into a continuous sequence, and this sequence is output as the spatial interval sequence between nodes.

[0030] The spatial difference summarization submodule analyzes the fluctuation segments of the boundary segment in the spatial distribution process based on the spatial interval sequence between nodes. Through the joint determination of frequency statistics and fluctuation amplitude, it screens out the key areas of spatial change and obtains spatial fluctuation parameters. The deviation of each segment's length value from the average interval is analyzed. The difference between each interval value and the average value of the entire sequence is compared. Segments with a difference higher than the average deviation are marked as "fluctuation segments". After arranging all fluctuation segments in order, the consecutive occurrences are further filtered out, i.e., whether there are three or more consecutive fluctuation segments. If so, it is marked as a fluctuation zone. The difference between the maximum and minimum interval values ​​in the zone is calculated to determine the fluctuation amplitude. At the same time, the frequency of such zones is statistically analyzed. If the fluctuation amplitude of a certain zone exceeds the standard deviation limit of the fluctuation of all segments, it is identified as a key area of ​​spatial change. For example, if the interval fluctuation range between nodes is generally concentrated between 25 meters and 5 meters, and there are three consecutive nodes with intervals of 15 meters, 48 ​​meters, and 19 meters respectively, it constitutes a strong fluctuation zone. By cross-judging and classifying the amplitude and frequency of each fluctuation zone, a list of key change areas is formed, and the zone number, start and end node numbers, and representative spatial fluctuation parameters are output.

[0031] The boundary morphology analysis submodule, based on spatial fluctuation parameters and combined with the functional area measurement results and boundary perimeter measurement results, judges the degree of boundary closure and enclosure compactness, analyzes the tortuousness of the path trend, and compares various morphological parameters with planning standard parameters to obtain key morphological features. For each functional area boundary outline, first determine whether the boundary is connected end to end to form a closed shape based on the order of its boundary nodes. Then obtain the entire enclosed area and boundary line length, and calculate the boundary closure degree and spatial compactness. The compactness value is between 0 and 1. The closer the value is to 1, the more regular the boundary shape is. Then compare the value with the planning requirements standard. For example, the compactness requirement for ecological conservation functional areas is not less than 0.6. If the calculated result of a boundary is 0.49, it is considered a low compactness shape. Next, calculate the angle change between each adjacent boundary segment in the boundary path. If the path before and after a certain point has too sharp an angle, it is marked as a sharp angle node, and the number of sharp angle occurrences is counted. For example, if a functional area has 12 sharp angle abrupt change nodes, exceeding the set allowable threshold of 8, the boundary tortuosity is considered too high. Finally, by comparing the indicators such as area, perimeter, closure, and number of sharp angles with the planning parameters item by item, determine whether there is a shape anomaly of the boundary and output the key shape feature results in the form of structural indicators.

[0032] like Figure 2 and Figure 4 As shown, the purpose offset module includes: The purpose optimization submodule analyzes the purpose type of spatial units based on key morphological features. For abnormal boundary segments within the functional area, it optimizes the purpose type coding of the spatial units corresponding to the abnormal segments. After adjustment, the purpose type coding of spatial units is classified to obtain the spatial unit purpose coding sequence. The identified abnormal boundary segments are used as optimization targets. First, the spatial unit number corresponding to the abnormal boundary segment is located. Then, the current land use type code of the spatial unit is read. The land use category corresponding to the code is extracted from the basic database, such as agricultural land, ecological land, and industrial land. Then, the land use weight value of this type in the overall land use structure within the functional area is obtained. The weight value is defined as the ratio of the area of ​​this type of land use to the total area of ​​the functional area. Next, it is determined whether the current land use type weight is lower than the median weight value of other major land use types in the functional area. If it is lower than 80% of the median value, it is initially judged as a weak land use type. For example, in a certain functional area, ecological land accounts for 40%, residential land accounts for 35%, and industrial land accounts for 2%. 5%. If the spatial unit corresponding to an abnormal boundary segment is designated as industrial land, its current use weight is 25%, which is lower than 80% of the median value of 37.5% for the other two categories, i.e., 30%. Therefore, it is marked as a weak use unit. Then, based on the principle of spatial adjacency, a set of spatial units within 100 meters of this unit is searched, and their use types are counted. The type with the highest frequency is extracted as the use optimization suggestion category. If there are 6 ecological land, 2 industrial land, and 1 agricultural land among the surrounding adjacent units, it is recommended to adjust the use type of the unit corresponding to the abnormal segment to ecological land. After adjustment, it is immediately rewritten into a new use type code and classified into the set of spatial units under the corresponding use type code to form a complete spatial unit use code sequence.

[0033] The usage weight comparison submodule compares the current usage weight of each spatial unit with the usage weight of neighboring spatial units based on the spatial unit usage coding sequence. It delineates the set of neighboring units by defining the neighborhood range, and statistically analyzes the distribution difference between the current unit usage weight and the neighboring usage weight to obtain the usage neighborhood difference quantity. Based on the updated spatial unit usage coding sequence, the current usage type of each spatial unit is extracted, and its corresponding usage weight value is obtained from the database. This weight value is defined as the area proportion of this type of usage within the current functional area. Then, a neighborhood range with a radius of 150 meters is defined with the center point of the unit as the center, and all spatial units falling within this neighborhood range are obtained. It is determined whether the usage type of the neighboring units is consistent with that of the current unit, and their respective usage weights are extracted. Subsequently, the usage weight of the current unit is compared with the average weight of units with the same usage in the neighborhood. If the weight of the current unit is greater than the average by more than 30%, it is marked as a weight surge. If a unit's weight is more than 30% below the average, it is marked as a unit with a sudden drop in weight; the rest are marked as units with consistent weight. For example, if a unit is designated as agricultural land and has a weight of 18%, the weights of its five neighboring agricultural land units are 12%, 14%, 13%, 15%, and 16%, respectively, with an average of 14%. The current weight is 18%, which is more than 30% above 14%, i.e., more than 18.2%, and does not constitute a sudden increase. If it is 22%, it is marked as a unit with a sudden increase. Then, all the differences after comparison are statistically analyzed, and the average value, standard deviation, and other values ​​of the differences are calculated to describe the degree of fluctuation between the current use and the neighboring uses. This difference is used as the neighborhood difference of use.

[0034] The purpose offset calculation submodule calculates the average distribution of purpose weights of each spatial unit based on the purpose neighborhood difference, filters spatial units with changes in purpose distribution, judges the distribution balance and purpose concentration, and obtains the purpose distribution offset. The formula is used to determine the distribution balance and the concentration of uses: ; The calculation is used to balance the offset parameters, where, Representative spatial unit For the type of use The purpose of the equalization offset parameter is to balance the offset parameter. Representative spatial unit Neighborhood usage types Average usage weight, Representative spatial unit In terms of usage type The weight of the use on, Representative spatial unit The number of neighborhood spatial units, Representative spatial unit The Neighborhood units in terms of usage type The weight of usage on; Application of equalization offset parameter It reflects spatial units In specific application type The above is a quantitative indicator of the difference and dispersion between the use weight of a spatial unit and the use weight distribution of its surrounding spatial units. By capturing the deviation of the current unit from the average use weight of its neighbors and the dispersion of use weight within the neighborhood, it comprehensively reflects the balance and concentration trend of the current spatial unit in terms of use distribution. The larger the value of this parameter, the more balanced the spatial unit is in terms of use type. The distribution on it is more different and uneven than that of the surrounding units as a whole.

[0035] For spatial units Corresponding application type To determine the offset, first extract the weight of the current use of the spatial unit. And determine the set of neighborhood spatial units within the neighborhood radius. For example, let's define a spatial unit. The original area for this unit was 1560 square meters, and the planned area for this unit is 2400 square meters. After normalization, the current use weight is... With a selected neighborhood radius of 300 meters, five neighborhood units were retrieved, with original usage areas of 1320, 1200, 1440, 1080, and 960 square meters, respectively, and corresponding planned areas of 2400, 2400, 2400, 2400, and 2400 square meters, respectively. The normalized neighborhood usage weight set is as follows: , , , , ; Calculate the neighborhood average use weight: ; Calculate the first term, which is the squared difference between the current use and the average use of the neighborhood: ; The second term is calculated as follows: the squared difference of the offset between each neighboring cell and the current application is as follows: ; ; ; ; ; Averaging the above results, we get: ; Add the two results together to calculate the application balance offset parameter: ; This value can be used as a basis for calculating the spatial balance and concentration of use of a spatial unit under the current use type. If the interval is [0.000, 0.02], the current result deviates from the upper limit and belongs to the object of significant change in use distribution, which can be used as the input unit for subsequent use adjustment. The formula, through the comprehensive representation of the discrete structure of the sum of squared use differences, can form a linkage feedback between the degree of use concentration and neighborhood consistency, and thus establish a use offset judgment method based on regional structure. This method avoids relying on subjective threshold setting behavior and constructs numerical criteria through two types of discrete sources of actual use distribution, providing a supporting basis for use coupling degree and subsequent ownership identification in the linkage process.

[0036] like Figure 2 and Figure 5 As shown, the coordination analysis module includes: The collaborative relationship judgment submodule determines the correspondence between the spatial unit's use type and the population density of the area based on the use distribution offset. It matches the use type of each spatial unit with the population data, calculates the correlation between the use and population distribution of each spatial unit, and obtains the use-population matching coefficient. First, determine the usage type of each spatial unit after optimization. Then, read the statistical population data of the area where each spatial unit is located, and establish a matching structure between population density values ​​and usage types on a spatial unit basis. Next, map the usage types of all spatial units within the functional area to the population grid data of the area. If a spatial unit covers an area with multiple population statistical units, summarize and sum the population unit data to form a unified population density value, defined as the number of residents per unit area. Then, group and categorize the usage types of each spatial unit. For all spatial units under each usage type, calculate the average and standard deviation of population density. Then, compare the population density corresponding to the current unit with the average population density of the same type of usage. If the difference is within the standard deviation range of the population density of the same type of usage, the unit is considered a unit. Within a certain range, units are marked as "matching units." Units exceeding the upper limit of the standard deviation are designated as "high-population units," while those below the lower limit are designated as "low-population units." This method is used to classify the coordination level between spatial use and population. For example, if the average population density in a certain type of industrial use is 300 people / km² and the standard deviation is 50, then a unit with a population density of 380 people / km², exceeding the upper limit of 350, is marked as high-population. Finally, the proportion of high-population, low-population, and matching units under each type of use is calculated to generate the correspondence between use type and population density. The use-population matching coefficient is formed by calculating the percentage of matching units out of all units for that use. This coefficient ranges from 0 to 1, with a value closer to 1 indicating a higher degree of matching. For example, if 18 units in a certain ecological use category are marked as matching units out of a total of 20, then the matching coefficient is 0.9.

[0037] The usage weight comparison submodule compares the current spatial unit usage weight with the average level of the same type of usage in the same region based on the usage population matching coefficient, and analyzes the distribution difference between the usage weight of each spatial unit and the usage weight of the same type of usage to obtain the weight balance parameter. The usage weight value of the current spatial unit is extracted and defined as the proportion of the unit's usage type to the total area of ​​all usage types within the same area. Then, all spatial units within the same area with the same usage type are classified, and the average usage weight value of units of the same type is calculated. The difference between the current unit's usage weight and this average value is then used to determine its suitability. If the difference is within ±10%, it is marked as a "balanced weight unit"; if it exceeds 10%, it is a "heavy weight unit"; and if it is less than 10%, it is a "slightly underweight unit". For example, if the current spatial unit is for residential use and its usage weight is 22%, while the average weight for residential uses in the same area is 18%, then the difference is 4%, which falls within ±10%. Within the interval, a unit is determined to be a weighted balanced unit. If the current use weight is 27%, the difference is 9%, which is still considered balanced. If it is 30%, exceeding 10% indicates an excessive weight. The above-mentioned labels are then statistically analyzed across all spatial units to form a classification ratio. The proportion of the three types of units under each spatial use type is analyzed to describe the stability of the distribution of each use type in the current area. At the same time, the difference of the current unit is summarized and normalized to form a weight difference value. This value is then compared with the mean of the difference values ​​of all units of the same type. If the deviation is less than 0.8 times the standard deviation of all difference values, the weight distribution of the unit is determined to be balanced, and the weight balance parameter of the unit is output as an evaluation basis.

[0038] The coordination analysis submodule analyzes the degree of matching between the use weight distribution and the population distribution based on the weight balance parameter and the population density of the spatial unit, judges the consistency of the two distributions, and obtains the use-population coupling degree. By combining spatial unit population density, the degree of matching between use weight distribution and population distribution is analyzed using the formula: ; Determine the consistency between the two distributions to obtain the degree of coupling between population and usage, where... Representing the The population coupling degree of the use of each spatial unit Representing the In the _ spatial unit, the _ Use weight of class purpose Representing the In the _ spatial unit, the _ Population density corresponding to the type of use Representing the The average population density across all uses of a spatial unit. Representing the The total number of space unit usage types; The land use-population coupling degree is an indicator used in the analysis of national land spatial planning to measure the degree of matching between the population distribution of each land use type and its use weight within a spatial unit. It reflects the coordination and consistency between land for different uses and the population distribution of each use within the spatial unit. The formula calculates the land use-population coordination within the entire spatial unit by weighting and normalizing the deviation of the population density of each type of use from the average population density within the spatial unit with the weight of that use, thereby obtaining the land use-population coupling degree of the spatial unit.

[0039] By retrieving land use classification data from each spatial unit, the corresponding number of land use types can be extracted. And obtain the area ratio of each use type as the use weight. For example, spatial unit numbered b1 is divided into three uses: residential land, commercial land, and green space, with a corresponding area of ​​2.5 km². 2 1.5km 2 and 1.0km 2 The total area is 5.0 km². 2 Then their respective weights are calculated as follows: , , Subsequently, actual population density data were obtained in the three use areas of this spatial unit, with the residential area having a density of 500 people / km². 2 Commercial area 800 people / km 2 Green area 200 people / km 2 The population density here is obtained by overlaying uniform gridded data with functional zones, and the average population density of its spatial units is [value missing]. To further improve the comparability of parameters with different dimensions, population density was normalized to obtain... , , , Substituting the above parameters into the formula for calculation, we get: ; ; The conclusion is ; Similarly, calculations are performed on spatial unit b2, and its land use types remain at three: industrial land, warehousing land, and infrastructure land, with each type having an area ratio of 2.0 km². 2 2.0km 2 1.0km 2 Total area 5.0 km² 2The weights were calculated as follows: , , The population density for the three types of uses is 300 people per km. 2 400 people / km 2 600 people / km 2 After normalization, they are respectively , , Its average population density is Substituting into the formula, we get: ; ; ; Therefore For spatial cell numbered b1, the calculated usage population coupling degree is: For the spatial cell numbered b2, the calculation result is: If the preset range for population coupling in the planning is: The high consistency between usage and population distribution indicates a high degree of coordination. The population distribution for the purpose of use deviates to some extent, and it is judged to be of general coordination. The significant differences in population distribution across different uses indicate low coordination. Then the calculation results of b1 and b2 are both in The intervals were classified as "generally coordinated." Among them, the coupling degree value of b1 was higher than that of b2, indicating that the matching difference between different use types and the population density they support was greater in unit b1, and the population deviation was more concentrated in the structure. b2, on the other hand, showed a population distribution that was closer to the average. This result indicates that under the current spatial unit structure, there is a mismatch between the local use functional zoning and the actual population carrying capacity in area b1, while the use and population matching relationship in area b2 is more compact.

[0040] like Figure 2 and Figure 6 As shown, the change identification module includes: The boundary change analysis submodule determines the location of boundary change points in spatial distribution based on the coupling degree of use and population, identifies the distribution of change points in the overall spatial structure, and determines the distribution characteristics of boundary changes by combining geographic coordinate data, thus obtaining the distribution characteristics of change points. Extract a list of spatial unit numbers for all instances where usage type coding adjustments or usage-population matching coefficient changes exceed a set threshold. Obtain the boundary coordinate data for each unit before and after the change. Then, determine whether the boundary has changed spatially by analyzing the coordinate difference. If any node on the unit boundary shifts by more than 5 meters, it is identified as a boundary change point. Mark the spatial location of this change point in geographic coordinate order and extract the center point coordinates of the spatial unit containing the change point as a reference point. Perform clustering based on functional zone numbers. Calculate the ratio of the number of change points in each functional zone to the total number of spatial units. If this ratio exceeds 30%, the functional zone is identified as a change point. Concentrated change areas are defined as areas with concentrated changes, while dispersed change areas are defined as areas with dispersed changes. For example, if a functional area contains 100 spatial units, and 35 of these units have boundary changes, then this area is considered a concentrated change area. Subsequently, a point layer is constructed using the coordinates of all change points, and their spatial distribution characteristics within the entire functional area are statistically analyzed. The distance of each change point relative to the geometric center of the functional area is extracted. If the average distance of all change points from the center does not exceed 30% of the radius of the functional area, it is considered a concentrated change. If the distribution exceeds 70%, it is considered a marginal change. Each change point is recorded with its functional area number, relative position, and concentration classification result, forming the distribution characteristics of boundary change points in the spatial structure.

[0041] The functional area relationship judgment submodule compares the functional area numbers and usage type codes before and after the change based on the distribution characteristics of the change points, analyzes the correspondence between the changed unit numbers and usage codes, filters out units whose usage codes have changed, and obtains the usage code change parameters. First, in the spatial units marked as change points, extract the functional area number and use type code before the change, and compare them one by one with the recorded values ​​after the change. If the functional area number remains the same but the use code changes, it is recorded as a use adjustment change unit. If the functional area number changes and the use code changes synchronously, it is recorded as a function migration change unit. Then, establish a comparison table of the original use code and the new use code according to the unit number, and determine whether there is a cross-category change in use type for each change unit. For example, changing from ecological use to industrial use is a cross-category change, while changing from agricultural land to forest land is a same-category change. Further screening is needed to identify key marked units. For example, unit A003 was originally designated as ecological forest land with a use code of E01, and after the change, it became urban construction land with a use code of U05. This indicates a change in use code. Record its number, original code, and new code, and mark the change category and degree. Then, calculate the percentage of units with changed use codes among all changed units. If the percentage exceeds 50% of the total number of changed units, it is defined as a broad use adjustment area; otherwise, it is a local use optimization area. Output the set of spatial units with changed use codes and their change category labels to obtain the use code change parameters.

[0042] The resource change measurement submodule compares the standardized resource reserve data of the change unit based on the use code change parameters, analyzes the distribution changes of the resource reserves of the change unit, calculates the change range of use type code and resource distribution, and obtains the use resource change index. The system retrieves the resource reserve data tables for each changed unit before and after the change, extracting the resource type, reserve value, and spatial range. The original data is standardized to thousands of tons or thousands of cubic meters. Then, the difference between the original and changed reserve values ​​for each changed unit is calculated. Finally, for each resource type, the average, maximum, minimum, and magnitude of the reserve increase / decrease for all changed units are statistically analyzed. For example, unit B012 originally had a reserve of 600,000 tons, and after the change, it has 450,000 tons, a difference of -150,000 tons. If the land use code is industrial construction land, its average industrial land use reserve before the change... With a value of 520 thousand tons and a standard deviation of 40 thousand tons, the resource change range of this unit is 28.8%, which falls within the medium fluctuation range. Subsequently, all units with changes are divided into three categories based on the magnitude of resource change: fluctuations less than 10% are considered stable, fluctuations between 10% and 30% are considered moderately changing, and fluctuations exceeding 30% are considered drasticly changing. Finally, the number of units and the total resource change under each category are summarized, and combined with the direction of change in use codes, it is determined whether there is a trend of resource allocation shifting to a specific use. The change ratio structure between the use type codes and resource reserve changes is output, thus obtaining the use resource change index.

[0043] like Figure 2 and Figure 7 As shown, the ownership selection module includes: The ownership identification submodule determines the ownership unit and usage type code corresponding to each change unit based on the resource change index, identifies the correspondence between the ownership unit and the usage code, organizes the ownership status of the change unit, and obtains the ownership unit ownership data. Extract the usage type code and corresponding resource change range value for each changed unit. Then, retrieve the current and historical registered ownership unit codes for that unit from the database. Establish an initial correspondence table between usage code, resource change range, and ownership unit according to unit number. Then, perform cross-checking on the usage code and ownership unit for each unit. If multiple usage codes appear under the same ownership unit, record it as "not unique usage". If the same usage code is repeatedly distributed in multiple ownership units, record it as "not unique ownership". Mark the two types of repetition separately, and count the number of unique usage but not unique ownership, unique ownership but not unique usage, both unique, and neither unique. Then calculate the proportion of each type of combination. The ownership attribution is divided into levels of clarity based on proportions. The criteria are as follows: "Highly Clear" when both are unique (≥90%), "Basically Clear" when between 60% and 90%, and "Ambiguous" when below 60%. Then, units with ambiguous ownership are selected and further prioritized based on the magnitude of resource changes. For example, unit B021 has a resource change magnitude of 35% and a usage code of U03. This code appears simultaneously in ownership units U07 and U11, so it is marked as a type with unique usage but not unique ownership. This unit is recorded as a unit with high resource fluctuation and ownership conflict. All units' codes, uses, resource change magnitudes, ownership status, and corresponding ownership unit codes are compiled to form ownership unit attribution data.

[0044] The resource distribution analysis submodule analyzes the distribution of standardized resource reserve data among various ownership units based on ownership unit attribution data, compares the distribution characteristics of resource reserves under each ownership unit, identifies key configurations for resource distribution changes, and obtains resource ownership distribution factors. All change units are categorized and summarized by ownership unit. The number of change units and total resource reserves under each ownership unit are calculated. Then, the resource proportion of each use code is broken down. If an ownership unit has more than two use codes and the difference in resource reserves under each use exceeds 20% of the total resources under that unit, it is classified as "multi-purpose resource imbalance type." If the use code within a unit is unique and the resource change is less than 10%, it is marked as "stable and single resource type." If the resource change is between 10% and 30%, it is "moderately variable resource type," and greater than 30%, it is "fluctuating resource type." This method is used to determine the resource allocation type for each ownership unit. For example, unit U... 05 contains 7 change units, covering two categories of usage codes: E01 and F04. Among them, E01 type resources account for 65% of the total reserves, and F04 type accounts for 35%, with a difference of 30%. The overall resource change range is 27%. This unit is marked as "multi-purpose resource with moderate change type". Subsequently, the resource concentration index under each ownership unit is extracted and superimposed with the change range for analysis. The concentration degree score is normalized and the critical range is defined: concentration degree less than 0.4 is "dispersed type", 0.4 to 0.7 is "intermediate type", and greater than 0.7 is "concentrated type". Combining the resource distribution type and concentration level of each unit, its resource ownership distribution factor is output to describe the structural characteristics of resource ownership of each unit.

[0045] The attribution optimization submodule determines the clarity of the attribution of each ownership unit based on the resource attribution distribution factor, compares the configuration with the single use type and the optimal range of resource distribution variation, and obtains the optimal attribution item. For each ownership unit, an optimization analysis is performed. First, a list of usage codes corresponding to all change units under that unit is retrieved. If the number of usage codes in the list is 1, it is considered a single usage type. If the number is greater than 1, they are sorted by percentage. If the highest percentage of usage codes exceeds 80%, it is also considered a single usage type; the rest are considered diverse usage types. Then, the resource change rate values ​​under that unit are averaged. If the result is less than 10%, it is marked as "optimal resource stability type," between 10% and 20% is "second best type," and more than 20% is "not best type." Finally, the single usage type and resource stability are combined for judgment. If a unit meets the requirements... If the purpose code is unique and the resource change rate is less than 10%, it is marked as the "optimal item". For example, if unit U03 has 5 change units, all of which are coded as F01 and the average resource change rate is 7%, it is determined to be the optimal item. If the purpose codes are F01 and F02, and F01 accounts for 85% and the average resource change rate is 9.5%, it is still marked as the optimal item. If the proportion of purpose codes does not meet the requirements and the change rate exceeds 20%, it is excluded from the preferred sequence. By statistically analyzing the number and proportion of units that meet the optimal conditions in each ownership unit, all configuration units that meet the requirements of single purpose and optimal resource change rate and their ownership units are output to obtain the optimal ownership item.

[0046] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A land spatial planning analysis system based on big data, characterized in that: The system includes: The boundary discrimination module is based on the ecological protection red line area. It analyzes the coordinates of the boundary nodes of the functional area, calculates the spatial distance between adjacent nodes, summarizes the spatial difference characteristics, and compares the area and perimeter with the planning standards to judge the spatial structure and obtain key morphological features. Based on the key morphological features, the application offset module optimizes the application type of spatial units, analyzes the application type encoding involved in abnormal boundary segments, compares the current application weight with the application weight of neighboring units, and determines the average distribution through the spatial neighborhood radius to obtain the application distribution offset. Based on the aforementioned usage distribution offset, the coordination analysis module determines the synergistic relationship between the spatial unit usage and the regional population density, compares the usage weight with the average level of similar usages, and combines population density analysis to determine the coordination between usage and population distribution, thereby obtaining the usage-population coupling degree. Based on the population coupling degree of the use, the change identification module analyzes the spatial distribution of boundary change points, determines the correspondence between functional area numbers and use type codes before and after the change, compares standardized resource reserve data, identifies changes in use and resources, and obtains use-resource change indicators.

2. The land spatial planning analysis system based on big data according to claim 1, characterized in that, The key morphological features include coherence parameters, compactness parameters, and boundary integrity indicators; the use distribution offset includes offset intensity, distribution heterogeneity, and spatial consistency; the use population coupling degree includes allocation parameters, synergy coefficients, and balance indicators; and the use resource change indicators include change magnitude, structural change parameters, and distribution anomaly coefficients.

3. The land spatial planning analysis system based on big data according to claim 1, characterized in that, The boundary discrimination module includes: The node distance calculation submodule is based on the ecological protection red line area. It analyzes the coordinate data of the functional area boundary nodes, uses spatial straight-line distance measurement for each group of adjacent nodes, gradually sorts out the changes in the length of the boundary segment, calculates the spatial interval distribution, and obtains the spatial interval sequence between nodes. The spatial difference summarization submodule analyzes the fluctuation segments of the boundary segment in the spatial distribution process based on the spatial interval sequence between the nodes. Through the joint determination of frequency statistics and fluctuation amplitude, it filters out the key areas of spatial change and obtains the spatial fluctuation parameters. Based on the spatial fluctuation parameters, combined with the functional area measurement results and the boundary perimeter measurement results, the boundary morphology analysis submodule determines the degree of boundary closure and enclosure compactness, analyzes the tortuousness of the path trend, and compares various morphological parameters with the planning standard parameters to obtain key morphological features.

4. The land spatial planning analysis system based on big data according to claim 1, characterized in that, The purpose offset module includes: Based on the key morphological features, the purpose optimization submodule analyzes the purpose type of spatial units, optimizes the purpose type coding of spatial units corresponding to abnormal boundary segments within the functional area, and classifies the purpose type coding of spatial units after adjustment to obtain the spatial unit purpose coding sequence. The usage weight comparison submodule compares the current usage weight of each spatial unit with the usage weight of neighboring spatial units based on the spatial unit usage encoding sequence, defines the set of neighboring units by the neighborhood range, and statistically analyzes the distribution difference between the current unit usage weight and the neighboring usage weight to obtain the usage neighborhood difference quantity. The purpose offset calculation submodule calculates the average distribution of purpose weights for each spatial unit based on the purpose neighborhood difference, filters spatial units where purpose distribution changes, judges the distribution balance and purpose concentration, and obtains the purpose distribution offset.

5. The land spatial planning analysis system based on big data according to claim 4, characterized in that, The formula used to determine the distribution balance and the concentration of use is: ； The calculation is used to balance the offset parameters, where, Representative spatial unit For the type of use The purpose of balancing offset parameters, Representative spatial unit Neighborhood usage types Average usage weight, Representative spatial unit In terms of usage type The weight of usage on, Representative spatial unit The number of neighborhood spatial units, Representative spatial unit The Neighborhood units in terms of usage type The weight of usage on.

6. The land spatial planning analysis system based on big data according to claim 1, characterized in that, The coordination analysis module includes: The collaborative relationship judgment submodule determines the correspondence between the spatial unit's use type and the population density of the area based on the use distribution offset. It matches the use type of each spatial unit with the population data, calculates the correlation between the use and population distribution of each spatial unit, and obtains the use-population matching coefficient. The use weight comparison submodule compares the use weight of the current spatial unit with the average level of the same type of use in the same region based on the use population matching coefficient, and analyzes the distribution difference between the use weight of each spatial unit and the use weight of the same type of use to obtain the weight balance parameter. The coordination analysis submodule analyzes the degree of matching between the use weight distribution and the population distribution based on the weight balance parameters and the population density of the spatial unit, judges the consistency of the two distributions, and obtains the use-population coupling degree.

7. The land spatial planning analysis system based on big data according to claim 6, characterized in that, The degree of matching between the use weight distribution and the population distribution is analyzed by combining the population density of spatial units, using the following formula: ； Determine the consistency between the two distributions to obtain the degree of coupling between population and usage, where... Representing the The population coupling degree of each spatial unit. Representing the In the _ spatial unit, the _ ... Use weight for class purposes Representing the In the _ spatial unit, the _ ... Population density corresponding to the type of use Representing the The average population density across all uses of a spatial unit. Representing the The total number of space unit usage types.

8. The land spatial planning analysis system based on big data according to claim 1, characterized in that, The change identification module includes: The boundary change analysis submodule determines the location of boundary change points in spatial distribution based on the aforementioned population coupling degree, identifies the distribution of change points in the overall spatial structure, and determines the distribution characteristics of boundary changes by combining geographic coordinate data, thereby obtaining the distribution characteristics of change points. The functional area relationship judgment submodule compares the functional area numbers and usage type codes before and after the change based on the distribution characteristics of the change points, analyzes the correspondence between the change unit numbers and usage codes, filters out units whose usage codes have changed, and obtains usage code change parameters. Based on the usage code change parameters, the resource change calculation submodule compares the standardized resource reserve data of the change unit, analyzes the distribution changes of the resource reserves of the change unit, calculates the change range of usage type code and resource distribution, and obtains the usage resource change index.

9. The land spatial planning analysis system based on big data according to claim 1, characterized in that, The system also includes: Based on the resource change index, the ownership selection module identifies the ownership unit and use type code of the change unit, analyzes the distribution of standardized resource reserve data, judges the clarity of ownership unit ownership, selects the configuration with single use and optimal resource distribution change range, and obtains the optimal ownership item. The optimal ownership criteria include uniqueness criteria and configuration optimization criteria.

10. The land spatial planning analysis system based on big data according to claim 9, characterized in that, The ownership selection module includes: The ownership identification submodule determines the ownership unit and usage type code corresponding to each change unit based on the usage resource change index, identifies the correspondence between the ownership unit and the usage code, organizes the ownership status of the change unit, and obtains ownership unit ownership data. Based on the ownership unit attribution data, the resource distribution analysis submodule analyzes the distribution of standardized resource reserve data in each ownership unit, compares the distribution characteristics of resource reserves under each ownership unit, identifies key configurations for resource distribution changes, and obtains the resource ownership distribution factor. The attribution optimization submodule determines the clarity of the attribution of each ownership unit based on the resource attribution distribution factor, compares the configuration with the single use type and the optimal range of resource distribution variation, and obtains the optimal attribution item.

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