A detection system and method for soil pollution

Abstract

The present invention relates to the technical field of soil pollution detection, and discloses a detection system and method for soil pollution. The method includes: collecting a plurality of soil samples in the area to be detected based on a preset distance. Obtaining the soil data of the plurality of soil samples and establishing a relational expression for each soil sample. Judging whether there is abnormal data in the relational expressions of each soil sample based on the expected distance metric, wherein: if there is abnormal data among the relational expressions of each soil sample, the soil sample corresponding to the abnormal data is recollected. If there is no abnormal data among the relational expressions of each soil sample, obtaining the average value of the soil data among the soil samples, and determining the soil pollution level of the area to be detected according to the average value of the soil samples. The present invention effectively eliminates data errors through precise soil sample collection, data analysis and anomaly detection, ensures the accuracy of soil pollution assessment, and thus provides a reliable basis for the implementation of pollution prevention and control measures.

Description

Technical Field

[0001] The present invention relates to the technical field of soil pollution detection, and in particular, to a detection system and method for soil pollution. Background Art

[0002] In the field of soil pollution monitoring and assessment, accurate soil sample collection and data analysis are the keys to determining the pollution level. The accuracy of soil pollution monitoring depends on multiple factors, including the representativeness of sample collection, the precision of data analysis, and the rationality of reflecting the pollution distribution.

[0003] However, traditional soil sample collection and data analysis methods often face many challenges in practical applications. First, data anomalies are a common problem. Due to the large spatial heterogeneity of soil samples, some soil samples may have abnormal data due to human errors during the collection process or interference from environmental factors. If such abnormal data is not identified, it will often seriously affect the accuracy of pollution assessment. Second, the uneven spatial distribution of samples is also a common problem. In some areas with complex terrain or uneven pollution distribution, traditional sampling methods may lead to unrepresentative sampling point distributions, thereby affecting the reliability of data analysis. In addition, when the scale of soil pollution detection is large, how to extract valuable information from thousands of soil samples, especially in areas where the pollution sources are widely distributed and the types of pollutants are numerous, traditional methods are difficult to effectively process such complex environmental data, thus affecting the final pollution assessment results.

[0004] Therefore, there is an urgent need to invent a technology for soil pollution to solve the problems that traditional soil sample collection and data analysis methods are difficult to effectively identify data anomalies, handle the uneven spatial distribution of samples, and cope with large-scale complex environmental data, resulting in the accuracy and reliability of pollution assessment being affected. Summary of the Invention

[0005] In view of this, the present invention proposes a detection system and method for soil pollution, aiming to solve the problems that traditional soil sample collection and data analysis methods are difficult to effectively identify data anomalies, handle the uneven spatial distribution of samples, and cope with large-scale complex environmental data, resulting in the accuracy and reliability of pollution assessment being affected.

[0006] The present invention proposes a detection method for soil pollution, including:

[0007] Collecting a plurality of soil samples in the area to be detected based on a preset distance;

[0008] Obtaining the soil data of the plurality of soil samples and establishing a relational expression for each of the soil samples;

[0009] Based on the predicted distance metric, determine whether there are abnormal data in each of the soil sample correlation formulas, where:

[0010] If there are abnormal data among the soil sample correlation formulas, re-collect the soil samples corresponding to the abnormal data;

[0011] If there are no abnormal data among the soil sample correlation formulas, obtain the average soil data among the soil samples, and determine the soil pollution level of the area to be detected based on the average soil sample.

[0012] Furthermore, when collecting a number of soil samples in the area to be detected based on a preset distance, it includes:

[0013] Obtain the detection area, soil particle size, soil porosity, soil temperature, soil hydraulic gradient, and physical properties of the water body in the soil in the area to be detected;

[0014] Determine the water permeability score of the area to be detected according to the soil particle size and soil porosity; determine the infiltration score of the area to be detected according to the soil particle size, soil porosity, soil temperature, soil hydraulic gradient, and physical properties of the water body in the soil;

[0015] Determine the preset collection distance according to the relationship between the water permeability score, infiltration score, and detection area:

[0016] ;

[0017] where d is the preset collection distance, T is the water permeability score of the area to be detected, P is the infiltration score of the area to be detected, A is the detection area of the area to be detected, and k1, k2, and K3 are weight coefficients, and k1, k2, and K3 are all not zero.

[0018] Furthermore, when determining the water permeability score of the area to be detected according to the soil particle size and soil porosity, it includes:

[0019] ;

[0020] where T is the water permeability score, Dmin is the minimum particle size of the soil particles, Dmax is the maximum particle size of the soil particles, D is the average particle size of the soil particles, is the soil porosity, and x1 and x2 are weight coefficients, and x1 and x2 are both not zero.

[0021] Furthermore, when determining the infiltration score of the area to be detected according to the soil particle size, soil porosity, soil temperature, soil hydraulic gradient, and physical properties of the water body in the soil, it includes:

[0022] ;

[0023] Wherein, P is the penetration score, Dmin is the minimum particle size of the soil, Dmax is the maximum particle size of the soil, D is the average particle size of the soil particles, is the soil porosity, α is the temperature coefficient, U is the actual soil temperature, △T is the preset soil temperature, △h is the head difference, L is the preset sampling depth of the soil sample, μ is the viscosity of the water body, and z1, z2, z3, z4, and z5 are weighting coefficients, and z1, z2, z3, z4, and z5 are all non-zero.

[0024] Further, when obtaining the soil data of a plurality of the soil samples, specifically, the chemical composition data contained in the soil samples, the concentration of each chemical substance, and the volatility of each chemical substance.

[0025] Further, when determining whether there is abnormal data in each soil sample correlation formula based on the predicted distance metric, it includes:

[0026] Obtain the mean value of the distance metrics between the distance metrics and determine it as the preset distance metric;

[0027] Obtain the distance metric between the soil sample and two adjacent soil samples, and obtain the mean value of the distance metric;

[0028] According to the relationship between the mean value of the distance metric and the preset distance metric, determine whether the soil sample correlation formula is abnormal data:

[0029] When the mean value of the distance metric is less than or equal to the preset distance metric, it is determined that the soil sample correlation formula is not the abnormal data;

[0030] When the mean value of the distance metric is greater than the preset distance metric, it is determined that the soil sample correlation formula is the abnormal data.

[0031] Further, when determining the soil pollution level of the area to be detected according to the mean value of the soil samples, it includes:

[0032] Obtain the mean value of the number of chemical components contained between the soil samples, the average concentration of chemical substances between the soil samples, and the average volatility of chemical substances between the soil samples;

[0033] According to the mean value of the number of chemical components contained between the soil samples, the average concentration of chemical substances between the soil samples, and the average volatility of chemical substances between the soil samples, determine the chemical substance pollution score, and according to the chemical substance pollution score, determine the soil pollution level of the area to be detected, wherein:

[0034] When the soil pollution level is the medium risk level or the high risk level, a warning message is sent.

[0035] Further, when determining the chemical substance pollution score according to the average value of the number of chemical components contained among the soil samples, the average concentration of chemical substances among the soil samples, and the average volatility of chemical substances among the soil samples, it includes:

[0036] ;

[0037] Among them, J is the chemical substance pollution score, n is the average value of the number of chemical components contained, M is the number of soil samples, ci is the average concentration of chemical substances among the soil samples, vi is the average volatility of chemical substances among the soil samples, f1, f2, and f3 are weight coefficients, and neither f1 nor f2 and f3 is zero; △n is the maximum number of chemical components contained in the soil sample, specifically:

[0038] Obtain the historical quantity of chemical components contained in the soil during each historical detection in the area to be detected, and determine the average value among the historical quantities of chemical components contained in the soil, and determine it as the maximum number of chemical components contained in the soil sample.

[0039] Further, when determining the soil pollution level of the area to be detected according to the chemical substance pollution score, it includes:

[0040] Determine the soil level of the area to be detected according to the relationship between the chemical substance pollution score and the first preset chemical substance pollution score and the second preset chemical substance pollution score configured in advance:

[0041] When the chemical substance pollution score is less than the first preset chemical substance pollution score, determine that the soil level of the area to be detected is the low risk level;

[0042] When the chemical substance pollution score is greater than or equal to the first preset chemical substance pollution score and less than the second preset chemical substance pollution score, determine that the soil level of the area to be detected is the medium risk level;

[0043] When the chemical substance pollution score is greater than or equal to the second preset chemical substance pollution score, determine that the soil level of the area to be detected is the high risk level;

[0044] Among them, the first preset chemical substance pollution score is less than the second preset chemical substance pollution score, and the magnitude relationship of the soil levels is sorted in sequence as: low risk level, medium risk level, and high risk level.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: By collecting soil samples based on a preset distance and establishing a sample correlation formula, it is possible to systematically analyze the soil sample data, thereby effectively identifying and processing data anomalies. In traditional soil detection methods, due to the uneven spatial distribution of soil samples and the influence of environmental factors, abnormal data often occurs during the collection process. If these abnormal data are not discovered and eliminated in a timely manner, it may lead to deviations in pollution assessment results, thereby affecting environmental protection and pollution control decisions. Through the preset distance and sample correlation formula of this method, these anomalies can be discovered and corrected more precisely, ensuring the accuracy of the data and greatly improving the reliability of pollution assessment. In addition, when there are no anomalies in the soil sample data, the method further determines the soil pollution level by obtaining the average value of the soil sample data. This processing method not only simplifies the data analysis process, but also comprehensively reflects the pollution status of the area to be detected more comprehensively and accurately by considering multiple sample data. Soil pollution assessment does not rely on a single data point. The average value calculation can overcome the local errors that may exist in a single sample, making the pollution assessment more representative, thereby improving the reliability and scientific nature of the assessment results. Finally, by using a measurement method based on the expected distance to determine whether the data is abnormal, manual intervention is avoided, thereby improving the detection efficiency. This automated data analysis method is applicable to large-scale and diverse soil pollution monitoring, can process a large amount of data in a short time, and greatly improves the efficiency of soil pollution detection. The application of this method is of great significance for large-scale soil pollution assessment and environmental monitoring, especially in areas where the pollution sources are widely distributed or the soil types are complex. Its fast and accurate assessment ability will provide more effective decision-making basis for government departments and environmental protection agencies.

[0046] On the other hand, the present application also provides a detection system for soil pollution, including:

[0047] A collection module configured to collect a plurality of soil samples in the area to be detected based on a preset distance;

[0048] An analysis module electrically connected to the collection module, the analysis module being configured to obtain the soil data of the plurality of soil samples and establish a correlation formula for each of the soil samples;

[0049] A judgment module electrically connected to the analysis module, the judgment module being configured to judge whether there is abnormal data in each of the soil sample correlation formulas based on an expected distance metric, where:

[0050] If there is abnormal data among the soil sample correlation formulas, the judgment module re-collects the soil samples corresponding to the abnormal data;

[0051] If there is no abnormal data among the relational expressions of the soil samples, the judgment module obtains the average value of the soil data among the soil samples, and determines the soil pollution level of the area to be detected according to the average value of the soil samples.

[0052] It can be understood that the detection system and method for soil pollution in the above embodiments of the present invention have the same beneficial effects and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to limit the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0054] Figure 1 is a flowchart of a detection method for soil pollution provided by an embodiment of the present invention;

[0055] Figure 2 is a functional block diagram of a detection system for soil pollution provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in combination with the embodiments.

[0057] As Figure 1 shown, in some embodiments of the present application, this embodiment provides a detection method for soil pollution, including:

[0058] Step S100, collecting a plurality of soil samples of the area to be detected based on a preset distance.

[0059] Specifically, when collecting a number of soil samples in the area to be detected based on a preset distance, it includes: obtaining the detection area and soil particle size of the area to be detected, determining the water permeability score of the area to be detected; according to the soil particle size, soil porosity, soil temperature, soil hydraulic gradient and physical properties of the water body in the soil. According to the soil particle size, soil porosity, soil temperature, soil hydraulic gradient and physical properties of the water body in the soil, determine the infiltration score of the area to be detected. Determine the preset collection distance according to the relationship between the water permeability score, infiltration score and detection area:

[0060] 。

[0061] Wherein, d is the preset collection distance, T is the water permeability score of the area to be detected, P is the infiltration score of the area to be detected, A is the detection area of the area to be detected, k1, k2 and K3 are weighting coefficients, and k1, k2 and K3 are all not zero.

[0062] Specifically, when determining the water permeability score of the area to be detected according to the soil particle size and soil porosity, it includes:

[0063] 。

[0064] Wherein, T is the water permeability score, Dmin is the minimum particle size of the soil particles, Dmax is the maximum particle size of the soil particles, D is the average particle size of the soil particles, is the soil porosity, x1 and x2 are weighting coefficients, and x1 and x2 are both not zero.

[0065] Specifically, when determining the infiltration score of the area to be detected according to the soil particle size, soil porosity, soil temperature, soil hydraulic gradient and physical properties of the water body in the soil, it includes:

[0066] 。

[0067] Wherein, P is the water permeability score, Dmin is the minimum particle size of the soil particles, Dmax is the maximum particle size of the soil particles, D is the average particle size of the soil particles, is the soil porosity, α is the temperature coefficient, T is the actual soil temperature, △T is the preset soil temperature, △h is the head difference, L is the preset collection depth of the soil sample, μ is the viscosity of the water body, z1, z2, z3, z4 and z5 are weighting coefficients, and z1, z2, z3, z4 and z5 are all not zero.

[0068] It is understandable that the water permeability score and the infiltration score of the soil are calculated based on the soil particle size, porosity, temperature, hydraulic gradient, and physical characteristics of the water body in the area to be detected. These scores provide a theoretical basis for subsequent soil sample collection, enabling the collection process to accurately reflect the hydrological characteristics of the area, and then reasonably designing the collection distance to ensure the comprehensiveness and accuracy of the samples. Specifically, the calculation of the water permeability score mainly relies on two key factors: soil particle size and porosity. The size of the soil particles (such as the minimum, maximum, and average particle sizes) directly affects the flow and infiltration ability of water. Soils with smaller particle sizes usually have lower water permeability, while soils with larger particles usually have higher water permeability. Porosity reflects the proportion of voids in the soil. Soils with higher porosity usually have stronger water permeability. The calculation formula of the water permeability score synthesizes these parameters and uses weight coefficients to weight the effects of particle size and porosity, thereby quantifying the water permeability ability of the area. The calculation of the infiltration score is more complex. In addition to soil particle size and porosity, it also combines factors such as the actual temperature of the soil, the head difference, and the soil hydraulic gradient. The influence of temperature on the infiltration performance is quantified by introducing a temperature coefficient. An increase in temperature usually reduces the viscosity of water, thus affecting the infiltration ability of water. In addition, the head difference and the soil hydraulic gradient are also key factors affecting permeability, and these factors can be obtained through actual measurement. The infiltration score is also weighted by comprehensively considering these influencing factors and using corresponding weight coefficients to obtain an accurate permeability assessment. Once the water permeability score and the infiltration score are calculated, these data will be used to determine the preset collection distance. The collection distance is not only affected by the water permeability and infiltration scores but also related to the total area of the area to be detected. The preset collection distance is determined based on the relationship between the water permeability score and the infiltration score, as well as their correlation with the detection area, combined with weight coefficients (k1, k2, k3) to determine the final collection range. This process ensures that the collected soil samples can accurately represent the overall soil characteristics of the area to be detected.

[0069] It can be seen that by obtaining multiple soil physical properties (such as particle size, porosity, temperature, hydraulic gradient, etc.) of the area to be detected and using these data to calculate the water permeability score and the infiltration score, the soil hydrological properties can be more accurately reflected. This comprehensive analysis can ensure the representativeness of soil samples, thereby improving the reliability of soil pollution detection results. Secondly, through the calculation of the water permeability score and the infiltration score, the water infiltration and flow capacity of the soil can be better reflected. The water permeability score provides a quantitative standard for evaluating the water penetration ability of the soil by considering the soil particle size and porosity, while the infiltration score combines multiple factors such as temperature, hydraulic gradient, and the viscosity of the water body, further improving the evaluation accuracy of soil permeability. This detailed scoring system helps to perform differential sampling and analysis according to different soil properties, thereby improving the accuracy of soil pollution detection. Thirdly, by determining the sampling distance reasonably based on the relationship between the water permeability score and the infiltration score and the detection area, combined with the weight coefficient, it ensures that the sampling points cover the overall characteristics of the area to be detected. This method can make the collected soil samples more comprehensive and representative by optimizing the spatial layout of sample collection, thus ensuring the scientific nature of soil pollution assessment results. In addition, using weight coefficients (such as x1, x2, k1, k2, k3, etc.) to weight factors such as soil particle size, porosity, and temperature can reasonably adjust the influence of different factors on the score. This can not only make the scoring system more accurate but also be flexibly adjusted according to the soil properties of different regions, thereby enhancing the adaptability and operability of soil pollution detection methods. This flexibility enables this method to be widely applicable to different types of soil pollution detection. Finally, by introducing multiple soil physical properties and an intelligent scoring system, the soil pollution assessment can be completed more quickly and accurately, especially in the detection of large-scale and complex areas, which has significant advantages. Using the preset distance and a scientific scoring system can not only improve the efficiency of the sampling process but also reduce manual intervention and increase the automation degree of soil pollution assessment, thereby providing more efficient and accurate technical support for environmental governance.

[0070] Step S200: Obtain the soil data of several soil samples and establish the relational expressions for each soil sample.

[0071] Specifically, when obtaining the soil data of several soil samples, it is specifically the chemical composition data contained in the soil samples, the concentration of each chemical substance, and the volatility of each chemical substance.

[0072] It is understandable that by obtaining the chemical composition data, the concentration of each chemical substance, and the volatile information of a number of soil samples, the chemical composition and its characteristics in the soil can be comprehensively and accurately analyzed. This provides key data support for the assessment and monitoring of soil pollution, helps identify potential pollutants in the soil, and evaluates their potential impact on the environment. At the same time, establishing the correlation formula of soil samples can help discover the interrelationships and laws among soil samples, thereby improving the accuracy of pollution source identification and the evaluation accuracy of pollution levels, providing a scientific basis for subsequent environmental protection and soil remediation.

[0073] Step S300: Based on the predicted distance metric, determine whether there are abnormal data in each soil sample correlation formula, where: If there are abnormal data among the soil sample correlation formulas, re-collect the soil samples corresponding to the abnormal data.

[0074] Specifically, when determining whether there are abnormal data in each soil sample correlation formula based on the predicted distance metric, it includes: obtaining the mean value of the distance metrics among the distance metrics and determining it as the preset distance metric. Obtain the distance metric between a soil sample and its two adjacent soil samples, and obtain the mean value of the distance metrics. Determine whether the soil sample correlation formula is abnormal data according to the relationship between the mean value of the distance metric and the preset distance metric: When the mean value of the distance metric is less than or equal to the preset distance metric, it is determined that the soil sample correlation formula is not abnormal data. When the mean value of the distance metric is greater than the preset distance metric, it is determined that the soil sample correlation formula is abnormal data.

[0075] It is understandable that by obtaining the mean value of the distance metrics among soil samples and comparing it with the preset distance metric, it is determined whether there are abnormal data. The preset distance metric is usually an ideal distance value set according to the distribution and collection conditions of soil samples and serves as a judgment criterion. In this way, the system can automatically screen out abnormal data that do not meet the preset criteria. Secondly, the mean value of the distance metric is an important judgment basis for whether the soil sample correlation formula is abnormal data. When the mean value of the distance metrics among soil samples is less than or equal to the preset distance metric, it indicates that the data relationship among these samples meets the expectations and the consistency among the data is good; while when the mean value of the distance metric is greater than the preset distance metric, it indicates that there is a large deviation in the correlation among soil samples, which may be due to collection errors or environmental factors resulting in abnormal data, and at this time the system will mark it as abnormal data. Finally, when it is determined that the correlation formula of a certain soil sample is abnormal data, a mechanism for re-collecting the soil sample will be automatically triggered. The technical principle of this method not only improves the accuracy of soil data but also can correct the data in a timely manner when the data is abnormal, ensuring the reliability of subsequent analysis and evaluation.

[0076] Step S400: If there is no abnormal data among the relational expressions of each soil sample, obtain the average soil data among the soil samples, and determine the soil pollution level of the area to be tested based on the average value of the soil samples.

[0077] Specifically, when determining the soil pollution level of the area to be tested based on the average value of the soil samples, it includes: obtaining the average value of the number of chemical components contained among the soil samples, the average concentration of chemical substances among the soil samples, and the average volatility of chemical substances among the soil samples. Determine the chemical substance pollution score based on the average value of the number of chemical components contained among the soil samples, the average concentration of chemical substances among the soil samples, and the average volatility of chemical substances among the soil samples, and determine the soil pollution level of the area to be tested based on the chemical substance pollution score, where: when the soil pollution level is a medium-risk level or a high-risk level, an early warning message is sent.

[0078] Specifically, when determining the chemical substance pollution score based on the average value of the number of chemical components contained among the soil samples, the average concentration of chemical substances among the soil samples, and the average volatility of chemical substances among the soil samples, it includes:

[0079] .

[0080] Among them, J is the chemical substance pollution score, n is the average value of the number of chemical components contained, M is the number of soil samples, ci is the average concentration of chemical substances among the soil samples, vi is the average volatility of chemical substances among the soil samples, f1, f2, and f3 are weight coefficients, and none of f1, f2, and f3 is zero. △n is the maximum number of chemical components contained in the soil sample. Specifically: obtain the historical number of chemical components contained in the soil during each historical test in the area to be tested, and determine it as the maximum number of chemical components contained in the soil sample based on the average value among the historical numbers of chemical components contained in the soil.

[0081] Specifically, when determining the soil pollution level of the area to be detected according to the chemical substance pollution score, it includes: determining the soil level of the area to be detected according to the relationship between the chemical substance pollution score and the pre-configured first preset chemical substance pollution score and the second preset chemical substance pollution score: when the chemical substance pollution score is less than the first preset chemical substance pollution score, it is determined that the soil level of the area to be detected is a low-risk level. When the chemical substance pollution score is greater than or equal to the first preset chemical substance pollution score and less than the second preset chemical substance pollution score, it is determined that the soil level of the area to be detected is a medium-risk level. When the chemical substance pollution score is greater than or equal to the second preset chemical substance pollution score, it is determined that the soil level of the area to be detected is a high-risk level. Among them, the first preset chemical substance pollution score is less than the second preset chemical substance pollution score, and the size relationship of the soil levels is sorted in turn as: low-risk level, medium-risk level, and high-risk level.

[0082] It is understandable that a chemical characteristic model of soil samples is established by obtaining the mean value of the number of chemical components, the average concentration of chemical substances, and the average volatility of chemical substances between soil samples. These data can effectively represent the soil quality status of the area to be detected and provide an accurate basis for subsequent pollution assessment. By analyzing the data of each soil sample, the universality and potential threats of chemical pollution in the soil can be revealed. Next, the chemical substance pollution score of the soil sample is comprehensively evaluated based on factors such as the number of chemical components, chemical concentration, and volatility of the soil sample. When calculating the pollution score, multiple weight coefficients (such as f1, f2, f3) are introduced, which enables the impact of different soil characteristics on the pollution score to be quantified, thus more accurately reflecting the degree of pollution. This scoring mechanism not only considers the current pollution level of the soil but also compares historical detection data to determine the change trend of the types and quantities of pollutants. On this basis, by comparing with the preset pollution score threshold, the soil pollution levels in polluted areas are divided into low-risk level, medium-risk level, and high-risk level. These thresholds are set according to historical data and environmental standards and can make a reasonable classification judgment on the soil pollution situation based on the magnitude of the chemical substance pollution score. When the pollution score exceeds a certain threshold, the system automatically classifies the area as medium-risk or high-risk level and triggers a warning message to prompt relevant departments to conduct further intervention or investigation. By comprehensively analyzing the data of multiple soil samples, not only the accuracy of the evaluation results is improved, but also the prediction and warning capabilities for potential pollution risks are strengthened. At the same time, using historical data as a reference basis makes the pollution assessment results have good historical comparability, which helps to understand the pollution trend and improve soil management measures. Finally, the core of the entire technical process lies in converting complex soil data into quantifiable pollution scores and making comprehensive judgments in combination with historical data and preset standards. In this way, areas with severe pollution can be automatically identified, providing a decision-making basis for environmental protection and soil remediation work.

[0083] It can be seen that through the precise analysis and mean calculation of soil sample data, the chemical composition and pollution status of the soil in the area to be detected can be comprehensively reflected. The mean value of the chemical composition quantity, the average concentration and volatility of chemical substances in soil samples, these important indicators can help accurately identify the types and distributions of pollutants. Through such data analysis, the comprehensiveness and efficiency of pollution detection are ensured, which helps to quickly evaluate the current situation of soil pollution in the preliminary stage. Secondly, the chemical substance pollution score is used to classify the soil pollution level, effectively dividing the pollution degree into low-risk, medium-risk and high-risk levels. By comparing these scores with the preset standard values, areas with relatively serious pollution can be identified in a timely manner, so as to take measures in the early stage to avoid the spread of pollution. This classification process improves the accuracy and operability of soil pollution assessment, helps environmental management departments reasonably arrange resources, and optimize pollution control plans. In addition, triggering warning information based on the chemical substance pollution score can give early warnings to potentially high-risk soil pollution areas and issue alarm information in a timely manner. Especially when the soil pollution reaches the medium-risk or high-risk level, the warning system will be automatically activated, providing a basis for emergency response for relevant departments. This intelligent warning mechanism can not only enhance the pollution prevention and control ability, but also improve the response efficiency to environmental accidents and reduce environmental hazards. In addition, multiple weight coefficients (f1, f2, f3) are introduced in the chemical substance pollution score during the assessment process, enabling comprehensive consideration of factors such as the types and concentrations of pollutants in different soil samples. This multi-dimensional scoring method makes the pollution assessment more detailed, not only considering the current pollution level, but also providing a more scientific assessment tool for long-term soil monitoring. This flexible scoring model helps to adapt to the complexity and diversity of soil pollution in different regions. Finally, by analyzing historical data and combining with the maximum quantity of chemical composition in soil samples, the accuracy of pollution assessment is further improved. Through the comparison of historical data, the system can better judge the change trend of pollution and adjust pollution control strategies accordingly. This method not only enhances the scientific nature of soil pollution detection, but also provides strong data support for environmental protection and policy making, ensuring the effective monitoring and treatment of soil pollution.

[0084] In the above embodiments, by collecting soil samples based on a preset distance and establishing sample correlation formulas, it is possible to systematically analyze soil sample data, thereby effectively identifying and processing data anomalies. In traditional soil detection methods, due to the uneven spatial distribution of soil samples and the influence of environmental factors, abnormal data often occurs during the collection process. If these abnormal data are not discovered and eliminated in a timely manner, it may lead to deviations in pollution assessment results, thereby affecting environmental protection and pollution control decisions. Through the preset distance and sample correlation formula of this method, these anomalies can be discovered and corrected more precisely, ensuring the accuracy of the data and greatly improving the reliability of pollution assessment. In addition, when there are no anomalies in the soil sample data, the method further determines the soil pollution level by obtaining the mean value of the soil sample data. This processing method not only simplifies the data analysis process, but also, by comprehensively considering multiple sample data, can more comprehensively and accurately reflect the pollution status of the area to be detected. Soil pollution assessment does not rely on a single data point alone. Mean value calculation can overcome the local errors that may exist in a single sample, making the pollution assessment more representative, thereby improving the reliability and scientific nature of the assessment results. Finally, by using a measurement method based on the predicted distance to determine whether the data is abnormal, manual intervention is avoided, thereby improving the detection efficiency. This automated data analysis method is applicable to large-scale and diverse soil pollution monitoring, can process a large amount of data in a short time, and greatly improves the efficiency of soil pollution detection. The application of this method is of great significance for large-scale soil pollution assessment and environmental monitoring, especially in areas where pollution sources are widely distributed or soil types are complex. Its fast and accurate assessment ability will provide more effective decision-making basis for government departments and environmental protection agencies.

[0085] In another preferred embodiment based on the above embodiments, as Figure 2 shown, this embodiment provides a detection system for soil pollution, including: a collection module, an analysis module, and a judgment module.

[0086] Specifically, the collection module is configured to collect a plurality of soil samples in the area to be detected based on a preset distance. The analysis module is electrically connected to the collection module. The analysis module is configured to obtain the soil data of the plurality of soil samples and establish correlation formulas for each soil sample. The judgment module is electrically connected to the analysis module. The judgment module is configured to determine whether there is abnormal data in each soil sample correlation formula based on the predicted distance measurement, where: if there is abnormal data among the soil sample correlation formulas, the judgment module re-collects the soil samples corresponding to the abnormal data. If there is no abnormal data among the soil sample correlation formulas, the judgment module obtains the mean value of the soil data among the soil samples and determines the soil pollution level of the area to be detected according to the soil sample mean value.

[0087] It is understandable that the detection system and method for soil pollution in each of the above embodiments of the present invention have the same beneficial effects and will not be elaborated herein.

[0088] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0089] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a combination of multiple flows and / or blocks

[0090] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one or more of the flows Figure 1 or a combination of multiple flows and / or blocks

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a combination of multiple flows and / or blocks

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A method for detecting soil pollution, characterized in that: include: Collecting a number of soil samples from the area to be tested based on a preset distance; Acquire soil data of a number of the soil samples and establish a correlation equation for each of the soil samples; Based on the preset distance metric, it is determined whether abnormal data appears in each of the soil sample association equations, wherein: If abnormal data appears between the soil sample association equations, the soil samples corresponding to the abnormal data are collected again; If no abnormal data appears between the soil sample correlation equations, then the mean value of the soil data between the soil samples is obtained, and the soil pollution level of the area to be detected is determined according to the mean value of the soil samples; Wherein, judging whether abnormal data appears in each of the soil sample association equations based on a preset distance metric includes: Obtaining a distance metric between each of the soil samples, and calculating a mean of the distance metrics between each of the distance metrics, and determining the mean of the distance metrics as the preset distance metric; Obtaining a distance measurement between the soil sample and two adjacent soil samples, and obtaining a mean value of the distance measurement; According to the relationship between the distance metric mean and the preset distance metric, it is determined whether the soil sample association equation is abnormal data.

2. The method for detecting soil pollution according to claim 1, characterized in that: When collecting several soil samples from the area to be tested based on a preset distance, including: Obtaining the detection area, soil particle size, soil porosity, soil temperature, soil hydraulic gradient and physical properties of water in the soil of the area to be detected; Determine the water permeability score of the area to be tested based on the soil particle size and soil porosity; determine the infiltration score of the area to be tested based on the soil particle size, soil porosity, soil temperature, soil hydraulic gradient and physical properties of water in the soil; The preset collection distance is determined according to the relationship between the water permeability score, the infiltration score and the detection area: ; Among them, d is the preset collection distance, T is the water permeability score of the area to be detected, P is the infiltration score of the area to be detected, A is the detection area of ​​the area to be detected, k1, k2 and K3 are weight coefficients, and k1, k2 and K3 are not 0.

3. The method for detecting soil pollution according to claim 2, characterized in that: Determining the water permeability score of the area to be tested based on the soil particle size and soil porosity includes: ; Wherein, T is the water permeability score, Dmin is the minimum particle size of soil particles, Dmax is the maximum particle size of soil particles, and D is the average particle size of soil particles. is the soil porosity, x1 and x2 are weight coefficients, and both x1 and x2 are not 0.

4. The method for detecting soil pollution according to claim 2, characterized in that: Determining the infiltration score of the area to be tested based on the soil particle size, soil porosity, soil temperature, soil hydraulic gradient and physical properties of water in the soil includes: ; Wherein, P is the permeability score, Dmin is the minimum particle size of soil particles, Dmax is the maximum particle size of soil particles, and D is the average particle size of soil particles. is the soil porosity, α is the temperature coefficient, U is the actual soil temperature, △T is the preset soil temperature, △h is the water head difference, L is the preset collection depth of the soil sample, μ is the viscosity of the water body, z1, z2, z3, z4 and z5 are weight coefficients, and z1, z2, z3, z4 and z5 are all non-zero.

5. The method for detecting soil pollution according to claim 1, characterized in that: The soil data of the soil samples are obtained specifically including the chemical composition data contained in the soil samples, the concentration of each chemical substance and the volatility of each chemical substance.

6. The method for detecting soil pollution according to claim 5, characterized in that: When judging whether abnormal data appears in each of the soil sample association equations based on a preset distance metric, it includes: When the distance metric mean is less than or equal to the preset distance metric, it is determined that the soil sample association equation is not the abnormal data; When the distance metric mean is greater than the preset distance metric, the soil sample association equation is determined to be the abnormal data.

7. The method for detecting soil pollution according to claim 6, characterized in that: Determining the soil pollution level of the area to be tested based on the soil sample mean value includes: Obtaining the average value of the chemical composition quantity contained in each of the soil samples, the average concentration of the chemical substances in each of the soil samples, and the average volatility of the chemical substances in each of the soil samples; According to the average value of the chemical composition quantity contained in each soil sample, the average concentration of the chemical substance between each soil sample and the average volatility of the chemical substance between each soil sample, the chemical substance pollution score is determined, and according to the chemical substance pollution score, the soil pollution level of the area to be tested is determined, wherein: When the soil pollution level is a medium risk level or a high risk level, an early warning message is sent.

8. The method for detecting soil pollution according to claim 7, characterized in that: Determining the chemical pollution score according to the average value of the chemical composition quantity contained in each soil sample, the average concentration of the chemical substance in each soil sample, and the average volatility of the chemical substance in each soil sample includes: ; Wherein, J is the chemical pollution score, n is the mean value of the chemical composition contained, M is the number of soil samples, ci is the average concentration of the chemical between the soil samples, vi is the average volatility of the chemical between the soil samples, f1, f2 and f3 are weight coefficients, and f1 is not zero with f2 and f3; △n is the maximum number of chemical compositions contained in the soil sample, specifically: The historical quantities of chemical compositions contained in the soil of the area to be tested during each historical test are obtained, and the maximum quantity of the chemical compositions contained in the soil sample is determined based on the quantity mean between the historical quantities of the chemical compositions contained in the soil.

9. The method for detecting soil pollution according to claim 8, characterized in that: Determining the soil pollution level of the area to be tested based on the chemical substance pollution score includes: Determine the soil grade of the area to be detected according to the relationship between the chemical pollution score and the pre-configured first preset chemical pollution score and second preset chemical pollution score: When the chemical substance pollution score is less than the first preset chemical substance pollution score, determining that the soil level of the area to be tested is a low risk level; When the chemical pollution score is greater than or equal to the first preset chemical pollution score, and the chemical pollution score is less than the second preset chemical pollution score, it is determined that the soil level of the area to be tested is a medium risk level; When the chemical substance pollution score is greater than or equal to the second preset chemical substance pollution score, it is determined that the soil level of the area to be tested is a high-risk level; Among them, the first preset chemical substance pollution score is less than the second preset chemical substance pollution score, and the size relationship of the soil levels is ranked in order: low risk level, medium risk level and high risk level.

10. A soil pollution detection system, using a soil pollution detection method according to any one of claims 1 to 9, characterized in that: include: A collection module, configured to collect a number of soil samples from the area to be tested based on a preset distance; An analysis module, electrically connected to the acquisition module, the analysis module being configured to obtain soil data of a number of the soil samples and establish a correlation equation for each of the soil samples; A judgment module is electrically connected to the analysis module, and is configured to judge whether abnormal data appears in each of the soil sample association equations based on a preset distance metric, wherein: If abnormal data appears between the soil sample association equations, the judgment module re-collects the soil samples corresponding to the abnormal data; If no abnormal data appears between the soil sample association equations, the judgment module obtains the mean value of the soil data between the soil samples, and determines the soil pollution level of the area to be detected based on the mean value of the soil samples.

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