Plot soil pollution condition survey information acquisition and analysis system

Abstract

The invention discloses a plot soil pollution condition survey information acquisition and analysis system, relates to the technical field of plot soil information acquisition and analysis, and aims to solve the problem of unclear soil pollution condition analysis. According to the method, abstract sample detection data is converted into a law conclusion with spatial significance through accurate association, standard processing, visual display and deep analysis, the accuracy and systematicness of spatial analysis are ensured, the practicability of a result is enhanced through visualization, and the accuracy and accuracy of the result are improved by combining the spatial distribution law of sample analysis. A pollution core area is delimited for an aggregation area, key parameters are counted, classified implementation of target area, physical and chemical property and pollutant detection is provided for traceability and key treatment, various sample sensors are accurately matched with detection items, a full-dimension detection system of basic attributes and pollution characteristics is formed, and the detection accuracy is improved. The grid point distribution method or the snakelike point distribution method is combined with the marked GPS coordinates, the sampling depth and the serial number, so that the point location distribution is uniform, and the space positioning traceability of the sample is realized.

Description

Technical Field

[0001] This invention relates to the field of soil information collection and analysis technology, specifically a soil pollution status investigation information collection and analysis system for land parcels. Background Technology

[0002] Existing soil pollution surveys suffer from numerous technical shortcomings. The diverse types of sensors lack standardized functionalities, and unstable equipment performance leads to large data errors and insufficient reliability. The sampling process lacks standardized procedures, resulting in arbitrary site selection, incomplete equipment preparation, inconsistent sampling procedures between surface and deep soil layers, and chaotic sample labeling, severely impacting data tracing and the accuracy of subsequent analysis. Inadequate sample preprocessing, ineffective control of environmental temperature, humidity, and electromagnetic interference, disorganized data recording, and crude and unstandardized handling of outliers and missing values ​​further degrade data quality. During pollution assessment, the correlation between data and spatial location is loose, standard matching is inaccurate, and analysis of the spatial distribution characteristics and vertical infiltration patterns of pollution is insufficient, making it difficult to accurately define the scope and level of pollution. This results in unscientific survey results that cannot provide a reliable basis for pollution remediation. Therefore, a systematic survey information collection and analysis system is urgently needed to overcome these bottlenecks. Summary of the Invention

[0003] The purpose of this invention is to provide a system for collecting and analyzing information on soil pollution in land parcels. Through precise correlation, standardized processing, intuitive display, and in-depth analysis, it transforms abstract detection data into spatially meaningful conclusions, ensuring both the accuracy and systematic nature of spatial analysis and enhancing the practicality of the results through visualization. By combining the spatial distribution patterns of sample analysis, it delineates pollution core areas in clustered regions and statistically analyzes key parameters, providing target areas for source tracing and key remediation. The classification and implementation of physical and chemical properties and pollutant detection enable various sample sensors to accurately match detection items, forming a comprehensive detection system of "basic attributes + pollution characteristics." The grid-based or serpentine point layout method, combined with labeled GPS coordinates, sampling depth, and numbering, ensures uniform point distribution and achieves spatial location and source tracing of samples, thus solving the problems in existing technologies.

[0004] To achieve the above objectives, the present invention provides the following technical solution: The system for collecting and analyzing information on soil pollution status at land parcels includes: The sample sensor management unit is used to verify various sample sensors and perform functional tests on them. The soil sample collection unit is used to collect soil samples from the soil collection area according to the soil collection requirements. The soil sample testing unit is used to test soil samples collected from the soil collection area using a sample sensor. The detection data preprocessing unit is used to preprocess the sample detection data. The detection data spatial analysis unit is used to combine the preprocessed sample detection data with the sample spatial location information for sample analysis; The data contamination assessment unit is used to assess sample contamination based on sample analysis results, and to perform visual data conversion on the sample contamination assessment data before transmitting it to the display terminal for data display.

[0005] Preferably, the sample sensor management unit is further configured to: Sample sensors include physical property sensors, chemical property sensors, pollution sensors, positioning sensors, and meteorological sensors; Physical property sensors include soil moisture sensors, temperature sensors, soil compaction sensors, and porosity sensors; chemical property sensors include soil pH sensors, electrical conductivity sensors, nutrient sensors, and redox potential sensors; pollution sensors include heavy metal sensors, organic pollutant sensors, and radioactive substance sensors. Before the sample sensors detect the sample, each sample sensor undergoes a functional test. Functional testing includes visual inspection, power-on testing, zero-point and range calibration, response time testing, and stability testing. After the functional tests are completed and passed, the sample sensor prepares the sample for testing.

[0006] Preferably, the soil sample collection unit is further used for: Before collecting soil samples from the soil collection area, a sampling plan is first formulated, including dividing the soil collection area according to the topography, soil type and land use history. After the soil collection area is divided, the sampling points are confirmed by grid method or serpentine method. At the same time, GPS coordinates, sampling depth and sampling number are marked on the confirmed sampling points. After the sampling plan is formulated, the sampling equipment is prepared. The sampling equipment includes tools, containers, and auxiliary equipment. Tools include stainless steel soil drills, bamboo strips, and ring cutters; containers include glass bottles and polyethylene bottles; auxiliary equipment includes GPS positioning devices, portable soil moisture meters, and sample labels. After the sampling equipment was prepared, staff carried out surface soil sampling and deep soil sampling. Topsoil collection was conducted as follows: staff used a GPS locator to determine the location of the sampling point. After the location of the sampling point was confirmed, staff used a soil drill to drill vertically into the soil to the specified depth, then rotated the soil drill to remove the soil core. The soil core was then mixed evenly to form a mixed sample. Approximately 1 kg of the mixed sample was taken using the quartering method and placed into a sample bottle. Deep soil collection involves: staff excavating a soil profile and using bamboo strips to collect soil from the side wall of the profile layer by layer, collecting approximately 500g of soil from each layer and placing it into a layered sample bottle; Soil samples collected from the surface soil and deep soil were separated and labeled. Finally, the soil samples were collected.

[0007] Preferably, the soil sample detection unit is further used for: Before testing soil samples, the samples are pre-treated, including air-drying, grinding, and packaging. Then, the testing environment needs to be managed, including temperature, humidity, and electromagnetic shielding; Soil samples were tested using physical property sensors, chemical property sensors, and pollution sensors. The testing process for physical properties includes soil moisture, temperature, compaction, and porosity; the testing process for chemical properties includes soil pH, electrical conductivity, and redox potential; and the testing process for pollutants includes soil heavy metals, organic pollutants, and radioactive substances. Record each test data point and create an electronic spreadsheet, including the sample number, test item, test time, sensor model, and test value. Finally, the soil samples were tested.

[0008] Preferably, the detection data preprocessing unit is further configured to: The sample test data was retrieved, and the integrity of the sample test data for each sample was checked. After the integrity check, outlier screening is performed by comparing the results against the acceptable range of the test items. Outliers in the screening are processed. If the error is confirmed to be caused by a faulty sample sensor or operational error, it is directly removed. If the cause cannot be confirmed, the data is retained but marked as data to be reviewed. After outlier handling, missing value handling is performed. For missing data, if the missing proportion is lower than the preset range, the mean of the detection of the same type of sample in the same region is used to fill the missing value and it is marked as the filled value. If the missing proportion is higher than the preset range, the missing value is recorded separately and is not used as the core data for subsequent analysis. After handling missing values, the data is standardized and checked for duplicate data. If duplicate data is found, the data is deleted. Finally, the data preprocessing of the sample detection data is completed.

[0009] Preferably, the process of confirming whether outlier values ​​are erroneous data caused by sample sensor malfunction or operational error includes: Extract the data values ​​obtained from the previous and next data collection points corresponding to the outliers; Determine whether the data values ​​obtained from the previous collection point and the data values ​​obtained from the next collection point are within the acceptable range, and obtain the acceptance test result; If either the data value obtained from the previous collection point or the data value obtained from the next collection point is outside the acceptable range, the abnormal value will be retained but marked as data to be reviewed. If the data values ​​obtained from the previous collection point and the data values ​​obtained from the next collection point are both within the acceptable range, then the data values ​​obtained from the previous collection point and the next collection point corresponding to the outlier are used to predict the data of the data collection point corresponding to the outlier, and the predicted data value of the data collection point to which the outlier belongs is obtained. Use outliers and predicted data to obtain the absolute deviation between outliers and predicted data; The absolute deviation is compared with a preset deviation threshold. If the absolute deviation exceeds the preset deviation threshold, it is determined that the data is erroneous due to a sample sensor malfunction or operational error. If the absolute deviation does not exceed the preset deviation threshold, the abnormal value is retained but marked as data to be reviewed.

[0010] Preferably, the data prediction for the data collection point corresponding to the outlier is performed using the data values ​​obtained from the preceding and following data collection points, to obtain the predicted data value for the data collection point to which the outlier belongs, including: Retrieve the data values ​​obtained from the previous and next data collection points corresponding to the outlier values; The average value of the data obtained from the previous and subsequent data collection points corresponding to the outlier is calculated to obtain the base prediction value. ;in, and These represent the data values ​​obtained from the previous and next data collection points corresponding to the outlier values, respectively. The first prediction value is obtained by using the data value obtained from the previous collection point and the data value obtained from the next collection point corresponding to the outlier. Retrieve the absolute deviation between the predicted data and the outlier value corresponding to all erroneous data from previous sampling points caused by sensor malfunctions or operational errors in the samples; The second prediction value is obtained by combining the absolute deviation between the predicted data value and the outlier value corresponding to all erroneous data caused by sensor failure or operational error of the samples before the outlier collection point with the first prediction value. The predicted values ​​corresponding to outliers are obtained by combining the second predicted value with the basic predicted value.

[0011] Preferably, the detection data spatial analysis unit is further used for: The preprocessed sample detection data is correlated with the corresponding sample spatial location information; The sample spatial location information includes GPS coordinates, sampling depth, sampling number, and area division information. The sampling number is used as a unique identifier to match the preprocessed sample detection data with the sample spatial location information, and the matching results in an associated dataset. The latitude and longitude coordinates collected by different devices in the associated dataset are converted into a unified geographic coordinate system. At the same time, they are classified into layers according to surface and depth. After the layer classification, the vertical spatial position of each sampling point is confirmed. The hierarchical and categorized associated datasets are visualized. The visualization process involves marking the location of all sampling points on an electronic map and using symbols of different colors and sizes to represent the numerical differences in sample test data. Then, distribution maps are drawn according to the test items or sampling depths. Finally, combined with the previously divided soil collection areas, a comparison chart of the mean values ​​of sample test data between regions is drawn. Based on the visualization processing results, the spatial distribution patterns of the associated data are analyzed, including clustering analysis, gradient change analysis, and boundary feature analysis. The sample analysis results of the sample detection data are obtained based on the spatial distribution pattern.

[0012] Preferably, the analytical data contamination assessment unit is further configured to: Retrieve soil environmental quality standard data from the database and confirm the limit standards for different pollutants under different land use types, including screening values ​​and control values; The assessment data in the sample analysis results were confirmed, including pollutant detection values, spatial location information and stratified data at each sampling point; The assessment data was matched with the soil environmental quality standard data item by item. The item-by-item matching was as follows: for each pollutant detection value at each sampling point, the corresponding pollutant limit in the standard was compared to determine whether the pollutant at that sampling point exceeded the standard, and the type of pollutant exceeding the standard, the location of the sampling point exceeding the standard, the sampling depth, and the multiple of exceeding the standard were recorded. Meanwhile, the spatial range of pollution is assessed based on the spatial distribution patterns in the sample analysis results. If the sample analysis results show that a certain type of pollutant is clustered in a specific area, the area is identified as the core pollution area, and the number of sampling points exceeding the standard, the average pollution concentration, and the coverage area within the core area are counted. If the characteristics are gradient changes, it is inferred that there is a pollution diffusion path. If the characteristics are boundary features, the boundary line between the polluted area and the non-polluted area is determined, and the pollution impact range is delineated.

[0013] Preferably, the analytical data contamination assessment unit is further configured to: Based on the stratified detection data and spatial analysis results of surface and deep soil, the vertical distribution of pollution is assessed. The vertical distribution is determined by comparing the concentration differences of the same type of pollutant in surface and deep soil to determine whether the pollution is in the surface or deep layer. If the deep soil exceeds the standard, the depth of pollution infiltration and the trend of pollution concentration changes in each layer are recorded. Finally, the soil pollution level was classified into clean areas, slightly polluted areas, moderately polluted areas, and heavily polluted areas. Finally, sample contamination assessment data is obtained, and the sample contamination assessment data is converted into a visualization report. After the conversion is completed, it is transmitted to the display terminal for data display.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The soil pollution status investigation information collection and analysis system provided by this invention ensures equipment accuracy through five functional tests, including visual inspection and zero-point calibration, solving the problems of inconsistent sensor types and insufficient data reliability in traditional methods. Simultaneously, the sampling process adopts a "regional division - scientific sampling point layout - standardized operation" procedure: regions are divided according to topography and soil type, and precise positioning is achieved using grid or serpentine sampling methods. Specialized equipment such as stainless steel soil drills are used to standardize the sampling operations for both surface and deep soil layers, achieving a unique binding of sample spatial information (GPS coordinates, depth, etc.) with a unique identifier. This overcomes the limitations of traditional sampling methods, such as "mismatch between points and areas" and "arbitrary operation," providing a high-quality sample foundation for subsequent analysis.

[0015] 2. The soil pollution status investigation information collection and analysis system provided by this invention eliminates interference during the detection stage through sample drying and grinding pretreatment, and ensures a stable detection environment by combining temperature and humidity control and electromagnetic shielding, while simultaneously recording the entire chain of information from "sample to equipment to time". During the pretreatment stage, a complete data chain of "detection-purification-standardization" is constructed through integrity checks, outlier classification (removing faulty data and marking data to be verified), differential missing value imputation (mean filling or separate recording), and standardized deduplication. This mechanism solves the problems of mixed outliers, coarse handling of missing values, and inconsistent formats in traditional data processing, ensuring the accuracy and reliability of the input data for analysis and laying a data foundation for subsequent spatial analysis.

[0016] 3. The soil pollution status investigation information collection and analysis system provided by this invention links detection data with spatial information through sampling numbers, unifies the coordinate system and classifies data hierarchically, and presents spatial distribution through electronic map visualization (color and size symbols show numerical differences). It innovatively introduces clustering, gradient change, and boundary feature analysis to accurately identify pollution core areas, diffusion paths, and boundaries. The pollution assessment process links to soil environmental quality standards, matching exceedance data item by item, and assesses vertical pollution distribution by comparing surface and deep layers, ultimately dividing the area into four pollution levels and displaying the results visually. This innovation solves the problems of traditional assessments that "emphasize numerical values ​​over spatial data" and "have vague standard matching," providing a precise decision-making basis for pollution source tracing and remediation zoning. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the process for collecting and analyzing soil pollution information for a plot of land according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] To address the problems in existing technologies, such as inconsistent sensor types and a lack of standardized detection functions leading to insufficient data reliability, incomplete sampling equipment preparation, non-standard collection procedures, inconsistent sampling operations between surface and deep soil layers, and inconsistent sample labeling affecting the accuracy of subsequent analysis, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution: The system for collecting and analyzing information on soil pollution status at land parcels includes: The sample sensor management unit is used to verify various sample sensors and perform functional tests on them. The soil sample collection unit is used to collect soil samples from the soil collection area according to the soil collection requirements. The soil sample testing unit is used to test soil samples collected from the soil collection area using a sample sensor. The detection data preprocessing unit is used to preprocess the sample detection data. The detection data spatial analysis unit is used to combine the preprocessed sample detection data with the sample spatial location information for sample analysis; The data contamination assessment unit is used to assess sample contamination based on sample analysis results, and to perform visual data conversion on the sample contamination assessment data before transmitting it to the display terminal for data display.

[0020] Specifically, the sample sensor management unit, through unified deployment and functional testing, ensures stable sample sensor performance, avoids acquisition deviations caused by equipment errors, and provides a reliable benchmark for subsequent data. The soil sample collection unit performs sampling according to standardized requirements, optimizing the sampling point layout based on real-time monitoring data from the sample sensors. This ensures that samples meet specifications for soil type and depth, while also improving the representativeness of samples for contaminated areas, solving the problem of "point-area mismatch" in traditional sampling. The soil sample testing unit employs standardized testing procedures, combined with professional equipment to reduce human error and significantly improve testing accuracy. The data preprocessing unit removes outliers and standardizes data formats through noise reduction, completion, and standardization, clearing data obstacles for subsequent analysis and preventing the evaluation results from being affected by raw data quality issues. The spatial analysis unit correlates preprocessed data with geographic coordinates and uses techniques such as spatial interpolation and hotspot analysis to present the spatial distribution characteristics of pollution, accurately locating highly polluted areas and diffusion paths. This overcomes the limitations of traditional numerical analysis, which emphasizes data over location, and provides crucial spatial evidence for pollution source tracing.

[0021] The sample sensor management unit is also used for: Sample sensors include physical property sensors, chemical property sensors, pollution sensors, positioning sensors, and meteorological sensors; Physical property sensors include soil moisture sensors, temperature sensors, soil compaction sensors, and porosity sensors; chemical property sensors include soil pH sensors, electrical conductivity sensors, nutrient sensors, and redox potential sensors; pollution sensors include heavy metal sensors, organic pollutant sensors, and radioactive substance sensors. Before the sample sensors detect the sample, each sample sensor undergoes a functional test. Functional testing includes visual inspection, power-on testing, zero-point and range calibration, response time testing, and stability testing. After the functional tests are completed and passed, the sample sensor prepares the sample for testing.

[0022] Specifically, physical property sensors (humidity, temperature, etc.), chemical property sensors (pH value, nutrients, etc.), and pollution sensors (heavy metals, organic pollutants, etc.) form a multi-dimensional monitoring network with positioning and meteorological sensors. This network can capture the basic physicochemical properties of the soil, accurately identify pollutants, and analyze the environmental impact on pollution by combining geographical location and meteorological conditions (such as precipitation and wind speed). It achieves a comprehensive correlation between "soil itself - pollutants - external environment," breaking through the limitations of the "one-sided information" of traditional single-type sensors. This provides three-dimensional data support for pollution cause analysis. Visual inspection can eliminate measurement deviations caused by physical damage to the sensors; power-on testing verifies circuit stability and avoids "false working" states of the equipment; zero-point and range calibration ensures accurate measurement range through standard material calibration, eliminating factory errors; response time testing ensures the sensor's real-time capture capability of soil parameter changes, avoiding the impact of lag on dynamic monitoring; and stability testing confirms that data fluctuations are within the allowable range through continuous observation, preventing short-term drift interference. Multi-stage testing forms a closed loop of "screening-verification-calibration," eliminating substandard sensors at the source and solving the data distortion problem caused by "equipment operating with defects" in traditional testing. The combination of classified sensors and rigorous testing enhances the specificity of the data. For example, the specialized configuration of heavy metal and organic pollutant sensors in the pollution sensor array can accurately capture concentration data of different pollution types. Functional testing ensures that various sample sensors maintain high accuracy in the measurement of specific parameters. For instance, after calibration, the pH sensor can accurately reflect the soil's acidity and alkalinity, providing a reliable basis for the "correlation analysis between pollutants and soil properties" in subsequent pollution assessments and avoiding judgment biases caused by sensor cross-interference or insufficient accuracy.

[0023] The soil sample collection unit is also used for: Before collecting soil samples from the soil collection area, a sampling plan is first formulated, including dividing the soil collection area according to the topography, soil type and land use history. After the soil collection area is divided, the sampling points are confirmed by grid method or serpentine method. At the same time, GPS coordinates, sampling depth and sampling number are marked on the confirmed sampling points. After the sampling plan is formulated, the sampling equipment is prepared. The sampling equipment includes tools, containers, and auxiliary equipment. Tools include stainless steel soil drills, bamboo strips, and ring cutters; containers include glass bottles and polyethylene bottles; auxiliary equipment includes GPS positioning devices, portable soil moisture meters, and sample labels. After the sampling equipment was prepared, staff carried out surface soil sampling and deep soil sampling. Topsoil collection was conducted as follows: staff used a GPS locator to determine the location of the sampling point. After the location of the sampling point was confirmed, staff used a soil drill to drill vertically into the soil to the specified depth, then rotated the soil drill to remove the soil core. The soil core was then mixed evenly to form a mixed sample. Approximately 1 kg of the mixed sample was taken using the quartering method and placed into a sample bottle. Deep soil collection involves: staff excavating a soil profile and using bamboo strips to collect soil from the side wall of the profile layer by layer, collecting approximately 500g of soil from each layer and placing it into a layered sample bottle; Soil samples collected from the surface soil and deep soil were separated and labeled. Finally, the soil samples were collected.

[0024] Specifically, in the pre-sampling planning stage, areas were divided based on topography, soil type, and land use history to ensure a precise match between the sampling range and the actual environmental characteristics, avoiding blind sampling. The use of grid or serpentine sampling methods, combined with labeled GPS coordinates, sampling depth, and numbering, ensured both uniform point distribution and spatial location traceability of samples, laying the foundation for subsequent spatial analysis. Sampling equipment was well-prepared and clearly categorized; tools such as stainless steel soil drills and bamboo strips were used to avoid metal tool contamination of samples; containers were differentiated into glass and polyethylene bottles to suit different testing needs; auxiliary equipment ensured accurate positioning and complete on-site information recording, reducing the risk of sample contamination and information omission from a hardware perspective. Sampling operations were standardized and meticulous; surface soil was collected vertically using stainless steel soil drills, mixed, and then quartered to ensure uniform and sufficient sample volume; deeper soil was collected through excavation profiles and layered bamboo strips to fully preserve the vertical distribution characteristics of the soil. Repackaging and labeling ensured a one-to-one correspondence between sample information and collection records, avoiding confusion.

[0025] To address the issues in existing technologies, such as improper sample preprocessing, uncontrolled detection environments, disorganized data recording, inadequate handling of outliers, missing values, duplicate values, and lack of data standardization, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution: The soil sample testing unit is also used for: Before testing soil samples, the samples are pre-treated, including air-drying, grinding, and packaging. Then, the testing environment needs to be managed, including temperature, humidity, and electromagnetic shielding; Soil samples were tested using physical property sensors, chemical property sensors, and pollution sensors. The testing process for physical properties includes soil moisture, temperature, compaction, and porosity; the testing process for chemical properties includes soil pH, electrical conductivity, and redox potential; and the testing process for pollutants includes soil heavy metals, organic pollutants, and radioactive substances. Record each test data point and create an electronic spreadsheet, including the sample number, test item, test time, sensor model, and test value. Finally, the soil samples were tested.

[0026] Specifically, the air-drying, grinding, and packaging steps strictly follow the testing specifications: air-drying removes excess moisture from the soil to avoid interference from moisture in the detection of chemical properties (such as pH value and conductivity); grinding ensures uniform sample particles, guaranteeing full contact between the sensor and the sample and reducing detection deviations caused by particle differences; packaging enables batch testing and backup of samples, ensuring the continuity of testing and reserving samples for retesting, solving the drawback of "one-time sample consumption" in traditional testing; stable control of temperature and humidity prevents environmental factors from affecting sensor performance (such as humidity changes that may cause electrochemical sensor drift); electromagnetic shielding isolates external electromagnetic signals from interfering with the electronic sensor, ensuring that high-precision detection of heavy metals, organic pollutants, etc., is not affected by electromagnetic noise. This environmental control ensures the stability of the testing from an external perspective, reducing errors caused by non-sample factors. The categorized implementation of physical and chemical property and pollutant testing allows various sensors to be precisely matched to the testing items: soil moisture and temperature sensors directly acquire physical parameters; chemical sensors such as pH and conductivity analyze soil chemical characteristics; and specialized sensors for heavy metals and organic pollutants focus on pollutants, forming a comprehensive testing system of "basic attributes + pollution characteristics." The clear testing process standardizes operational steps, avoiding cross-contamination of sensors caused by cross-project testing, while ensuring no testing items are omitted, covering the key indicators required for soil pollution investigation. Spreadsheets record information such as sample number, testing item, time, sensor model, and test value, constructing a complete data chain of "sample-testing process-result." This recording method not only facilitates later data verification and anomaly tracing (e.g., when a sample's test value is abnormal, the device calibration record can be traced through the sensor model), but also provides a standardized format for statistical analysis and sharing of data, enhancing the application value of the data.

[0027] The detection data preprocessing unit is also used for: The sample test data was retrieved, and the integrity of the sample test data for each sample was checked. After the integrity check, outlier screening is performed by comparing the results against the acceptable range of the test items. Outliers in the screening are processed. If the data is confirmed to be erroneous due to sensor failure or operational error, it is directly removed. If the cause cannot be confirmed, the data is retained but marked as data to be reviewed. After outlier handling, missing value handling is performed. For missing data, if the missing proportion is lower than the preset range, the mean of the detection of the same type of sample in the same region is used to fill the missing value and it is marked as the filled value. If the missing proportion is higher than the preset range, the missing value is recorded separately and is not used as the core data for subsequent analysis. After handling missing values, the data is standardized and checked for duplicate data. If duplicate data is found, the data is deleted. Finally, the data preprocessing of the sample detection data is completed.

[0028] Specifically, by comprehensively retrieving data and verifying project integrity, the "data incompleteness" problem caused by record-keeping omissions was eliminated at the source. This ensured that the test data for each sample had no critical missing data at the project level, avoiding bias in subsequent analysis due to incomplete data. This established the first line of defense for data quality. Outliers were screened against the acceptable range, and judgment criteria were clarified to avoid subjective assumptions. Differentiated processing of outliers (direct removal of data caused by malfunctions and marking data with unclear causes for further verification) eliminated clearly invalid data while retaining potentially valuable information, balancing data purification and information integrity. This solved the problem of information loss caused by the "one-size-fits-all" approach in traditional processing. Differentiated processing was based on the proportion of missing data: low proportions of missing data were processed within the same area. The mean of samples of the same type in the domain is filled and labeled, which reduces the completion error by leveraging the correlation of similar samples and retains clues for data traceability through labeling. High proportion of missing data is recorded separately and excluded from core analysis to avoid interference from the analysis results due to a large amount of filled data. This ensures that subsequent conclusions are based on high-quality data and effectively avoids the risk of "analysis dominated by speculative data". Standardization and unification eliminate the dimensional differences between different detection items and provide a unified benchmark for cross-item data comparison and comprehensive analysis. Duplicate data deletion avoids the impact of redundant information on analysis efficiency and reduces data storage and computing costs. Through the full-process design of "integrity check - outlier handling - missing value filling - standardization and deduplication", a closed-loop optimization of data quality is formed. By strictly screening and processing to remove invalid data, and by using scientific strategies to retain and restore valuable information, while standardization and deduplication ensure data standardization, the final high-quality output data provides accurate and consistent input for subsequent spatial analysis and pollution assessment, greatly improving the scientificity and credibility of soil pollution investigation results.

[0029] Specifically, to determine whether outlier values ​​are due to sensor malfunction or operational errors, the following steps are taken: Extract the data values ​​obtained from the previous and next data collection points corresponding to the outliers; Determine whether the data values ​​obtained from the previous collection point and the data values ​​obtained from the next collection point are within the acceptable range, and obtain the acceptance test result; If either the data value obtained from the previous collection point or the data value obtained from the next collection point is outside the acceptable range, the abnormal value will be retained but marked as data to be reviewed. If the data values ​​obtained from the previous collection point and the data values ​​obtained from the next collection point are both within the acceptable range, then the data values ​​obtained from the previous collection point and the next collection point corresponding to the outlier are used to predict the data of the data collection point corresponding to the outlier, and the predicted data value of the data collection point to which the outlier belongs is obtained. Use outliers and predicted data to obtain the absolute deviation between outliers and predicted data; The absolute deviation is compared with a preset deviation threshold. If the absolute deviation exceeds the preset deviation threshold, it is determined that the data is erroneous due to sensor failure or operational error. If the absolute deviation does not exceed the preset deviation threshold, the abnormal value is retained but marked as data to be reviewed.

[0030] The technical effects of the above solution are as follows: In soil pollution investigations, data collected by sensors (such as heavy metal content and pollutant concentration) are the foundation for analyzing soil conditions. By correlating and verifying data from different collection points, outliers caused by "sensor malfunction / operational error" and other reasons (such as local anomalies in the soil itself or temporary fluctuations in the natural environment) are distinguished. For example, if a heavy metal sensor in a certain plot of soil produces an outlier due to equipment malfunction, it will interfere with the judgment of pollution distribution. This embodiment accurately determines the outlier, avoiding such erroneous data from affecting subsequent analysis of the pollution range and degree, ensuring the authenticity and reliability of the data used for pollution investigations. Based on the passivity of the data before and after, outliers are distinguished as either "pending verification" or "malfunction / error". For real local anomalies that may exist in complex soil environments (such as sudden changes in pollutant concentration caused by underground pipe leakage), the data is marked as pending verification and retained, which can be further verified later by geological exploration, laboratory testing, etc. For erroneous data caused by equipment or operational problems, after clear determination, it can be removed or marked for correction, realizing hierarchical control of data quality, which not only does not miss real soil anomaly information, but also cleans up erroneous data and improves the overall data quality. If erroneous data (caused by sensor malfunction or operational error) is not identified, it can lead to biases in soil pollution distribution models and pollution level assessments. For example, mistakenly including high-concentration anomalies caused by a malfunction in the analysis may exaggerate the extent of pollution. This embodiment promptly identifies and processes such erroneous data, making subsequent data-driven pollution status analyses (such as pollution source tracing and risk zoning) more accurate. This assists investigators in accurately grasping the true extent of soil pollution at a site, providing a reliable basis for remediation plan development and environmental management decisions. Soil pollution investigations require long-term, continuous data collection. The standardized processing of anomalies in this embodiment ensures the reliability of the data sequence, facilitating dynamic analysis (such as the migration patterns of pollutants over time and pollution fluctuations in different seasons). The correlation between data from previous and subsequent collection points also aligns with the spatial (adjacent collection points) and temporal (continuous monitoring) correlation characteristics of soil pollution data, making data analysis more closely reflect the actual patterns of soil pollution propagation and change, and improving the scientific rigor of the investigation and analysis. When a sensor malfunction or operational error is identified, the system can promptly trigger an early warning, reminding investigators to inspect the equipment and standardize operations. For example, if a soil sampling equipment sensor malfunction is not detected in time, subsequent data collection will be affected. This embodiment can identify such problems as early as possible, ensuring the continuous and effective conduct of the investigation, reducing rework and data loss due to equipment or operational issues, and improving the efficiency and reliability of the investigation. The retention and labeling of outliers (pending verification, malfunction / error type) establishes a full-process traceability archive for soil pollution investigation data. During subsequent verification and auditing, the processing of outlier data can be clearly traced, explaining the reliability of the data and its causes, enhancing the credibility of the investigation results. Simultaneously, it also provides data references for optimizing sensor layout and improving investigation operation procedures (such as identifying frequently malfunctioning sampling points and adjusting equipment or sampling strategies), contributing to the continuous optimization of the system and the investigation work.

[0031] Specifically, using the data values ​​obtained from the preceding and following data collection points corresponding to the outlier, data prediction is performed on the data collection points corresponding to the outlier to obtain the predicted data values ​​for the data collection points to which the outlier belongs, including: Retrieve the data values ​​obtained from the previous and next data collection points corresponding to the outlier values; The average value of the data obtained from the previous and subsequent data collection points corresponding to the outlier is calculated to obtain the base prediction value. ;in, and These represent the data values ​​obtained from the previous and next data collection points corresponding to the outlier values, respectively. The first prediction value is obtained by using the data value obtained from the previous collection point and the data value obtained from the next collection point corresponding to the outlier. The first prediction is obtained using the following formula:

[0032] in, Indicates the first predicted value; This indicates the preset difference reference value; This represents the nonlinear adjustment coefficient, with a value range of 0.7-1.2; Retrieve the absolute deviation between the predicted data and the outlier value corresponding to all erroneous data collected before the outlier point that was determined to be caused by sensor malfunction or operational error. The second prediction value is obtained by combining the absolute deviation between the predicted data value corresponding to all erroneous data caused by sensor failure or operational error before the abnormal value collection point and the abnormal value with the first prediction value. The second prediction is obtained using the following formula:

[0033] in, Indicates the second forecast quantity; This indicates the number of erroneous data points caused by sensor malfunctions or operational errors prior to the outlier collection point. This represents the outlier value corresponding to the i-th erroneous data. This represents the base prediction value corresponding to the i-th erroneous data point; This represents the absolute deviation between the predicted value and the outlier corresponding to the i-th erroneous data point; specifically, It is the deviation of historical erroneous data, divided by The absolute deviation, after being exponentialized and averaged, is used to "aggregate and refine" dispersed historical fault deviations into a correction for the current prediction. For example, in soil surveys, abnormal deviations caused by sensor faults at different historical times can be integrated using this formula to create a comprehensive correction trend, reducing the interference or erroneous judgments of similar faults on the current prediction. The formula uses exponentiation of historical deviations... By combining and averaging, the individual differences in historical faults (different fault deviation magnitudes and scenarios) are generalized into a universal correction quantity to adapt to the current possible fault scenarios. For example, different location sensor faults exhibit different deviation behaviors. Through aggregation calculation, common correction rules applicable to the current anomaly prediction are extracted, making the value of historical fault data easier to reuse in subsequent applications.

[0034] The predicted values ​​corresponding to outliers are obtained by combining the second predicted value with the basic predicted value.

[0035] The predicted value corresponding to the outlier is obtained by the following formula:

[0036] in, X represents the predicted value corresponding to the outlier; X represents the basic predicted value corresponding to the outlier.

[0037] The technical effect of the above technical solution is as follows: Existing technologies often use only simple interpolation (such as front and back averages) when predicting data. This embodiment introduces... (Nonlinear prediction based on the difference between preceding and following data and the reference value) and (Combining the correction amount of historical error data bias), predictions are corrected from two dimensions: the local characteristics of the current data (difference between previous and subsequent points) and the feedback from historical error data (patterns of past fault deviations). For example, in soil pollution surveys, not only are the mean values ​​of data before and after the current anomaly point considered, but also the anomaly deviation patterns caused by historical sensor faults are correlated, making the predicted values ​​more closely match the actual data distribution, improving the accuracy of anomaly point predictions, and reducing errors caused by simple predictions. For scenarios such as soil pollution surveys, data is greatly affected by the environment (e.g., soil heterogeneity) and equipment (sensor fluctuations). (Nonlinear adjustment coefficient) adjustment The response to the difference is constructed using the bias distribution of historical error data. It can adapt to the nonlinear and fluctuating characteristics of data. Compared with the fixed-mode prediction of existing technologies, it can better cope with complex working conditions, such as the differences in diffusion characteristics of different plots and different pollutants in soil pollutant concentration monitoring. This embodiment can be achieved through parameters ( (Historical data) adaptive adjustment improves prediction adaptability. Fault correlation analysis deepens the reuse of historical fault data: Existing technologies rarely utilize historical "sensor fault / operational error" data to assist current predictions. This embodiment retrieves the deviation of historical error data to construct... This enables historical fault information to inform current predictions. For example, in site surveys, if sensors in a certain area have frequently failed due to high soil moisture, their historical biases can be used to correct current predictions of anomalies in the same area and of the same type. This uncovers the value of fault data, making predictions more closely correlated with equipment fault patterns and helping to identify potential equipment problems (such as prompting sensor protection optimization in areas with frequent faults). Fault impact quantification correction: through... The formula aggregates historical deviations (∑, exponentiation, and mean operations) to quantify the impact of historical faults on current predictions. It transforms the abstract concept of "historical faults potentially interfering with current data" into concrete correction quantities. Compared to existing technologies that rely on experience to estimate fault impacts, this approach is more quantitative and scientific, enabling prediction results to isolate or adapt to the deviations caused by faults, thus improving the reliability of the prediction stage in data quality control. The data quality control closed loop strengthens the linkage between prediction and anomaly detection: This prediction scheme, as a sub-process of the "anomaly confirmation" stage, outputs predicted values ​​for subsequent deviation comparison and fault determination. Compared to the loose correlation between prediction and fault determination in existing technologies, this achieves deep linkage between "anomaly identification - prediction correction - fault determination." In the soil pollution survey system, from sensor-collected anomalies to deviation determination using multi-dimensional predicted values, a data quality control closed loop is formed, improving the ability to distinguish between "true anomalies (sudden changes in actual soil pollution)" and "false anomalies (equipment / operational problems)," ensuring the quality of survey data. Dynamic iterative optimization: As the system operates, historical erroneous data continuously accumulates... It can be continuously updated based on new data, enabling dynamic iteration of the prediction model. Existing prediction models are often static and fixed. This embodiment can adapt to factors such as equipment aging and environmental changes during long-term monitoring. For example, during long-term soil survey monitoring, sensors gradually drift. After updating historical fault deviation data, the prediction correction capability is optimized in sync, allowing data quality control and prediction functions to be continuously upgraded as the system operates.

[0038] To address the issues of inconsistent data correlation, inaccurate standard matching, and insufficient spatial and vertical pollution assessment in existing soil pollution assessment technologies, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution: The detection data spatial analysis unit is also used for: The preprocessed sample detection data is correlated with the corresponding sample spatial location information; The sample spatial location information includes GPS coordinates, sampling depth, sampling number, and area division information. The sampling number is used as a unique identifier to match the preprocessed sample detection data with the sample spatial location information, and the matching results in an associated dataset. The latitude and longitude coordinates collected by different devices in the associated dataset are converted into a unified geographic coordinate system. At the same time, they are classified into layers according to surface and depth. After the layer classification, the vertical spatial position of each sampling point is confirmed. The hierarchical and categorized associated datasets are visualized. The visualization process involves marking the location of all sampling points on an electronic map and using symbols of different colors and sizes to represent the numerical differences in sample test data. Then, distribution maps are drawn according to the test items or sampling depths. Finally, combined with the previously divided soil collection areas, a comparison chart of the mean values ​​of sample test data between regions is drawn. Based on the visualization processing results, the spatial distribution patterns of the associated data are analyzed, including clustering analysis, gradient change analysis, and boundary feature analysis. The sample analysis results of the sample detection data are obtained based on the spatial distribution pattern.

[0039] Specifically, through a comprehensive design encompassing data association, coordinate unification, hierarchical classification, visualization processing, and spatial pattern analysis, the scientific rigor and practicality of spatial dimension analysis in soil pollution investigations have been significantly enhanced. Its advantages lie in the precise processing across multiple dimensions. The data association process uses the sampling number as a unique identifier, achieving precise matching between preprocessed data and spatial information such as GPS coordinates and sampling depth. This ensures that each set of detection data corresponds to a specific spatial location, fundamentally avoiding the problem of "data and location misalignment." This lays a solid foundation for accurate data association in subsequent spatial analysis. Unifying the latitude and longitude collected by different devices to the same geographic coordinate system eliminates spatial positioning deviations caused by differences in device coordinate systems. Hierarchical classification by surface and deep layers, along with clearly defined vertical spatial locations, breaks through the limitations of traditional planar analysis, constructing a... The "horizontal + vertical" three-dimensional spatial data framework better reflects the vertical distribution characteristics of soil pollution. Numerical differences in sampling points are marked on electronic maps using color and size symbols, enabling rapid identification of high- and low-value areas. Distribution maps drawn according to detection items or depth can specifically present the spatial distribution of specific pollutants or different soil layers. Regional mean comparison maps clearly show the pollution differences between regions, transforming complex spatial data into intuitive visual information, lowering the barrier to understanding for non-professionals, and facilitating a rapid grasp of pollution spatial patterns. Cluster analysis can identify concentrated pollution distribution areas, providing crucial clues for source tracing. Gradient change analysis can capture the gradual trend of pollutants from high to low levels, inferring diffusion paths. Boundary feature analysis can identify abrupt changes in pollution levels in different areas, aiding in the delineation of pollution control zones. These analyses reveal the spatial distribution patterns of pollution, breaking through the limitations of purely numerical analysis in reflecting spatial correlations. This makes the sample analysis results more closely aligned with actual pollution scenarios. Through precise correlation, standardized processing, intuitive display, and in-depth analysis, abstract detection data is transformed into spatially meaningful patterns. This ensures both the accuracy and systematic nature of spatial analysis and enhances the practicality of the results through visualization, providing strong spatial support for interpreting the spatial distribution of soil pollution, tracing pollution sources, and formulating remediation plans.

[0040] The data contamination assessment unit is also used for: Retrieve soil environmental quality standard data from the database and confirm the limit standards for different pollutants under different land use types, including screening values ​​and control values; The assessment data in the sample analysis results were confirmed, including pollutant detection values, spatial location information and stratified data at each sampling point; The assessment data was matched with the soil environmental quality standard data item by item. The item-by-item matching was as follows: for each pollutant detection value at each sampling point, the corresponding pollutant limit in the standard was compared to determine whether the pollutant at that sampling point exceeded the standard, and the type of pollutant exceeding the standard, the location of the sampling point exceeding the standard, the sampling depth, and the multiple of exceeding the standard were recorded. Meanwhile, the spatial range of pollution is assessed based on the spatial distribution patterns in the sample analysis results. If the sample analysis results show that a certain type of pollutant is clustered in a specific area, the area is identified as the core pollution area, and the number of sampling points exceeding the standard, the average pollution concentration, and the coverage area within the core area are counted. If the characteristics are gradient changes, it is inferred that there is a pollution diffusion path. If the characteristics are boundary features, the boundary line between the polluted area and the non-polluted area is determined, and the pollution impact range is delineated.

[0041] Based on the stratified detection data and spatial analysis results of surface and deep soil, the vertical distribution of pollution is assessed. The vertical distribution is determined by comparing the concentration differences of the same type of pollutant in surface and deep soil to determine whether the pollution is in the surface or deep layer. If the deep soil exceeds the standard, the depth of pollution infiltration and the trend of pollution concentration changes in each layer are recorded. Finally, the soil pollution level was classified into clean areas, slightly polluted areas, moderately polluted areas, and heavily polluted areas. Finally, sample contamination assessment data is obtained, and the sample contamination assessment data is converted into a visualization report. After the conversion is completed, it is transmitted to the display terminal for data display.

[0042] Specifically, soil environmental quality standards are retrieved from the database to clarify the screening and control values ​​of pollutants under different land use types, providing an authoritative basis for the assessment. Pollutant detection values ​​at sampling points are matched with corresponding standard limits, accurately recording the types, locations, depths, and multiples of exceedances. This avoids subjective ambiguity in the assessment, making the judgment of "whether it exceeds the standard" and "the degree of exceedance" systematic and ensuring the standardization and comparability of the assessment results. Combined with the spatial distribution patterns of the samples, pollution core areas are delineated in clustered areas, and key parameters are statistically analyzed, providing target areas for source tracing and key remediation. Diffusion paths are inferred through gradient changes, allowing for early prediction of pollution spread trends. The affected area is delineated based on boundary characteristics, accurately defining the remediation boundary. This overcomes the limitations of traditional "single-point assessments" in reflecting the overall pollution pattern, making the assessment results more closely aligned with the actual pollution situation. By comparing pollutant concentrations in surface and deep soil layers, the vertical distribution characteristics of pollution can be clearly identified. For cases of excessive levels in deep soil, the infiltration depth and concentration trends are recorded to determine whether pollution is spreading underground and at what rate. This provides crucial evidence for preventing groundwater pollution and overcomes the limitations of focusing solely on surface pollution. The soil is divided into four levels, including clean and slightly polluted areas, making the differences in pollution levels readily apparent. This facilitates the development of differentiated remediation plans based on different levels—for example, remediation measures can be taken in slightly polluted areas, while emergency control is required in heavily polluted areas. This avoids the waste of a "one-size-fits-all" approach to remediation. Visualized reports and terminal displays enhance the practicality of the results, transforming complex assessment data into intuitive charts. This allows decision-makers to quickly grasp the overall picture of pollution and supports cross-departmental information sharing, shortening the response cycle from assessment to remediation.

[0043] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A system for collecting and analyzing information on soil pollution status in a land parcel, characterized in that, include: The sample sensor management unit is used to verify and test the functionality of various sample sensors. The soil sample collection unit is used to collect soil samples according to soil collection requirements. The soil sample testing unit is used to test the collected soil samples using a sample sensor. The detection data preprocessing unit is used to preprocess the sample detection data; The detection data spatial analysis unit is used to analyze the preprocessed sample detection data by combining the sample spatial location information. The detection data preprocessing unit is also used for: Retrieve sample test data and check the integrity of each sample's data; For the test data after integrity check, outliers are screened by comparing them with the acceptable range of the test items; Outliers are processed. If the error is confirmed to be caused by a faulty sample sensor or operational error, it is directly removed. If the cause is unknown, the data is retained and marked as data to be reviewed. For outliers, the data values ​​of the corresponding preceding and following data collection points are extracted to determine whether the preceding and following data values ​​are within the acceptable range in order to help confirm the cause of the outlier.

2. The soil pollution status investigation information collection and analysis system for land parcels according to claim 1, characterized in that, The detection data spatial analysis unit is also used for: The preprocessed sample detection data is correlated with the corresponding sample spatial location information; The sample spatial location information includes GPS coordinates, sampling depth, sampling number, and area division information. The sampling number is used as a unique identifier to match the preprocessed sample detection data with the sample spatial location information, and the matching results in an associated dataset. The latitude and longitude coordinates collected by different devices in the associated dataset are converted into a unified geographic coordinate system. At the same time, they are classified into layers according to surface and depth. After the layer classification, the vertical spatial position of each sampling point is confirmed. The hierarchical and categorized associated datasets are visualized. The visualization process involves marking the location of all sampling points on an electronic map and using symbols of different colors and sizes to represent the numerical differences in sample test data. Then, distribution maps are drawn according to the test items or sampling depths. Finally, combined with the previously divided soil collection areas, a comparison chart of the mean values ​​of sample test data between regions is drawn. Based on the visualization processing results, the spatial distribution patterns of the associated data are analyzed, including clustering analysis, gradient change analysis, and boundary feature analysis. The sample analysis results of the sample detection data are obtained based on the spatial distribution pattern.

3. The soil pollution status investigation information collection and analysis system for land parcels according to claim 2, characterized in that, The sample sensor management unit is also used for: Sample sensors include physical property sensors, chemical property sensors, pollution sensors, positioning sensors, and meteorological sensors; Physical property sensors include soil moisture sensors, temperature sensors, soil compaction sensors, and porosity sensors; chemical property sensors include soil pH sensors, electrical conductivity sensors, nutrient sensors, and redox potential sensors; pollution sensors include heavy metal sensors, organic pollutant sensors, and radioactive substance sensors. Before the sample sensors detect the sample, each sample sensor undergoes a functional test. Functional testing includes visual inspection, power-on testing, zero-point and range calibration, response time testing, and stability testing. After the functional tests are completed and passed, the sample sensor prepares the sample for testing.

4. The soil pollution status investigation information collection and analysis system for land parcels according to claim 3, characterized in that, The soil sample collection unit is also used for: Before collecting soil samples from the soil collection area, a sampling plan should be developed, including dividing the collection area according to the topography, soil type and land use history, using the grid method or serpentine method to confirm the sampling points, and marking the spatial location information of the sampling points. Once the sampling plan is determined, prepare the sampling equipment, which includes tools, containers, and auxiliary equipment. Soil samples were obtained through surface soil sampling and deep soil sampling. Surface soil sampling involved obtaining a mixed sample at the confirmed sampling point, while deep soil sampling involved obtaining stratified samples according to the soil profile. The collected soil samples were aliquoted and labeled to complete the soil sample collection process.

5. The soil pollution status investigation information collection and analysis system for land parcels according to claim 4, characterized in that, The soil sample detection unit is also used for: Before testing soil samples, they must be pre-treated, including air-drying, grinding, and packaging. Then, the testing environment needs to be managed, including temperature, humidity, and electromagnetic shielding; Soil samples were tested using physical property sensors, chemical property sensors, and pollution sensors. The testing process for physical properties includes soil moisture, temperature, compaction, and porosity; the testing process for chemical properties includes soil pH, electrical conductivity, and redox potential; and the testing process for pollutants includes soil heavy metals, organic pollutants, and radioactive substances. Record each test data point and create an electronic spreadsheet, including the sample number, test item, test time, sensor model, and test value. Finally, the soil samples were tested.

6. The soil pollution status investigation information collection and analysis system for land parcels according to claim 5, characterized in that, The detection data preprocessing unit is also used for: After outlier handling, missing value handling is performed. For missing data, if the missing proportion is lower than the preset range, the mean of the detection of the same type of sample in the same region is used to fill the missing value and it is marked as the filled value. If the missing proportion is higher than the preset range, the missing value is recorded separately and is not used as the core data for subsequent analysis. After handling missing values, the data is standardized and checked for duplicate data. If duplicate data is found, the data is deleted. Finally, the data preprocessing of the sample detection data is completed.

7. The soil pollution status investigation information collection and analysis system for land parcels according to claim 6, characterized in that, To determine whether outlier values ​​are due to sample sensor malfunction or operational errors, the following steps are also required: If either the data value obtained from the previous collection point or the data value obtained from the next collection point is outside the acceptable range, the abnormal value will be retained but marked as data to be reviewed. If the data values ​​obtained from the previous collection point and the data values ​​obtained from the next collection point are both within the acceptable range, then the data values ​​obtained from the previous collection point and the next collection point corresponding to the outlier are used to predict the data of the data collection point corresponding to the outlier, and the predicted data value of the data collection point to which the outlier belongs is obtained. Use outliers and predicted data to obtain the absolute deviation between outliers and predicted data; The absolute deviation is compared with a preset deviation threshold. If the absolute deviation exceeds the preset deviation threshold, it is determined that the data is erroneous due to a sample sensor malfunction or operational error. If the absolute deviation does not exceed the preset deviation threshold, the abnormal value is retained but marked as data to be reviewed.

8. The soil pollution status investigation information collection and analysis system for land parcels according to claim 7, characterized in that, Using the data values ​​obtained from the preceding and following data collection points corresponding to the outlier, data prediction is performed on the data collection points corresponding to the outlier, obtaining the predicted data values ​​for the data collection points to which the outlier belongs, including: Retrieve the detection data from the previous and next data collection points corresponding to the outlier, and calculate the basic predicted value based on these two types of data; The basic prediction value is corrected by combining the historical absolute deviation between the predicted value and the outlier value corresponding to the erroneous data before the outlier collection point caused by sample sensor failure or operational error. The corrected results are used to obtain the predicted data values ​​for the collection points to which the outliers belong.

9. The soil pollution status investigation information collection and analysis system for land parcels according to claim 8, characterized in that, It also includes a data pollution assessment unit, which is used to conduct pollution assessment based on sample analysis results and convert the assessment data into visual data before transmitting it to a display terminal for display. The data contamination assessment unit is also used for: Retrieve soil environmental quality standards and clarify the pollutant limits corresponding to different land use types; Confirm the assessment data in the sample analysis results, including pollutant detection values, spatial location information, and stratified data at each sampling point; The assessment data was matched with the aforementioned soil environmental quality standards one by one to determine whether the pollutants at each sampling point exceeded the standards and to record the relevant information on exceeding the standards. The spatial extent of pollution is assessed by combining the spatial distribution patterns of the samples. Based on the aggregation, gradient changes, or boundary characteristics of pollutants, the core pollution area, pollution diffusion path, or boundary line between polluted and non-polluted areas are determined respectively.

10. The soil pollution status investigation information collection and analysis system for land parcels according to claim 9, characterized in that, The data contamination assessment unit is also used for: Based on the stratified detection data and spatial analysis results of surface soil and deep soil, the vertical distribution of pollution is assessed. The vertical distribution is determined by comparing the concentration differences of the same type of pollutant in surface soil and deep soil to determine whether the pollution is in the surface or deep layer. If the deep soil exceeds the standard, the depth of pollution infiltration and the trend of pollution concentration changes in each layer are recorded. Finally, the soil pollution level was classified into clean areas, slightly polluted areas, moderately polluted areas, and heavily polluted areas. Finally, sample contamination assessment data is obtained, and the sample contamination assessment data is converted into a visualization report. After the conversion is completed, it is transmitted to the display terminal for data display.

Images