Ecological hydrological index monitoring space-time dimension extension method based on stereoscopic monitoring network

Abstract

The invention discloses an ecological hydrological index monitoring space-time dimension extension method based on a three-dimensional monitoring network, and relates to the technical field of environment monitoring, and the method specifically comprises the following steps: extracting temperature and humidity response time points, calculating an interval difference, carrying out the clustering labeling to generate a lagging unit, normalizing brightness to construct a difference sequence, and carrying out the adjustment and recombination to obtain a brightness matrix. And combining a water level extraction consistent area merging boundary, positioning sampling layer output parameters, and rearranging brightness to generate a three-dimensional image body. According to the method, temperature and humidity response time points are extracted, difference values are quantified, a response set of spatial correlation is constructed, a brightness structure is adjusted in combination with a difference factor sequence, the reflection of vertical information to a dynamic process is enhanced, consistent areas are identified by utilizing a trend direction, boundaries are merged, and structural parameters are extracted by positioning a change dense hierarchy. Compression reconstruction and main response layer extraction of a trend-oriented region are realized, the brightness reconstruction consistency and the spatial change capture capability are improved, and the recognition precision of deep ecological hydrological features and the regional structure analysis capability are enhanced.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method for expanding the spatiotemporal dimensions of eco-hydrological index monitoring based on a three-dimensional monitoring network. Background Technology

[0002] The field of environmental monitoring technology mainly includes the monitoring and assessment of natural ecosystems and environmental elements at different spatial and temporal scales. Core aspects include real-time observation of elements such as atmosphere, water bodies, soil, and organisms, dynamic identification of change processes, and comprehensive analysis. It covers remote sensing monitoring technology, ground observation networks, multi-source data integration, and model-driven analysis, forming an environmental information monitoring system that integrates data acquisition, processing, fusion, and expression, providing a technical foundation and decision support for ecological security, water resource management, and regional sustainable development.

[0003] Among them, the spatiotemporal dimension expansion method of eco-hydrological index monitoring based on three-dimensional monitoring network refers to the method of jointly acquiring key eco-hydrological parameters of land and water bodies by constructing a nested multi-level monitoring system such as high-resolution satellites, UAV hyperspectral and lidar, and ground eddy observation. The research focuses on technical issues such as the low inversion accuracy of eco-hydrological indicators in complex underlying surface areas and the difficulty in obtaining vertical deep information. Traditional eco-hydrological index monitoring usually adopts a method of combining single optical remote sensing images with field sampling. It obtains surface ecological information through methods such as vegetation index spectral identification or water body single-band inversion, and estimates hydrological parameters within a certain depth range through ground sampling observation. This method relies on fixed points, has limited spatial coverage, and is insufficient in response to temporal changes, making it difficult to meet the needs of capturing the dynamic characteristics of high-frequency and multi-dimensional eco-hydrological systems.

[0004] Existing technologies rely on single optical remote sensing images combined with in-situ sampling for parameter inversion. The resulting eco-hydrological information is limited to shallow surfaces and fixed observation points, failing to cover high-frequency dynamic changes within the region. When the target area has complex underlying surfaces or delayed hydrological responses, traditional methods lack analytical mechanisms for temporal differences and response delays, leading to insufficient spatial hierarchy representation and unclear trend evolution identification. In practical applications, poor vertical structural coordination among multi-temporal remote sensing images results in distorted analysis of deep eco-hydrological characteristics or trend capture deviations, limiting the improvement of systematic analysis capabilities and response accuracy. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network. The technical solution is as follows:

[0006] A method for expanding the spatiotemporal dimensions of eco-hydrological index monitoring based on a three-dimensional monitoring network includes the following steps: S1: Obtain the response time series of temperature and humidity within the remote sensing period, extract the temperature response time point and humidity response time point of each pixel, calculate the time interval difference, perform spatial clustering of the interval difference according to the geographic coordinates of the pixel, and classify and label the clustering results according to the response differences to generate a set of hysteresis units. S2: Call the temperature and humidity combination data of each unit in the hysteresis unit set, perform normalization processing and establish a difference factor sequence, adjust the vertical brightness values ​​of the corresponding pixels in the original image layer by layer, reorganize the brightness information matrix, and classify and map according to spatial index to generate a brightness matrix. S3: Call the image brightness change information in the brightness matrix, combine it with the trend direction sequence in the groundwater level monitoring sequence data, extract the spatial regions with consistent directions, merge the region boundaries through the inter-pixel trend continuity features, and generate a trend region map; S4: Call the spatial coordinates of the trend region map, extract the vertical layer thickness configuration in the corresponding image, combine the change order in the trend continuation segment, perform inter-layer compression, locate the layer with the most dense trend fluctuation as the sampling layer index, and output the structural parameter set.

[0007] As a further embodiment of the present invention, the hysteresis unit set includes response difference categories, spatial clustering units, and time difference indices; the brightness matrix includes normalized brightness values, recombination ratio factors, and spatial mapping indexes; the trend region map includes trend direction blocks, trend boundary lines, and trend consistency regions; and the structural parameter set includes compressed hierarchical indexes, layer thickness configuration sequences, and trend fluctuation dense layers.

[0008] As a further aspect of the present invention, step S1 specifically comprises: S101: Obtain the response time series of temperature and humidity within the remote sensing period, extract the time point when each pixel executes the response signal, record the time when the fluctuation amplitude first exceeds the set change threshold in the temperature sequence as the temperature response time point, record the time when the peak value of the same pixel in the humidity sequence appears as the humidity response time point, and establish the pixel response time point sequence. The change threshold is set based on the sum of the standard deviation and the average rate of change of the corresponding pixel temperature sequence; S102: Call the pixel response time point sequence, calculate the time interval difference between the temperature response time point and the humidity response time point of each pixel, extract the geographic coordinate information of the corresponding pixel, match it with the time difference, construct a gridded distribution structure according to geographic spatial proximity, and merge pixels with the same time difference in each spatial unit to generate response interval distribution data. S103: Call the response interval distribution data, perform cluster analysis on the pixel set according to the time difference distribution characteristics in each spatial unit and the spatial proximity relationship of the pixels in the grid, divide the spatial region into multiple response difference type regions based on the time interval directionality and quantity characteristics in the clustering results, and encode, label and spatially map the type regions to generate a hysteresis unit set.

[0009] As a further aspect of the present invention, step S2 specifically includes: S201: Call the temperature and humidity combination data of each unit in the lag unit set, obtain the temperature and humidity data sequence in the corresponding time period, perform standard normalization on the data sequence according to the difference between the peak and valley values ​​in each unit, establish a normalized temperature and humidity difference index set, and generate a temperature and humidity difference factor sequence. S202: Based on the normalization result of each factor in the temperature and humidity difference factor sequence, match the position of the corresponding pixel in the original image in the vertical structure, perform layer-by-layer scaling on the brightness data in the vertical layer, and replace the gray values ​​in sequence according to the factor size order of the unit in each layer to obtain the vertical brightness adjustment dataset. S203: Call the vertical brightness adjustment dataset, construct a two-dimensional grid index relationship according to the spatial coordinates of the pixels, remap the brightness value of each adjusted pixel to the corresponding spatial position, aggregate the vertical hierarchy structure according to the grid division, establish a spatial hierarchy synthesis matrix, and generate a brightness matrix.

[0010] As a further aspect of the present invention, the specific formula for performing layer-by-layer scaling on the brightness data in the vertical hierarchy is as follows: ; Calculate the vertical brightness adjustment coefficient value; in, This represents the coefficient value of the z-th pixel after vertical brightness adjustment in the x-th layer. This represents the brightness value of the z-th pixel in the x-th vertical layer of the original image. This represents the weight coefficient of the k-th temperature and humidity difference factor for the z-th pixel in the x-th layer. This represents the normalized result of the k-th temperature and humidity difference factor in the x-th layer. This represents the average of all normalization factors in the x-th layer. Let x be the number of temperature and humidity difference factors in the x-th layer. This represents the spatial index number of a pixel in a two-dimensional image. Indicates the index number of the hierarchy in the vertical structure. This indicates the number of the temperature and humidity difference factor within the x-th layer.

[0011] As a further aspect of the present invention, step S3 specifically comprises: S301: Call the image brightness change information in the brightness matrix, extract the change direction according to the monotonically increasing and decreasing order of brightness values ​​in the time series, filter and mark the regions with continuous and consistent change directions, and establish region sequence classification according to pixel index to generate brightness direction distribution data. S302: Call the brightness direction distribution data to obtain the groundwater level monitoring sequence at the corresponding location, extract the direction of the change slope of each time period in the water level sequence, match the water level change direction with the brightness change direction, mark the pixel group with the same direction and record the spatial number, and generate a direction matching relationship set; S303: Call the direction matching relationship set to perform boundary positioning processing on continuous spatial units. Based on the brightness direction and trend sequence continuity between adjacent pixels, perform trend merging analysis and delineate continuous boundaries. Perform unified encoding processing on the regional spatial range to generate a trend region map.

[0012] As a further aspect of the present invention, step S4 specifically comprises: S401: Call the spatial coordinates of the trend area map, extract the vertical layer structure of the corresponding area in the original image, obtain the layer thickness data, integrate the layer spacing in multiple areas, construct a structure mapping table according to the coordinate index, and generate vertical layer thickness configuration data. S402: Call the vertical layer thickness configuration data, obtain the trend continuation segment inside each trend region, perform continuity detection on the change amplitude between adjacent layers, adjust the interlayer spacing according to the trend continuation direction, generate compressed layer structure data, and obtain the layer compression ratio value. S403: Call the layer compression ratio value, analyze the trend change gradient distribution in the compressed structure, select the layer with the largest change amplitude in the region as the sampling layer of the response intensity concentration, extract the corresponding index position and structural parameters, integrate the regional structural information, and generate a structural parameter set.

[0013] As a further aspect of the present invention, the layer compression ratio value is called to analyze the gradient distribution of trend changes in the compressed structure, and the layer with the largest change amplitude in the region is selected as the sampling layer of the response intensity concentration. The corresponding index position and structural parameters are extracted, and the specific formula for calculating the change amplitude in the region is as follows: ; Calculate the magnitude of change; in, This represents the magnitude of change at the i-th structural level. This represents the local gradient change rate of the j-th sub-region in the i-th structural level. This represents the compressibility response coefficient of the j-th subregion in the i-th structural level. This represents the number of anomalous mutation points contained in the i-th structural level. This represents the total number of sub-regions in the i-th structural level. Represents the hierarchical index number. This represents the number of the sub-region in the i-th structural level.

[0014] As a further aspect of the present invention, the method further includes: S5: Call the layer thickness configuration and sampling index in the structure parameter set, combine the grayscale distribution of the brightness matrix and the spatial coordinates of the trend region map, adjust the layer spacing according to the compression ratio, locate the main response layer according to the sampling index, and rearrange the brightness sequence to replace the original layer level to generate a three-dimensional image volume; The three-dimensional image volume includes the main response layer brightness, the compressed hierarchical structure, and the rearranged brightness sequence.

[0015] As a further aspect of the present invention, step S5 specifically includes: S501: Call the layer thickness configuration and sampling index in the structure parameter set, combine the spatial coordinate information in the trend region map, perform coordinate mapping processing on the vertical structure of the image in each region, adjust the original layer spacing according to the compression ratio set in the structure parameters, and generate compressed layer spacing data. S502: Call the compressed layer spacing data and the sampling index value in the structural parameter set, locate the main response layer in the vertical structure according to the coordinate position of the sampling layer in the trend area, establish a mapping relationship between the position of the main response layer and the compressed layer structure, and generate the sampling layer position positioning result. The main response layer refers to the vertical layer position in a segment where the brightness change rate is greater than the average change rate of the region and is located in a continuous and consistent brightness gradient direction. S503: Call the sampling layer positioning results and the pixel grayscale sequence in the brightness matrix, rearrange the grayscale information of each trend region according to the sampling layer order, replace the layer structure of the corresponding position in the original image with the rearranged brightness data, integrate all image regions in spatial order, and generate a three-dimensional image volume.

[0016] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following: In this embodiment of the invention, by dynamically extracting the differences in temperature and humidity response of pixels within a remote sensing period and performing spatial clustering annotation, a set of spatial units with response difference attributes can be established. Furthermore, the brightness numerical ratio is reconstructed by driving the normalized temperature and humidity factor, thereby achieving fine control of the vertical brightness structure. Based on this, by combining the trend-consistent region and the direction of groundwater level change, the identification and boundary merging of trend spatial blocks are completed. Finally, the most densely fluctuating layer is located to form a set of structural parameters, so that the brightness changes and trend evolution within the spatial region are consistent, improving the responsiveness to temporal features and the completeness of spatial structure expression, realizing the compressed positioning of vertical layers and the reconstruction mapping of the main response layer, and improving the sensitivity identification and expression analysis capabilities of eco-hydrological dynamic changes. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the workflow of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0021] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0022] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] Please see Figure 1 This invention provides a technical solution: a method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network, comprising the following steps: S1: Obtain the response time series of temperature and humidity within the remote sensing period, extract the temperature response time point and humidity response time point of each pixel, calculate the time interval difference, perform spatial clustering of the interval difference according to the geographic coordinates of the pixel, and classify and label the clustering results according to the response differences to generate a set of hysteresis units. The hysteresis unit set includes response difference categories, spatial clustering units, and time difference indices; The specific steps of S1 are as follows: S101: Obtain the response time series of temperature and humidity within the remote sensing period, extract the time point when each pixel executes the response signal, record the time when the fluctuation amplitude first exceeds the set change threshold in the temperature sequence as the temperature response time point, record the time when the peak value of the same pixel in the humidity sequence appears as the humidity response time point, and establish the pixel response time point sequence. The change threshold is set based on the sum of the standard deviation and the average change amplitude of the corresponding pixel temperature sequence; S102: Call the pixel response time point sequence, calculate the time interval difference between the temperature response time point and the humidity response time point of each pixel, extract the geographic coordinate information of the corresponding pixel, match it with the time difference, construct a gridded distribution structure according to geographic spatial proximity, and merge pixels with the same time difference in each spatial unit to generate response interval distribution data. S103: Call the response interval distribution data, perform cluster analysis on the pixel set according to the time difference distribution characteristics in each spatial unit and the spatial proximity relationship of the pixels in the grid, divide the spatial region into multiple response difference type regions based on the time interval directionality and quantity characteristics in the clustering results, and encode, label and spatially map the type regions to generate a hysteresis unit set.

[0025] In this embodiment, the system first initiates a time-series acquisition process for eco-hydrological data for each pixel within the monitoring area. This process covers a complete remote sensing monitoring cycle, for example, a continuous 72-hour monitoring period. During this period, the system uses infrared thermal imaging sensors and multispectral humidity sensors deployed in the monitoring network to collect temperature data sequences of the land cover layer and humidity data sequences of the shallow soil or vegetation canopy at 15-minute sampling intervals. The system preprocesses the acquired raw sequence data, removing null values ​​caused by sensor malfunctions or atmospheric obstruction, and then proceeds to the response time point extraction stage. For the temperature sequence, the system needs to determine a specific change threshold to lock the response start time. Specifically, the system calculates the standard deviation of the temperature data for that pixel over the entire 72-hour period, and simultaneously calculates the average of the absolute values ​​of temperature changes between adjacent sampling points. The sum of the standard deviation and the average change is defined as the change threshold. The system iterates through the temperature sequence, finding the first moment when the absolute value of the fluctuation exceeds the change threshold, and marks it as the temperature response time point. Next, the system retrieves peak data within the same time window from the humidity sequence and records the moment when the humidity peak occurs as the humidity response time point.

[0026] After completing the single-point time-series analysis, the system calculates the time interval difference between the temperature response time point and the humidity response time point for each pixel. For example, if the temperature of a pixel fluctuates significantly at 10:15 and the humidity reaches its peak at 11:00, the time interval difference for that pixel is 45 minutes. The system extracts the geospatial coordinates (longitude and latitude) of each pixel and binds this coordinate information to the calculated time interval difference. Subsequently, the system performs gridding based on geospatial proximity, dividing the monitoring area into several standard grid units with a side length of 500 meters. Within each grid unit, the system traverses all the pixels contained therein, merging those pixels whose time interval difference is within a set tolerance range. For example, if the tolerance is set to 5 minutes, pixels with time interval differences of 43 minutes and 47 minutes are considered consistent. Based on this merging result, the system generates response interval distribution data.

[0027] Based on this, the system calls upon response interval distribution data and performs cluster analysis by combining the spatial proximity of grid cells. The system employs a density-based spatial clustering algorithm to aggregate spatially adjacent grid cells with similar time interval difference distribution characteristics into the same cluster. For the clustering results, the system further analyzes the directionality (e.g., positive or negative lag) and quantitative characteristics (e.g., the mean lag duration) of the time intervals within each cluster, thereby dividing the entire monitoring area into different response difference type regions. The system assigns unique codes to these regions; for example, "short lag - high density" regions are coded as Type-A, and "long lag - low density" regions as Type-B. These codes are then mapped back to a two-dimensional spatial map, ultimately generating a set of lag cells containing response difference categories, spatial clustering units, and temporal difference indicators. Table 1 below shows the response time and classification results of some pixels during the processing.

[0028] Table 1. Pixel Response Time and Cluster Classification Data for Monitoring Area As shown in Table 1, the system classifies pixels with different characteristics into different categories such as Type-A or Type-B based on the calculated time interval difference and spatial location, thus completing the construction of the hysteresis unit set and providing a basis for the spatial and temporal attributes for subsequent brightness matrix reorganization.

[0029] Furthermore, S2: Call the temperature and humidity combination data of each unit in the hysteresis unit set, perform normalization processing and establish a difference factor sequence, adjust the vertical brightness values ​​of the corresponding pixels in the original image layer by layer, reorganize the brightness information matrix, and classify and map according to the spatial index to generate a brightness matrix. The brightness matrix includes normalized brightness values, recombination scaling factors, and spatial mapping indexes; The specific steps of S2 are as follows: S201: Call the temperature and humidity combination data of each unit in the lag unit set, obtain the temperature and humidity data sequence in the corresponding time period, perform standard normalization on the data sequence according to the difference between the peak and valley values ​​in each unit, establish a normalized temperature and humidity difference index set, and generate a temperature and humidity difference factor sequence. S202: Based on the normalization result of each factor in the temperature and humidity difference factor sequence, match the position of the corresponding pixel in the original image in the vertical structure, perform layer-by-layer scaling on the brightness data in the vertical layer, and replace the gray values ​​in sequence according to the factor size order of the unit in each layer to obtain the vertical brightness adjustment dataset. The specific formula for performing layer-by-layer scaling on the brightness data in the vertical hierarchy is as follows: ; Calculate the vertical brightness adjustment coefficient value; in, Indicates the first The pixel in the first Vertical brightness adjustment coefficient value of the layer, Indicates the first line in the original graph. The pixel in the first Brightness values ​​in the vertical layer hierarchy. Indicates the first The pixel in the first The first in the layer The weighting coefficients of each temperature and humidity difference factor. Indicates the first The first in the layer Normalization results of temperature and humidity difference factors Indicates the first The average of all normalization factors in the layer. For the first The number of temperature and humidity difference factors in the layer, This represents the spatial index number of a pixel in a two-dimensional image. Indicates the index number of the hierarchy in the vertical structure. Indicates the first Numbering of the temperature and humidity difference factor within the layer; S203: Call the vertical brightness adjustment dataset, construct a two-dimensional grid index relationship according to the spatial coordinates of the pixels, remap the brightness value of each adjusted pixel to the corresponding spatial position, aggregate the vertical hierarchy structure according to the grid division, establish a spatial hierarchy synthesis matrix, and generate a brightness matrix.

[0030] In this embodiment, the system calls the set of hysteresis units generated in step S1, and for each hysteresis unit in the set, obtains its corresponding temperature and humidity combination data. The system extracts the complete temperature and humidity data sequence of the unit within a specified time period, and performs normalization processing on these sequences to eliminate the influence of dimensions. The processing logic is as follows: the system first identifies the peak and trough values ​​of each data sequence within the unit, and calculates the difference between the peak and trough values ​​as the range; then, it subtracts the trough value from each specific value in the sequence, and divides the result by the range to obtain a normalized value between 0 and 1. Based on these normalization results, the system establishes a set of normalized temperature and humidity difference indicators and generates a temperature and humidity difference factor sequence.

[0031] The system adjusts the vertical brightness values ​​of corresponding pixels in the original remote sensing image layer by layer based on the normalization results in the temperature and humidity difference factor sequence. First, the system matches the specific layer position of each pixel in the original image's vertical structure (for example, the image may contain multiple vertical layers such as the surface layer, lower canopy, and upper canopy). Then, the system uses a specific weighted calculation logic to scale the brightness data of each layer. The specific logic is described as follows: The system first obtains the initial brightness value of a specified pixel in the original image at a specified vertical layer. Simultaneously, the system determines the weight coefficients of all temperature and humidity difference factors within that layer and their corresponding normalization results. During calculation, the system multiplies the weight coefficient of each temperature and humidity difference factor with its corresponding normalization result, obtaining multiple product terms; then, these product terms are summed to obtain a weighted sum. Finally, the system multiplies this weighted sum with the initial brightness value to obtain the numerator. Regarding the denominator, the system calculates the arithmetic mean of all normalization factors within the layer and adds 1 to this mean as the denominator. Finally, the system divides the numerator by the denominator to obtain the vertical brightness adjustment coefficient value for that pixel at that layer, i.e., the adjusted brightness value.

[0032] For example, the spatial index number of the cell being processed is set to 500, and the vertical hierarchy index is level 3. This level contains 2 temperature and humidity difference factors (i.e., the number of factors is 2).

[0033] The parameter settings are as follows: In the original image, pixel number 500 had an initial brightness value of 150 in layer 3.

[0034] The weighting coefficient of the first temperature and humidity difference factor is set to 0.6, and its normalization result is calculated to be 0.8 through the aforementioned steps.

[0035] The weighting coefficient of the second temperature and humidity difference factor is set to 0.4, and its normalization result is calculated to be 0.5 through the aforementioned steps.

[0036] Based on the above data, the calculation process is as follows: First, calculate the weighted sum: 0.6 multiplied by 0.8 equals 0.48; 0.4 multiplied by 0.5 equals 0.20; the sum of the two equals 0.68.

[0037] Calculate the numerator: multiply the initial brightness value of 150 by the weighted sum of 0.68 to get 102.

[0038] Calculate the denominator: First, calculate the average of all normalization factors, which is the sum of 0.8 and 0.5 divided by 2, to get 0.65; then add 1 to 0.65 to get 1.65.

[0039] The final adjusted brightness value is calculated as follows: dividing the numerator 102 by the denominator 1.65 yields approximately 61.82.

[0040] The results indicate that, after weighted adjustment by the temperature and humidity difference factor, the brightness value of this pixel in the third layer is suppressed, reflecting the actual radiation characteristics of this layer under specific temperature and humidity response conditions.

[0041] Following the above logic, the system sequentially traverses each level of each pixel in the lag unit set, replacing all grayscale values ​​to generate a vertical brightness adjustment dataset. Finally, the system constructs a grid index relationship based on the two-dimensional spatial coordinates of the pixels, remaps the adjusted brightness values ​​back to their original geographical locations, and aggregates the originally discrete vertical hierarchical structure according to the grid division to establish a brightness matrix containing spatial hierarchical synthesis information.

[0042] Furthermore, S3: Call the image brightness change information in the brightness matrix, combine it with the trend direction sequence in the groundwater level monitoring sequence data, extract the spatial regions with consistent directions, merge the region boundaries through the inter-pixel trend continuity features, and generate a trend region map; The trend area map includes trend direction blocks, trend boundary lines, and trend consistency areas; The specific steps for S3 are as follows: S301: Call the image brightness change information in the brightness matrix, extract the change direction according to the monotonically increasing and decreasing order of brightness values ​​in the time series, filter and mark the regions with continuous and consistent change directions, and establish region sequence classification according to pixel index to generate brightness direction distribution data. S302: Call the brightness direction distribution data to obtain the groundwater level monitoring sequence at the corresponding location, extract the direction of the change slope of each time period in the water level sequence, match the water level change direction with the brightness change direction, mark the pixel group with the same direction and record the spatial number, and generate a direction matching relationship set; S303: Call the direction matching relationship set to perform boundary positioning processing on continuous spatial units. Based on the brightness direction and trend sequence continuity between adjacent pixels, perform trend merging analysis and delineate continuous boundaries. Perform unified encoding processing on the regional spatial range to generate a trend region map.

[0043] In this embodiment, the system calls the brightness matrix generated in step S2 to extract image brightness change information. The system scans the brightness value of each pixel along the time axis, identifies the monotonically increasing or decreasing sequence, and thus extracts the direction of brightness change (e.g., continuous brightening is marked as positive, and continuous darkening is marked as negative). The system compares the change directions of adjacent pixels spatially, filters out regions with continuous and consistent change directions, marks them, and simultaneously establishes a region sequence classification according to pixel index to generate brightness direction distribution data.

[0044] Subsequently, the system incorporates groundwater level monitoring sequence data. The system acquires measured water level data from groundwater monitoring wells at corresponding geographical locations, calculates the slope of the water level change for each time period in the sequence, and extracts the direction of the water level rise and fall trend. The system then spatially matches the direction of water level change with the previously extracted direction of brightness change. For example, if the brightness of pixels in a certain area continuously increases, and the corresponding groundwater level monitoring data shows an upward trend in water level (with a positive slope), then the two are determined to be in the same direction. The system marks these groups of pixels with consistent directions and records their spatial numbers, generating a set of direction matching relationships.

[0045] Finally, the system calls the direction matching relationship set to locate the boundaries of continuous spatial units. The system checks whether there is a continuity interruption in the brightness direction or trend sequence between adjacent pixels. If adjacent pixels have the same direction, they are merged into the same region; if the direction changes abruptly (e.g., from positive to negative), a boundary line is drawn at that point. The system performs unified encoding processing on the delineated continuous regions, assigning each independent region a unique identifier code, and finally generating a trend area map containing trend direction blocks, trend boundary lines, and trend consistency areas. This process spatially couples and partitions the surface radiation variation characteristics with underground hydrological processes.

[0046] Furthermore, S4: Call the spatial coordinates of the trend region map, extract the vertical layer thickness configuration in the corresponding image, combine the change order in the trend continuation segment, perform inter-layer compression, locate the layer with the most dense trend fluctuation as the sampling layer index, and output the structural parameter set. The set of structural parameters includes compressed hierarchical index, layer thickness configuration sequence, and trend fluctuation dense layer. The specific steps for S4 are as follows: S401: Call the spatial coordinates of the trend area map, extract the vertical layer structure of the corresponding area in the original image, obtain the layer thickness data, integrate the layer spacing in multiple areas, construct a structure mapping table according to the coordinate index, and generate vertical layer thickness configuration data. S402: Call the vertical layer thickness configuration data, obtain the trend continuation segment inside each trend region, perform continuity detection on the change amplitude between adjacent layers, adjust the interlayer spacing according to the trend continuation direction, generate compressed layer structure data, and obtain the layer compression ratio value. S403: Call the layer compression ratio value, analyze the trend change gradient distribution in the compressed structure, select the layer with the largest change amplitude in the region as the sampling layer of the response intensity concentration, extract the corresponding index position and structural parameters, integrate the regional structural information, and generate a set of structural parameters. The specific formula for calculating the range of change within the region is as follows: ; Calculate the magnitude of change; in, Representing the The range of change at each structural level Representing the In the first structural level The rate of change of local gradient in each sub-region Representing the In the first structural level Compression response coefficients of each subregion Representing the The number of anomalous mutation points contained in each structural level. Representing the The total number of sub-regions in each structural level Represents the hierarchical index number. Representing the Numbering of sub-regions in the structural hierarchy.

[0047] In this embodiment, the system utilizes the trend region map generated by S3 to extract the vertical layer thickness configuration information of each defined trend region from the original image. The system first acquires the original layer thickness data of each layer within the region and integrates the interlayer spacing of multiple pixels within the region to construct a structure mapping table. Next, the system analyzes the trend continuation segments within the region and performs continuity detection on the change magnitude between adjacent layers. Based on the direction of trend continuation, the system adjusts the compression ratio of the spacing between layers, compressing redundant layers with insignificant trend changes while retaining the spatial proportion of layers with significant changes, thereby generating compressed layer structure data and the corresponding layer compression ratio value.

[0048] Based on this, the system further analyzes the gradient distribution of trend changes in the compressed structure, aiming to select the level with the largest change amplitude as the sampling level. The system uses specific logical operations to quantify the change amplitude of each structural level.

[0049] The logical description is as follows: First, for the i-th structural level, the system obtains the local gradient change rate and the corresponding compression response coefficient for all sub-regions within that level. The system multiplies the local gradient change rate of each sub-region by its corresponding compression response coefficient to obtain a weighted gradient term. Next, the system sums the weighted gradient terms for all sub-regions within that level to obtain the total weighted gradient.

[0050] Simultaneously, the system counts the number of anomalous mutation points within this structural level, adds 1 to this number, and then takes the square root of the result as an adjustment factor. The system then divides the sum of the aforementioned weighted gradients by this adjustment factor to obtain the adjusted weighted gradient mean.

[0051] On the other hand, the system calculates the arithmetic mean of the local gradient change rates of all sub-regions within this level, i.e., the unweighted gradient mean.

[0052] Finally, the system calculates the absolute value of the difference between the adjusted weighted gradient mean and the unweighted gradient mean, and defines this absolute value as the change magnitude of the i-th structural level.

[0053] For example, for the 4th level (i=4) within a certain trend region, this level contains 3 sub-regions (n=3).

[0054] The parameter settings are as follows: Subregion 1: Local gradient change rate is 10, and compression response coefficient is 0.9.

[0055] Subregion 2: The local gradient change rate is 12, and the compression response coefficient is 0.95.

[0056] Sub-region 3: The local gradient change rate is 11, and the compression response coefficient is 0.9.

[0057] The number of abnormal mutation points detected in this level is 3 (M=3).

[0058] The calculation process is as follows: Calculate the weighted gradient term: Subregion 1: 10 multiplied by 0.9 equals 9.

[0059] Subregion 2: 12 multiplied by 0.95 equals 11.4.

[0060] Subregion 3: 11 multiplied by 0.9 equals 9.9.

[0061] Calculate the weighted gradient sum: 9 + 11.4 + 9.9 = 30.3.

[0062] Calculate the adjustment factor: the number of outliers is 3 plus 1, which equals 4, and the square root of 4 is 2.

[0063] Calculate the adjusted weighted gradient mean: 30.3 divided by 2 equals 15.15.

[0064] Calculate the unweighted gradient mean: (10+12+11) divided by 3, that is, 33 divided by 3, which equals 11.

[0065] Calculate the change range: 15.15 minus 11 equals 4.15, and the absolute value is still 4.15.

[0066] The result indicates that the magnitude of change at level 4 is 4.15.

[0067] After calculating the change magnitude of all levels, the system compares the 4.15 of level 4 with the 2.5 calculated for level 5 and the 3.0 calculated for level 3, selecting level 4, which has the largest value, as the level with the most concentrated trend fluctuations, i.e., the main response layer. The system extracts the index position and related structural parameters of this level and integrates them into a structural parameter set. Table 2 below shows the calculation parameters for different levels and the final selection results.

[0068] Table 2. Calculation and Screening Results of Change Amplitude at Each Level within the Trend Region As shown in Table 2, Layer-04, having the largest variation range, was positioned as the sampling layer by the system, and its corresponding structural parameters were output to the structural parameter set.

[0069] Furthermore, S5: Call the layer thickness configuration and sampling index in the structure parameter set, combine the grayscale distribution of the brightness matrix and the spatial coordinates of the trend region map, adjust the layer spacing according to the compression ratio, locate the main response layer according to the sampling index, and rearrange the brightness sequence to replace the original layer level to generate a three-dimensional image volume; The three-dimensional image volume includes the main response layer brightness, the compressed hierarchical structure, and the rearranged brightness sequence; The specific steps of S5 are as follows: S501: Call the layer thickness configuration and sampling index in the structure parameter set, combine the spatial coordinate information in the trend region map, perform coordinate mapping processing on the vertical structure of the image in each region, adjust the original layer spacing according to the compression ratio set in the structure parameters, and generate compressed layer spacing data. S502: Call the compressed layer spacing data and the sampling index value in the structural parameter set, locate the main response layer in the vertical structure according to the coordinate position of the sampling layer in the trend area, establish a mapping relationship between the position of the main response layer and the compressed layer structure, and generate the sampling layer position positioning result. The main response layer refers to the vertical layer position in a segment where the brightness change rate is greater than the average change rate of the region and is located in a continuous and consistent brightness gradient direction. S503: Call the sampling layer positioning results and the pixel grayscale sequence in the brightness matrix, rearrange the grayscale information of each trend region according to the sampling layer order, replace the layer structure of the corresponding position in the original image with the rearranged brightness data, integrate all image regions in spatial order, and generate a three-dimensional image volume.

[0070] In this embodiment, the system calls the structural parameter set output by S4 (including layer thickness configuration and sampling index), and combines it with the brightness matrix generated by S2 and the trend region map generated by S3 to construct a 3D image volume. First, based on the spatial coordinate information in the trend region map, the system maps the vertical structure of the image to a specific geographic grid. Then, the system uses the compression ratio set in the structural parameter set to physically adjust the interlayer spacing of the original image. Specifically, the system reduces the thickness of layers that were determined in S4 to have small changes and be non-primary responses according to the compression ratio (e.g., compressing the original thickness of 10 units to 2 units), thereby freeing up display space for key information within the limited vertical data space and generating compressed interlayer spacing data.

[0071] Next, the system calls the compressed layer spacing data and sampling index values ​​to precisely locate the main response layer in the adjusted vertical structure. The system maps the "most densely trending layer" selected in S4 (such as Layer-04 in the previous example) to the compressed coordinate system, ensuring that this layer is located at the core of the visual or data analysis. The system establishes a strict index mapping relationship between the position of this main response layer and the other compressed layer structures to generate the sampling layer location results. In this process, the main response layer is explicitly defined as the vertical layer position in a segment where the brightness change rate is greater than the average change rate of the region and the brightness gradient direction is continuous and consistent.

[0072] Finally, based on the sampling layer location results, the system calls the pixel grayscale sequence in the brightness matrix to rearrange the grayscale information within each trend region. The system replaces the high-weight brightness data of the main response layer with the core display layer and fills the compressed layers with the brightness data of the secondary layers. The system integrates all rearranged and structurally adjusted image regions in spatial order, ultimately generating a complete three-dimensional image volume. This three-dimensional image volume intuitively displays the key vertical response characteristics of eco-hydrological indicators, highlighting the impact of groundwater level changes on specific surface or canopy depths.

[0073] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0074] It should be understood that the term "and / or" in this article merely describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0075] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0076] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0077] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0082] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0083] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network, characterized in that, The method includes: S1: Obtain the response time series of temperature and humidity within the remote sensing period, extract the temperature response time point and humidity response time point of each pixel, calculate the time interval difference, perform spatial clustering of the interval difference according to the geographic coordinates of the pixel, and classify and label the clustering results according to the response differences to generate a set of hysteresis units. S2: Call the temperature and humidity combination data of each unit in the hysteresis unit set, perform normalization processing and establish a difference factor sequence, adjust the vertical brightness values ​​of the corresponding pixels in the original image layer by layer, reorganize the brightness information matrix, and classify and map according to spatial index to generate a brightness matrix. S3: Call the image brightness change information in the brightness matrix, combine it with the trend direction sequence in the groundwater level monitoring sequence data, extract the spatial regions with consistent directions, merge the region boundaries through the inter-pixel trend continuity features, and generate a trend region map; S4: Call the spatial coordinates of the trend region map, extract the vertical layer thickness configuration in the corresponding image, combine the change order in the trend continuation segment, perform inter-layer compression, locate the layer with the most dense trend fluctuation as the sampling layer index, and output the structural parameter set.

2. The method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network according to claim 1, characterized in that, The hysteresis unit set includes response difference categories, spatial clustering units, and time difference indices; the brightness matrix includes normalized brightness values, recombination ratio factors, and spatial mapping indexes; the trend region map includes trend direction blocks, trend boundary lines, and trend consistency zones; and the structural parameter set includes compressed hierarchy indexes, layer thickness configuration sequences, and trend fluctuation dense layers.

3. The method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain the response time series of temperature and humidity within the remote sensing period, extract the time point when each pixel executes the response signal, record the time when the fluctuation amplitude first exceeds the set change threshold in the temperature sequence as the temperature response time point, record the time when the peak value of the same pixel in the humidity sequence appears as the humidity response time point, and establish the pixel response time point sequence. The change threshold is set based on the sum of the standard deviation and the average rate of change of the corresponding pixel temperature sequence; S102: Call the pixel response time point sequence, calculate the time interval difference between the temperature response time point and the humidity response time point of each pixel, extract the geographic coordinate information of the corresponding pixel, match it with the time difference, construct a gridded distribution structure according to geographic spatial proximity, and merge pixels with the same time difference in each spatial unit to generate response interval distribution data. S103: Call the response interval distribution data, perform cluster analysis on the pixel set according to the time difference distribution characteristics in each spatial unit and the spatial proximity relationship of the pixels in the grid, divide the spatial region into multiple response difference type regions based on the time interval directionality and quantity characteristics in the clustering results, and encode, label and spatially map the type regions to generate a hysteresis unit set.

4. The method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Call the temperature and humidity combination data of each unit in the lag unit set, obtain the temperature and humidity data sequence in the corresponding time period, perform standard normalization on the data sequence according to the difference between the peak and valley values ​​in each unit, establish a normalized temperature and humidity difference index set, and generate a temperature and humidity difference factor sequence. S202: Based on the normalization result of each factor in the temperature and humidity difference factor sequence, match the position of the corresponding pixel in the original image in the vertical structure, perform layer-by-layer scaling on the brightness data in the vertical layer, and replace the gray values ​​in sequence according to the factor size order of the unit in each layer to obtain the vertical brightness adjustment dataset. S203: Call the vertical brightness adjustment dataset, construct a two-dimensional grid index relationship according to the spatial coordinates of the pixels, remap the brightness value of each adjusted pixel to the corresponding spatial position, aggregate the vertical hierarchy structure according to the grid division, establish a spatial hierarchy synthesis matrix, and generate a brightness matrix.

5. The method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network according to claim 4, characterized in that, The specific formula for performing layer-by-layer scaling on the brightness data in the vertical hierarchy is as follows: ； Calculate the vertical brightness adjustment coefficient value; in, This represents the coefficient value of the z-th pixel after vertical brightness adjustment in the x-th layer. This represents the brightness value of the z-th pixel in the x-th vertical layer of the original image. This represents the weight coefficient of the k-th temperature and humidity difference factor for the z-th pixel in the x-th layer. This represents the normalized result of the k-th temperature and humidity difference factor in the x-th layer. This represents the average of all normalization factors in the x-th layer. Let x be the number of temperature and humidity difference factors in the x-th layer. This represents the spatial index number of a pixel in a two-dimensional image. Indicates the index number of the hierarchy in the vertical structure. This indicates the number of the temperature and humidity difference factor within the x-th layer.

6. The method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Call the image brightness change information in the brightness matrix, extract the change direction according to the monotonically increasing and decreasing order of brightness values ​​in the time series, filter and mark the regions with continuous and consistent change directions, and establish region sequence classification according to pixel index to generate brightness direction distribution data. S302: Call the brightness direction distribution data to obtain the groundwater level monitoring sequence at the corresponding location, extract the direction of the change slope of each time period in the water level sequence, match the water level change direction with the brightness change direction, mark the pixel group with the same direction and record the spatial number, and generate a direction matching relationship set; S303: Call the direction matching relationship set to perform boundary positioning processing on continuous spatial units. Based on the brightness direction and trend sequence continuity between adjacent pixels, perform trend merging analysis and delineate continuous boundaries. Perform unified encoding processing on the regional spatial range to generate a trend region map.

7. The method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network according to claim 6, characterized in that, The specific steps for S4 are as follows: S401: Call the spatial coordinates of the trend area map, extract the vertical layer structure of the corresponding area in the original image, obtain the layer thickness data, integrate the layer spacing in multiple areas, construct a structure mapping table according to the coordinate index, and generate vertical layer thickness configuration data. S402: Call the vertical layer thickness configuration data, obtain the trend continuation segment inside each trend region, perform continuity detection on the change amplitude between adjacent layers, adjust the interlayer spacing according to the trend continuation direction, generate compressed layer structure data, and obtain the layer compression ratio value. S403: Call the layer compression ratio value, analyze the trend change gradient distribution in the compressed structure, select the layer with the largest change amplitude in the region as the sampling layer of the response intensity concentration, extract the corresponding index position and structural parameters, integrate the regional structural information, and generate a structural parameter set.

8. The method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network according to claim 7, characterized in that, The layer compression ratio value is used to analyze the gradient distribution of trend changes in the compressed structure. The layer with the largest change amplitude within the region is selected as the sampling layer for the concentration of response intensity. The corresponding index position and structural parameters are extracted. The specific formula for calculating the change amplitude within the region is as follows: ； Calculate the magnitude of change; in, This represents the magnitude of change at the i-th structural level. This represents the local gradient change rate of the j-th sub-region in the i-th structural level. This represents the compressibility response coefficient of the j-th subregion in the i-th structural level. This represents the number of anomalous mutation points contained in the i-th structural level. This represents the total number of sub-regions in the i-th structural level. Represents the hierarchical index number. This represents the number of the sub-region in the i-th structural level.

9. The method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network according to claim 1, characterized in that, The method further includes: S5: Call the layer thickness configuration and sampling index in the structure parameter set, combine the grayscale distribution of the brightness matrix and the spatial coordinates of the trend region map, adjust the layer spacing according to the compression ratio, locate the main response layer according to the sampling index, and rearrange the brightness sequence to replace the original layer level to generate a three-dimensional image volume; The three-dimensional image volume includes the main response layer brightness, the compressed hierarchical structure, and the rearranged brightness sequence.

10. The method for expanding the spatiotemporal dimension of eco-hydrological index monitoring based on a three-dimensional monitoring network according to claim 9, characterized in that, The specific steps of S5 are as follows: S501: Call the layer thickness configuration and sampling index in the structure parameter set, combine the spatial coordinate information in the trend region map, perform coordinate mapping processing on the vertical structure of the image in each region, adjust the original layer spacing according to the compression ratio set in the structure parameters, and generate compressed layer spacing data. S502: Call the compressed layer spacing data and the sampling index value in the structural parameter set, locate the main response layer in the vertical structure according to the coordinate position of the sampling layer in the trend area, establish a mapping relationship between the position of the main response layer and the compressed layer structure, and generate the sampling layer position positioning result. The main response layer refers to the vertical layer position in a segment where the brightness change rate is greater than the average change rate of the region and is located in a continuous and consistent brightness gradient direction. S503: Call the sampling layer positioning results and the pixel grayscale sequence in the brightness matrix, rearrange the grayscale information of each trend region according to the sampling layer order, replace the layer structure of the corresponding position in the original image with the rearranged brightness data, integrate all image regions in spatial order, and generate a three-dimensional image volume.

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