A multi-dimensional weight plate value thermal map generation method

Abstract

This invention discloses a method for generating a multi-dimensional weighted heatmap of land value, relating to the field of land data analysis technology. The method includes: acquiring a set of multi-dimensional value quantification parameters for land parcels; mapping these parameters to a geographic raster matrix using a GIS geographic coordinate system to construct a spatiotemporal cube data structure; extracting various environmental interference factors corresponding to residential comfort; constructing a multi-factor fusion color mixing function; calculating the comprehensive environmental masking weight; processing the basic value color layer to generate an environmental interference layer; extracting the comprehensive value index from the spatiotemporal cube data structure; using a dynamic clustering algorithm to achieve adaptive grading and determine grading threshold intervals; building a non-linear color mapping lookup table; mapping the threshold intervals to a preset gradient color spectrum to generate a color mapping layer; constructing a multi-level layer stack structure; overlaying the environmental interference layer and the color mapping layer; and using a graphics rendering pipeline combined with a fragment shader to complete pixel blending operations to generate a land value heatmap.

Description

Technical Field

[0001] This invention relates to the field of land data analysis technology, and in particular to a method for generating a multi-dimensional weighted heatmap of land value. Background Technology

[0002] In the fields of geographic information applications such as urban planning and real estate valuation, the heat map of regional value is a core visualization tool used to intuitively present the distribution of comprehensive regional value.

[0003] Chinese invention patent with publication number CN121503921A discloses a land value assessment method based on multi-source heterogeneous data fusion. By collecting four types of multi-source heterogeneous data—baseline status, planning conditions, market feedback, and social perception—it constructs an assessment system containing multi-level indicators. The analytic hierarchy process (AHP) is used to determine the weight of each indicator and calculate the land value index by weighting it level by level. Finally, the land value index is mapped to a preset geographic grid unit using ArcGIS Pro. The corresponding color depth is matched according to the value index value to generate and output a land value heat map.

[0004] The aforementioned existing technology generates heat maps by directly mapping a single value index to a fixed color system, which results in the heat maps failing to intuitively and accurately reflect the attenuation effect of various environmental interference factors on the overall value of the sector. Summary of the Invention

[0005] To address the technical problem that existing technologies generate heatmaps by directly mapping a single value index to a fixed color system, resulting in heatmaps that cannot intuitively and accurately reflect the attenuation effect of multiple environmental interference factors on the overall value of a sector, this invention provides a multi-dimensional weighted method for generating sector value heatmaps.

[0006] The technical solution adopted in this invention is: a method for generating a multi-dimensional weighted sector value heatmap, comprising the following steps:

[0007] Step 1: Obtain the multidimensional value quantification parameter set of the sector, and map the multidimensional value quantification parameter set of the sector to the geographic raster matrix based on the GIS geographic coordinate system to generate a spatiotemporal cube data structure.

[0008] Step 2: Extract various environmental interference factors corresponding to living comfort from the spatiotemporal cube data structure, construct a color mixing function that integrates multiple environmental interference factors, and calculate the comprehensive environmental masking weight; process the basic value color layer based on the comprehensive environmental masking weight to generate the environmental interference layer;

[0009] Step 3: Extract the comprehensive value index from the spatiotemporal cube data structure, perform adaptive grading on the comprehensive value index through a dynamic clustering algorithm, generate grading threshold intervals, construct a nonlinear color mapping lookup table, map the grading threshold intervals to a preset gradient color spectrum, and generate a color mapping layer.

[0010] Step 4: Construct a multi-level layer stack structure, overlay the environmental interference layer and the color mapping layer onto the multi-level layer stack structure, and perform pixel blending operations on the overlay layers through the fragment shader based on the graphics rendering pipeline to generate a heat map of the sector value.

[0011] Preferably, the step of mapping the multidimensional value quantification parameter set of the plate to a geographic raster matrix based on the GIS geographic coordinate system to generate a spatiotemporal cube data structure includes:

[0012] The system calculates the value gradient features in the multidimensional value quantification parameter set of the landmass, and performs non-uniform grid division on the target geographic space based on the value gradient features. For high-value areas with value gradient values ​​greater than a first preset threshold, data mapping is completed using fine-grained grids of the first size. For medium-value areas with value gradient values ​​greater than or equal to a second preset threshold and less than or equal to the first preset threshold, data mapping is completed using medium-grained grids of the third size. For low-value areas with value gradient values ​​less than the second preset threshold, data mapping is completed using coarse-grained grids of the second size. A non-uniform geographic grid matrix is ​​constructed based on the hybrid division results of fine-grained, medium-grained, and coarse-grained grids. The multidimensional value quantification parameter set of the landmass is completely mapped to the non-uniform geographic grid matrix to generate a spatiotemporal cube data structure.

[0013] Preferably, step 2 includes the following:

[0014] Extract various environmental interference factors corresponding to living comfort from the spatiotemporal cube data structure. These environmental interference factors include light pollution intensity, noise pollution intensity, and dust pollution intensity.

[0015] Obtain the functional attributes of the block to which the grid cell belongs, including residential, commercial and industrial types;

[0016] Based on the functional attributes of the aforementioned modules, a corresponding interference weight coefficient is assigned to each type of environmental interference factor.

[0017] A color mixing function that integrates multiple environmental interference factors is constructed, and the comprehensive environmental masking weight is calculated by combining the interference weight coefficients with the values ​​of each environmental interference factor.

[0018] Based on the comprehensive environmental masking weight processing of the basic value color layer, an environmental interference layer is generated.

[0019] Preferably, a color mixing function integrating multiple environmental interference factors is constructed, and the comprehensive environmental masking weight is calculated by combining the interference weight coefficients with the values ​​of each environmental interference factor, including:

[0020] Obtain the regional baseline environmental reference value of the target geospace, wherein the regional baseline environmental reference value corresponds to the original environmental value of each environmental disturbance factor;

[0021] The values ​​of each environmental disturbance factor are normalized with the corresponding background environmental benchmark value of the region to obtain standardized environmental disturbance values.

[0022] A nonlinear mapping function is constructed to map the standardized environmental disturbance values ​​into single-factor masking weights;

[0023] Based on the aforementioned interference weight coefficients, the weights of each single-factor masking are weighted and summed to obtain the comprehensive environmental masking weights; wherein, the mapping relationship of the nonlinear mapping function matches the visual perception characteristics of human eyes regarding color attenuation.

[0024] Preferably, constructing the nonlinear color mapping lookup table includes:

[0025] Set the value index range and the warning color band mapping range;

[0026] By using a segmented interpolation algorithm, different segments of the value index range are mapped to corresponding color systems, and the warning color band mapping range is mapped to warning colors, generating a color lookup table containing warning breakpoints.

[0027] Preferably, the step of mapping different segments of the value index range to corresponding color systems using a segmented interpolation algorithm includes:

[0028] Obtain the functional attributes of the grid unit corresponding to the segment, and based on the functional attributes of the segment, call the preset differential interpolation parameter set; use the differential interpolation parameter set to perform piecewise interpolation on the value index interval to generate a non-linear color gradient that matches the segment type.

[0029] Preferably, the generation of the sector value heatmap includes:

[0030] Define a fragment shader procedure, which includes a heatmap texture sampler, an environment mask texture sampler, and a time variable;

[0031] A sinusoidal perturbation factor is generated based on the time variable. The sinusoidal perturbation factor is coupled with the luminance component of the color mapping layer to generate a heatmap texture with dynamic lighting effects. The heatmap texture is written into the frame buffer and output.

[0032] Preferably, the sinusoidal perturbation factor is coupled with the luminance component of the color mapping layer to generate a heatmap texture with dynamic lighting effects; the heatmap texture is written to the frame buffer and output, including:

[0033] Obtain the vector boundary data of the target section, and perform spatial clipping on the color mapping layer based on the vector boundary data to generate a boundary-constrained color mapping texture;

[0034] The sinusoidal perturbation factor is coupled with the luminance component of the boundary-constrained color mapping texture to generate a dynamic light effect texture defined within the plate boundary.

[0035] Obtain the rendering load parameters of the current device, and perform adaptive downsampling processing on the dynamic light effect texture based on the rendering load parameters to generate a target heatmap texture that adapts to the current rendering load.

[0036] Write the target heatmap texture into the frame buffer and output it.

[0037] The beneficial effects of this invention are as follows: by integrating the multidimensional value quantification parameters of the plate with various environmental interference factors into the spatiotemporal cube data structure, the unified carrying of value data and environmental interference data can be achieved; by constructing a comprehensive environmental masking weight to generate an environmental interference layer, and superimposing the environmental interference layer and the color mapping layer through a multi-level layer stack and using the graphics rendering pipeline to complete pixel mixing rendering, the attenuation effect of various environmental interferences on the comprehensive value of the plate can be intuitively presented, effectively improving the completeness of the visualization representation of the plate value heat map. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating the method of the present invention. Detailed Implementation

[0039] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0040] To address the technical problems of existing technologies that cannot integrate various environmental disturbance factors affecting living comfort into the land area value heatmap rendering process and cannot intuitively represent the impact of environmental disturbances on the decline of land area comprehensive value, this embodiment provides a multi-dimensional weighted land area value heatmap generation method, such as... Figure 1 As shown, it includes the following steps:

[0041] Step 1: Obtain the multidimensional value quantification parameter set of the sector, and map the multidimensional value quantification parameter set of the sector to the geographic raster matrix based on the GIS geographic coordinate system to generate a spatiotemporal cube data structure.

[0042] Among them, the multi-dimensional value quantification parameter set of the urban area is a multi-dimensional quantitative data set used to comprehensively characterize the comprehensive value of the urban geographical area, including the location value parameter, supporting facility value parameter, ecological environment value parameter, market transaction value parameter, and environmental disturbance parameters related to living comfort.

[0043] A geographic raster matrix is ​​a two-dimensional spatial matrix formed by combining several independent geographic raster units after the geographic space of a target city is divided according to the geographic coordinate system. Each geographic raster unit carries the value parameters and environmental interference data of the corresponding geographic space.

[0044] The spatiotemporal cube data structure refers to a three-dimensional data storage structure that uses a geographic raster matrix as the spatial basis and integrates multi-dimensional value quantification parameters of the plate, multiple types of environmental interference factors, and dynamically adjusted weight parameters. It can realize the unified storage and rapid retrieval of value data, environmental data, and weight data.

[0045] To address the precision imbalance inherent in existing technologies employing globally uniform resolution geographic raster matrices—where using uniform coarse rasteres in high-value areas leads to a loss of value detail representation, while using uniform fine rasteres in low-value areas generates redundant data and wastes computing resources—and to resolve the technical problem of failing to balance the value representation accuracy of heatmaps with rendering efficiency, one possible implementation involves mapping the multi-dimensional value quantification parameter set of the affected area to a geographic raster matrix based on a GIS geographic coordinate system to generate a spatiotemporal cube data structure, including:

[0046] The value gradient features of the multidimensional value quantification parameter set of the calculation block are used to perform non-uniform grid partitioning on the target geographic space based on the value gradient features; the value gradient is calculated using the following formula:

[0047]

[0048] Among them The value gradient of the current grid cell, This represents the overall value of the current grid cell. This represents the combined value of adjacent grid cells. This represents the spatial distance between the current grid cell and its adjacent grid cells. It is the minimum distance threshold, set according to the accuracy of the target area data, used to avoid the risk that the gradient value tends to infinity when D approaches 0.

[0049] For high-value areas with value gradient values ​​greater than a first preset threshold, data mapping is performed using fine-grained rasters of the first size; for medium-value areas with value gradient values ​​greater than or equal to a second preset threshold and less than or equal to the first preset threshold, data mapping is performed using medium-grained rasters of the third size; for low-value areas with value gradient values ​​less than the second preset threshold, data mapping is performed using coarse-grained rasters of the second size; a non-uniform geographic raster matrix is ​​constructed based on the hybrid partitioning results of fine-grained, medium-grained, and coarse-grained rasters; the multi-dimensional value quantification parameter set of the blocks is completely mapped to the non-uniform geographic raster matrix to generate a spatiotemporal cube data structure.

[0050] Step 2: Extract various environmental interference factors corresponding to living comfort from the spatiotemporal cube data structure, construct a color mixing function that integrates multiple environmental interference factors, and calculate the comprehensive environmental masking weight; process the basic value color layer based on the comprehensive environmental masking weight to generate the environmental interference layer.

[0051] It should be noted that environmental disturbance factors refer to negative environmental parameters that directly affect the living comfort of a residential area, specifically including three categories: light pollution intensity, noise pollution intensity, and dust pollution intensity.

[0052] A color blending function is a specialized function model used to fuse the values ​​of multiple environmental interference factors and calculate the environmental masking weights. The comprehensive environmental masking weight refers to the weighting parameters calculated based on the weighted average of multiple environmental interference factors, used to characterize the degree to which environmental interference attenuates the base color layer.

[0053] The basic value color layer refers to a basic color layer generated solely based on the sector's comprehensive value index, without incorporating any environmental interference factors. The environmental interference layer refers to a separate layer specifically representing the attenuation effect of environmental interference after processing the basic value color layer based on the comprehensive environmental masking weight.

[0054] To address the technical problems of the inability to integrate and characterize multiple types of environmental interference factors, the lack of differentiation in the sensitivity of environmental interference to different functional modules, the imbalance in interference characterization caused by differences in the background environment of different regions, and the inconsistency between color attenuation and the characteristics of human visual perception, in one possible implementation, step 2 includes the following:

[0055] First, three types of environmental disturbance factors—light pollution intensity, noise pollution intensity, and dust pollution intensity—corresponding to livability in the spatiotemporal cube data structure are extracted. The functional attributes of each geographic raster unit (residential, commercial, or industrial) are obtained, and a corresponding disturbance weight coefficient is assigned to each type of environmental disturbance factor based on these functional attributes. Then, the regional baseline environmental benchmark values ​​for each type of environmental disturbance factor within the target geographic space are obtained. Finally, the actual monitored values ​​of each type of environmental disturbance factor and the corresponding regional baseline environmental benchmark values ​​are substituted into the standardized environmental disturbance value calculation formula.

[0056]

[0057] in For the first Standardized environmental disturbance values ​​for environmental disturbance factors. For the first Actual monitoring values ​​of environmental disturbance factors, For the first Regional background environmental baseline values ​​for environmental disturbance factors. Values , , , Represents the intensity of light pollution. Represents the intensity of noise pollution. Represents the intensity of dust pollution;

[0058] After calculating the standardized environmental interference values, a logarithmic nonlinear mapping function matching the visual perception characteristics of the human eye is constructed. The subjective perception of color occlusion and environmental interference by the human eye does not follow a linear change law, but conforms to the Weber-Fechner visual perception law: the sensitivity to weak environmental interference is high, and the perceived value will decrease significantly with a small increase in interference; when the intensity of environmental interference reaches a certain level, the visual perception of the human eye tends to saturate, and if the intensity of interference is further increased, the subjective feeling will no longer increase synchronously and linearly.

[0059] Therefore, linear mapping is not suitable. This invention uses a monotonically increasing, convex saturated logarithmic nonlinear curve as the mapping form: the curve has a large slope and the weight changes are sensitive in the low-interference interval, while the curve's slope gradually converges and the weight tends to the upper limit of saturation in the high-interference interval, closely matching the real visual perception of the human eye. The nonlinear mapping function is constructed as follows:

[0060]

[0061] in: For the first The single-factor masking weights corresponding to environmental interference factors are constrained in the range of... ; For the first Standard environmental interference values ​​for environmental interference factors; The maximum value of the standardized environmental disturbance values ​​for all grid cells within the target geographic space; This is the visual correction factor, with a value range of [value range missing]. It is used to fine-tune the visual effect of color decay and adapt the display effect of heat map.

[0062] Substituting the standard environmental disturbance values ​​of each grid cell into the aforementioned nonlinear mapping function, the constraints can be obtained grid-by-grid mapping. Interval, single-factor masking weights that conform to the laws of human visual perception.

[0063] Substitute the various interference weight coefficients and their corresponding single-factor masking weights into the comprehensive environment masking weight calculation formula:

[0064]

[0065] in To incorporate environmental masking weights, , , These are the interference weighting coefficients corresponding to light pollution intensity, noise pollution intensity, and dust pollution intensity, respectively. , , These are the single-factor masking weights corresponding to the three types of environmental interference factors.

[0066] The comprehensive environmental shading weight is obtained by weighted summation. The overall environmental shading weight values ​​are constrained under the following conditions: In this range, a larger weight value indicates stronger environmental interference and a greater degree of attenuation of the color value of the area. Based on the comprehensive environmental occlusion weight, pixel-level brightness and color saturation attenuation processing is performed on the basic value color layer. Specifically, the original brightness value of the corresponding pixel in each geographic raster in the basic value color layer is extracted. With color saturation The attenuation is calculated using the environmental attenuation mapping formula:

[0067]

[0068] In the formula: This represents the pixel brightness value after attenuation. This represents the pixel color saturation after attenuation. This is the brightness attenuation adjustment coefficient, with a value range of... This is used to match the human eye's perception of the brightness and darkness of a heatmap; The overall environmental shading weight is determined.

[0069] The pixel colors that have undergone both brightness and saturation attenuation are reassigned, and the entire image is reconstructed grid by grid to generate an independent layer that only represents the effect of value color attenuation caused by environmental interference. This layer is called the environmental interference layer.

[0070] It should be noted that the interference weighting coefficient is objectively set based on the sensitivity of the functional attributes of the area to environmental disturbances. Residential areas are the most sensitive, corresponding to the largest interference weighting coefficient; commercial areas are the next most sensitive, corresponding to a medium interference weighting coefficient; and industrial areas are the least sensitive, corresponding to the smallest interference weighting coefficient. The regional background environmental baseline value is objectively determined based on long-term environmental monitoring statistics of the target geographic space.

[0071] This step can integrate multiple environmental interference factors to achieve comprehensive characterization, combine the functional attributes of the plates to allocate interference weights differently, calibrate the background environmental differences in different areas, and match the visual perception characteristics of color decay of the human eye, so that the attenuation effect of environmental interference on the comprehensive value of the plates can be expressed more accurately and more in line with actual application scenarios.

[0072] Step 3: Extract the comprehensive value index from the spatiotemporal cube data structure, perform adaptive grading on the comprehensive value index using a dynamic clustering algorithm, generate grading threshold intervals, construct a nonlinear color mapping lookup table, map the grading threshold intervals to a preset gradient color spectrum, and generate a color mapping layer.

[0073] It should be noted that the comprehensive value index refers to a quantitative value in the spatiotemporal cube data structure used to comprehensively represent the overall value level of a sector. The dynamic clustering algorithm is a clustering algorithm that adaptively classifies value levels based on the actual spatial distribution characteristics of the comprehensive value index, without requiring manually preset fixed classification thresholds.

[0074] The tiered threshold range refers to the range of comprehensive value index values ​​corresponding to different value levels, adaptively generated by a dynamic clustering algorithm. The non-linear color mapping lookup table is a table model used to map the comprehensive value index range to corresponding colors, including warning breakpoints and differentiated color gradients matched with the functional attributes of the sectors.

[0075] To address the technical issues of existing value grading methods failing to adapt to actual data distribution and neglecting outliers that cause grading threshold shifts, the implementation process first extracts the comprehensive value index from the spatiotemporal cube data structure. This comprehensive value index undergoes anti-outlier preprocessing, such as Winsorize truncation to limit values ​​to ±3 standard deviations and eliminate extreme outliers. Subsequently, an anti-outlier dynamic clustering algorithm, such as DBSCAN or K-medoids, is used to adaptively grade the preprocessed comprehensive value index, generating a grading threshold range that adapts to the data distribution.

[0076] Define the standard value range and warning color band mapping range for the comprehensive value index; obtain the functional attributes of the corresponding geographic grid units; based on the functional attributes of the units, call the preset differentiated interpolation parameter set; and perform interpolation calculation on the value index range using a piecewise interpolation algorithm. The interpolation calculation adopts the following piecewise interpolation formula:

[0077]

[0078] in These are the color parameters after interpolation. The color reference parameters are the starting points of the segmented intervals. The color reference parameters are the end points of the segmented intervals. This is the normalized value of the comprehensive value index within the segmented intervals.

[0079] The above formula generates a non-linear color gradient that matches the functional attributes of the module; the warning color band mapping range is mapped to a dedicated warning color, generating a non-linear color mapping lookup table containing warning breakpoints; finally, the graded threshold range is mapped to a preset gradient color spectrum, generating a color mapping layer.

[0080] This step can adaptively classify value levels, generate differentiated non-linear color gradients based on the functional attributes of the blocks, and set warning color bands to make the value color expression of the heat map more in line with the actual application scenarios of urban blocks, effectively improving the intuitiveness and accuracy of block value assessment.

[0081] Step 4: Construct a multi-level layer stack structure, overlay the environmental interference layer and the color mapping layer onto the multi-level layer stack structure, and perform pixel blending operations on the overlay layers through the fragment shader based on the graphics rendering pipeline to generate a heat map of the sector value.

[0082] It should be noted that the multi-level layer stack structure refers to a multi-level layer storage structure used to overlay and store environmental interference layers, color mapping layers, and basic geographic base maps, which can realize the orderly overlay and management of multiple layers.

[0083] The graphics rendering pipeline refers to the standardized rendering workflow that combines hardware and software to complete layer rendering, pixel blending, and lighting effects processing. A fragment shader is a dedicated rendering program used to color, blend, and process the lighting effects of pixels in overlay layers. Pixel blending operations are standardized rendering operations that fuse and calculate the pixel parameters of the ambient noise layer and the color map layer.

[0084] To address the technical issues of coarse rendering due to layer overlays, out-of-bounds dynamic lighting effects, and poor multi-device rendering compatibility in existing technologies, the implementation process first constructs a multi-level layer stack structure, orderly stacking the environment interference layer and color mapping layer into this structure. A fragment shader program is defined, comprising a heatmap texture sampler, an environment mask texture sampler, and a time variable. Based on the time variable, a sinusoidal fluctuation perturbation factor is generated using a sinusoidal fluctuation factor calculation formula.

[0085]

[0086] in The sinusoidal wave disturbance factor. The perturbation frequency for dynamic lighting effects. The time variable is used; the vector boundary data of the target plate is obtained, and spatial clipping is performed on the color mapping layer based on the vector boundary data to generate a boundary-constrained color mapping texture; the sinusoidal fluctuation disturbance factor and the luminance component of the boundary-constrained color mapping texture are coupled through a luminance coupling calculation formula:

[0087]

[0088] in These are the brightness parameters after coupling. The original brightness parameters for boundary-constrained color mapping textures. It is the sinusoidal wave disturbance factor.

[0089] After coupling, a dynamic light effect texture is generated that appears only within the plate boundary; the rendering load parameters of the currently running device are obtained, and adaptive downsampling processing is performed on the dynamic light effect texture based on the rendering load parameters to generate a target heat map texture that adapts to the current rendering load; based on the graphics rendering pipeline, pixel blending operations are performed on the overlay layer through the fragment shader, the target heat map texture is written to the frame buffer and output to generate a plate value heat map.

[0090] It should be noted that the perturbation frequency of dynamic lighting effects is objectively set based on the visual comfort of the human eye to avoid the frequency being too high or too low and affecting the visual effect; the threshold of rendering load parameters is objectively divided according to the hardware configuration standards of different terminal devices; the adaptive downsampling ratio is dynamically adjusted according to the rendering load level, and the higher the rendering load, the greater the downsampling ratio.

[0091] This step enables precise pixel-by-pixel rendering of multiple layers, strictly confining dynamic lighting effects within the boundaries of the plates and improving the visual regularity of the heatmap; at the same time, it adaptively adjusts rendering parameters according to the device's rendering load, effectively improving the smoothness and adaptability of the heatmap on different terminal devices.

[0092] In summary, this invention, through a complete technical solution involving non-uniform grid division, multi-environmental interference factor fusion calculation, segment functional attribute adaptation, differentiated nonlinear color mapping, and dynamic rendering optimization, optimizes the entire process from data storage, interference calculation, color mapping, to rendering output. It effectively solves a series of technical problems in existing technologies, such as incomplete visualization of segment value heatmaps, grid precision imbalance, distorted representation of environmental interference, lack of color differentiation, and poor rendering adaptability. The generated segment value heatmap can accurately, completely, and intuitively represent the comprehensive value of segments and the attenuation effect of environmental interference.

[0093] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A multi-dimensional weight plate value heat map generation method, characterized in that, Includes the following steps: Step 1: Obtain the multidimensional value quantification parameter set of the sector, and map the multidimensional value quantification parameter set of the sector to the geographic raster matrix based on the GIS geographic coordinate system to generate a spatiotemporal cube data structure. Step 2: Extract various environmental interference factors corresponding to living comfort from the spatiotemporal cube data structure, construct a color mixing function that integrates multiple environmental interference factors, and calculate the comprehensive environmental masking weight. Based on the comprehensive environmental masking weight processing of the basic value color layer, an environmental interference layer is generated. Step 3: Extract the comprehensive value index from the spatiotemporal cube data structure, perform adaptive grading on the comprehensive value index through a dynamic clustering algorithm, generate grading threshold intervals, construct a nonlinear color mapping lookup table, map the grading threshold intervals to a preset gradient color spectrum, and generate a color mapping layer. Step 4: Construct a multi-level layer stack structure, overlay the environmental interference layer and the color mapping layer onto the multi-level layer stack structure, and perform pixel blending operations on the overlay layers through the fragment shader based on the graphics rendering pipeline to generate a heat map of the sector value.

2. The method of claim 1, wherein, The process of mapping the multidimensional value quantification parameter set of the plate to a geographic raster matrix based on the GIS geographic coordinate system to generate a spatiotemporal cube data structure includes: The system calculates the value gradient features in the multidimensional value quantification parameter set of the landmass, and performs non-uniform grid division on the target geographic space based on the value gradient features. For high-value areas with value gradient values ​​greater than a first preset threshold, data mapping is completed using fine-grained grids of the first size. For medium-value areas with value gradient values ​​greater than or equal to a second preset threshold and less than or equal to the first preset threshold, data mapping is completed using medium-grained grids of the third size. For low-value areas with value gradient values ​​less than the second preset threshold, data mapping is completed using coarse-grained grids of the second size. A non-uniform geographic grid matrix is ​​constructed based on the hybrid division results of fine-grained, medium-grained, and coarse-grained grids. The multidimensional value quantification parameter set of the landmass is completely mapped to the non-uniform geographic grid matrix to generate a spatiotemporal cube data structure.

3. The method of claim 1, wherein, Step 2 includes the following: Extract various environmental interference factors corresponding to living comfort from the spatiotemporal cube data structure. These environmental interference factors include light pollution intensity, noise pollution intensity, and dust pollution intensity. Obtain the functional attributes of the block to which the grid cell belongs, including residential, commercial and industrial types; Based on the functional attributes of the aforementioned modules, a corresponding interference weight coefficient is assigned to each type of environmental interference factor. A color mixing function that integrates multiple environmental interference factors is constructed, and the comprehensive environmental masking weight is calculated by combining the interference weight coefficients with the values ​​of each environmental interference factor. Based on the comprehensive environmental masking weight processing of the basic value color layer, an environmental interference layer is generated.

4. The method of claim 3, wherein, A color mixing function integrating multiple environmental interference factors is constructed. Combining the interference weight coefficients with the values ​​of each environmental interference factor, the comprehensive environmental masking weight is calculated, including: Obtain the regional baseline environmental reference value of the target geospace, wherein the regional baseline environmental reference value corresponds to the original environmental value of each environmental disturbance factor; The values ​​of each environmental disturbance factor are normalized with the corresponding background environmental benchmark value of the region to obtain standardized environmental disturbance values. A nonlinear mapping function is constructed to map the standardized environmental disturbance values ​​into single-factor masking weights; Based on the aforementioned interference weight coefficients, the weights of each single-factor masking are weighted and summed to obtain the comprehensive environmental masking weights; wherein, the mapping relationship of the nonlinear mapping function matches the visual perception characteristics of human eyes regarding color attenuation.

5. The method for generating a multi-dimensional weighted heatmap of sector value according to claim 4, characterized in that, The construction of the nonlinear color mapping lookup table includes: Set the value index range and the warning color band mapping range; By using a segmented interpolation algorithm, different segments of the value index range are mapped to corresponding color systems, and the warning color band mapping range is mapped to warning colors, generating a color lookup table containing warning breakpoints.

6. The method for generating a multi-dimensional weighted heatmap of sector value according to claim 5, characterized in that, The step of mapping different segments of the value index range to corresponding color systems using a segmented interpolation algorithm includes: Obtain the functional attributes of the grid unit corresponding to the segment, and based on the functional attributes of the segment, call the preset differential interpolation parameter set; use the differential interpolation parameter set to perform piecewise interpolation on the value index interval to generate a non-linear color gradient that matches the segment type.

7. The method for generating a multi-dimensional weighted heatmap of sector value according to claim 1, characterized in that, The generated sector value heatmap includes: Define a fragment shader procedure, which includes a heatmap texture sampler, an environment mask texture sampler, and a time variable; A sinusoidal perturbation factor is generated based on the time variable. The sinusoidal perturbation factor is coupled with the luminance component of the color mapping layer to generate a heatmap texture with dynamic lighting effects. The heatmap texture is written into the frame buffer and output.

8. The method for generating a multi-dimensional weighted heatmap of sector value according to claim 7, characterized in that, The sinusoidal perturbation factor is coupled with the luminance component of the color mapping layer to generate a heatmap texture with dynamic lighting effects. Writing the heatmap texture to the frame buffer and outputting it includes: Obtain the vector boundary data of the target section, and perform spatial clipping on the color mapping layer based on the vector boundary data to generate a boundary-constrained color mapping texture; The sinusoidal perturbation factor is coupled with the luminance component of the boundary-constrained color mapping texture to generate a dynamic light effect texture defined within the plate boundary. Obtain the rendering load parameters of the current device, and perform adaptive downsampling processing on the dynamic light effect texture based on the rendering load parameters to generate a target heatmap texture that adapts to the current rendering load. Write the target heatmap texture into the frame buffer and output it.

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