A water quality and soil detection and analysis method and system based on multi-standard automatic evaluation

Abstract

The application discloses a water quality and soil detection and analysis method and system based on multi-standard automatic evaluation and belongs to the technical field of environmental monitoring data processing. The system comprises a data import module, an intelligent identification module, a batch sample processing module, a multi-standard evaluation module, a comprehensive analysis module, a water chemistry verification module and a result output module. The system automatically analyzes the Excel table structure through an intelligent identification algorithm, extracts index names, units, detection results and background values, supports batch sample format identification, can analyze multiple samples at a time, integrates three environmental quality standards of underground water GB / T 14848-2017, surface water GB 3838-2002 and soil GB 36600-2018, automatically performs quality classification evaluation, calculates single pollution index P i , exceeding multiple, Nemerow comprehensive pollution index P N , performs water chemistry anion and cation balance verification, Shukarev classification, TDS and hardness checking, and supports single sample and batch sample export and summary table generation. The application solves the problems of weak Excel data recognition ability, batch sample processing difficulty, lack of multi-standard automatic switching, single comprehensive evaluation method and insufficient data quality checking in the prior art, and greatly improves the efficiency and accuracy of detection data analysis.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, specifically to a method and system for water and soil quality testing and analysis based on multi-standard automatic evaluation, applicable to the analysis of monitoring data, pollution assessment, and quality classification of groundwater, surface water, and soil environmental quality. Background Technology

[0002] With increasing environmental awareness and growing demand for environmental monitoring, water and soil testing have become important tools for environmental assessment. Currently, my country has promulgated several environmental quality standards, such as GB / T 14848-2017 "Groundwater Quality Standard", GB 3838-2002 "Surface Water Environmental Quality Standard", and GB 36600-2018 "Soil Environmental Quality Standard for Construction Land Soil Pollution Risk Control".

[0003] Existing methods for analyzing test data have the following shortcomings: 1. Low efficiency of manual calculation: After obtaining test data, testing laboratories need to manually consult standard documents, compare limits item by item, and calculate pollution indices, which is labor-intensive and prone to errors; 2. Difficulty in batch sample processing: For batch testing of multiple samples, existing software is mostly in single-sample analysis mode, which cannot efficiently process batch sample data and requires repeated import and calculation; 3. Weak Excel data recognition capability: Existing software has strict requirements for Excel table formats and cannot automatically recognize the table formats of different laboratories, requiring users to manually adjust the data format or input it item by item; 4. Lack of automatic switching between multiple standards: Different testing items require the application of different environmental quality standards, but existing software is mostly based on a single standard and cannot automatically select and switch standards according to the testing items; 5. Limited comprehensive evaluation methods: There is a lack of professional evaluation methods such as the Nemerow Comprehensive Pollution Index and the Shukalev Water Chemical Classification, which cannot meet the needs of environmental assessment reports; 6. Insufficient data quality verification: For water chemical data, there is a lack of quality control measures such as anion and cation balance verification and TDS verification, which may lead to the adoption of erroneous data.

[0004] Therefore, there is a need for an intelligent analysis system that can automatically recognize Excel data, support batch sample processing, automatic evaluation of multiple standards, comprehensive pollution assessment, and data quality verification. Summary of the Invention

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a water and soil quality detection and analysis system based on multi-standard automatic evaluation, including a data import module, an intelligent identification module, a batch sample processing module, a multi-standard evaluation module, a comprehensive analysis module, a water chemistry verification module, and a result output module. The module includes: 1. A data import module supporting uploads of various Excel formats such as XLSX, XLS, CSV, and ODS, allowing file import via drag-and-drop or click, automatic worksheet parsing, and support for multiple sheet selection; 2. An intelligent recognition module automatically searches for keywords related to indicators, units, test results, and background values ​​within the first 15 rows, achieving fuzzy matching of indicators through string normalization algorithms, verifying unit compatibility, and identifying batch sample layouts; 3. A batch sample processing module automatically extracts the sample list, independently separates test data for each sample, supports rapid switching between multiple samples, and exports batch analysis results and generates summary tables; 4. A multi-standard evaluation module integrates three major environmental quality standards: groundwater GB / T 14848-2017, surface water GB 3838-2002, and soil GB36600-2018, automatically matching the corresponding standard based on the test items to complete water and soil quality classification and exceedance determination; 5. A comprehensive analysis module calculates the single pollution index P. i Exceeding limits, pollution accumulation index, and Nemerow composite pollution index P N 6. The water chemistry verification module is used to convert ion concentrations into equivalent concentrations, complete anion and cation balance verification, Shukalev classification, TDS verification and hardness verification, and realize water chemistry data quality judgment; 7. The result output module is used for single sample report export, batch sample multi-worksheet export, automatic saving of analysis history, and realization of traceability of detection data and analysis process.

[0006] Compared with existing technologies, this invention has the following advantages: 1. Intelligent recognition and strong compatibility: It automatically recognizes the structure of Excel spreadsheets, supports multiple format layouts, eliminates the need for manual data format adjustment, and significantly improves work efficiency; 2. Batch processing and efficiency improvement: It supports automatic identification and processing of batch samples, allowing analysis of multiple samples with a single import, and batch export to generate summary tables, improving efficiency by more than 10 times; 3. Multi-standard integration and automatic switching: It integrates three major environmental quality standards for groundwater, surface water, and soil, automatically selecting the standard based on the testing item, eliminating the need for manual review of standard documents; 4. Comprehensive evaluation and professional reliability: It provides single-item pollution... 5. Professional evaluation methods such as pollution index, Nemerow comprehensive pollution index, and Shukalev water chemical classification meet the needs of environmental impact assessment reports; 6. Quality verification and reliable data: Built-in quality control measures such as anion and cation balance verification, TDS verification, and hardness verification ensure data quality and prevent the use of erroneous data; 7. Historical records and traceability: Automatically saves analysis records, supports viewing historical samples and re-exporting, ensuring the traceability of the analysis process and meeting quality management requirements; 8. Offline operation and data security: Adopts the Electron desktop application architecture, can run without an internet connection, and test data is not uploaded to the server, ensuring data security. Attached image description: Figure 1 This is a schematic diagram of the overall architecture of the system of the present invention; Figure 2 This is a flowchart illustrating the intelligent recognition module of the present invention; Figure 3 This is a flowchart illustrating the batch sample processing module of the present invention; Figure 4 This is a flowchart illustrating the multi-standard evaluation module of the present invention; Figure 5 This is a schematic diagram of the calculation process of the water chemistry verification module of the present invention; Figure 6 This is a schematic diagram of the user interface of the present invention.

[0007] Detailed Implementation: The present invention will be further described in detail below with reference to specific embodiments.

[0008] Example 1: Batch Sample Analysis of Groundwater

[0009] Step 1: Data Preparation. The user prepares an Excel file with the following format: Row 1: Name of the detection indicator (color, odor and taste, turbidity, pH, total hardness, etc.); Row 2: Unit (platinum cobalt colorimetric unit, description, NTU, dimensionless, mg / L, etc.); Row 3: Background value (optional); Row 4 and below: Sample number and corresponding test results.

[0010] Step 2: File Import. Users upload Excel files by dragging and dropping or clicking. The system automatically parses the file and identifies the worksheets.

[0011] Step 3: Intelligent Recognition. The system executes an intelligent recognition algorithm, which includes: First, checking if the third row of data in the Excel spreadsheet contains the keyword "sample number". If the keyword is detected, the index position of the sample column is determined and the search stops. Second, checking if the fourth row of the spreadsheet contains the identifier "background value". If it is, the spreadsheet is marked as having a background value row. Finally, starting from the first row of data, all data rows are traversed, the sample names in each row are extracted, and a sample list is generated. The final result is the return of the sample column index, the background value row index, the sample list, and the position information of the first column of data.

[0012] Step 4: Sample Selection. The system displays a list of identified samples (e.g., dxs-01, dxs-02, dxs-03, etc.), and the user selects the sample to be analyzed.

[0013] Step 5: Data Extraction. The system extracts test data for the selected sample as follows: Starting from the initial data column, it iterates through all data columns in the table, extracting the corresponding indicator name, unit, test result, and background value information for each column in sequence; the extracted indicator names are matched with the system's built-in indicator library, and the units are checked for compatibility; only the test data with successfully matched indicators and compatible units are retained, and the corresponding indicator, test result, and background value information are summarized and returned.

[0014] Step 6: Quality Classification. The system classifies the tested indicators according to GB / T 14848-2017 "Groundwater Quality Standard". The specific rules are as follows: For pH value, if the measured value is in the range of 6.5~7.5, it is classified as Class I; if the measured value is in the range of 6.5~8.5, it is classified as Class III; if the measured value is in the range of 5.5~9.0, it is classified as Class IV; and if it exceeds the above range, it is classified as Class V. For numerical indicators, the measured value is compared with the corresponding limit values ​​of Class I to Class IV in sequence. If the measured value is ≤ Class I limit, it is classified as Class I; if the measured value is ≤ Class II limit, it is classified as Class II; if the measured value is ≤ Class III limit, it is classified as Class III; if the measured value is ≤ Class IV limit, it is classified as Class IV; and if it exceeds the Class IV limit, it is classified as Class V.

[0015] Step 7: Pollution Index Calculation. The system calculates the pollution index from the groundwater monitoring data, specifically including: 1. Exceedance Multiple Calculation: Divide the measured value by the Class III limit, then subtract 1 to obtain the exceedance multiple; 2. P ki Pollution index calculation: The difference between the detected value and the background value is divided by the Class III limit to obtain P. ki3. Pollution index; Pollution classification determination: based on P ki Pollution index values ​​are classified into levels, if P ki If the value is ≤0, it is considered uncontaminated; 0 <P ki If the value is ≤1, it is considered light pollution. <P ki If the concentration is ≤2, it is considered moderately polluted. <P ki If the concentration is ≤3, it is considered heavily polluted. ki A value greater than 3 indicates severe pollution.

[0016] Step 8: Comprehensive Evaluation. The system determines the overall quality category as the worst category among all indicators.

[0017] Step 9: Export Results. Users can choose: 1. Export Current Sample: Generate a single Excel file; 2. Batch Export: Select multiple samples to generate a multi-worksheet Excel file, including a summary table.

[0018] Step 10: Historical Records. The system automatically saves analysis records, including sample name, timestamp, input data, and calculation results, which users can view and re-export at any time.

[0019] Example 2: Comprehensive Assessment of Soil Pollution

[0020] Step 1: Data Import. Users upload an Excel file containing soil testing data, including heavy metal indicators such as arsenic, cadmium, hexavalent chromium, copper, lead, mercury, and nickel.

[0021] Step 2: Parameter Selection. User Selection: 1. Land Use Type: Category I (Residential / School, etc.) or Category II (Industrial / Commercial, etc.); 2. Standard Type: Filter Value or Control Value.

[0022] Step 3: Calculation of Single Pollution Index. The system first determines the corresponding evaluation standard value based on the standard type and land use type selected by the user: 1-Standard Value Selection: If the standard type selected by the user is a screening value, the corresponding screening value is matched according to the land use type (Category 1 / Category 2); if the standard type selected by the user is a control value, the corresponding control value is matched according to the land use type (Category 1 / Category 2); 2. Single Pollution Index P i Calculation: Divide the detected value by the selected standard value to obtain the individual pollution index P. i 3. Pollution level determination: based on P i The values ​​are classified into levels, if P i If the concentration is ≤0.7, it is considered clean (safe). <P i ≤1.0 is considered fairly clean, 1.0 <P i A concentration of ≤2.0 is considered lightly polluted. <P iA concentration of ≤3.0 is considered moderate pollution. i A value >3.0 indicates severe pollution.

[0023] Step 4: Nemerow Comprehensive Pollution Index. The system calculates the Nemerow Comprehensive Pollution Index and determines the comprehensive pollution level based on the soil testing data. The specific steps are as follows: 1. Average value calculation: Calculate the average value of all individual pollution indices P. i The sum, divided by P i The quantity, to obtain P i The average value (P) avg ); 2. Maximum value extraction: from all individual pollution indices P i Select the P with the largest value from the middle i (P) max ); 3. Nemerow index calculation: First calculate P avg The square of P max The sum of the squares of the products, divided by 2, and the square root of the result, yields the Nemerow Comprehensive Pollution Index (P). N ); 4. Comprehensive pollution level determination: based on P N The values ​​are classified into levels, if P N If the concentration is ≤0.7, it is considered clean (safe). <P N ≤1.0 is considered fairly clean, 1.0 <P N A concentration of ≤2.0 is considered lightly polluted. <P N If the concentration is ≤3.0, it is considered moderate pollution. N A value >3.0 indicates severe pollution.

[0024] Step 5: Results Output. The system generates an Excel-formatted analysis report for soil pollution assessment. This report includes the following core contents: basic sample information, detection values ​​for each indicator, selected evaluation standard values, individual pollution index Pi, pollution level of each indicator, and Nemerow comprehensive pollution index P. N Numerical values ​​and P-based N The overall pollution level was determined.

[0025] Example 3: Validation of water chemistry data

[0026] Step 1: Data Input. The user inputs the water chemical ion concentration data to be verified into the system. The concentration unit is mg / L. The cations include sodium ion (Na⁺), potassium ion (K⁺), calcium ion (Ca²⁺), and magnesium ion (Mg²⁺). The anions include bicarbonate ion (HCO3⁻), carbonate ion (CO3²⁻), sulfate ion (SO4²⁻), and chloride ion (Cl⁻).

[0027] Step 2: Equivalent Conversion. The system performs equivalent conversion on the input ion concentration data. First, it presets the equivalent masses of each ion: sodium ion (Na⁺) is 23, potassium ion (K⁺) is 39.1, calcium ion (Ca²⁺) is 20.04, magnesium ion (Mg²⁺) is 12.15, bicarbonate ion (HCO₃⁻) is 61, carbonate ion (CO₃²⁻) is 30, sulfate ion (SO₄²⁻) is 48.03, and chloride ion (Cl⁻) is 35.45. Then, it divides the concentration value of each ion (in mg / L) by the corresponding equivalent mass to obtain the equivalent concentration of each ion (in meq / L).

[0028] Step 3: Verification of cation and anion balance. The system verifies the cation-anion balance of the converted ion equivalent concentrations. The specific procedure is as follows: 1. Calculation of total cations: Divide the sodium ion (Na⁺) concentration by 23, potassium ion (K⁺) concentration by 39.1, calcium ion (Ca²⁺) concentration by 20.04, and magnesium ion (Mg²⁺) concentration by 12.15. Sum the above calculation results to obtain the total cations; 2. Calculation of total anions: Divide the bicarbonate ion (HCO₃⁻) concentration by 61, carbonate ion (CO₃²⁻) concentration by 30, sulfate ion (SO₄²⁻) concentration by 48.03, and chloride ion (Cl⁻) concentration by 35.45. Sum the above calculation results to obtain the total anions; 3. Calculation of relative error: First calculate the difference between the total anions and the total cations, then divide it by the sum of the total cations and anions. Multiply the calculation result by 100% to obtain the relative error (RE); 4. Data quality assessment: If the absolute value of the relative error is ≤5%, the water chemistry data is considered to be of acceptable quality; if the absolute value of the relative error is >5%, the data is considered to be of unacceptable quality and the data is questionable.

[0029] Step 4: Shukarev Water Chemical Type Naming. Based on the equivalent concentration data after anion-cation balance verification, the system completes the Shukarev water chemical type naming and mineralization classification. The specific process is as follows: 1. Ion Equivalent Percentage Calculation: Anion Percentage: Calculate the sum of bicarbonate and carbonate ion concentrations, chloride ion concentration, and sulfate ion concentration as percentages of the total anion concentration; Cation Percentage: Calculate the sum of calcium ion concentration, magnesium ion concentration, sodium ion concentration, and potassium ion concentration as percentages of the total cation concentration; 2. Dominant Ion Determination: Using an ion equivalent percentage > 25% as the criterion, the anion type is determined based on the ions with a percentage exceeding 25% (including bicarbonate, chloride, and sulfate), and the cation type is determined based on the ions with a percentage exceeding 25% (including calcium, magnesium, and sodium); 3. Mineralization Classification: Classified according to total dissolved solids (TDS) values. TDS < 1000 mg / L is classified as Class A freshwater, and 1000 mg / L ≤ TDS < 3000 mg / L is classified as Class B. Class C is slightly brackish water, with TDS between 3000 mg / L and 10000 mg / L; Class D is saline water, with TDS between 10000 mg / L and 50000 mg / L; and Class E is brine, with TDS ≥ 50000 mg / L. 4. Naming of water chemical types: The determined anion type, cation type, and mineralization grade letter are combined to form a water chemical type name in the format of "Anion Type - Cation Type Water - Mineralization Letter".

[0030] Step 5: TDS and Hardness Verification. The system verifies the TDS (Total Dissolved Solids) and hardness of the water chemistry data. The specific process is as follows: 1. TDS Verification: First, calculate the sum of all ion concentrations, subtract 0.5 times the bicarbonate ion (HCO3⁻) concentration to obtain the calculated TDS value; then calculate the difference between the calculated and measured TDS values, divide by the measured TDS value, and multiply by 100% to obtain the relative TDS error; if the absolute value of the relative TDS error is <15%, the TDS data quality is considered acceptable. 2. Hardness Verification: Multiply the calcium ion (Ca²⁺) concentration by 2.497, add the magnesium ion (Mg²⁺) concentration multiplied by 4.116 to obtain the calculated hardness value; then calculate the difference between the calculated and measured hardness values, divide by the measured hardness value, and multiply by 100% to obtain the relative hardness error; if the absolute value of the relative hardness error is <10%, the hardness data quality is considered acceptable.

[0031] Step 6: Results Output. The system generates an analysis report for water chemistry data verification, which includes the following core contents: the determination results of anion and cation balance verification, a detailed table of ion equivalent concentrations, the water chemistry type determined based on the Shukalev classification method, the mineralization classification results, and the quality determination results of TDS and hardness verification.

[0032] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method and system for water and soil quality detection and analysis based on multi-standard automatic evaluation, characterized in that, The system includes a data import module, an intelligent identification module, a batch sample processing module, a multi-standard evaluation module, a comprehensive analysis module, a water chemistry verification module, and a result output module. The data import module receives and parses test data files in Excel format, supporting XLSX, XLS, CSV, and ODS formats. The intelligent identification module automatically identifies the indicator names, units, test results, and background value columns in the Excel spreadsheet, processes different naming methods using a fuzzy matching algorithm, and performs unit compatibility verification. The batch sample processing module identifies batch sample formats, extracts test data from multiple samples, and supports rapid switching between samples and batch export. The multi-standard evaluation module automatically classifies and evaluates the test data according to different environmental standards, specifically including classifying groundwater according to the Groundwater Quality Standard GB / T 14848-2017 (Class IV), classifying surface water according to the Surface Water Environmental Quality Standard GB 3838-2002 (Class IV and worse), and evaluating screening and control values ​​according to the Construction Land Soil Pollution Risk Control Standard GB36600-2018. The comprehensive analysis module calculates the pollution index and performs a comprehensive evaluation, specifically including the single pollution index P. i Calculation, exceeding the standard multiple calculation, Nemerow comprehensive pollution index P N The system includes calculation of pollution accumulation index; the water chemistry verification module is used to verify the quality of water chemistry data, specifically including calculation of relative error (RE) of anion and cation balance, automatic naming of Shukalev water chemistry types, and cross-verification of TDS, hardness, and conductivity; the results output module is used to generate analysis reports and export Excel files, supporting single sample export and batch sample export, and the exported files include summary tables.

2. The system according to claim 1, characterized in that, The intelligent recognition module includes a header recognition unit, an indicator matching unit, and a unit verification unit. The header recognition unit searches for rows containing keywords such as indicator, unit, detection result, and background value within the first 15 rows of the Excel spreadsheet to determine the header row position and column indices. The indicator matching unit matches the indicator names in the Excel spreadsheet with the system's built-in standard indicator library, using string normalization to remove spaces and standardize bracket format, supporting both exact and containment matching. The unit verification unit compares the units in the Excel spreadsheet with the system's standard units to determine unit compatibility, classifying them into three levels: matching, discrepancy, and non-matching.

3. The system according to claim 1, characterized in that, The identification method of the batch sample processing module Includes the following steps: S1: Detect whether the 3rd row of the Excel spreadsheet contains the following keywords as sample numbers, and determine the sample column index; S2: Detect whether the 4th row is a background value row. If it contains background value keywords, mark the background value row index; S3: Starting from the 4th or 5th row, extract all non-empty cells in the sample column as a sample name list; S4: For each sample, starting from the data start column, extract the indicator name, unit, test result and background value column by column; S5: Match the extracted data with the system indicator library and filter out items with mismatched units; S6: Supports users to select any sample for analysis, or to select multiple samples in batches for export.

4. The system according to claim 1, characterized in that, The multi-standard evaluation module's evaluation method for groundwater includes: for numerical indicators, determining the water quality category based on a comparison of the detected value with the Class IV limit; for pH values, using a range judgment: 6.5-7.5 is Class I, 6.5-8.5 is Class III, 5.5-9.0 is Class IV, and the rest are Class V; for textual indicators, such as odor and taste, and visible matter, determining whether the test result is "absent"—if yes, it is Class I; otherwise, it exceeds Class V; calculating the exceedance multiple: exceedance multiple = (detected value / Class III limit) - 1; calculating P... ki Pollution Index: P ki = (Detected value - Background value) / Class III limit; according to P ki Pollution classification based on values: P ki ≤0 indicates no contamination, 0 <P ki ≤1 indicates light pollution, 1 <P ki ≤2 indicates moderate pollution, 2 <P ki ≤3 indicates heavy pollution, P ki >3 indicates severe pollution.

5. The system according to claim 1, characterized in that, The evaluation method for surface water by the multi-standard evaluation module includes: for the basic items in Table 1, a five-category classification is adopted, with dissolved oxygen as a reverse indicator; the higher the value, the better the category. For pH value, a range judgment is adopted: 6-9 is Class I, and the rest is Class V (worst). For the supplementary items for centralized drinking water surface water sources in Table 2 and the specific items in Table 3, a single limit value judgment is adopted; if the detected value is ≤ the limit, it is considered compliant; otherwise, it is considered non-compliant. The multiple of non-compliance is calculated as follows: for positive indicators, multiple of non-compliance = (detected value / Class III limit) - 1; for reverse indicators, multiple of non-compliance = (Class III limit - detected value) / Class III limit.

6. The system according to claim 1, characterized in that, The comprehensive analysis module's soil evaluation methods include: selecting the corresponding evaluation standard based on land use type (Class I or Class II) and standard type (screening value or control value); and calculating the individual pollution index: P. i = Detected value / Standard value; Calculate the pollution accumulation index: Accumulation index = Detected value / Background value; Based on P i Pollution levels are classified based on values: P i ≤0.7 is considered clean (safe), 0.7 <P i ≤1.0 is considered fairly clean, 1.0 <P i ≤2.0 indicates slight pollution; 2.0 <P i ≤3.0 indicates moderate pollution, P i A score of >3.0 indicates severe pollution; calculate the Nemerow comprehensive pollution index: P N = √[(P_average² + P_maximum²) / 2], where P_average is the average of all individual pollution indices, and P_maximum is the maximum value among all individual pollution indices; according to P N The value determines the overall pollution level.

7. The system according to claim 1, characterized in that, The calculation method of the water chemistry verification module includes: dividing each ion concentration (mg / L) by the corresponding equivalent mass to convert it to equivalent concentration (meq / L); calculating the total cations: ∑cations = Na⁺ / 23 + K⁺ / 39.1 + Ca²⁺ / 20.04 + Mg²⁺ / 12.15; calculating the total anions: ∑anions = HCO⁻ / 61 + CO⁻ / 30 + SO₄²⁻ / 48.03 + Cl⁻ / 35.45; calculating the relative error: RE = [(∑anions - ∑cations) / (∑cations + ∑anions)] × 100%; judging data quality: |RE| ≤ 5% is acceptable, |RE| > 5% is acceptable. 5% is considered unqualified; Shukalev water chemistry type naming is performed: based on the percentage of anion and cation equivalents, the dominant ion (>25%) is determined, and named according to the letter format of anion type-cation type water-mineralization grade; TDS verification is performed: TDS calculated value = ∑ ion concentration - 0.5 × HCO3⁻ concentration, compared with the measured value, an error <15% is considered qualified; Hardness verification is performed: hardness calculated value = 2.497 × Ca²⁺ + 4.116 × Mg²⁺, compared with the measured value, an error <10% is considered qualified.

8. The system according to claim 1, characterized in that, The results output module includes single-sample export, batch-sample export, historical record management, and data traceability functions. The single-sample export function generates an Excel file containing sample information, test results, quality classification, contamination index, and comprehensive evaluation. The batch-sample export function generates an independent worksheet for each sample and a summary table containing the overall quality category, number of test indicators, number of exceedance indicators, and analysis time for all samples. The historical record management function automatically saves sample data and results for each analysis, supporting viewing historical records, reloading historical samples, and exporting historical samples. The data traceability function records sample names, analysis timestamps, input data, and calculation results, ensuring the traceability of the analysis process.

9. The system according to claim 1, characterized in that, It also includes an activation authorization module, a trial period management module, and a usage duration tracking module; the activation authorization module uses the HMAC-SHA256 encryption algorithm to generate a unique device identifier and activation code, and the activation code format is device ID|expiration timestamp|HMAC signature, ensuring that the activation code is bound to the device and cannot be forged; the trial period management module calculates the trial period based on the cumulative usage duration to prevent the trial period from being reset by modifying the system time or clearing the cache. The usage time tracking module is used to record the time increment of each time the software is opened, which is accumulated to the total usage time to detect time rollback cheating behavior.

10. A method and system for water and soil quality detection and analysis based on multi-standard automatic evaluation, characterized in that, Includes the following steps: S1: Receive the test data file in Excel format uploaded by the user; S2: Automatically recognize the structure of the Excel spreadsheet and determine the columns containing the indicator name, unit, test result, and background value; S3: Extract the test data, match it with the system's built-in standard indicator library, and filter out data with mismatched units; S4: Select the corresponding environmental quality standard based on the type of test item (groundwater / surface water / soil); S5: Perform quality classification evaluation for each detection indicator, and calculate the pollution index and the exceedance multiple; S6: Conduct a comprehensive pollution evaluation, and calculate the Nemerow comprehensive pollution index or water chemistry verification index; S7: Generates an analysis report and outputs the results file in Excel format; S8: Saves analysis records to a historical database, supporting subsequent queries and exports.

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