Ring layer potential domain expansion algorithm-based scenery winning area vitality boundary demarcation method

Abstract

The invention belongs to the technical field of space boundary demarcation, and discloses a landscape winning area vitality boundary demarcation method based on a circle layer potential domain expansion algorithm, and the method specifically comprises the following steps: constructing a space vitality index system of a landscape winning area based on two dimensions of humanity interaction force and environmental bearing capacity; multi-source heterogeneous data related to the spatial vitality index system in the scenic area is collected and preprocessed, and a vitality information database of the scenic area is constructed; utilizing the vitality information database to quantify and comprehensively evaluate the vitality indexes in each grid unit, and generating a space vitality grid map reflecting the vitality level of the scenic area; performing grading processing on the vitality values in the spatial vitality grid map, extracting vitality contour lines and dividing into multiple levels of vitality circle layers; the problems of strong subjectivity, insufficient accuracy and poor dynamic adaptability of a vitality boundary demarcation method in the prior art are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of spatial boundary delineation, and in particular relates to a method for delineating the vitality boundary of a scenic spot based on a circle potential domain expansion algorithm. Background Art

[0002] With the development of the economy and society and the improvement of people's living standards, public demand for high-quality tourism and leisure environments continues to increase, placing higher demands on the landscape quality and functional optimization of scenic spots. In this context, scientifically delineating the vitality boundary, where visitor activity spaces are highly coupled to the landscape environment, has become a key technical foundation for the sustainable development and refined management of scenic spots. However, existing methods often rely on visitor behavior data, spatial thermal analysis, or empirical judgment, combined with spatial factors such as local topography, transportation accessibility, and scenic spot distribution. While these methods can initially reflect the spatial distribution of visitor activity, they lack systematic spatial hierarchical modeling and analysis of dynamic expansion mechanisms, making it difficult to accurately depict the dynamic adaptive relationship between visitor activity ranges and scenic area environmental conditions. Furthermore, existing vitality space evaluation systems rely on a single indicator and lack the coordinated integration of multidimensional factors such as visitor behavior characteristics, environmental carrying capacity, and the intensity of human interaction. This results in a single indicator system structure and weak correlation, making it difficult to fully reflect the generation logic and dynamic evolution characteristics of vitality space. The evaluation results are relatively one-sided and cannot effectively support the spatial optimization and refined management needs of scenic spots. Furthermore, traditional methods for defining vitality boundaries often rely on empirically defined thresholds, cluster analysis, or contour extraction. These methods are crude and subjective, lacking a quantitative extraction mechanism based on mathematical modeling. This makes it difficult to effectively capture the changing patterns of visitor spatial utilization. This leads to fuzzy and inconsistent boundary delineation, as well as discrepancies with the actual distribution of spatial vitality. This compromises the objectivity and accuracy of the delineation results. Consequently, existing methods for defining vitality boundaries suffer from strong subjectivity, insufficient precision, and poor dynamic adaptability. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method for delineating the vitality boundaries of scenic spots based on the circle potential expansion algorithm, which solves the problems in the existing technology that the vitality boundary delineation methods are highly subjective, lack accuracy and have poor dynamic adaptability.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] The method for defining the vitality boundary of scenic spots based on the circle potential expansion algorithm specifically includes the following steps:

[0006] Based on the dual dimensions of human interaction and environmental carrying capacity, a spatial vitality index system for scenic spots is constructed;

[0007] Collecting multi-source heterogeneous data related to the spatial vitality index system in the scenic area and preprocessing, constructing the vitality information database of the scenic area;

[0008] Using the vitality information database, quantifying and comprehensively evaluating the vitality index in each grid unit, and generating a spatial vitality grid map reflecting the vitality level of the scenic area;

[0009] Classifying the vitality values in the spatial vitality grid map, extracting the vitality contour lines and dividing the multi-level vitality circle layers;

[0010] Defining the potential domain expansion coefficient, and based on the spatial distribution characteristics of the vitality cumulative value in each level of the vitality circle layer showing exponential decay with the corresponding expansion radius, integrating the circle layer potential domain expansion algorithm;

[0011] Based on the circle layer potential domain expansion algorithm, calculating the spatial utilization degree of each level of the vitality circle layer, and fitting to generate a spatial utilization degree curve;

[0012] According to the spatial utilization degree curve, determining the optimal vector boundary layer of the scenic area, and superimposing it on the spatial vitality grid map of the scenic area to generate a spatial vitality boundary layer.

[0013] The human interaction dimension includes aggregation, diversity and benefit to the people;

[0014] The vitality index in aggregation includes the tourist aggregation index;

[0015] The vitality index in diversity includes the activity richness index;

[0016] The vitality index in benefit to the people includes the accessibility index and the point of interest density index;

[0017] The environmental carrying capacity dimension includes safety and ecology;

[0018] The vitality index in safety includes the terrain relief index, the catchment index and the aerosol optical thickness index;

[0019] The vitality index in ecology includes the vegetation coverage index, the habitat quality index and the soil conservation amount index.

[0020] Collecting multi-source heterogeneous data related to the spatial vitality index system in each grid unit and preprocessing, constructing the vitality information database of the scenic area, including the following steps:

[0021] Collecting multi-source heterogeneous data related to the spatial vitality index system in each grid unit and preprocessing, constructing the vitality information database of the scenic area, including the following steps:

[0022] A pre-processing process for data fusion and standardization of multi-source heterogeneous data to unify the spatial reference of each data. The pre-processing process includes unified coordinate system projection, resampling to a unified spatial resolution, format conversion, invalid value removal and noise filtering, and unified data time window;

[0023] Based on the pre-processed multi-source heterogeneous data, calculate the various vitality indicators in the spatial vitality index system;

[0024] All vitality index calculation results in each grid cell are output in a unified raster format, and each vitality index forms an independent indicator layer with the same spatial reference;

[0025] Combined with the existing management zoning or ecological control boundaries of the scenic area, statistical processing is performed on each indicator layer to extract the statistical parameters of each vitality indicator in each grid cell. The statistical parameters include entropy value, maximum value, minimum value and average value.

[0026] All processed indicator layers with spatial references are superimposed and integrated to build a vitality information database of scenic spots.

[0027] The vitality index in each grid cell is quantified and comprehensively evaluated to generate a spatial vitality grid map reflecting the vitality level of the scenic spot. The specific steps include:

[0028] Standardize the data of each vitality indicator in each grid cell to generate a dimensionless standardized indicator matrix;

[0029] By analyzing the information distribution characteristics, the degree of dispersion and the ability to distinguish each vitality index in the overall data of each index within the grid unit are calculated, and the relative importance coefficient of each vitality index is determined;

[0030] Based on the relative importance coefficient of each vitality indicator, weights are assigned to each vitality indicator;

[0031] The weights of each vitality indicator are weighted and synthesized with the dimensionless standardized indicator matrix to obtain a comprehensive evaluation matrix;

[0032] Sorting the data of each vitality indicator based on its relative distance from the ideal state to generate a comprehensive vitality score;

[0033] The comprehensive vitality score is assigned to the corresponding grid cell to generate a spatial vitality grid map reflecting the vitality level of the scenic spot.

[0034] The vitality values ​​in the spatial vitality grid map are graded, vitality contours are extracted, and multi-level vitality circles are divided. The specific steps include:

[0035] The vitality value is divided into a preset number of continuous vitality grades by using a standard difference segmentation method, to form a gradient interval from low vitality to high vitality;

[0036] The polygon boundary of the grid area corresponding to each vitality grade in the spatial vitality grid map is extracted to generate a closed vitality contour line, and the vector boundary layer is converted through vectorization processing;

[0037] The geometric smoothing and topological correction of each vector boundary layer are performed to construct a multi-level vitality ring layer with continuous spatial hierarchical relationship;

[0038] An attribute field is assigned to each level of vitality ring layer, and the attribute field includes ring layer number, vitality average value and ring layer area.

[0039] Based on the ring potential domain expansion algorithm, a potential domain expansion coefficient is defined, and a spatial utilization degree calculation formula is constructed, which includes the following steps:

[0040] The potential domain expansion coefficient is defined to measure the overall vitality structure characteristics of the scenic spot, and comprehensively reflects the spatial agglomeration intensity and distribution balance state of the vitality in the scenic spot. The expression of the potential domain expansion coefficient is as follows:

[0041]

[0042] In the formula, c represents the vitality aggregation value; E n represents the vitality distribution entropy;

[0043] The vitality aggregation value c is used to reflect the vitality aggregation degree of the scenic spot, and the expression of the vitality aggregation value c is as follows:

[0044]

[0045] In the formula, f(x) and f1(x) are both curve equations, and the curve equation takes the number x of each ring layer as the independent variable of the horizontal coordinate, and takes the expansion radius corresponding to each level of vitality ring layer as the dependent variable of the vertical coordinate; x represents the ring layer number, x∈[1, Q];

[0046] f(x) represents the actual expansion radius curve of the ring layer; A is the area surrounded by f(x) and the coordinate horizontal axis;

[0047] f1(x) represents the ideal expansion radius curve of the ring layer; A h is the area surrounded by f1(x) and the coordinate horizontal axis;

[0048] Q represents the preset number of vitality grades;

[0049] According to the spatial vitality grid map, a local vitality center point is identified, a spatial sub-area corresponding to the local vitality center is set, and the vitality distribution entropy E n is calculated based on the spatial sub-area. The calculation formula is:

[0050]

[0051] Where, the vitality distribution entropy E n Used to measure the distribution balance of vitality; p i It represents the ratio of the cumulative vitality value of the i-th spatial sub-area to the total vitality value of the scenic area; n is the number of spatial sub-areas;

[0052] Based on the spatial distribution characteristics of the exponential decay of the cumulative vitality value with the corresponding expansion radius in each level of vitality circle, and combined with the potential domain expansion coefficient, the calculation formula of space utilization is constructed as follows:

[0053]

[0054] Where θ x V is the space utilization of the xth level vitality circle; x R is the ratio of the cumulative vitality value in the x-th level vitality circle to the total vitality value in the scenic area; x The expansion radius of the x-level vitality circle The proportion of the expansion radius of the largest vitality circle; is an exponential decay term, which is used to reflect the trend of decreasing vitality with distance expansion.

[0055] Calculating the vitality concentration value c specifically includes the following steps:

[0056] Extract polygonal boundaries of various levels of vitality circles from the spatial vitality grid map;

[0057] Calculate the area S of each vitality circle x , and converted into expansion radius

[0058] The circle numbers of each level of vitality circle and the expansion radius of the corresponding vitality circle are used to form a sequence

[0059] Sequences corresponding to each level of vitality circle Perform interpolation fitting to construct the expression of the actual expansion radius curve f(x) of the circle layer:

[0060]

[0061] Assuming that the scenic area expands outward in a fixed scale increment, the newly added area S of each level of vitality circle is x Should remain constant, or expand radius As the level of the vitality circle increases linearly, the expression for constructing the ideal expansion radius curve f1(x) of the circle is:

[0062]

[0063] Integrate f(x) and f1(x) in the interval x∈[1, Q] to obtain A and A h ;

[0064] A and A h Substitute the value of c into the formula for calculating the value of c to obtain the value of c.

[0065] Calculate the value of E n , which specifically includes the following steps:

[0066] In the spatial vitality grid map, set a fixed neighborhood window, extract the maximum value in the neighborhood of each pixel, and construct an extreme value layer R focal (i,j) with the following expression:

[0067] R focal (i,j) = max]R(i',j')|(i',j')

[0068] Extract the local peak point by difference calculation, and the difference calculation formula is as follows:

[0069] D(i,j) = R focal (i,j) - R(i',j')

[0070] When the difference D(i,j) is zero, the pixel is the local vitality center point;

[0071] Convert the identified local vitality center point to a vector point and unify it to a consistent spatial reference system;

[0072] Based on the identified local vitality center point, use the nearest neighborhood dominance principle to generate a spatial influence domain with the local vitality center point as the control point through the shortest distance allocation rule to form a Voronoi diagram;

[0073] Each Voronoi polygon region is defined as a spatial sub-region to realize the spatial segmentation corresponding to the local vitality center point in the scenic area;

[0074] Aggregate and count the vitality values in each spatial sub-region to obtain the vitality cumulative value V i in the i-th spatial sub-region;

[0075] Calculate the sum of the vitality values in the scenic area to obtain the total vitality value V total ;

[0076] According to the vitality cumulative value V i in the i-th spatial sub-region and the total vitality value V total , calculate p i , and the specific calculation formula is as follows:

[0077]

[0078] Substitute p i Into the calculation formula of vitality distribution entropy E n , the vitality distribution entropy E n .

[0079] Based on the circle layer potential domain expansion algorithm, the spatial utilization degree of each level of vitality circle layer is calculated, and the spatial utilization degree curve is generated by fitting, specifically including the following steps:

[0080] Substitute the calculated vitality aggregation value c and vitality distribution entropy E n Into the potential domain expansion coefficient expression, the potential domain expansion coefficient γ is calculated;

[0081] Based on the spatial vitality grid map, the corresponding V x , R x Parameter values of each level of vitality circle layer are extracted and calculated;

[0082] Combined with γ, V x , R x Parameter values, the spatial utilization degree of each level of vitality circle layer is calculated;

[0083] The circle layer number x and the spatial utilization degree θ x Of the corresponding vitality circle layer are both composed of sequence (x, θ x );

[0084] Interpolation fitting is carried out on each group of sequence (x, θ x ), and the spatial utilization degree curve is generated.

[0085] According to the spatial utilization degree curve, the optimal vector boundary layer of the scenic spot is determined, and is superimposed on the spatial vitality grid map of the scenic spot to generate the spatial vitality boundary layer, specifically including the following steps:

[0086] Based on the spatial utilization degree curve, the maximum point of spatial utilization degree is extracted;

[0087] The vector boundary layer of the vitality circle layer corresponding to the circle layer number of the maximum point of spatial utilization degree is the optimal vector boundary layer;

[0088] The optimal vector boundary layer is superimposed on the spatial vitality grid map of the scenic spot to generate the spatial vitality boundary layer.

[0089] The beneficial effects of the present application are:

[0090] 1. This invention builds a spatial vitality index system that comprehensively integrates multi-source heterogeneous data such as visitor location service data, point of interest distribution data, and remote sensing images, achieving accurate identification and quantitative evaluation of the vitality space of scenic spots. By introducing a circle potential expansion algorithm, using pixels as the basic unit for circle division and space utilization calculation, it effectively avoids the subjectivity caused by relying on empirical judgment in traditional methods, thereby achieving high precision and high objectivity in boundary extraction.

[0091] By regularly collecting and updating visitor location service data, point of interest heat data, and remote sensing monitoring data, the dynamic characteristics of changes in tourist flow and scenic area environmental conditions are reflected in real time. Based on this real-time data, the potential domain expansion coefficient is dynamically adjusted, the vitality index is regularly recalculated, and the spatial vitality grid map is updated, thereby adjusting the circle division and spatial utilization calculation. In this way, the potential domain expansion coefficient can dynamically change according to the latest data, thereby ensuring that the delineation of the vitality boundary always reflects the current distribution status of the scenic area's vitality, significantly improving the dynamic adaptability of the boundary delineation, and solving the problems of the existing technology of vitality boundary delineation methods, which are highly subjective, lacking in accuracy, and poor dynamic adaptability.

[0092] 2. The present invention processes the vitality grid map in a hierarchical manner and extracts vitality contour lines to divide the multi-level vitality circles, so that the vitality boundary extraction is more in line with the actual spatial expansion law, thereby enhancing the matching of the boundary demarcation results with the geographic space structure; by calculating the spatial utilization of vitality circles at all levels and fitting the spatial utilization curve, the present invention can provide a scientific basis for the gradient development of scenic spots and the optimized construction of vitality zones, thereby promoting the rational reorganization of the spatial structure of scenic spots and the optimization and upgrading of functional layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0094] Figure 1 It is a flow chart of the method for delineating the vitality boundary of a scenic spot according to the present invention;

[0095] Figure 2 is a spatial activity grid diagram in an embodiment of the present invention;

[0096] Figure 3 Schematic diagram of the ten-level vitality circle structure in an embodiment of the present invention;

[0097] Figure 4 Schematic diagram of the thirty-level vitality circle structure in an embodiment of the present invention;

[0098] Figure 5 20-level vitality circle structure diagram in an embodiment of the present invention;

[0099] Figure 6 is the vitality distribution Voronoi diagram in an embodiment of the present invention;

[0100] Figure 7 is a graph of expansion radius in an embodiment of the present invention;

[0101] Figure 8 is a diagram showing changes in space utilization in an embodiment of the present invention;

[0102] Figure 9 It is the vitality boundary map delineated by the method of the present invention;

[0103] Figure 10 It is a vitality boundary map delineated by the outsourcing concave polygon method in the prior art;

[0104] Figure 11 It is a vitality boundary graph delineated by the Densi-Graph curve method in the prior art. DETAILED DESCRIPTION

[0105] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0106] like Figures 1 to 11 As shown in FIG, the method for defining the vitality boundary of a scenic spot based on the circle potential expansion algorithm specifically includes the following steps:

[0107] Based on the dual dimensions of human interaction and environmental carrying capacity, a spatial vitality index system for scenic spots is constructed. The spatial vitality index system is shown in Table 1.

[0108] Collect and pre-process multi-source heterogeneous data related to the spatial vitality index system in scenic spots to build a vitality information database of scenic spots;

[0109] Using the vitality information database, the vitality indicators in each grid unit are quantified and comprehensively evaluated to generate a spatial vitality grid map reflecting the vitality level of the scenic area;

[0110] The vitality values ​​in the spatial vitality grid map are graded, vitality contours are extracted, and multi-level vitality circles are divided;

[0111] The potential domain expansion coefficient is defined, and based on the spatial distribution characteristics of the exponential decay of the cumulative value of the vitality in each level of the vitality circle layer with the corresponding expansion radius, the circle layer potential domain expansion algorithm is integrated;

[0112] Based on the circle layer potential domain expansion algorithm, the spatial utilization degree of each level of the vitality circle layer is calculated, and the spatial utilization degree curve is fitted and generated;

[0113] According to the spatial utilization degree curve, the optimal vector boundary layer of the scenic spot is determined, and is superimposed on the spatial vitality grid map of the scenic spot to generate the spatial vitality boundary layer.

[0114] The human interaction force dimension includes aggregation, diversity and benefit to the people;

[0115] The vitality index in aggregation includes the tourist aggregation index (A1);

[0116] The vitality index in diversity includes the activity richness index (B1);

[0117] The vitality index in benefit to the people includes the accessibility index (C1) and the point of interest (POI) density index (C2);

[0118] The environmental carrying capacity dimension includes safety and ecology;

[0119] The vitality index in safety includes the terrain relief degree index (D1), the catchment degree index (D2) and the aerosol optical depth index (D3);

[0120] The vitality index in ecology includes the vegetation coverage index (E1), the habitat quality index (E2) and the soil retention amount index (E3);

[0121] Table 1: Spatial vitality index system of scenic spot

[0122]

[0123] Collect multi-source heterogeneous data related to the spatial vitality index system in each grid cell and preprocess to construct the vitality information database of the scenic spot, including the following steps:

[0124] Collect multi-source heterogeneous data covering the characteristics of human interaction and environmental carrying capacity in each grid cell, including the multi-source heterogeneous data including the tourist location service (LBS) data, the point of interest (POI) distribution data, the remote sensing image, the digital elevation model (DEM), the aerosol optical depth (AOD) data and the road traffic data;

[0125] The data fusion and standardization preprocessing procedure of multi-source heterogeneous data makes each data uniform in spatial reference. The preprocessing procedure includes uniform coordinate system projection, resampling uniform spatial resolution, format conversion, invalid value elimination and noise filtering, and uniform data time window.

[0126] Preferably, the format conversion can adopt GeoTIFF or Shapefile.

[0127] GeoTIFF: a raster file format used to store raster data such as remote sensing images, digital elevation models, etc.

[0128] Shapefile: a vector file format used to store vector data such as geographic boundaries, points, lines, and surfaces.

[0129] Based on the preprocessed multi-source heterogeneous data, each item of the spatial vitality index system is calculated.

[0130] The calculation results of all vitality indexes in each grid cell are output in a unified raster format, and each vitality index forms an independent index layer with the same spatial reference.

[0131] Based on the existing management division or ecological control boundary of the scenic spot, statistical parameters of each vitality index in each grid cell are extracted by statistical processing of each index layer. The statistical parameters include entropy, maximum value, minimum value, and average value.

[0132] All index layers with spatial reference are integrated by superimposition to construct the vitality information database of the scenic spot.

[0133] Quantify and comprehensively evaluate the vitality indexes in each grid cell to generate a spatial vitality grid map reflecting the vitality level of the scenic spot, which includes the following steps:

[0134] Standardize each item of vitality index data in each grid cell, unify the numerical scale by interval normalization method, distinguish positive and negative indexes, and generate a dimensionless standardized index matrix.

[0135] Through information distribution characteristic analysis, calculate the dispersion degree and discrimination ability of each item of vitality index data in the overall data of each index in the grid cell, and determine the relative importance coefficient of each vitality index.

[0136] Based on the relative importance coefficient of each vitality index, weight assignment is performed on each item of vitality index to ensure the objectivity and repeatability of the weight result.

[0137] Weight the weight of each item of vitality index and the dimensionless standardized index matrix to obtain a comprehensive evaluation matrix.

[0138] Sorting the data of each vitality indicator based on its relative distance from the ideal state to generate a comprehensive vitality score;

[0139] Assign the vitality comprehensive score value to the corresponding grid unit to generate a spatial vitality grid map reflecting the vitality level of the scenic spot;

[0140] In the examples of this application, the Taihu West Mountain Scenic Area in Suzhou, Jiangsu Province was used as the research area, and multi-source heterogeneous data covering human interaction and environmental carrying characteristics were collected, including: GF-1 satellite remote sensing images acquired on May 9, 2023, 4012 tourist location-based service (LBS) data collected through the Sina Weibo open platform in the past five years, 1793 point of interest (POI) distribution data extracted from the AutoNavi map open platform, 30-meter resolution ASTER GDEM digital elevation model data, and OpenStreetMap road network data combined with local correction information, with a total of 3706 road nodes and line segments extracted;

[0141] By analyzing the information distribution characteristics, the dispersion and discrimination ability of each vitality index data in the overall data of each index in the grid unit were calculated, and the relative importance coefficient of each vitality index was determined. Finally, the weight range of the vitality index was obtained, which was 0.06 to 0.13. The specific distribution is shown in Table 2.

[0142] In the weighted calculation stage, the weights of each vitality indicator were weighted and synthesized with the dimensionless standardized indicator matrix to obtain a comprehensive evaluation matrix. The vitality indicator data were further sorted based on their relative distance from the ideal state to generate a comprehensive vitality score. The results showed that the vitality comprehensive score range of the Xishan Scenic Area was 0.324–0.857, with an average value of 0.561. Finally, the vitality comprehensive score was assigned to the corresponding grid cell to generate a spatial vitality grid map reflecting the vitality level of the scenic area (see Figure 2). Figure 2 ), different gray levels correspond to the relative level of regional vitality;

[0143] Table 2 Weights and statistics of spatial vitality indicators of scenic spots

[0144]

[0145] The vitality values ​​in the spatial vitality grid map are graded, vitality contours are extracted, and multi-level vitality circles are divided. The specific steps include:

[0146] The standard deviation segmentation method is used to divide the vitality value into a preset number of continuous vitality levels, forming a gradient interval from low vitality to high vitality;

[0147] Polygonal boundaries are extracted for the grid areas corresponding to each vitality level in the spatial vitality grid map to generate closed vitality contour lines, which are then converted into vector boundary layers through vectorization processing.

[0148] Perform geometric smoothing and topological correction on each vector boundary layer to construct a multi-level vitality circle with continuous spatial hierarchical relationships;

[0149] Assign attribute fields to each level of vitality circle, including circle number, vitality mean and circle area;

[0150] In the vitality circle test of the embodiment, it was found that when the number of circles is less than 20 levels (such as 15 levels, 10 levels), it is difficult to carefully depict the local fluctuation characteristics of the vitality space, and the problem of "over-simplification" of the boundary is likely to occur. For example, when the number of circles is 10, the schematic diagram of the multi-level vitality circle structure is as follows: Figure 3 As shown; when the number of circles exceeds 20 (such as 25 or 30), the boundary differences between the circles tend to be blurred, affecting the efficiency of identifying the vitality expansion trend. For example, when the number of circles is 10, the schematic diagram of the multi-level vitality circle structure is as follows Figure 4 Therefore, considering the spatial expression accuracy and subsequent calculation stability, it is finally decided to divide the vitality circle into 20 levels. The corresponding multi-level vitality circle structure diagram is as follows Figure 5 shown.

[0151] Based on the potential expansion algorithm, the potential expansion coefficient is defined and the space utilization calculation formula is constructed, which specifically includes the following steps:

[0152] The potential expansion coefficient is defined to measure the overall vitality structure characteristics of scenic spots and comprehensively reflect the spatial agglomeration intensity and distribution equilibrium state of vitality in scenic spots. The expression of the potential expansion coefficient is as follows:

[0153]

[0154] In the formula, c represents the vitality concentration value; E n represents the entropy of vitality distribution;

[0155] The vitality concentration value c is used to reflect the degree of vitality concentration in a scenic area. The expression for constructing the vitality concentration value c is as follows:

[0156]

[0157] Where, f(x) and f1(x) are both curve equations, in which the circle number x is the independent variable of the horizontal coordinate and the expansion radius corresponding to each level of vitality circle is the dependent variable of the vertical coordinate; x represents the circle number, x∈[1,Q];

[0158] f(x) represents the actual expansion radius curve of the circle layer; A is the area surrounded by f(x) and the coordinate horizontal axis;

[0159] f1(x) represents the ideal expansion radius curve of the circle layer; A h is the area surrounded by f1(x) and the coordinate horizontal axis;

[0160] Q represents the number of preset vitality levels;

[0161] According to the spatial vitality grid map, a local vitality center point is identified, a spatial sub-region corresponding to the local vitality center is set, and a vitality distribution entropy E n is calculated based on the spatial sub-region.

[0162]

[0163] In the formula, the vitality distribution entropy E n is used to measure the distribution balance of vitality; p i represents the proportion of the cumulative value of the vitality in the i-th spatial sub-region to the total value of the vitality in the scenic area; n is the number of spatial sub-regions.

[0164] Based on the spatial distribution characteristics that the cumulative value of the vitality in each level of the vitality circle layer decays exponentially with the corresponding expansion radius, and combined with the potential domain expansion coefficient, the spatial utilization degree calculation formula is as follows:

[0165]

[0166] In the formula, θ x is the spatial utilization degree of the x-th level of the vitality circle layer; V x is the proportion of the cumulative value of the vitality in the x-th level of the vitality circle layer to the total value of the vitality in the scenic area; R x is the expansion radius of the x-th level of the vitality circle layer is the proportion of the expansion radius of the maximum vitality circle layer; is an exponential decay term, which is used to reflect the decreasing trend of the vitality with the distance expansion;

[0167] The above-mentioned vitality aggregation value c, vitality distribution entropy E n , and potential domain expansion coefficient are integrated into a circle layer potential domain expansion algorithm.

[0168] The vitality aggregation value c is calculated, which specifically includes the following steps:

[0169] The polygon boundary of each level of the vitality circle layer is extracted from the spatial vitality grid map;

[0170] The area S x of each vitality circle layer is calculated, and the expansion radius r

[0171] After normalization, the circle layer number of each level of vitality circle layer and the expansion radius of the corresponding vitality circle layer both constitute a sequence

[0172] The sequence corresponding to each level of vitality circle layer Interpolation fitting is performed to construct the expression of the actual expansion radius curve f(x) of the circle layer as follows:

[0173]

[0174] Assuming that the scenic area expands outward in a fixed scale increasing manner in space, the newly added area S of each level of vitality circle layer x should be kept constant, or the expansion radius should linearly increase with the level of the vitality circle layer, and the expression of the ideal expansion radius curve f1(x) of the circle layer is constructed as follows:

[0175]

[0176] Integrating f(x) and f1(x) in the interval x∈[1, Q] respectively, A and A h are obtained.

[0177] A and A h are substituted into the calculation formula of the vitality aggregation value c to obtain the vitality aggregation value c.

[0178] In the embodiments of the application, Simpson numerical integration method is used to integrate the two curves respectively, and the vitality aggregation value c = A / Ah = 0.75 is calculated.

[0179] The vitality distribution entropy E is calculated n , and specifically includes the following steps:

[0180] In the spatial vitality grid map, a fixed neighborhood window is set, the maximum value in the neighborhood of each pixel is extracted, and an extreme value layer R focal (i,j) is constructed, and the expression is as follows:

[0181] R focal (i,j) = max]R(i',j')|(i',j')

[0182] Local peak points are extracted by difference calculation, and the difference calculation formula is as follows:

[0183] D(i,j) = R focal (i,j) - R(i',j')

[0184] When the difference D(i,j) is zero, the pixel is a local vitality center point.

[0185] The identified local vitality center points are converted into vector points and unified to a consistent spatial reference system.

[0186] Based on the identified local vitality center points, the nearest neighbor dominance principle is used to generate the spatial influence domain with each local vitality center point as the control point through the shortest distance allocation rule to form a Voronoi diagram, such as Figure 6 As shown;

[0187] Each Voronoi polygon area is defined as a spatial sub-area, which realizes the spatial segmentation corresponding to the local vitality center point in the scenic area;

[0188] The vitality values ​​in each spatial sub-area are aggregated and statistically analyzed to obtain the cumulative vitality value V in the i-th spatial sub-area. i ;

[0189] The total vitality value V is calculated by accumulating the total vitality value in the scenic area. total ;

[0190] According to the cumulative vitality value V in the i-th spatial sub-region i and total vitality V total Calculate p i , the specific calculation formula is as follows:

[0191]

[0192] will p i Substitute the vitality distribution entropy E n In the calculation formula, the vitality distribution entropy E is calculated n ;

[0193] In the embodiment of this application, the vitality distribution entropy value E is finally obtained. n =2.47.

[0194] Based on the potential expansion algorithm, the spatial utilization of each level of vitality circles is calculated, and the spatial utilization curve is generated by fitting. The specific steps include:

[0195] The calculated vitality concentration value c and vitality distribution entropy E n Substitute into the potential expansion coefficient expression to calculate the potential expansion coefficient γ;

[0196] In the research experiment of the embodiment of the present application, c and E n Substituting into the potential expansion coefficient calculation formula, the system potential expansion coefficient γ≈0.216 is calculated;

[0197] Extract and calculate the V corresponding to each level of vitality circle based on the spatial vitality grid map x 、R x Parameter value;

[0198] Combined with γ, V x 、R xParameter values, calculate the space utilization of each level of vitality circle;

[0199] The circle number x and the space utilization θ of the corresponding active circle x All form a sequence (x,θ x );

[0200] For each set of sequences (x, θ x ) to perform interpolation fitting and generate a space utilization curve, such as Figure 8 As shown;

[0201] γ、V x 、R x and θ x Together they constitute the vitality structured parameters of the circles. In the research and experiments of the embodiments of this application, the vitality structured parameters of the vitality circles at all levels are shown in Table 3;

[0202] The optimal vector boundary layer of the scenic area is determined based on the spatial utilization curve and superimposed on the spatial vitality raster map of the scenic area to generate the spatial vitality boundary layer. The specific steps include:

[0203] Based on the space utilization curve, extract the maximum point of space utilization;

[0204] The vector boundary layer of the vitality circle corresponding to the circle number of the point with the maximum spatial utilization is the optimal vector boundary layer;

[0205] The optimal vector boundary layer is superimposed on the spatial vitality raster map of the scenic area to generate a spatial vitality boundary layer;

[0206] By first fitting and generating a space utilization curve and then extracting the maximum point from the space utilization curve, the interference of local fluctuations in the original space utilization data on the identification of the optimal vector boundary layer can be avoided, which has higher stability and representativeness.

[0207] Table 3 Circle Vitality Structural Parameters

[0208]

[0209]

[0210] As can be seen from Table 3, in the research and experiment of the embodiment of the present application, the space utilization θ x The maximum point of the value is located in the 9th circle layer. The vector boundary layer of the 9th circle layer is extracted and superimposed on the spatial vitality grid map of the Xishan Scenic Area. Graphic clipping and visual optimization are performed, and finally the vitality boundary map of the Xishan Scenic Area is obtained (such as Figure 9 shown);

[0211] The method of the present application is combined with the outer concave polygon method and the Densi-Graph curve method (such as Figure 10 、 11 As shown in the figure, the accuracy of the boundary delineation is compared, and the boundary accuracy evaluation formula is:

[0212]

[0213] Where: The ratio of the cumulative vitality value of the circle where the vitality boundary is located to the total vitality value. is the ratio of the coverage area of ​​the vitality boundary to the total area. The larger the μ value, the larger the proportion of vitality accumulation value can be accommodated within the smaller boundary coverage area, and the higher the fitting and accuracy of the boundary shape.

[0214] The boundary accuracy of the vitality boundary delineated by the method of the present application, the outer concave polygon method, and the Densi-Graph curve method are 2.31, 1.59, and 0.65, respectively. It can be concluded that the delineation accuracy based on the circle potential domain expansion curve is significantly improved.

[0215] The present invention realizes the accurate identification and quantitative evaluation of the vitality space of scenic spots by constructing a spatial vitality index system and comprehensively integrating multi-source heterogeneous data such as tourist location service data, point of interest distribution data, and remote sensing images; by introducing the circle potential expansion algorithm, circle division and space utilization calculation are performed with pixels as the basic unit, which effectively avoids the subjectivity problem caused by reliance on experience judgment in traditional methods, thereby achieving high precision and high objectivity in boundary extraction; by dynamically adjusting the potential expansion coefficient, by regularly collecting and updating tourist location service data, point of interest heat data and remote sensing monitoring data, the dynamic characteristics of tourist flow changes and scenic area environmental conditions are reflected in real time; based on these real-time data, the vitality index is regularly recalculated and the spatial vitality is updated raster map, and then adjust the circle division and the calculation of spatial utilization; in this way, the potential expansion coefficient can change dynamically according to the latest data, thereby ensuring that the delineation of the vitality boundary always reflects the current vitality distribution status of the scenic area, and significantly improving the dynamic adaptability of the boundary delineation; by hierarchically processing the vitality raster map and extracting vitality contour lines to divide multi-level vitality circles, the vitality boundary extraction is made more in line with the actual spatial expansion law, thereby enhancing the matching of the boundary delineation result with the geographic space structure; by calculating the spatial utilization of vitality circles at all levels and fitting the spatial utilization curve, the present invention can provide a scientific basis for the gradient development of scenic spots and the optimized construction of vitality zones, thereby promoting the rational reorganization of the spatial structure of scenic spots and the optimization and upgrading of functional layout.

[0216] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0217] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. A method for defining the vitality boundary of a scenic spot based on a circle potential expansion algorithm, characterized in that: The specific steps include: Based on the dual dimensions of human interaction and environmental carrying capacity, a spatial vitality index system for scenic spots is constructed; Collect and pre-process multi-source heterogeneous data related to the spatial vitality index system in scenic spots to build a vitality information database of scenic spots; Using the vitality information database, the vitality indicators in each grid unit are quantified and comprehensively evaluated to generate a spatial vitality grid map reflecting the vitality level of the scenic area; The vitality values ​​in the spatial vitality grid map are graded, vitality contours are extracted, and multi-level vitality circles are divided; The potential domain expansion coefficient is defined, and based on the spatial distribution characteristics of the exponential decay of the cumulative value of vitality in each level of vitality circle with the corresponding expansion radius, the potential domain expansion algorithm of the circle is integrated; Based on the potential expansion algorithm of the circle, the spatial utilization of each level of vitality circles is calculated, and the spatial utilization curve is generated by fitting; The optimal vector boundary layer of the scenic area is determined according to the spatial utilization curve and superimposed on the spatial vitality raster map of the scenic area to generate the spatial vitality boundary layer.

2. The method for defining the vitality boundary of a scenic spot according to claim 1, characterized in that: The dimensions of human interaction include aggregation, diversity and benefit to the people; The vitality index in agglomeration includes the tourist concentration index; The indicators of vitality in diversity include activity richness indicators; The vitality index in benefiting the people includes the accessibility index and the density index of points of interest; The environmental carrying capacity dimensions include safety and ecology; The safety indicators include terrain relief index, water collection index and aerosol optical depth index; Ecological vitality indicators include vegetation coverage index, habitat quality index and soil conservation index.

3. The method for defining the vitality boundary of a scenic spot according to claim 2, characterized in that: The multi-source heterogeneous data related to the spatial vitality index system in each grid cell are collected and pre-processed to construct a vitality information database of scenic spots. The specific steps include: Collect multi-source heterogeneous data covering human interaction and environmental carrying characteristics in each grid cell. The multi-source heterogeneous data includes visitor location service data, point of interest distribution data, remote sensing imagery, digital elevation model, aerosol optical depth data, and road traffic data; A pre-processing process for data fusion and standardization of multi-source heterogeneous data to unify the spatial reference of each data. The pre-processing process includes unified coordinate system projection, resampling to a unified spatial resolution, format conversion, invalid value removal and noise filtering, and unified data time window; Based on the pre-processed multi-source heterogeneous data, calculate the various vitality indicators in the spatial vitality index system; All vitality index calculation results in each grid cell are output in a unified raster format, and each vitality index forms an independent indicator layer with the same spatial reference; Combined with the existing management zoning or ecological control boundaries of the scenic area, statistical processing is performed on each indicator layer to extract the statistical parameters of each vitality indicator in each grid cell. The statistical parameters include entropy value, maximum value, minimum value and average value. All processed indicator layers with spatial references are superimposed and integrated to build a vitality information database of scenic spots.

4. The method for defining the vitality boundary of a scenic spot according to claim 3, characterized in that: The vitality index in each grid cell is quantified and comprehensively evaluated to generate a spatial vitality grid map reflecting the vitality level of the scenic spot. The specific steps include: Standardize the data of each vitality indicator in each grid cell to generate a dimensionless standardized indicator matrix; By analyzing the information distribution characteristics, the degree of dispersion and the ability to distinguish each vitality index in the overall data of each index within the grid unit are calculated, and the relative importance coefficient of each vitality index is determined; Based on the relative importance coefficient of each vitality indicator, weights are assigned to each vitality indicator; The weights of each vitality indicator are weighted and synthesized with the dimensionless standardized indicator matrix to obtain a comprehensive evaluation matrix; Sorting the data of each vitality indicator based on its relative distance from the ideal state to generate a comprehensive vitality score; The comprehensive vitality score is assigned to the corresponding grid cell to generate a spatial vitality grid map reflecting the vitality level of the scenic spot.

5. The method for defining the vitality boundary of a scenic spot according to claim 4, characterized in that: The vitality values ​​in the spatial vitality grid map are graded, vitality contours are extracted, and multi-level vitality circles are divided. The specific steps include: The standard deviation segmentation method is used to divide the vitality value into a preset number of continuous vitality levels, forming a gradient interval from low vitality to high vitality; Polygonal boundaries are extracted for the grid areas corresponding to each vitality level in the spatial vitality grid map to generate closed vitality contour lines, which are then converted into vector boundary layers through vectorization processing. Perform geometric smoothing and topological correction on each vector boundary layer to construct a multi-level vitality circle with continuous spatial hierarchical relationships; Assign attribute fields to each level of vitality circle, including circle number, average vitality and circle area.

6. The method for defining the vitality boundary of a scenic spot according to claim 5, characterized in that: The potential domain expansion coefficient is defined, and based on the spatial distribution characteristics of the exponential decay of the cumulative vitality value of each level of vitality circle with the corresponding expansion radius, the potential domain expansion algorithm of the integrated circle is integrated, which specifically includes the following steps: The potential expansion coefficient is defined to measure the overall vitality structure characteristics of scenic spots and comprehensively reflect the spatial agglomeration intensity and distribution equilibrium state of vitality in scenic spots. The expression of the potential expansion coefficient is as follows: In the formula, c represents the vitality concentration value; E n represents the entropy of vitality distribution; The vitality concentration value c is used to reflect the degree of vitality concentration in a scenic area. The expression for constructing the vitality concentration value c is as follows: Where, f(x) and f1(x) are both curve equations, in which the circle number x is the independent variable of the horizontal coordinate and the expansion radius corresponding to each level of vitality circle is the dependent variable of the vertical coordinate; x represents the circle number, x∈[1,Q]; f(x) represents the actual expansion radius curve of the circle; A is the area enclosed by f(x) and the horizontal axis of the coordinate; f1(x) represents the ideal expansion radius curve of the circle; A h is the area enclosed by f1(x) and the horizontal axis of the coordinate system; Q represents the number of preset vitality levels; According to the spatial vitality grid map, the local vitality center point is identified, the spatial sub-area corresponding to the local vitality center is set, and the vitality distribution entropy E is constructed based on the spatial sub-area. n The calculation formula is: Where, the vitality distribution entropy E n Used to measure the distribution balance of vitality; p i It represents the ratio of the cumulative vitality value of the i-th spatial sub-area to the total vitality value of the scenic area; n is the number of spatial sub-areas; Based on the spatial distribution characteristics of the exponential decay of the cumulative vitality value with the corresponding expansion radius in each level of vitality circle, and combined with the potential domain expansion coefficient, the calculation formula of space utilization is constructed as follows: Where θ x V is the space utilization of the xth level vitality circle; x R is the ratio of the cumulative vitality value in the x-th level vitality circle to the total vitality value in the scenic area; x The expansion radius of the x-level vitality circle The proportion of the expansion radius of the largest vitality circle; is an exponential decay term, which is used to reflect the trend of decreasing vitality with distance expansion.

7. The method for defining the vitality boundary of a scenic spot according to claim 6, characterized in that: Calculating the vitality concentration value c specifically includes the following steps: Extract polygonal boundaries of various levels of vitality circles from the spatial vitality grid map; Calculate the area S of each vitality circle x , and converted into expansion radius The circle numbers of each level of vitality circle and the expansion radius of the corresponding vitality circle are used to form a sequence Sequences corresponding to each level of vitality circle Perform interpolation fitting to construct the expression of the actual expansion radius curve f(x) of the circle layer: Assuming that the scenic area expands outward in a fixed scale increment, the newly added area S of each level of vitality circle is x Should remain constant, or expand radius As the level of the vitality circle increases linearly, the expression for constructing the ideal expansion radius curve f1(x) of the circle is: Integrate f(x) and f1(x) in the interval x∈[1, Q] to obtain A and A h ; A and A h Substitute into the vitality concentration value c calculation formula to obtain the vitality concentration value c.

8. The method for defining the vitality boundary of a scenic spot according to claim 7, characterized in that: Calculate the vitality distribution entropy E n , specifically including the following steps: In the spatial activity grid map, a fixed neighborhood window is set to extract the maximum value in the neighborhood of each pixel and construct the extreme value layer R. focal (i,j), the expression is as follows: R focal (i,j)=max{R(i′,j′)|(i′,j′)} The local peak point is extracted by difference calculation. The difference calculation formula is as follows: D(i,j)=R focal (i,j)-R(i′,j′) When the difference D(i,j) is zero, the pixel is the local activity center; Convert the identified local vitality center points into vector points and unify them into a consistent spatial reference system; Based on the identified local vitality centers, the nearest neighbor dominance principle is used to generate the spatial influence domain with each local vitality center as the control point through the shortest distance allocation rule to form a Voronoi diagram. Each Voronoi polygon area is defined as a spatial sub-area, which realizes the spatial segmentation corresponding to the local vitality center point in the scenic area; The vitality values ​​in each spatial sub-area are aggregated and statistically analyzed to obtain the cumulative vitality value V in the i-th spatial sub-area. i ; The total vitality value V is calculated by accumulating the total vitality value in the scenic area. total ; According to the cumulative vitality value V in the i-th spatial sub-region i and total vitality V total Calculate p i , the specific calculation formula is as follows: will p i Substitute the vitality distribution entropy E n In the calculation formula, the vitality distribution entropy E is calculated n .

9. The method for defining the vitality boundary of a scenic spot according to claim 8, characterized in that: Based on the potential expansion algorithm, the spatial utilization of each level of vitality circles is calculated, and the spatial utilization curve is generated by fitting. The specific steps include: The calculated vitality concentration value c and vitality distribution entropy E n Substitute into the potential expansion coefficient expression to calculate the potential expansion coefficient γ; Extract and calculate the V corresponding to each level of vitality circle based on the spatial vitality grid map x 、R x Parameter value; Combined with γ, V x 、R x Parameter values, calculate the space utilization of each level of vitality circle; The circle number x and the space utilization θ of the corresponding active circle x All form a sequence (x,θ x ); For each set of sequences (x, θ x ) to perform interpolation fitting and generate the space utilization curve.

10. The method for defining the vitality boundary of a scenic spot according to claim 9, characterized in that: The optimal vector boundary layer of the scenic area is determined based on the spatial utilization curve and superimposed on the spatial vitality raster map of the scenic area to generate the spatial vitality boundary layer. The specific steps include: Based on the space utilization curve, extract the maximum point of space utilization; The vector boundary layer of the vitality circle corresponding to the circle number of the point with the maximum spatial utilization is the optimal vector boundary layer; The optimal vector boundary layer was overlaid on the spatial vitality raster map of the scenic area to generate a spatial vitality boundary layer.

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