Salinization monitoring data visualization method and system

Abstract

The invention relates to the technical field of data visualization, in particular to a salinization monitoring data visualization method and system, and the method comprises the following steps: collecting sensor and remote sensing salinity information, unifying the spatial position, forming a salinity spatial data layer, analyzing the distribution sequence and morphological characteristics of the salinity in a unit, and determining the salinization monitoring data. And identifying structure change, constructing a staged evolution sequence, judging an early warning level in combination with a time difference and a space communication relationship, and generating a risk distribution and closed boundary visualization result. According to the method, unified spatial organization is carried out on multi-source salinity information in a geographic unit, and an expression result capable of reflecting internal structure characteristics is constructed in combination with position sequence and distribution form analysis, so that salinity change is converted into spatial structure evolution from discrete numerical values, and an evolution stage is clear through continuous monitoring period comparison; the risk level is combined with the spatial communication relationship, stable region identification and boundary closed expression are realized, and the continuity, definition and interpretability of salinization risk presentation are improved.

Description

Technical Field

[0001] This invention relates to the field of data visualization technology, and in particular to a method and system for visualizing salinization monitoring data. Background Technology

[0002] Data visualization technology refers to a collection of technologies centered around the acquisition, organization, expression, and presentation of data. Core aspects include the collection and format standardization of multi-source data, the organization of temporal and spatial attributes, the construction of data hierarchy and relationships, and the design of user-oriented visual representation methods. It typically transforms complex data into understandable and analyzable information through methods such as map layer overlay, time series display, hierarchical expression of indicators, and interactive interfaces, playing a fundamental supporting role in scenarios such as natural resource management, ecological environment monitoring, agricultural production, and risk early warning. Traditional methods for visualizing salinization monitoring data involve organizing information such as soil salinity, groundwater depth, remote sensing inversion results, and ground survey data during salinization monitoring and management, and then displaying them in the form of tables, statistical charts, or single static distribution maps. The technical aspects mainly include summarizing and calculating monitoring data from a single time period or a limited area, marking data according to administrative units or sample point locations, classifying data into levels based on predetermined thresholds, and generating static result maps. These methods rely on manual selection of data sources and the creation of separate maps or reports to reflect the salinization status, making it difficult to uniformly organize and present multi-source, heterogeneous data with continuous spatiotemporal characteristics within the same representation system.

[0003] Traditional methods of visualizing salinization monitoring data focus on summarizing information from a single time period or local area, relying on manual processing and static maps. The data sources are scattered and the organization is fragmented, making it difficult to reflect the continuous relationship between multi-source data in time and space. There is a lack of structured description between spatial location and monitoring results. The change process is mainly judged indirectly by comparing different result maps, which cannot clearly present the evolution of salinity distribution patterns. This leads to a lag in risk identification, blurred regional boundaries, and difficulty in providing stable support for refined management and dynamic early warning. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for visualizing salinization monitoring data, comprising the following steps: S1: Acquire the output information of soil salinity monitoring sensors and remote sensing pixels within the geographic unit, analyze the correspondence between the spatial coordinates of the monitoring points and the acquisition time, calculate the location distribution of the monitoring points in the geographic unit grid, adjust the spatial arrangement order of the monitoring points, and generate a salinity spatial data layer. S2: Based on the salinity spatial data layer, extract the salinity distribution order within the same geographical unit, calculate the spatial distribution statistical characteristic value, compare the distribution in the central region with the overall distribution status, filter the spatial locations that fall into the salinization determination interval, and generate unit salinization structure visualization information. S3: Based on the visualized information of the unit's salinization structure, compare the differences between adjacent monitoring periods, analyze the structural changes, determine the location of structural change nodes, adjust the segmentation of the salinization time axis, and establish a phased salinization evolution display sequence. S4: Based on the staged salinization evolution display sequence, calculate the mapping relationship between salinity intervals and warning levels, determine the warning level affiliation of geographical units, select warning units of the same level to construct a continuous spatial region, compare the stability of warning performance of adjacent units, and obtain a salinization risk level distribution layer.

[0005] As a further embodiment of the present invention, the salinity spatial data layer includes geographic unit coding, monitoring point spatial index, temporal consistency identifier, salinity attribute set, and spatial topology marker. The unit salinization structure visualization information includes salinity gradient type, spatial concentration index, central offset feature, salinization judgment ratio, and structural morphology label. The staged salinization evolution display sequence includes evolution stage identifier, stage duration, structurally stable segment marker, change node index, and stage comparison parameter. The salinization risk level distribution layer includes risk level coding, continuous risk blocks, stability discrimination identifier, spatial coverage, and regional consistency label.

[0006] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain soil salinity monitoring sensor and remote sensing pixel output information within the geographic unit, extract the spatial coordinate field of the monitoring sensor and the collection timestamp, perform alignment verification for the pixel grid number, perform one-to-one mapping between coordinate values ​​and time series, and generate a spatiotemporal alignment coordinate matrix. S102: Based on the spatiotemporal alignment coordinate matrix, calculate the row and column positions of the monitoring points in the geographic unit grid index, perform ascending order reordering on the row and column index sequence, determine the consistency between the monitoring point index and the geographic unit boundary index, remove the boundary index markers, integrate the corresponding monitoring point salinity value records, perform structured binding between the salinity values ​​and the grid position identifier, and generate a salinity spatial data layer.

[0007] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the salinity spatial data layer, extract the salinity numerical sequence corresponding to the grid within the same geographic unit, perform sorting operation according to the grid index order, summarize the difference results of adjacent salinity values ​​in continuous space, and generate a salinity spatial distribution sequence. S202: Based on the salinity spatial distribution sequence, calculate the salinity span value corresponding to the first and last indices, extract the salinity mean value of the index segment in the middle of the salinity spatial distribution sequence, compare it with the salinity mean value of the whole sequence, mark the grid indices that deviate from the whole mean value and fall into the salinization judgment interval threshold, and obtain the salinization spatial index set. S203: Call the salinization spatial index set, count the proportion of the total number of the geographic unit in all grid indices, combine the changes in the distribution density of the index in the sequence, identify the spatial distribution morphology transfer status, map the proportion value and morphology identifier into graphic representation units, and generate unit salinization structure visualization information.

[0008] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the visualization information of the unit salting structure, collect the graphic unit code and proportion field corresponding to adjacent monitoring cycles, retrieve the corresponding values ​​of the same spatial index in the two cycles, compare the proportion difference and morphological identification difference between the two cycles, arrange the difference results according to the index sequence, and generate a cycle difference quantification matrix. S302: Based on the period difference quantization matrix, obtain the initial setting reference value sequence and align the period index, perform deviation operation on the period difference and the reference value, determine the grid index position when the deviation exceeds the salting change discrimination threshold, record the cross position index and the corresponding period number into a key-value structure, and obtain the change node index table. S303: Call the change node index table, adjust the segment position of the salinization time axis and update the segment boundary index, perform consistency judgment on the morphological identifiers within the segment and retain the segments where the judgment is successful, and perform sequence splicing of the segment order with the corresponding visualization graphic unit to establish a staged salinization evolution display sequence.

[0009] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the staged salinization evolution display sequence, extract the geographical unit salinity interval boundary value sequence, calculate the interval boundary value and warning level, perform mapping assignment for the salinity interval code, and generate a warning level mapping data frame. S402: Based on the warning level mapping data frame, determine the warning level of the geographic unit and output the level identifier, filter the unit indexes of the same level and perform connectivity discrimination according to spatial adjacency, aggregate the unit indexes of connected units into a set of regions, and obtain the set of connected region identifiers of the same level. S403: Call the same level connected region identifier set, compare the stability of the early warning performance of adjacent units and calculate the level fluctuation count value, filter the regions whose level fluctuation count value is lower than the stability discrimination threshold, perform spatial labeling on the filtered regions according to the unified identifier, and obtain the salinization risk level distribution layer.

[0010] As a further aspect of the present invention, the process of extracting the boundary value sequence of salinity intervals of geographical units and calculating the interval boundary value and the warning level is specifically limited to: dividing the salinity values ​​within the same geographical unit at equal intervals based on the salinity distribution information, wherein the salinity interval boundary value sequence includes at least three consecutive boundary values, and the salinity difference between adjacent boundary values ​​remains consistent. Based on the salinity interval boundary value sequence, each salinity interval is mapped one-to-one with a warning level, and the warning levels are set to no less than three levels in order from low to high; when performing mapping assignment for the salinity interval code, the salinity interval code is converted into the corresponding warning level identifier, and the warning level identifier is written into the warning level mapping data frame in the form of a field; In the process of comparing the stability of warning performance of adjacent units and calculating the level fluctuation count, the level fluctuation count is limited to the total number of times the warning level of adjacent geographical units changes, and the stability discrimination threshold is limited to no more than half of the total number of warning levels, and the level fluctuation count is filtered.

[0011] As a further aspect of the present invention, the method further includes step S5: S5: Using the distribution of spatial units surrounding the risk area in the salinization risk level distribution layer, determine whether the warning level has changed hierarchically and maintains a consistent spatial position, filter to form a boundary candidate unit band, compare the spatial adjacency and connectivity of the boundary candidate units, adjust the expansion range of the stable area, complete the spatial closure check, convert the closure result into a displayable boundary element, and generate a salinization risk boundary visualization result. The visualization results of the salinization risk boundary include risk boundary line elements, closed area identifiers, boundary continuity attributes, spatial extension range annotations, and boundary stability characteristics.

[0012] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Using the distribution of spatial units surrounding the risk area in the salinization risk level distribution layer, obtain the outer unit index and the corresponding early warning level sequence, determine whether the difference between the level of the outer unit and the adjacent risk unit crosses the level change threshold and the position index is continuous, filter the index that meets the condition and arrange it in a circumferential order to generate a boundary candidate unit band. S502: Based on the boundary candidate unit band, compare the connectivity marker states in the candidate unit spatial adjacency matrix, determine whether the connectivity markers meet the discrimination conditions of a single ring structure, perform index replacement on unit sequences that do not meet the conditions, and synchronously update the stable region extension range index set to obtain closed candidate connected sequences; S503: Call the closed candidate connected sequence, complete the spatial closure check and calculate the closure discrimination value of the first and last indexes. If the closure discrimination value meets the closure threshold condition, it is converted into a boundary coordinate point set. The boundary coordinate point set is encoded into a line feature attribute structure according to the display order to generate a visualization result of the saltification risk boundary.

[0013] A data visualization system for monitoring salinization includes: The spatial integration module acquires the output information of soil salinity monitoring sensors and remote sensing pixels within a geographic unit, generates a spatiotemporal alignment coordinate matrix, obtains the spatial coordinates and acquisition time of monitoring points, corrects for time consistency, calculates the index position of monitoring points in the geographic unit grid, determines the relationship between monitoring points and geographic unit boundaries, and constructs a salinity spatial data layer. The structural analysis module, based on the salinity spatial data layer, extracts the spatial sorting relationship of salinity values ​​within the same geographical unit, calculates the distribution span and concentration pattern, compares the distribution center with the overall state, filters the spatial indexes that enter the salinization determination interval, and forms unit salinization structure visualization information. The evolution segmentation module compares the structural feature vectors corresponding to adjacent monitoring periods based on the visualization information of the unit's salinization structure, calculates the position of structural change nodes, adjusts the time axis segmentation according to the change nodes, selects time segments with consistent structural performance, and generates a staged salinization evolution display sequence. The risk mapping module, based on the staged salinization evolution display sequence, identifies the mapping relationship between salinity intervals and warning levels, determines the warning level affiliation of geographical units, filters geographical units of the same level, and generates a salinization risk level distribution layer. The boundary construction module, based on the salinization risk level distribution layer, analyzes the consistency of the risk level of the geographical units surrounding the risk area, determines the spatial location of the hierarchical change in the warning level, filters the candidate boundary unit zone, analyzes the spatial adjacency relationship, adjusts the extension range of the stable area, completes the spatial closure verification, and generates the visualization result of the salinization risk boundary.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by uniformly organizing multi-source salinity information within a geographical unit and combining location order and distribution pattern analysis, an expression result reflecting internal structural characteristics is constructed, transforming salinity changes from discrete numerical values ​​into spatial structural evolution. By comparing continuous monitoring cycles, the evolutionary stages are clearly identified, forming a traceable time series performance. At the same time, risk levels are combined with spatial connectivity to achieve stable area identification and boundary closure expression, improving the continuity, clarity, and interpretability of salinization risk presentation. Attached Figure Description

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

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

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

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

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

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

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

[0022] Please see Figure 1 This invention provides a method for visualizing salinization monitoring data, comprising the following steps: S1: Acquire soil salinity monitoring sensor and remote sensing pixel output information within the geographic unit, analyze the correspondence between the spatial coordinates of the monitoring points and the acquisition time, calculate the location distribution of the monitoring points in the geographic unit grid, adjust the spatial arrangement order of the monitoring points, determine the matching status of the monitoring points and the geographic unit boundary, integrate spatial location identifiers and salinity records, and generate a salinity spatial data layer. S2: Based on the salinity spatial data layer, extract the salinity distribution order within the same geographical unit, calculate the spatial distribution statistical characteristic value, compare the distribution in the central region with the overall distribution status, filter the spatial locations that fall into the salinization judgment interval, calculate the proportion of space occupied within the unit, identify the distribution morphology change characteristics, and generate unit salinization structure visualization information. S3: Based on the visualized information of unit salinization structure, compare the differences between adjacent monitoring periods, analyze the structural changes, determine the location of structural change nodes, adjust the segmentation of the salinization time axis, select time segments with consistent structural performance, and establish a staged salinization evolution display sequence. S4: Based on the salt distribution information corresponding to the staged salinization evolution display sequence, calculate the mapping relationship between salinity intervals and warning levels, determine the warning level affiliation of geographical units, select warning units of the same level to construct continuous spatial regions, compare the stability of warning performance of adjacent units, spatially label stable regions according to unified identifiers, and obtain the salinization risk level distribution layer. S5: Utilize the distribution of spatial units surrounding the risk area in the salinization risk level distribution layer to determine if the warning level has changed hierarchically while maintaining a consistent spatial position. Select and form boundary candidate unit zones, compare the spatial adjacency and connectivity of the boundary candidate units, adjust the expansion range of the stable area, complete the spatial closure check, and convert the closure results into displayable boundary elements to generate a visual result of the salinization risk boundary. The salinity spatial data layer includes geographic unit codes, spatial indexes of monitoring points, temporal consistency identifiers, salinity attribute sets, and spatial topological markers. The unit salinization structure visualization information includes salinity gradient type, spatial concentration index, central offset characteristics, salinization judgment ratio, and structural morphology label. The staged salinization evolution display sequence includes evolution stage identifiers, stage duration, structurally stable segment markers, change node indexes, and stage comparison parameters. The salinization risk level distribution layer includes risk level codes, continuous risk blocks, stability discrimination identifiers, spatial coverage, and regional consistency labels. The salinization risk boundary visualization results include risk boundary line elements, closed area identifiers, boundary continuity attributes, spatial extension range annotations, and boundary stability characteristics.

[0023] Please see Figure 2The specific steps of S1 are as follows: S101: Obtain soil salinity monitoring sensor and remote sensing pixel output information within the geographic unit, extract the spatial coordinate field of the monitoring sensor and the collection timestamp, perform alignment verification for the pixel grid number, perform one-to-one mapping between coordinate values ​​and time series, and generate a spatiotemporal alignment coordinate matrix. Agrogeographic unit numbered Unit-05 was selected, covering a rectangular area with longitudes from 118.200 to 118.210 and latitudes from 37.500 to 37.510. A soil conductivity sensor array deployed 20cm below the surface was polled via an RS-485 bus interface at a sampling frequency of 10 minutes. The parsed raw data frames contained the sensor's unique hardware ID and its latitude and longitude coordinates based on the WGS-84 coordinate system. and UTC collection timestamp Simultaneously, multispectral remote sensing images of the corresponding area are loaded, and the images are divided into a 10m × 10m pixel grid. Each grid is assigned a unique geocode ID. For alignment verification, a time synchronization window threshold is set. Minutes are used to extract only the remote sensing image metadata falling within that time window. Regarding coordinate mapping, each sensor coordinate is traversed. Determine whether the condition is met. and ,in For a given pixel grid, if the following conditions are met, the sensor reading is bounded to that grid ID. For example, if the coordinates of sensor S-01 are (118.205...). 37.505), time is 2025-12-16-10:00:00, successfully matched grid Grid-1024, the remote sensing acquisition time corresponding to the grid is 10:02:00, the time difference between the two is 2 minutes less than the 5-minute threshold, the binding is successful, and the spatiotemporal alignment coordinate matrix is ​​generated.

[0024] S102: Based on the spatiotemporal alignment coordinate matrix, calculate the row and column positions of the monitoring points in the geographic unit grid index, perform ascending order reordering on the row and column index sequence, determine the consistency between the monitoring point index and the geographic unit boundary index, remove the boundary index markers, integrate the corresponding monitoring point salinity value records, perform structured binding between salinity values ​​and grid position identifiers, and generate a salinity spatial data layer. Based on the bottom left corner origin of the geographic unit Given a grid resolution of 10m, calculate the row and column indices of the monitoring points within the grid. The calculation method is as follows , ,in and Given a grid step size in the latitude and longitude directions, an ascending reordering operation is performed on the generated row and column index sequence to ensure that the data is linearly distributed according to spatial location. Boundary consistency is then checked, and the maximum number of rows for a geographic unit is set to . The maximum number of columns is If the calculated index satisfies and If the condition is met, the point is considered valid; otherwise, the index is marked as "-1" and removed from the dataset. For monitoring points that pass the verification, the recorded soil salinity conductivity (EC value) is extracted. If multiple monitoring points exist within the same grid (e.g., S-01 and S-02 both fall within Grid-1024), the arithmetic mean is calculated as the salinity value for that grid. The processed salinity value (e.g., 2.45 dS / m) is then compared with the grid location identifier. Structured key-value pairs are formed to construct a salinity spatial data layer; Table 1: Results of Spatiotemporal Alignment and Gridding of Monitoring Points Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the salinity spatial data layer, extract the salinity numerical sequence corresponding to the grid within the same geographic unit, perform sorting operation according to the grid index order, summarize the difference results of adjacent salinity values ​​in continuous space, and generate a salinity spatial distribution sequence. Salt values ​​are extracted from the effective grid within geographic unit Unit-05 in row-major order to form the original numerical sequence. ,in To determine the total number of valid grid cells, perform a quicksort on the grid index to ensure sequence... Strictly corresponding to continuous scan lines in space, for the sequence Perform a first-order difference operation to calculate the salinity gradient between adjacent grid cells. traversal ,Will The results are stored in the difference result set and retained. As the basic distribution values, they are combined to generate a spatial distribution sequence of salinity. If the salt values ​​of three consecutive grids are The difference result is This generates a spatial distribution sequence of salinity.

[0025] S202: Based on the salinity spatial distribution sequence, calculate the salinity span value corresponding to the first and last indices, extract the salinity mean value of the index segment in the middle of the salinity spatial distribution sequence, perform a numerical comparison with the salinity mean value of the overall sequence, mark the grid indices that deviate from the overall mean value and fall into the salinization judgment interval threshold, and obtain the salinization spatial index set. Extract the first valid value from the sequence and last significant value Calculate the salinity span value Define the middle index segment of the sequence as the index range within Calculate the mean salinity within the subsequences between the segments. Calculate the complete sequence overall mean The deviation judgment threshold is set as follows: (i.e., 20%), and set the absolute threshold for salting determination as . dS / m (set according to local severe salinization standards), scan each grid individually, and when one of the following two conditions is met: 1. Grid salinity value 2. Grid salt content and difference ratio and At that time, the grid index will be used. Mark them as potential salting points and store the marked indices in the salting space index set. For example, if A certain grid value The deviation rate is ,and (set up ), obtain the salted spatial index set.

[0026] S203: Call the salinization spatial index set, count the proportion of the number of all grid indices of the geographic unit, combine the distribution density changes of the index in the sequence, identify the spatial distribution morphology transfer status, map the proportion value and morphology identifier into graphic expression units, and generate unit salinization structure visualization information. Statistical Index Set Number of elements Calculate the total number of grid cells in the geographic unit. The proportion of Analyze the index set Density variations in the spatial sequence are calculated using a sliding window (10 grid cells). If the local density jumps from low (<30%) to high (>70%), it is identified as a "morphological clustering" state; if the density remains at a uniform level (40%-60%), it is identified as a "morphological dispersion" state. A mapping rule is then established: if... And the state is "aggregated", mapped to "red solid rectangle"; if The state is "diffuse", which is mapped to "yellow hollow circles", generating visualization information of the unit salting structure.

[0027] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the visualization information of unit salting structure, collect the corresponding graphic unit codes and proportion fields of adjacent monitoring cycles, retrieve the corresponding values ​​of the same spatial index in two cycles, compare the proportion difference and morphological identification difference between the two cycles, arrange the difference results according to the index sequence, and generate a cycle difference quantification matrix. Call two adjacent monitoring cycles (November 2025) and Visual information of unit salting structure (December 2025), extracting the same spatial index. exist percentage of time and graphic shape identifiers ,as well as Moment and Calculate the proportional difference At the same time, compare the morphological identifiers, if Then morphological difference marker Set it to 1, otherwise set it to 0. Arranged according to the index sequence, a periodic difference quantization matrix is ​​generated.

[0028] S302: Based on the period difference quantization matrix, obtain the initial setting benchmark value sequence and align the period index. Perform deviation operation on the period difference quantity and the benchmark value. Determine the grid index position when the deviation exceeds the salting change discrimination threshold. Record the cross position index and the corresponding period number into a key-value structure and obtain the change node index table. Based on monitoring data from the past five years for the region, the average salinity change rate at each grid location during the same period was calculated and used as the initial baseline value sequence. For example, the average rate of change of a certain grid during the same period Quantize the period difference matrix and the corresponding benchmark value Alignment, perform offset calculation The threshold for judging salinization changes was set as follows: (i.e., 15% abnormal fluctuation), traverse the sequence, and determine. The position of a certain grid of And the benchmark ,but If the value exceeds the threshold of 0.15, the location is determined to be a "mutation node," and the index that meets the condition is added. With the current period number Combining key-value pairs Store it in the changed node index table.

[0029] S303: Call the change node index table, adjust the segment position of the salting time axis and update the segment boundary index, perform consistency judgment on the morphological identifiers within the segment and retain the segments where the judgment is successful, and stitch the segment order with the corresponding visual graphic unit to establish a staged salting evolution display sequence. Indexed by mutation nodes on the timeline Using the index as a boundary, the continuous time series is divided into multiple segments. If a sudden change occurs at a certain point, the timeline is divided into... and Two segments; within each segment, check the morphological identifiers. For consistency, if more than 90% of the grid shape identifiers in a certain segment are the same (e.g., all are "red solid rectangles"), the segment is judged as valid and retained; otherwise, it is regarded as a transitional noise segment and smoothed. The retained segments are spliced ​​in chronological order, and the corresponding visual graphic units (e.g., "red solid rectangle" represents the severe salinization stage) are associated with the corresponding time period to construct a staged salinization evolution display sequence. Table 2: Quantification of Periodic Differences and Determination of Change Nodes As shown in Table 2, the difference of Grid-1025 is 0.23, which is far greater than the baseline value of 0.05. The calculated deviation of 0.18 is greater than the threshold of 0.15. Therefore, it is identified as a mutation node with increased salinization. The results show that the location has a significant salinization deterioration that exceeds the natural fluctuation range in the T2 cycle.

[0030] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the salt distribution information corresponding to the staged salinization evolution display sequence, extract the boundary value sequence of geographical unit salt intervals, calculate the interval boundary value and warning level, perform mapping assignment for the salt interval code, and generate a warning level mapping data frame. Extract current salinity distribution information, set salinity interval boundary value sequences, and define non-salinized areas according to the "Soil Environmental Quality Standard". Mild moderate Severe (Unit: dS / m), the corresponding warning levels are assigned values ​​as follows: 0 (no risk), 1 (low risk), 2 (medium risk), 3 (high risk), and the salt content of each grid is iterated. This will be mapped to the aforementioned interval; for example, the salt content of the Grid-1024 grid is 2.45 dS / m, falling within... The interval is mapped and assigned the value of Level 2 (medium risk), generating a warning level mapping data frame.

[0031] S402: Based on the warning level mapping data frame, determine the warning level of the geographic unit and output the level identifier, filter the unit index of the same level and perform connectivity discrimination according to spatial adjacency, aggregate the unit indexes that are connected into a set of regions, and obtain the set of connected region identifiers of the same level. Output the level identifier for each grid cell, filter out the cell indices with a level identifier of "3 (high risk)," and use the 8-neighborhood connectivity algorithm (checking the neighboring grid cells in the 8 directions around the target grid cell) to determine whether high-risk cells are spatially adjacent. If the grid cells are adjacent... With grid If both are at level 3, they are determined to be connected. Using breadth-first search (BFS), the high-risk connected units are aggregated into the same region set and assigned a unique region ID (such as Region-01) to obtain the set of connected region identifiers of the same level.

[0032] S403: Call the same level connected region identifier set, compare the stability of the early warning performance of adjacent units and calculate the level fluctuation count value, filter the regions whose level fluctuation count value is lower than the stability judgment threshold, perform spatial labeling on the filtered regions according to the unified identifier, and obtain the salinization risk level distribution layer. For each connected region (e.g., Region-01), the level data of the grid within the region over the past 10 monitoring periods are retrieved to calculate the "level fluctuation count value". This refers to the total number of times the grid level within the region changes (e.g., from 2 to 3, or from 3 to 2), with a stability threshold set as follows. Next, if a certain area This indicates that the area is in a long-term stable high-risk state and has continuous harmfulness, meeting the labeling conditions. The area is selected, and according to the risk level (e.g., level 3 corresponds to dark red), the grid of the area is uniformly filled with color and the boundary is outlined on the GIS layer to generate a salinization risk level distribution layer.

[0033] Please see Figure 6 The specific steps of S5 are as follows: S501: Utilize the distribution of spatial units surrounding the risk area in the salinization risk level distribution layer to obtain the outer unit index and the corresponding early warning level sequence. Determine if the difference between the outer unit and the adjacent risk unit level crosses the level change threshold and the position index is continuous. Filter out the indexes that meet the conditions and arrange them in a circumferential order to generate the boundary candidate unit band. Lock the set of regions already marked as risk areas (e.g., Region-01), expand outwards to obtain the set of "outer unit" indices immediately adjacent to the region's edge, and read the warning level of the outer units. Let the warning level inside the risk area be 1. (e.g., level 3), the outer unit level is Set the threshold for hierarchical changes to: (i.e., grade difference) Check each peripheral unit individually; if (For example, the inner value is 3, the outer value is 1, and the difference is 2), and the outer units that meet the conditions are spatially adjacent, then the indices are arranged in a clockwise circular order to generate a boundary candidate unit band.

[0034] S502: Based on the boundary candidate unit band, compare the connectivity marker states in the candidate unit spatial adjacency matrix, determine whether the connectivity markers meet the discrimination conditions of a single ring structure, perform index replacement on unit sequences that do not meet the conditions, and synchronously update the stable region extension range index set to obtain closed candidate connected sequences; Construct the spatial adjacency matrix of candidate unit bands, matrix elements Representation unit and If a node is adjacent, the value is 0; otherwise, the value is 0. Check if the graph structure corresponding to the matrix satisfies a "single-ring structure," meaning each node has exactly two connected neighbor nodes (in a circular path). If a node is found to have only one neighbor (broken) or more than two neighbors (branched), perform an index replacement: search if there are any remaining nodes in the node's 8-neighborhood that satisfy the condition. If a spare unit exists, the node is replaced to repair the break or remove the forked branch. The repaired peripheral unit index is included in the "stable region extended range index set" to obtain the closed candidate connected sequence.

[0035] S503: Call the closed candidate connected sequence, complete the spatial closure check and calculate the closure discrimination value of the first and last indexes. If the closure discrimination value meets the closure threshold condition, it is converted into a boundary coordinate point set. The boundary coordinate point set is encoded into a line feature attribute structure according to the display order, and the salting risk boundary visualization result is generated. Extract the first unit and the last unit Calculate the Euclidean distance between the two points using their center coordinates. Set the closure threshold to Rice (i.e.) (Allowing an error of one diagonal grid), if If the closure condition is met, extract the center coordinates of the units in the sequence one by one to generate a point set. The point set is encoded into a vector line feature (Polyline), and the risk level and region ID are recorded in the attribute table to generate a visualization result of the salinization risk boundary; Table 3: Boundary Candidate Unit Sequence and Closure Criterion Table As shown in Table 3, the coordinates of the first grid in the sequence, Grid-201, are (500 350). The coordinates of the last grid Grid-250 are (500350). 4150310), calculate the distance between the first and last ends. The distance is less than the threshold of 14.2 meters, so the sequence is determined to form a closed loop. A closed risk warning red line will be generated accordingly. The results show that the boundary of the risk area has been successfully extracted and the geometric shape is complete, which can be used for precise isolation or governance scope definition.

[0036] Please see Figure 7 A data visualization system for salinization monitoring, comprising: The spatial integration module acquires the output information of soil salinity monitoring sensors and remote sensing pixels within a geographic unit, generates a spatiotemporal alignment coordinate matrix, obtains the spatial coordinates and acquisition time of monitoring points, corrects for time consistency, calculates the index position of monitoring points in the geographic unit grid, determines the relationship between monitoring points and geographic unit boundaries, and constructs a salinity spatial data layer. The structural analysis module, based on the salinity spatial data layer, extracts the spatial ordering relationship of salinity values ​​within the same geographic unit, calculates the distribution span and concentration pattern, compares the distribution center with the overall state, filters the spatial indexes that enter the salinization determination interval, and forms a visual information of unit salinization structure. The evolution segmentation module compares the structural feature vectors corresponding to adjacent monitoring periods based on the unit salinization structure visualization information, calculates the position of structural change nodes, adjusts the time axis segmentation according to the change nodes, selects time segments with consistent structural performance, and generates a staged salinization evolution display sequence. The risk mapping module, based on the phased salinization evolution display sequence, identifies the mapping relationship between salinity intervals and warning levels, determines the warning level affiliation of geographical units, filters geographical units of the same level, and generates a salinization risk level distribution layer. The boundary construction module, based on the salinization risk level distribution layer, analyzes the consistency of the risk level of the geographical units surrounding the risk area, determines the spatial location of the hierarchical change in the warning level, filters the candidate boundary unit zones, analyzes the spatial adjacency relationship, adjusts the extension range of the stable area, completes the spatial closure verification, and generates the visualization result of the salinization risk boundary.

[0037] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for visualizing salinization monitoring data, characterized in that, Includes the following steps: S1: Acquire the output information of soil salinity monitoring sensors and remote sensing pixels within the geographic unit, analyze the correspondence between the spatial coordinates of the monitoring points and the acquisition time, calculate the location distribution of the monitoring points in the geographic unit grid, adjust the spatial arrangement order of the monitoring points, and generate a salinity spatial data layer. S2: Based on the salinity spatial data layer, extract the salinity distribution order within the same geographical unit, calculate the spatial distribution statistical characteristic value, compare the distribution in the central region with the overall distribution status, filter the spatial locations that fall into the salinization determination interval, and generate unit salinization structure visualization information. S3: Based on the visualized information of the unit's salinization structure, compare the differences between adjacent monitoring periods, analyze the structural changes, determine the location of structural change nodes, adjust the segmentation of the salinization time axis, and establish a phased salinization evolution display sequence. S4: Based on the staged salinization evolution display sequence, calculate the mapping relationship between salinity intervals and warning levels, determine the warning level affiliation of geographical units, select warning units of the same level to construct a continuous spatial region, compare the stability of warning performance of adjacent units, and obtain a salinization risk level distribution layer.

2. The method for visualizing salinization monitoring data according to claim 1, characterized in that, The salinity spatial data layer includes geographic unit codes, monitoring point spatial indexes, temporal consistency identifiers, salinity attribute sets, and spatial topological markers. The unit salinization structure visualization information includes salinity gradient type, spatial concentration index, central offset characteristics, salinization judgment ratio, and structural morphology label. The staged salinization evolution display sequence includes evolution stage identifiers, stage duration, structurally stable segment markers, change node indexes, and stage comparison parameters. The salinization risk level distribution layer includes risk level codes, continuous risk blocks, stability discrimination identifiers, spatial coverage, and regional consistency labels.

3. The method for visualizing salinization monitoring data according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain soil salinity monitoring sensor and remote sensing pixel output information within the geographic unit, extract the spatial coordinate field of the monitoring sensor and the collection timestamp, perform alignment verification for the pixel grid number, perform one-to-one mapping between coordinate values ​​and time series, and generate a spatiotemporal alignment coordinate matrix. S102: Based on the spatiotemporal alignment coordinate matrix, calculate the row and column positions of the monitoring points in the geographic unit grid index, perform ascending order reordering on the row and column index sequence, determine the consistency between the monitoring point index and the geographic unit boundary index, remove the boundary index markers, integrate the corresponding monitoring point salinity value records, perform structured binding between the salinity values ​​and the grid position identifier, and generate a salinity spatial data layer.

4. The method for visualizing salinization monitoring data according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Based on the salinity spatial data layer, extract the salinity numerical sequence corresponding to the grid within the same geographic unit, perform sorting operation according to the grid index order, summarize the difference results of adjacent salinity values ​​in continuous space, and generate a salinity spatial distribution sequence. S202: Based on the salinity spatial distribution sequence, calculate the salinity span value corresponding to the first and last indices, extract the salinity mean value of the index segment in the middle of the salinity spatial distribution sequence, compare it with the salinity mean value of the whole sequence, mark the grid indices that deviate from the whole mean value and fall into the salinization judgment interval threshold, and obtain the salinization spatial index set. S203: Call the salinization spatial index set, count the proportion of the total number of the geographic unit in all grid indices, combine the changes in the distribution density of the index in the sequence, identify the spatial distribution morphology transfer status, map the proportion value and morphology identifier into graphic representation units, and generate unit salinization structure visualization information.

5. The method for visualizing salinization monitoring data according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Based on the visualization information of the unit salting structure, collect the graphic unit code and proportion field corresponding to adjacent monitoring cycles, retrieve the corresponding values ​​of the same spatial index in the two cycles, compare the proportion difference and morphological identification difference between the two cycles, arrange the difference results according to the index sequence, and generate a cycle difference quantification matrix. S302: Based on the period difference quantization matrix, obtain the initial setting reference value sequence and align the period index, perform deviation operation on the period difference and the reference value, determine the grid index position when the deviation exceeds the salting change discrimination threshold, record the cross position index and the corresponding period number into a key-value structure, and obtain the change node index table. S303: Call the change node index table, adjust the segment position of the salinization time axis and update the segment boundary index, perform consistency judgment on the morphological identifiers within the segment and retain the segments where the judgment is successful, and perform sequence splicing of the segment order with the corresponding visualization graphic unit to establish a staged salinization evolution display sequence.

6. The method for visualizing salinization monitoring data according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Based on the staged salinization evolution display sequence, extract the geographical unit salinity interval boundary value sequence, calculate the interval boundary value and warning level, perform mapping assignment for the salinity interval code, and generate a warning level mapping data frame. S402: Based on the warning level mapping data frame, determine the warning level of the geographic unit and output the level identifier, filter the unit indexes of the same level and perform connectivity discrimination according to spatial adjacency, aggregate the unit indexes of connected units into a set of regions, and obtain the set of connected region identifiers of the same level. S403: Call the same level connected region identifier set, compare the stability of the early warning performance of adjacent units and calculate the level fluctuation count value, filter the regions whose level fluctuation count value is lower than the stability discrimination threshold, perform spatial labeling on the filtered regions according to the unified identifier, and obtain the salinization risk level distribution layer.

7. The method for visualizing salinization monitoring data according to claim 6, characterized in that, The process of extracting the boundary value sequence of salinity intervals of geographical units and calculating the interval boundary value and warning level is specifically defined as follows: based on the salinity distribution information, the salinity values ​​within the same geographical unit are divided at equal intervals, and the salinity interval boundary value sequence includes at least three consecutive boundary values, with the salinity difference between adjacent boundary values ​​remaining consistent. Based on the salinity interval boundary value sequence, each salinity interval is mapped one-to-one with a warning level, and the warning levels are set to no less than three levels in order from low to high; when performing mapping assignment for the salinity interval code, the salinity interval code is converted into the corresponding warning level identifier, and the warning level identifier is written into the warning level mapping data frame in the form of a field; In the process of comparing the stability of warning performance of adjacent units and calculating the level fluctuation count, the level fluctuation count is limited to the total number of times the warning level of adjacent geographical units changes, and the stability discrimination threshold is limited to no more than half of the total number of warning levels, and the level fluctuation count is filtered.

8. The method for visualizing salinization monitoring data according to claim 1, characterized in that, The method further includes step S5: S5: Using the distribution of spatial units surrounding the risk area in the salinization risk level distribution layer, determine whether the warning level has changed hierarchically and maintains a consistent spatial position, filter to form a boundary candidate unit band, compare the spatial adjacency and connectivity of the boundary candidate units, adjust the expansion range of the stable area, complete the spatial closure check, convert the closure result into a displayable boundary element, and generate a salinization risk boundary visualization result. The visualization results of the salinization risk boundary include risk boundary line elements, closed area identifiers, boundary continuity attributes, spatial extension range annotations, and boundary stability characteristics.

9. The method for visualizing salinization monitoring data according to claim 8, characterized in that, The specific steps of S5 are as follows: S501: Using the distribution of spatial units surrounding the risk area in the salinization risk level distribution layer, obtain the outer unit index and the corresponding early warning level sequence, determine whether the difference between the level of the outer unit and the adjacent risk unit crosses the level change threshold and the position index is continuous, filter the index that meets the condition and arrange it in a circumferential order to generate a boundary candidate unit band. S502: Based on the boundary candidate unit band, compare the connectivity marker states in the candidate unit spatial adjacency matrix, determine whether the connectivity markers meet the discrimination conditions of a single ring structure, perform index replacement on unit sequences that do not meet the conditions, and synchronously update the stable region extension range index set to obtain closed candidate connected sequences; S503: Call the closed candidate connected sequence, complete the spatial closure check and calculate the closure discrimination value of the first and last indexes. If the closure discrimination value meets the closure threshold condition, it is converted into a boundary coordinate point set. The boundary coordinate point set is encoded into a line feature attribute structure according to the display order to generate a visualization result of the saltification risk boundary.

10. A data visualization system for monitoring salinization, characterized in that, The system is used to implement the salinization monitoring data visualization method according to any one of claims 1-9, the system comprising: The spatial integration module acquires the output information of soil salinity monitoring sensors and remote sensing pixels within a geographic unit, generates a spatiotemporal alignment coordinate matrix, obtains the spatial coordinates and acquisition time of monitoring points, corrects for time consistency, calculates the index position of monitoring points in the geographic unit grid, determines the relationship between monitoring points and geographic unit boundaries, and constructs a salinity spatial data layer. The structural analysis module, based on the salinity spatial data layer, extracts the spatial sorting relationship of salinity values ​​within the same geographical unit, calculates the distribution span and concentration pattern, compares the distribution center with the overall state, filters the spatial indexes that enter the salinization determination interval, and forms unit salinization structure visualization information. The evolution segmentation module compares the structural feature vectors corresponding to adjacent monitoring periods based on the visualization information of the unit's salinization structure, calculates the position of structural change nodes, adjusts the time axis segmentation according to the change nodes, selects time segments with consistent structural performance, and generates a staged salinization evolution display sequence. The risk mapping module, based on the staged salinization evolution display sequence, identifies the mapping relationship between salinity intervals and warning levels, determines the warning level affiliation of geographical units, filters geographical units of the same level, and generates a salinization risk level distribution layer. The boundary construction module, based on the salinization risk level distribution layer, analyzes the consistency of the risk level of the geographical units surrounding the risk area, determines the spatial location of the hierarchical change in the warning level, filters the candidate boundary unit zone, analyzes the spatial adjacency relationship, adjusts the extension range of the stable area, completes the spatial closure verification, and generates the visualization result of the salinization risk boundary.

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