Processing method for water quality detection data of new and old wetlands

Abstract

The application provides a kind of processing method for new and old wetland water quality detection data, belongs to digital data processing technical field, it includes: carrying out sample point setting to new wetland and old wetland;Realize sample point setting standardization, the water quality of new wetland and old wetland is detected by the way of field detection and analysis detection to sample point;Field detection data and laboratory detection data are analyzed and processed by improved method;The data report of water quality detection of new wetland and old wetland is generated.The application solves the problem of insufficient space representation, improves the spatial coverage accuracy of detection data, realizes the synergy of detection means, solves the problem of incomplete index coverage and insufficient detection accuracy, realizes the double guarantee of on-site rapid screening and laboratory accurate verification;Solve the technical problems that traditional methods cannot accurately locate the difference source and cannot quantify the overall water quality difference;It can also provide clear and operable decision basis for new wetland construction.

Description

Technical Field

[0001] This invention belongs to the field of digital data processing technology, and specifically relates to a method for processing water quality testing data of new and old wetlands. Background Technology

[0002] Wetlands are among the world's most biodiverse and ecologically functional ecosystems, serving as vital habitats for numerous aquatic plants and animals and playing a crucial role in urban flood control and water purification. However, with continuous urban expansion and infrastructure development, wetlands are facing the threat of large-scale degradation and habitat destruction. Therefore, to address this challenge, many countries have successively launched artificial wetland and ecological restoration projects to achieve the dual goals of ecological restoration and green city construction.

[0003] Specifically, wetlands play a vital role in maintaining ecological balance, regulating climate, and purifying water quality. Before undertaking any new wetland construction project, a comprehensive and accurate understanding of the water quality of both the new wetland and existing wetlands—that is, wetland water quality testing—is crucial for assessing the necessity and feasibility of the new wetland construction and predicting its impact on the surrounding ecological environment.

[0004] Currently, as mentioned in the patent publication number "CN120293586A", wetland water quality testing generally suffers from problems such as insufficient comprehensiveness of testing indicators, lack of systematic testing methods, and poor accuracy and comparability of test results, making it difficult to meet the stringent requirements for water quality assessment in the early stages of new wetland construction. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for processing water quality testing data from both new and old wetlands. This method can systematically, comprehensively, and accurately test the water quality of both new and old wetlands, obtain comparable data, and provide a strong basis for decision-making regarding the construction of new wetlands.

[0006] The present invention employs the following technical solution.

[0007] A method for processing water quality testing data from both new and old wetlands includes:

[0008] Step 1: Set up sample points with differentiated sampling methods for new and old wetlands;

[0009] Step 2: Conduct water quality testing at sampling points in both new and old wetlands using on-site testing and analysis methods;

[0010] Step 3: Analyze and process the field test data and laboratory test data using improved statistical methods;

[0011] Step 4: Generate data reports on water quality testing for both new and old wetlands.

[0012] Preferably, step 1 specifically includes:

[0013] In both new and old wetlands, the central area of ​​the water body and the area near the edge of the water body are both important considerations. Water quality testing points were set up at various locations, with one point located at the center of each wetland. Each water quality sampling point is located at a distance from the edge of the water body. Distributed at a distance of meters and evenly distributed around the water body A water quality testing sampling point.

[0014] Preferably, in step 1, the distance from the edge of the water body The area at a distance of meters is the edge layer of spatial stratification, while the central area of ​​the water body is the central layer of spatial stratification.

[0015] Preferably, in step 2, the method for conducting water quality testing using on-site testing includes:

[0016] Using a portable water quality analyzer equipped with pH, ​​dissolved oxygen, temperature, and conductivity detection functions, real-time on-site monitoring was conducted at each designated sampling point. This involved immersing the probe of the portable water quality analyzer into the water sample at an appropriate depth, and recording the data for each indicator after the analyzer readings stabilized. This process was repeated for each sampling point for each indicator. Next, perform duplicate detection. The average value of the index data from each sampling point is used as the on-site detection data for that index.

[0017] Preferably, in step 2, the method for water quality testing using analytical methods includes:

[0018] Sufficient water samples were collected at each sampling point using sampling bottles. After collection, the samples were sent to the laboratory where total phosphorus, total nitrogen, and suspended solids were analyzed to obtain data. Each water sample was analyzed for each specific indicator. Sub-analysis and detection, computational analysis and detection The average value of the index data from each test is taken as the laboratory test data for that index in the water sample.

[0019] Preferably, step 3 specifically includes:

[0020] Step 3-1: Organize the on-site testing data and laboratory testing data;

[0021] Step 3-2: Perform outlier handling on-site and laboratory test data;

[0022] Step 3-3: Apply improved statistical methods to perform statistical analysis on the field test data and laboratory test data after outlier processing.

[0023] Preferably, in step 3-1, the on-site test data and laboratory test data are summarized and organized to create a data table. The data table includes wetland type, sample location, test index type, test data and test time information.

[0024] Preferably, in step 3-2, the Grubbs criterion is used to identify and process outliers in the field test data and laboratory test data. That is, if the difference between a certain test data and the average value of the group of test data is greater than the critical value calculated by the Grubbs criterion, the test data is determined to be an outlier, is removed, and the test data of the corresponding sample point of the outlier is supplemented.

[0025] Preferably, step 3-3 specifically includes:

[0026] Step 3-3-1: Perform stratified calculation of basic statistical parameters;

[0027] Step 3-3-2: Calculate the dynamic ecological weight parameters of various indicator data;

[0028] Step 3-3-3: Apply improved statistical methods to perform statistical analysis.

[0029] Preferably, step 3-3-1 includes:

[0030] Stratified average and overall average The calculation is performed using the following formula:

[0031]

[0032] in ,exist for Time indicates the central layer, in for Time indicates the edge layer; Indicates layering The number of sample points, Indicates the number of samples in the central layer. Indicates the number of samples in the edge layer. Indicates layering The A certain type of indicator data from a sample point;

[0033] Stratified standard deviation and overall standard deviation The value is calculated using the following formula:

[0034]

[0035] in This represents the standard deviation of the central layer. This represents the standard deviation of the edge layer;

[0036] Stratified coefficient of variation and overall coefficient of variation The value is calculated using the following formula:

[0037]

[0038] in for Stratified standard deviation of the strata; for robust median The calculation is performed using the following formula:

[0039] ;

[0040] in Indicates the spatial weighting coefficient, introducing the spatial weighting coefficient. , This represents the median of a certain type of indicator data in the central layer. This represents the median of a certain type of indicator data in the edge layer.

[0041] Preferably, step 3-3-2 includes:

[0042] The formula for calculating the dynamic ecological weight parameter is:

[0043] ;

[0044] in Indicators Dynamic ecological weight parameters, Indicators Ecological impact index. This indicates the number of data types for the indicator.

[0045] Preferably, step 3-3-3 includes:

[0046] Step 3-3-3-1: Calculate the differences in stratified indicators;

[0047] Step 3-3-3-2: Calculate the weighted index differences;

[0048] Step 3-3-3-3: Construct a comprehensive difference index.

[0049] Preferably, step 3-3-3-1 includes:

[0050] For each indicator The absolute difference and relative difference rate of the central and peripheral layers are calculated separately to locate spatial difference hotspots.

[0051] absolute difference value The calculation formula is: ,in Indicating the stratification of the new wetland index The average value, Indicating the stratification of old wetlands index The average value;

[0052] relative difference rate The calculation formula is: ;

[0053] The method for locating hotspots with spatial differences is as follows:

[0054] like The study determined that there were significant spatial differences between the new and old wetlands.

[0055] like It was determined that there was a moderate spatial difference between the new and old wetlands;

[0056] like It was determined that there were slight spatial differences between the new and old wetlands.

[0057] Preferably, step 3-3-3-2 includes:

[0058] Combining dynamic ecological weights Calculate each indicator Weighted difference value :

[0059]

[0060] in , Indicators representing new wetlands The overall average, Indicators representing old wetlands The overall average.

[0061] Preferably, step 3-3-3-3 includes:

[0062] Construct a comprehensive water quality difference index between new and old wetlands The calculation formula is as follows:

[0063] ;

[0064] in Indicates to Indicators obtained by applying the Min-Max normalization method The normalized value of the weighted difference;

[0065] Construct a comprehensive water quality difference index between new and old wetlands Then, the following judgment is made:

[0066] exist At that time, the overall water quality difference between the new wetland and the old wetland was determined to be slight.

[0067] exist At that time, the overall water quality difference between the new wetland and the old wetland was determined to be moderate.

[0068] exist At that time, the overall water quality difference between the new wetland and the old wetland was determined to be significant.

[0069] Preferably, step 4 includes:

[0070] Based on the statistical analysis results, write a report on the analysis and processing of water quality testing data for both new and old wetlands. The report should include the purpose of the testing, the testing methods, the testing results, the result analysis, the conclusions, and the recommendations.

[0071] The beneficial effects of the present invention are as follows, compared with the prior art:

[0072] This invention includes setting up sampling points in both new and existing wetlands; standardizing sampling point setup; applying on-site testing and analysis to water quality testing at sampling points in both new and existing wetlands; analyzing and processing on-site and laboratory testing data using improved methods; and generating water quality testing data reports for both new and existing wetlands. This invention solves the problem of insufficient spatial representativeness, improves the spatial coverage accuracy of testing data, achieves collaborative testing methods, addresses the issues of incomplete indicator coverage and insufficient testing accuracy, and provides dual assurance of rapid on-site screening and accurate laboratory verification. It also solves the technical problems of traditional methods being unable to accurately locate the source of differences and quantify overall water quality differences; and can provide clear and actionable decision-making basis for the construction of new wetlands. Attached Figure Description

[0073] Figure 1 This is a flowchart of the method for processing water quality testing data of new and old wetlands in this invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0075] like Figure 1 As shown, a method for processing water quality testing data from both new and old wetlands includes:

[0076] Step 1: Set up sample points with differentiated sampling methods for new and old wetlands;

[0077] In a preferred but non-limiting embodiment of the present invention, step 1 specifically includes:

[0078] In both new and old wetlands, the central area of ​​the water body and the area near the edge of the water body are both important considerations. Water quality sampling points are set up at various locations (X value is reasonably determined based on the wetland size and actual conditions; generally, X value ranges from 5 to 10 meters for small wetlands with an area of ​​less than 1000 square meters, from 10 to 20 meters for medium-sized wetlands with an area of ​​1000 to 10000 square meters, and from 20 to 50 meters for large wetlands with an area of ​​more than 10000 square meters). A sampling point is also set up at the center of the water body in each wetland. Each water quality sampling point is located at a distance from the edge of the water body. Distributed at a distance of meters and evenly distributed around the water body Water quality testing points ( and The value is determined based on the wetland area; for small wetlands with an area of ​​less than 1000 square meters, Take 1, Select 3; medium-sized wetlands with an area of ​​1000-10000 square meters. Take 2, Take 5; large wetlands with an area greater than 10,000 square meters, Take 3. (Take 8). For each water quality testing sampling point, GPS positioning equipment can be used to accurately record the coordinates of each water quality testing sampling point.

[0079] In step 1, taking a new wetland and its adjacent old wetland as an example, the new wetland has an area of ​​approximately 5000 square meters, and the old wetland has an area of ​​approximately 8000 square meters. Two sampling points are set at the center of the new wetland, labeled N-C1 and N-C2; five sampling points are evenly set along the perimeter at a distance of 15 meters from the edge of the new wetland, labeled N-E1, N-E2, N-E3, N-E4, and N-E5. Similarly, two sampling points are set at the center of the old wetland, labeled O-C1 and O-C2; and five sampling points are evenly set along the perimeter at a distance of 15 meters from the edge of the old wetland, labeled O-E1, O-E2, O-E3, O-E4, and O-E5. The coordinates of each sampling point are accurately recorded using a GPS positioning device.

[0080] Step 1 standardizes the sample point setup, solves the problem of insufficient spatial representativeness, and improves the spatial coverage accuracy of the detection data.

[0081] Existing technologies often fail to reflect the overall water quality of wetlands due to haphazard sampling point placement (such as only placing points in localized areas). This step achieves two major technical effects through a sampling point design that combines "scale adaptation" and "spatial stratification":

[0082] Differentiated site layout to suit different types of wetlands:

[0083] Based on the wetland area (small wetlands <1000㎡, medium-sized wetlands 1000-10000㎡, large wetlands >10000㎡), the number of central sampling points was set accordingly. =1 / 2 / 3) and the number of edge samples ( =3 / 5 / 8), and specify the distance between edge sampling points (5-10 meters for small wetlands, 10-20 meters for medium-sized wetlands, and 20-50 meters for large wetlands). For example, in a medium-sized new wetland of 5000㎡, 2 sampling points are set in the center and 5 sampling points are evenly set at the edge. Compared with the traditional simple layout of "1 center + 2 edges", the sampling point coverage density is increased by 67%, which can effectively capture the water quality difference between the wetland center (water quality stable zone) and the edge (land source interference zone) and avoid the bias of "local data replacing the whole" caused by sparse sampling points.

[0084] GPS positioning and fixed sampling points ensure data traceability and reproducibility.

[0085] This step requires recording the coordinates of all sample points using GPS (such as the precise recording of the coordinates of N-C1 in the example). This not only facilitates "retesting at the same location" in the subsequent construction of new wetlands, but also provides a unified spatial benchmark for water quality comparison at different times (such as before construction and one year after construction), solving the problem of "fuzzy sample point locations and inability to reproduce" in traditional testing, and improving the spatial comparability of data by more than 90%.

[0086] In a preferred but non-limiting embodiment of the present invention, in step 1, the central region of the water body and the region from the edge of the water body... At a distance of 1 meter, spatial stratification occurs, which is the distance from the edge of the water body. The area at a distance of meters is the edge layer of spatial stratification, while the central area of ​​the water body is the central layer of spatial stratification.

[0087] Step 2: Conduct water quality testing at sampling points in both new and old wetlands using on-site testing and analysis methods;

[0088] In a preferred but non-limiting embodiment of the present invention, the method for water quality testing using on-site detection in step 2 includes:

[0089] Water quality testing using on-site methods is known as portable water quality analyzer testing. This involves using a portable water quality analyzer (such as the Hach HQ40d multi-parameter water quality analyzer) equipped with functions for measuring pH, dissolved oxygen (DO), temperature, and conductivity. Real-time on-site testing is performed at each designated sample point. The portable water quality analyzer probe is fully immersed in the water sample at an appropriate depth (generally 20-30 cm underwater) according to the instrument's operating specifications. Once the portable water quality analyzer readings stabilize, the data for each indicator are recorded. This process is repeated for each sample point and for each indicator. Second-rate( (Generally, the value is 3), for duplicate detection. The average value of the index data from each sampling point is used as the on-site detection data for that index. The on-site detection data includes index data such as pH, dissolved oxygen (DO), temperature, and conductivity.

[0090] In a preferred but non-limiting embodiment of the present invention, the method for water quality testing using analytical detection in step 2 includes:

[0091] Laboratory analysis is a method of water quality testing that utilizes analytical methods. This involves collecting sufficient water samples (generally 1000-2000 ml per sample point) using clean, uncontaminated sampling bottles at each sampling point to ensure representativeness. After collection, the samples are properly preserved and sent to the laboratory as soon as possible, following relevant standards and specifications (such as HJ494-2009 "Technical Guidelines for Water Quality Sampling"). In the laboratory, the total phosphorus (TP), total nitrogen (TN), and suspended solids (TSS) of the water samples are analyzed. The indicators were analyzed and tested to obtain indicator data. Total phosphorus was detected using the ammonium molybdate spectrophotometric method (according to GB / T11893-1989 "Determination of Total Phosphorus in Water - Ammonium Molybdate Spectrophotometric Method"); total nitrogen was detected using the alkaline potassium persulfate digestion ultraviolet spectrophotometric method (according to GB / T11894-1989 "Determination of Total Nitrogen in Water - Alkaline Potassium Persulfate Digestion Ultraviolet Spectrophotometric Method"); and suspended solids were detected using the gravimetric method (according to GB / T11901-1989 "Determination of Suspended Solids in Water - Gravimetric Method"). Each water sample was analyzed for each indicator. Sub-analysis detection ( (Generally, the value is taken as 2), calculation, analysis, and detection. The average value of the index data from each test is taken as the laboratory test data for that index in the water sample.

[0092] Step 2 enables collaborative testing methods, resolving issues of incomplete indicator coverage and insufficient testing accuracy, and achieving dual assurance of rapid on-site screening and precise laboratory verification.

[0093] Existing technologies often rely on a single detection method (such as on-site detection or laboratory analysis alone), resulting in a trade-off between real-time performance and accuracy. This step achieves three major technical benefits through the synergy of "portable instruments + standard laboratory methods":

[0094] Real-time on-site monitoring captures dynamic water quality parameters:

[0095] Portable water quality analyzers such as the Hach HQ40d were used to test "fluctuating indicators" such as pH, DO, and temperature on-site, and the average value was obtained by "repeatedly testing at the same location three times". The design of (=3) reduces the random error of portable water quality meters. For example, in the DO detection at the N-C1 sample point of the new wetland, the three readings were 6.4, 6.5, and 6.6 mg / L, with an average of 6.5 mg / L. Compared with a single detection, the random error of the data was reduced by 40%, which can reflect the real-time dissolved oxygen status of the wetland water (such as the difference in DO between morning and afternoon), and provide dynamic data support for subsequent analysis of the impact of aquatic plant photosynthesis on DO.

[0096] Standardized laboratory testing ensures the accuracy of key indicators:

[0097] For core indicators such as TP, TN, and TSS that require complex pretreatment, we strictly follow national standard methods (e.g., TP uses the GB / T11893 ammonium molybdate spectrophotometric method) and use the "average of two analyses" method. =2) Control system errors. For example, the TP detection at the N-C1 sample point yielded 0.14 mg / L and 0.16 mg / L in two parallel experiments, with an average of 0.15 mg / L and a relative deviation of only 6.7%, far below the industry-allowed deviation standard of 10%. This ensures the detection accuracy of pollution indicators such as TP and TN, providing reliable data for assessing the "nitrogen and phosphorus purification capacity" of wetlands.

[0098] The indicators are comprehensive, meeting the multi-dimensional assessment needs of new wetland construction:

[0099] This step simultaneously covers seven core indicators: physical indicators (temperature, TSS), chemical indicators (pH, DO, TP, TN), and electrochemical indicators (conductivity). Compared to traditional testing methods that only cover three to four indicators, this represents a 100% increase in indicator coverage. For example, conductivity can be used to help determine water salinity (e.g., in coastal wetlands), and TSS can reflect water turbidity (e.g., in wetlands affected by sediment input). This allows for a comprehensive assessment of the basic water quality conditions of new wetlands, avoiding "one-sided assessments" caused by missing indicators (e.g., measuring only DO while ignoring TP, thus failing to detect potential phosphorus pollution risks).

[0100] Step 3: Analyze and process the field test data and laboratory test data using improved statistical methods;

[0101] In a preferred but non-limiting embodiment of the present invention, step 3 specifically includes:

[0102] Step 3-1: Organize the on-site testing data and laboratory testing data;

[0103] In a preferred but non-limiting embodiment of the present invention, in step 3-1, the field test data and laboratory test data are summarized and organized to establish a data table. The data table includes information such as wetland type (new wetland or old wetland), sample point location (whether the sample point belongs to the central layer or the edge layer and the specific coordinates of the sample point), test index type (seven index types such as temperature, TSS, pH, DO, TP, TN, and conductivity), test data (test data corresponding to the test index type), and test time.

[0104] Step 3-2: Perform outlier handling on-site and laboratory test data;

[0105] In a preferred but non-limiting embodiment of the present invention, in step 3-2, outlier judgment and processing of field test data and laboratory test data are performed using methods such as the Grubbs criterion. That is, if the difference between a certain test data and the average value of the group of test data is greater than the critical value calculated by the Grubbs criterion, the test data is determined to be an outlier, is removed, and the test data of the corresponding sample point of the outlier is supplemented.

[0106] Step 3-3: Apply improved statistical methods to perform statistical analysis on the field test data and laboratory test data after outlier processing.

[0107] In step 3-3, statistical analysis is performed on the field and laboratory test data after processing various outliers in both the new and old wetlands, calculating statistical parameters such as mean, standard deviation, and coefficient of variation. By comparing the statistical parameters of the same indicators between the new and old wetlands, improved statistical methods are applied to analyze the differences in overall water quality and dispersion between the two wetlands.

[0108] In a preferred but non-limiting embodiment of the present invention, step 3-3 specifically includes:

[0109] In the statistical analysis of water quality in existing new and old wetlands, t-tests are often used. However, the limitations of t-tests (dependence on normality, homogeneity of variance, and sensitivity to extreme values) are well known. Furthermore, traditional statistical parameter calculations can only reflect data characteristics in a single way and cannot achieve "precise difference positioning" by combining wetland ecological characteristics.

[0110] This approach, through "expanding hierarchical statistical dimensions + introducing dynamic weight parameters + constructing a multi-dimensional difference quantification model," overcomes the limitations of traditional t-test methods, forming an improved technical solution: First, it integrates the "center-edge" spatial stratification characteristics of wetland water bodies into statistical parameter calculations for the first time, solving the analytical bias caused by neglecting spatial differences in traditional methods; second, it innovatively introduces "indicator ecological weights" to correct the differences in the impact of different water quality indicators on wetland functions; and third, it constructs a "comprehensive difference index" to achieve quantitative assessment from single indicator differences to overall water quality differences, providing a more accurate decision-making basis for the construction of new wetlands.

[0111] Step 3-3-1: Perform stratified calculation of basic statistical parameters;

[0112] To address the water quality differences between the central and peripheral layers of wetland water bodies (e.g., the peripheral layer is more affected by terrestrial inputs and is prone to high TSS and high TP), the traditional "overall sample statistics" is optimized into a two-level calculation logic of "stratified statistics + overall summary" to ensure that the statistical parameters can reflect spatial heterogeneity.

[0113] In a preferred but non-limiting embodiment of the present invention, step 3-3-1 includes:

[0114] Stratified average and overall average The calculation is performed using the following formula:

[0115]

[0116] in ,exist for Time indicates the central layer, in for The time represents the edge layer, avoiding the spatial differences that are masked by the overall statistics of traditional methods; Indicates layering The number of sample points, Indicates the number of samples in the central layer. Indicates the number of samples in the edge layer. Indicates layering The The data of a certain type of indicator for a sample point is the field test data or laboratory test data after outlier processing.

[0117] Stratified standard deviation and overall standard deviation The value is calculated using the following formula:

[0118]

[0119] in This represents the standard deviation of the central layer. It represents the standard deviation of the edge layer, based on the combined calculation of the layered variance, which more accurately reflects the overall dispersion and avoids the excessive interference of edge extreme values ​​on the overall standard deviation in traditional methods;

[0120] Stratified coefficient of variation and overall coefficient of variation The value is calculated using the following formula:

[0121]

[0122] in for Stratified standard deviation; by stratified coefficient of variation By comparison, regions with high dispersion can be located, whereas traditional methods can only obtain the overall coefficient of variation. The source of the dispersion cannot be determined.

[0123] For the robust median The calculation is performed using the following formula:

[0124] ;

[0125] in Indicates the spatial weighting coefficient, introducing the spatial weighting coefficient. (Values ​​range from 0.4 to 0.6, adjusted according to wetland type: small wetlands) It can be 0.4, medium-sized wetland It can be 0.5, as large wetlands have a large central area. (It can be 0.6), correcting the shortcoming of the traditional median that ignores the spatial proportion. This represents the median of a certain type of indicator data in the central layer. This represents the median of a certain type of indicator data in the edge layer.

[0126] Step 3-3-2: Calculate the dynamic ecological weight parameters of various indicator data;

[0127] In a preferred but non-limiting embodiment of the present invention, step 3-3-2 includes:

[0128] Different types of water quality indicators have different weights influencing wetland ecological functions (e.g., dissolved oxygen (DO) directly affects the survival of aquatic organisms, so its weight should be higher than that of pH). Traditional methods treat all indicators equally, failing to reflect the varying ecological importance of each indicator. This step innovatively introduces dynamic ecological weight parameters. ( The data types (such as pH, DO, TP, etc.) are dynamically adjusted based on the needs of wetland ecological functions.

[0129] The formula for calculating the dynamic ecological weight parameter is:

[0130] ;

[0131] in Indicators (i.e., the type of indicator data is) The dynamic ecological weight parameters of ) Indicators The ecological impact index is determined by referring to the "Technical Specification for Wetland Ecological Quality Assessment" (LY / T3253-2021) to determine the initial data as the base score, and then making adjustments based on the specific requirements for revising the new wetland construction goals (such as constructing "water purification wetlands," TP, TN, etc.). The data will be scored 10 points higher than the initial data; the construction of "biological habitat wetlands" will be awarded by DO. (Add 10 points to the initial data). This indicates the number of data types for the indicator.

[0132] Dynamic ecological weight Breaking through the limitations of traditional fixed weights, it achieves a precise match between the importance of indicators and wetland construction goals.

[0133] Step 3-3-3: Apply improved statistical methods to perform statistical analysis.

[0134] Traditional methods rely solely on "parameter numerical comparison + significance test" to determine differences, failing to quantify the "degree of overall water quality difference." This step constructs a three-level difference quantification model of "stratified difference - indicator weight - comprehensive index," enabling a systematic comparison from single indicators to overall water quality.

[0135] In a preferred but non-limiting embodiment of the present invention, step 3-3-3 includes:

[0136] Step 3-3-3-1: Calculate the differences in stratified indicators (spatial dimension);

[0137] In a preferred but non-limiting embodiment of the present invention, step 3-3-3-1 includes:

[0138] For each indicator The absolute difference and relative difference rate of the central and peripheral layers are calculated separately to locate spatial difference hotspots.

[0139] absolute difference value The calculation formula is: ,in Indicating the stratification of the new wetland index The average value, Indicating the stratification of old wetlands index The average value;

[0140] relative difference rate The calculation formula is: ;

[0141] The method for locating hotspots with spatial differences is as follows:

[0142] like The study determined that there were significant spatial differences between the new and old wetlands.

[0143] like It was determined that there was a moderate spatial difference between the new and old wetlands;

[0144] like It was determined that there were slight spatial differences between the new and old wetlands.

[0145] Step 3-3-3-2: Calculate the weighted index differences (ecological dimension);

[0146] In a preferred but non-limiting embodiment of the present invention, step 3-3-3-2 includes:

[0147] Combining dynamic ecological weights Calculate each indicator Weighted difference value Highlighting the differentiated contributions of key ecological indicators:

[0148]

[0149] in It reflects the indicators of new and old wetlands. Overall average difference Indicators representing new wetlands The overall average, Indicators representing old wetlands The overall average;

[0150] Step 3-3-3-3: Construct a comprehensive difference index (overall dimension);

[0151] In a preferred but non-limiting embodiment of the present invention, step 3-3-3-3 includes:

[0152] Construct a comprehensive water quality difference index between new and old wetlands The formula for quantifying the overall water quality variation is as follows:

[0153] ;

[0154] in Indicates to Indicators obtained by applying the Min-Max normalization method The normalized value of the weighted difference;

[0155] Construct a comprehensive water quality difference index between new and old wetlands Then, the following judgment is made:

[0156] exist At that time, the overall water quality difference between the new wetland and the old wetland was determined to be slight, indicating that the water quality of the new wetland and the old wetland were similar, and the ecological management model of the old wetland could be used as a reference for the construction of the new wetland.

[0157] exist At that time, the overall water quality difference between the new wetland and the old wetland was determined to be moderate, which indicates that some water quality indicators of the new wetland are better or worse than those of the old wetland (whether they are better or worse depends on the value of the water quality indicator data), and targeted optimization is required (e.g., if the TP difference is large, phosphorus interception measures should be strengthened).

[0158] exist When the overall water quality difference between the new wetland and the old wetland is determined to be significant, it indicates that the water quality difference between the new wetland and the old wetland is significant, and the construction plan of the new wetland needs to be re-evaluated (such as adjusting the hydrological design and plant configuration).

[0159] Step 3 breaks through the "generalization" limitations of traditional statistical analysis. It designs a unique logic of "stratified statistics + dynamic weighting + comprehensive index" for the "spatial stratification characteristics" and "ecological correlation of indicators" of wetland water quality. This solves the technical problems that traditional methods cannot accurately locate the source of differences and cannot quantify the overall water quality differences.

[0160] Compared with existing methods, step 3 has better technical effects: First, through hierarchical statistics, specific difference areas of "center / edge" can be located, providing a basis for optimizing the spatial layout of new wetlands; second, through dynamic weights, the analysis results are ensured to be consistent with the wetland construction goals, avoiding "irrelevant indicators interfering with decision-making"; third, through the comprehensive difference index, the leap from "qualitative description" to "quantitative assessment" is achieved, improving the scientific nature of decision-making.

[0161] Step 4: Generate data reports on water quality testing for both new and old wetlands.

[0162] In a preferred but non-limiting embodiment of the present invention, step 4 includes:

[0163] Based on the statistical analysis results, a data analysis and processing report on water quality testing for both new and existing wetlands will be prepared. The report will cover the testing objectives, testing methods, testing results (presenting various indicator data and statistical parameters in a combination of charts and text), results analysis (explaining the characteristics of water quality in new and existing wetlands and the reasons for differences), conclusions, and recommendations. The report format will follow relevant industry standards and specifications to ensure accurate, clear, and complete data presentation, providing reliable data support for subsequent decisions related to the construction of new wetlands.

[0164] One specific implementation of step 4 is as follows:

[0165] Step 4, based on the testing process and data processing logic of the patented technology solution, provides a standardized writing framework, key points and examples for the five core modules of the report: "testing purpose, testing method, test results, result analysis, conclusions and recommendations", to ensure that the report is scientific, standardized and decision-supporting.

[0166] I. Method for writing the "Testing Purpose": Clearly define the core objectives and application scenarios of the test.

[0167] 1. Writing Logic

[0168] It is necessary to focus on the core needs of "preliminary assessment for new wetland construction" and clearly explain the direct purpose (understanding water quality), indirect purpose (supporting decision-making), and scope (wetland objects and indicator objects to be tested) of the testing, avoiding vague statements.

[0169] 2. Content Framework

[0170] The opening paragraph clarifies the background of the testing (the necessity of constructing new wetlands);

[0171] The direct objective of the testing is to compare the differences in water quality between new and old wetlands and obtain key indicator data.

[0172] Define the application objectives of the testing (to provide data support for the necessity assessment, scheme optimization, and ecological integration of new wetland construction);

[0173] Define the scope of the test (specific wetland name / location, type of test indicator).

[0174] 3. Example (in conjunction with patent embodiments)

[0175] "To assess the necessity and feasibility of constructing a new wetland (approximately 5,000 square meters, hereinafter referred to as the 'new wetland') in [XX area], and to compare its water quality with that of the surrounding existing wetland (approximately 8,000 square meters, hereinafter referred to as the 'old wetland'), this test systematically detects and analyzes the core water quality indicators of the two wetlands, including pH, dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN), and suspended solids (TSS). The aim is to understand the overall water quality level, spatial distribution characteristics, and significant differences between the two wetlands, and to provide scientific data support for optimizing the construction plan of the new wetland (such as vegetation configuration and edge protection design), predicting ecological benefits, and ensuring ecological integration with the old wetland."

[0176] II. Writing Methods for "Testing Methods": Reproduce the testing process and demonstrate the standardization and reproducibility of the methods. 1. Writing Logic

[0177] The test results must strictly correspond to the three major steps of "sample setting, testing methods, and data processing" in the patented technical solution, and be written according to the "process-oriented" approach. Each step must clearly define the "operation standards, parameter values, and reference specifications" to ensure that others can reproduce the testing process using this method.

[0178] 2. Content Framework (written in modules)

[0179] (1) Sample point setting method

[0180] Describe the classification and scale of wetlands (area and location of new / old wetlands);

[0181] Clearly define the sampling point stratification logic (center / edge), the basis for determining the quantity (X1 / X2 values ​​adapted to the area), and the edge distance (X value).

[0182] Supplement the sampling point marking and positioning methods (such as GPS coordinate recording).

[0183] Example: "New wetland (5000㎡, medium-sized wetland): 2 sampling points (marked N-C1, N-C2) are set at the center of the water body, and 5 sampling points (marked N-E1~N-E5) are evenly set along the perimeter at a distance of 15 meters from the edge; Old wetland (8000㎡, medium-sized wetland): 2 sampling points (marked O-C1, O-C2) are set at the center of the water body, and 5 sampling points (marked O-E1~O-E5) are set at a distance of 15 meters from the edge. All sampling points are located using GPS (e.g., N-C1 coordinates: XX°XX′XX″N, XX°XX′XX″E), according to the parameter settings of "medium-sized wetland X=10-20 meters, X1=2, X2=5" in the patented technical solution."

[0184] (2) Detection methods

[0185] The test is divided into two parts: “On-site testing” and “Laboratory testing”. Each part must specify “instruments / equipment, testing indicators, operating procedures, and number of repetitions”.

[0186] Laboratory tests must be labeled with the national standards (GB / T series) upon which they are based to ensure that the methods are compliant.

[0187] Example: "① On-site testing: Use a Hach HQ40d multi-parameter water quality analyzer to test pH, DO, temperature, and conductivity; immerse the probe 25 cm underwater (patent recommends 20-30 cm), record the readings after they stabilize, and repeat the test 3 times for each sample point and each indicator (X3=3), and take the average value; ② Laboratory testing: Collect water samples from each sample point in a 1000 mL brown bottle (rinse 3 times before collection), and send them for testing in an ice bath within 2 hours; TP is tested using GB / T11893-1989 ammonium molybdate spectrophotometry, TN is tested using GB / T11894-1989 alkaline potassium persulfate digestion ultraviolet spectrophotometry, and TSS is tested using GB / T11901-1989 gravimetric method. Perform 2 parallel experiments for each water sample and each indicator (X4=2), and take the average value."

[0188] (3) Data processing methods

[0189] Explain the dimensions of the data processing tables (wetland type, sampling point location, indicators, detection values, etc.).

[0190] Clearly define outlier handling methods (such as the application conditions of the Grubbs criterion and the determination of critical values);

[0191] List the parameters for statistical analysis (mean, standard deviation, coefficient of variation) and the significance test method (t-test, P<0.05 is considered significant).

[0192] Example: "① Data processing: Establish a data table according to "wetland type - sampling point location - detection index - detection value - detection time"; ② Outlier handling: Use Grubbs' criterion (95% confidence level). If the deviation of the data from the mean is greater than the critical value (e.g., the critical value is 2.176 when n=10), it is judged as an outlier and additional detection is performed; ③ Statistical analysis: Calculate the mean, standard deviation and coefficient of variation of each index of the two wetlands, and use t test to judge the significance of the difference in mean (P<0.05 is significant)."

[0193] 3. Method for writing "Test Results": Use "charts + text" to present the data intuitively and highlight the core differences.

[0194] 1. Writing Logic

[0195] Following the approach of "first compiling overall statistics, then comparing at different levels", we first summarize the core statistical parameters in tables, then use charts (bar charts, line charts) to show the key differences, and extract the "numerical comparison conclusions" in the text to avoid simply listing data.

[0196] 2. Content Framework (Hierarchical Presentation)

[0197] (1) Summary of overall statistical parameters (in tabular form)

[0198] Table column dimensions: indicator type (pH, DO, etc.), wetland type (new / old), mean, standard deviation, coefficient of variation, t-test p-value, significance of difference (significant / not significant).

[0199] (2) Spatial layer differences display (charts + text)

[0200] Use a line chart to show the changes in "center-edge" indicators (such as the trend of TP concentration from the center to the edge).

[0201] Extract the hierarchical differences in the text (such as whether the edge indicators are higher than the center, and whether the dispersion is greater).

[0202] Example: "Spatial stratification differences (see...") Figure 1 (Graph showing TP concentration changes between center and edge in new and old wetlands). The average TP concentration at the center of the new wetland was 0.12 mg / L, and at the edge it was 0.14 mg / L, with the edge concentration being 16.7% higher than the center. The average TP concentration at the center of the old wetland was 0.15 mg / L, and at the edge it was 0.22 mg / L, with the edge concentration being 46.7% higher than the center. Furthermore, the standard deviation of TP at the edge of the old wetland (0.03 mg / L) was significantly greater than that at the edge of the new wetland (0.02 mg / L), indicating that the edge of the old wetland was more significantly affected by exogenous pollution and experienced greater water quality fluctuations.

[0203] (3) Outlier handling results (text description)

[0204] Please describe the location of the outlier sample, the type of index, the original value, the processing method, and the supplementary test results.

[0205] Example: "During data processing, it was found that the original pH value of the new wetland edge sampling point N-E3 was 9.5 (the mean of the same group of data was 7.3). According to the Grubbs criterion (n=7, confidence level 95%, critical value 2.02), the deviation of this value (2.2) is greater than the critical value, and it is judged as an outlier. After supplementary testing, the mean pH value of the N-E3 sampling point was 7.4, which is in good consistency with the same group of data. The original outlier has been replaced for subsequent statistics."

[0206] IV. Methodology for Writing "Results Analysis": From "Data Differences" to "Interpretation of Causes," relating to wetland characteristics

[0207] 1. Writing Logic

[0208] Following the approach of "first analyzing the differences in indicators, then comprehensively interpreting the overall water quality, and finally analyzing the reasons for the differences in wetland attributes (new / old)," we must avoid merely focusing on data descriptions and instead explore the ecological and environmental factors behind the differences.

[0209] 2. Content Framework (Dimensional Analysis)

[0210] (1) Characteristics and reasons for the differences in each indicator

[0211] For each significant difference indicator (such as DO, TP, TN), first explain the "difference performance" (new wetlands are better / worse than old wetlands), and then interpret the reasons in conjunction with wetland type (new wetlands are undisturbed, old wetlands have been used for a long time) and ecological conditions (aquatic plants, external inputs);

[0212] Example: "①DO index: The average DO of the new wetland (6.3 mg / L) was significantly higher than that of the old wetland (6.0 mg / L), and the coefficient of variation (4.8%) was lower. This may be because the new wetland is a planned area, and the water body has not been disturbed by human activities for a long time. Aquatic plants (such as reeds and cattails) grow vigorously, have strong photosynthetic oxygen production capacity, and have high water transparency and good dissolved oxygen diffusion efficiency. On the other hand, the old wetland has long received drainage from surrounding farmland, and the water body has a slightly higher degree of eutrophication. Excessive algae reproduction consumes some dissolved oxygen, resulting in a lower DO level and greater fluctuations. ②TP index: The average TP of the new wetland (0.13 mg / L) was significantly lower than that of the old wetland (0.18 mg / L). In particular, the TP concentration at the edge of the old wetland increased sharply. It is speculated that the edge of the old wetland is close to farmland, and fertilizer runoff during the rainy season leads to increased phosphorus input. On the other hand, the new wetland currently has no external pollution input, and the amount of phosphorus released from the sediment is small. Therefore, the phosphorus concentration is lower and the spatial distribution is more uniform."

[0213] (2) Comparison of overall water quality characteristics

[0214] Based on the differences in various indicators, the overall water quality level of the two wetlands was determined (referencing the "Surface Water Environmental Quality Standard" GB3838-2002), and the advantages and disadvantages of the new wetland in terms of water quality were summarized.

[0215] Example: "Overall, the water quality of the new wetland is better than that of the old wetland: DO meets the Class III standard of GB3838-2002 (≥5mg / L), and TP and TN are close to the Class III standard (TP≤0.2mg / L, TN≤1.0mg / L). Moreover, the spatial dispersion of each indicator is small, and the water quality stability is good. Although the DO of the old wetland meets the standard, the concentrations of TP and TN are relatively high (close to the Class IV standard). In particular, the water quality fluctuates significantly in the peripheral areas due to external pollution, and the overall ecological function is slightly weaker than that of the new wetland."

[0216] (3) Interpretation of non-significant difference indicators

[0217] For indicators with no significant differences (such as pH and TSS), explain the reasons for the small differences to demonstrate the comprehensiveness of the analysis;

[0218] Example: "The pH levels of the two wetlands showed no significant difference (7.3 for the new wetland and 7.2 for the old wetland), both falling within the neutral range (6.5-8.5). This is because the two wetlands are located in the same hydrological unit, have the same water supply type (both atmospheric precipitation and surrounding groundwater), and have similar buffering capacities, resulting in stable pH levels with minimal differences. Although the TSS level was slightly higher in the old wetland (28 mg / L vs 25 mg / L), there was no significant difference. This is presumably related to the fact that there is currently no large-scale sediment input in either wetland (no construction has been carried out in the new wetland, and the vegetation cover around the old wetland is good)."

[0219] 5. Methods for writing "Conclusions and Recommendations": Conclusions should be concise and based on data, and recommendations should be actionable and feasible.

[0220] 1. The Logic and Framework for Writing the "Conclusion"

[0221] The conclusions should be "data-driven and highly condensed," avoiding repetitive analysis and clearly stating the conclusions regarding the differences in water quality between the two wetlands and the overall water quality.

[0222] Example: "1. Conclusion on differences in indicators: The new wetland had significantly better DO (6.3 mg / L), TP (0.13 mg / L), and TN (1.2 mg / L) than the old wetland (6.0 mg / L, 0.18 mg / L, and 1.5 mg / L, respectively), while pH and TSS showed no significant differences; 2. Conclusion on spatial characteristics: The water quality at the edge of the old wetland (especially TP) was significantly worse than that at the center, and the dispersion was greater, while the water quality difference between the center and edge of the new wetland was small; 3. Conclusion on overall water quality: The overall water quality of the new wetland was close to the Class III standard of GB3838-2002 and had good stability; the TP and TN of the old wetland were relatively high, and the edge was significantly affected by external pollution, so the overall water quality was slightly worse."

[0223] 2. Logic and Framework for Writing "Suggestions"

[0224] The recommendations should be "closely aligned with the test results and tailored to the construction of new wetlands," and should be categorized into "necessity recommendations, scheme optimization recommendations, and ecological integration recommendations" to ensure feasibility.

[0225] Example: "1. Recommendation on the necessity of construction: Based on the fact that the water quality of the new wetland is significantly better than that of the old wetland, and that the old wetland has edge pollution problems, it is recommended to promote the construction of the new wetland to make up for the shortcomings in the region's water purification capacity; 2. Recommendation on scheme optimization: ① To address the TP pollution problem at the edge of the old wetland, a 20-meter-wide vegetation buffer zone (such as planting calamus and water chestnut) should be added to the edge of the new wetland to intercept land-based phosphorus input; ② Utilizing the DO advantage of the new wetland, aerobic purification plants (such as goldfish algae) should be planted in the central area to enhance nitrogen and phosphorus removal; 3. Recommendation on ecological connection: After the construction of the new wetland, the hydrological connectivity with the old wetland should be monitored regularly to avoid the water quality of the new wetland being affected by the spread of pollution from the sediment of the old wetland. At the same time, through the design of ecological corridors, the biological exchange between the two wetlands should be promoted to enhance the overall ecosystem stability."

[0226] The report prepared in step 4 can fully reproduce the test results of the patented technology solution, and at the same time provide a clear and operable decision-making basis for the construction of new wetlands, which meets the industry reporting standards and the application requirements of patented technology.

[0227] The beneficial effects of the present invention are as follows, compared with the prior art:

[0228] This invention includes setting up sampling points in both new and existing wetlands; standardizing sampling point setup; applying on-site testing and analysis to water quality testing at sampling points in both new and existing wetlands; analyzing and processing on-site and laboratory testing data using improved methods; and generating water quality testing data reports for both new and existing wetlands. This invention solves the problem of insufficient spatial representativeness, improves the spatial coverage accuracy of testing data, achieves collaborative testing methods, addresses the issues of incomplete indicator coverage and insufficient testing accuracy, and provides dual assurance of rapid on-site screening and accurate laboratory verification. It also solves the technical problems of traditional methods being unable to accurately locate the source of differences and quantify overall water quality differences; and can provide clear and actionable decision-making basis for the construction of new wetlands.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention without departing from the spirit and scope of the present invention. Any modifications or equivalent substitutions should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for processing water quality testing data from both new and old wetlands, characterized in that, include: Step 1: Set up sample points with differentiated sampling methods for new and old wetlands; Step 2: Conduct water quality testing at sampling points in both new and old wetlands using on-site testing and analysis methods; Step 3: Analyze and process the field test data and laboratory test data using improved statistical methods; Step 4: Generate data reports on water quality testing for both new and old wetlands; Step 3 specifically includes: Step 3-1: Organize the on-site testing data and laboratory testing data; Step 3-2: Perform outlier handling on-site and laboratory test data; Step 3-3: Apply improved statistical methods to perform statistical analysis on the field test data and laboratory test data after outlier processing; In step 3-1, the field test data and laboratory test data are summarized and organized to create a data table. The data table includes wetland type, sampling point location, test index type, test data and test time information. In step 3-2, the Grubbs criterion is used to identify and process outliers in the field test data and laboratory test data. That is, if the difference between a certain test data and the average value of the group of test data is greater than the critical value calculated by the Grubbs criterion, the test data is determined to be an outlier, removed, and the test data of the corresponding sample point for the outlier is supplemented. Step 3-3 specifically includes: Step 3-3-1: Perform stratified calculation of basic statistical parameters; Step 3-3-2: Calculate the dynamic ecological weight parameters of various indicator data; Step 3-3-3: Apply improved statistical methods to perform statistical analysis; Step 3-3-3 includes: Step 3-3-3-1: Calculate the differences in stratified indicators; Step 3-3-3-2: Calculate the weighted index differences; Step 3-3-3-3: Construct a comprehensive difference index; Step 3-3-3-1 includes: For each indicator The absolute difference and relative difference rate of the central and peripheral layers are calculated separately to locate spatial difference hotspots. absolute difference value The calculation formula is: ,in Indicating the stratification of the new wetland index The average value, Indicating the stratification of old wetlands index The average value; relative difference rate The calculation formula is: ; The method for locating hotspots with spatial differences is as follows: like The study determined that there were significant spatial differences between the new and old wetlands. like It was determined that there was a moderate spatial difference between the new and old wetlands; like It was determined that there were slight spatial differences between the new and old wetlands; Step 3-3-3-2 includes: Combining dynamic ecological weights Calculate each indicator Weighted difference value : in , Indicators representing new wetlands The overall average, Indicators representing old wetlands The overall average; Step 3-3-3-3 includes: Construct a comprehensive water quality difference index between new and old wetlands The calculation formula is as follows: ;in Indicates to Indicators obtained by applying the Min-Max normalization method The normalized value of the weighted difference; Construct a comprehensive water quality difference index between new and old wetlands Then, the following judgment is made: exist At that time, the overall water quality difference between the new wetland and the old wetland was determined to be slight. exist At that time, the overall water quality difference between the new wetland and the old wetland was determined to be moderate. exist At that time, the overall water quality difference between the new wetland and the old wetland was determined to be significant.

2. The method for processing water quality testing data of new and old wetlands according to claim 1, characterized in that, Step 1 specifically includes: In both new and old wetlands, the central area of ​​the water body and the area near the edge of the water body are both important considerations. Water quality testing points were set up at various locations, with one point located at the center of each wetland. Each water quality sampling point is located at a distance from the edge of the water body. Distributed at a distance of meters and evenly distributed around the water body One water quality testing sampling point; In step 1, the distance from the edge of the water body The area at a distance of meters is the edge layer of spatial stratification, while the central area of ​​the water body is the central layer of spatial stratification.

3. The method for processing water quality testing data of new and old wetlands according to claim 2, characterized in that, In step 2, the method of conducting water quality testing using on-site testing methods includes: Using a portable water quality analyzer equipped with pH, ​​dissolved oxygen, temperature, and conductivity detection functions, real-time on-site monitoring was conducted at each designated sampling point. This involved immersing the probe of the portable water quality analyzer into the water sample at an appropriate depth, and recording the data for each indicator after the analyzer readings stabilized. This process was repeated for each sampling point for each indicator. Next, perform duplicate detection. The average value of the index data from each sampling point is taken as the on-site detection data of that index. In step 2, the method of water quality testing using analytical methods includes: Sufficient water samples were collected at each sampling point using sampling bottles. After collection, the samples were sent to the laboratory where total phosphorus, total nitrogen, and suspended solids were analyzed to obtain data. Each water sample was analyzed for each specific indicator. Sub-analysis and detection, computational analysis and detection The average value of the index data from each test is taken as the laboratory test data for that index in the water sample.

4. The method for processing water quality testing data of new and old wetlands according to claim 3, characterized in that, Step 3-3-1 includes: Stratified average and overall average The calculation is performed using the following formula: in ,exist for Time indicates the central layer, in for Time indicates the edge layer; Indicates layering The number of sample points, Indicates the number of samples in the central layer. Indicates the number of samples in the edge layer. Indicates layering The A certain type of indicator data from a sample point; Stratified standard deviation and overall standard deviation The value is calculated using the following formula: in This represents the standard deviation of the central layer. This represents the standard deviation of the edge layer; Stratified coefficient of variation and overall coefficient of variation The value is calculated using the following formula: ;in for Stratification standard deviation of the strata; For the robust median The calculation is performed using the following formula: ;in Indicates the spatial weighting coefficient, introducing the spatial weighting coefficient. , This represents the median of a certain type of indicator data in the central layer. This represents the median of a certain type of indicator data in the edge layer.

5. The method for processing water quality testing data of new and old wetlands according to claim 4, characterized in that, Step 3-3-2 includes: The formula for calculating the dynamic ecological weight parameter is: ;in Indicators Dynamic ecological weight parameters, Indicators Ecological impact index. This indicates the number of data types for the indicator.

6. The method for processing water quality testing data of new and old wetlands according to claim 1, characterized in that, Step 4 includes: Based on the statistical analysis results, write a report on the analysis and processing of water quality testing data for both new and old wetlands. The report should include the purpose of the testing, the testing methods, the testing results, the result analysis, the conclusions, and the recommendations.

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