Soil environment monitoring method and system based on stratified sampling

Abstract

The present invention relates to the field of soil monitoring technology, and discloses a soil environment monitoring method and system based on stratified sampling, including a data acquisition module and an intelligent monitoring module. The soil environment monitoring method and system based on stratified sampling sets a target area through a data acquisition module, sets a number of monitoring points in each target area, collects surface samples for each monitoring point, and forms a surface sample data set. The intelligent monitoring module analyzes the surface density of the surface samples in each target area, and then determines whether the monitoring points have homogeneity. If they do not have homogeneity, the monitoring points need to be reset and re-collected, covering soil characteristics in multiple dimensions. The data acquisition module performs stratified collection of deep samples for monitoring points with homogeneity to form a deep sample data set. The intelligent monitoring module analyzes the density difference and index difference between different deep samples, and then determines whether the deep samples have abnormalities, and outputs the judgment results. The environmental monitoring has high accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of soil monitoring, and in particular to a soil environment monitoring method and system based on stratified sampling. Background Art

[0002] Stratified sampling is a commonly used soil environmental monitoring method, especially suitable for studying the physical, chemical and biological properties of soil at different depths. The core principle of stratified sampling is to divide the soil profile into multiple layers according to a certain depth or characteristic, usually based on the soil's cultivated layer, subsurface layer and deeper parent material layer. The advantage of this method is that it can reflect the heterogeneous characteristics of soil at different depths. For example, the surface soil is often rich in organic matter and nutrients and is significantly affected by human activities and natural processes, while the deeper soil reflects more long-term geological and hydrological processes. Through stratified sampling, samples can be collected separately for these layers to deeply analyze the physical and chemical properties of the soil and their changes with depth. Studying the vertical change law of soil is of great significance to solving a series of key environmental problems. For example, nutrient distribution research can reveal the efficiency of nutrient acquisition by plant roots and guide agricultural production. Water migration analysis helps to understand soil moisture dynamics, thereby optimizing irrigation management and water resource utilization. Vertical distribution monitoring of pollutant accumulation can help assess the migration risk of heavy metals, pesticides or other harmful substances, and provide a basis for soil remediation and environmental governance.

[0003] At present, the traditional soil environmental monitoring method based on stratified sampling has significant limitations, especially in the surface soil. The surface soil is greatly affected by a variety of external factors, such as human activities, wind and sand erosion, precipitation erosion, and the decomposition of biological residues. These confounding factors will significantly change the physical and chemical properties of the surface soil, resulting in a decrease in the representativeness and accuracy of the sampling results. Due to the large heterogeneity between samples, it is easy to ignore the influence of surface confounding factors, making it difficult to ensure the homogeneity between samples, resulting in low judgment accuracy. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a soil environment monitoring method and system based on stratified sampling, which has the advantages of multi-dimensional coverage of soil characteristics and high environmental monitoring accuracy. It solves the problem that the traditional soil environment monitoring method based on stratified sampling ignores the heterogeneity between samples and has low environmental monitoring accuracy.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a soil environment monitoring method based on stratified sampling, comprising the following steps:

[0008] Step 1: N target areas are set, and several monitoring points are set in each target area. Surface samples are collected from each monitoring point to form a surface sample data set;

[0009] Step 2: According to the surface sample data set, analyze the surface density BM of the surface samples in each target area, and then set the fixed range fluctuation threshold BDY and the range threshold JCY to determine whether all monitoring points in a single area are homogeneous. If not, return to step 1. If so, continue to step 3.

[0010] Step 3: Conduct stratified collection of deep samples at homogeneous monitoring points, and obtain the indicator data of all deep samples through monitoring devices to form a deep sample data set;

[0011] Step 4: According to the deep sample data set, analyze the density difference Mdc and index difference Zbc between different deep samples, then set the fixed range of density difference threshold MDY and index difference threshold ZBY to determine whether there are any abnormalities in the deep samples of the monitoring point, and output the corresponding judgment results.

[0012] Soil environment monitoring system based on stratified sampling, including data acquisition module and intelligent monitoring module;

[0013] The data acquisition module is composed of a surface data unit and a deep data unit. The surface data unit is used to collect a surface sample data set, which includes the collected data of surface samples of all monitoring points. The deep data unit is used to collect a deep sample data set, which includes the indicator data of all deep samples.

[0014] The intelligent monitoring module consists of a surface analysis unit, a deep analysis unit and an environmental management unit. The surface analysis unit analyzes and generates the corresponding surface density BM based on the surface sample data set, and then sets a fixed range of fluctuation threshold BDY and extreme difference threshold JCY to determine whether all monitoring points are homogeneous, and transmits the judgment result to the deep analysis unit through the network. The deep analysis unit analyzes the density difference Mdc and index difference Zbc between different deep samples for the monitoring points with homogeneity in the deep sample data set. The deep analysis unit sets a fixed range of density difference threshold MDY and index difference threshold ZBY, and then combines the density difference Mdc and index difference Zbc to determine whether there is an abnormality in the deep sample of the monitoring point, and outputs the corresponding judgment result.

[0015] Compared with the prior art, the present invention provides a soil environment monitoring method and system based on stratified sampling, which has the following beneficial effects:

[0016] 1. The present invention sets N target areas through the data acquisition module. Several monitoring points are set in each target area. Surface samples are collected for each monitoring point to form a surface sample data set. The intelligent monitoring module analyzes the surface density BM of the surface samples in each target area according to the surface sample data set, and then sets a fixed range of fluctuation threshold BDY and extreme difference threshold JCY to determine whether all monitoring points in a single area are homogeneous. If the fluctuation rate Bdl of the surface density in the i-th target area is i Exceeding the fluctuation threshold BDY or the range JC of the surface density in the i-th target area i Exceeding the extreme value threshold JCY indicates that the monitoring points in the target area are not homogeneous, and it is necessary to reset the monitoring points and collect surface samples at each monitoring point until the fluctuation rate Bdl of the surface density in the target area is included in the fluctuation threshold BDY, and the extreme value JC of the surface density in the target area is included in the extreme value threshold JCY, thus covering the soil characteristics in multiple dimensions.

[0017] 2. The present invention performs stratified collection of deep samples at monitoring points with homogeneity through a data acquisition module, and obtains the index data of all deep samples through a monitoring device to form a deep sample data set. The intelligent monitoring module analyzes the density difference Mdc and the index difference Zbc between different deep samples according to the deep sample data set, and then sets a fixed range of density difference threshold MDY and index difference threshold ZBY to determine whether there is an abnormality in the deep samples of the monitoring point. If any value in the density difference Mdc exceeds the density difference threshold MDY or the mass concentration difference of polycyclic aromatic hydrocarbons in the index difference Zbc exceeds the index difference threshold ZBY, it indicates that an abnormality exists in the deep samples of the monitoring point, accurately locates the problem area, and comprehensively reflects the health status of the regional soil environment, providing a scientific basis and operation plan for soil pollution monitoring, environmental assessment and ecological restoration, and the environmental monitoring has high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a step diagram of the method of the present invention;

[0019] Figure 2 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The traditional soil environmental monitoring method based on stratified sampling has significant limitations, especially in the surface soil. The surface soil is greatly affected by a variety of external factors, such as human activities, wind and sand erosion, precipitation erosion, and the decomposition of biological residues. These confounding factors will significantly change the physical and chemical properties of the surface soil, resulting in a decrease in the representativeness and accuracy of the sampling results. Since there may be a large heterogeneity between samples, it is easy to ignore the influence of surface confounding factors, and it is difficult to ensure the homogeneity between samples.

[0022] This results in low judgment accuracy. Therefore, a soil environment monitoring method and system based on stratified sampling is provided. Figure 1 , the soil environment monitoring method based on stratified sampling includes the following steps:

[0023] Step 1: N target areas are set, and several monitoring points are set in each target area. Surface samples are collected from each monitoring point to form a surface sample data set;

[0024] The expression of the surface sample data set is {Y1 q 、Y2 q 、Y3 q 、...、Yn q}, Y1 s To Yn s are the collection data of surface samples from the first to the nth monitoring points, respectively. The collection data include collection depth, collection time, volume and dry weight. q represents the area number corresponding to each surface sample. In a single area, the collection depth, collection time and volume of surface samples of all monitoring points are the same, which ensures the consistency of depth, time and volume of surface sample collection in the target area, and helps to improve the comparability and reliability of the data.

[0025] Step 2: According to the surface sample data set, analyze the surface density BM of the surface samples in each target area, and then set the fixed range fluctuation threshold BDY and the range threshold JCY to determine whether all monitoring points in a single area are homogeneous. If not, return to step 1. If so, continue to step 3.

[0026] The calculation process of surface density BM is as follows:

[0027] S11. According to the sample data set, count the collected data of surface samples of all monitoring points in the i-th target area, i∈N, the total number of monitoring points in the i-th target area is a, and the corresponding number of surface samples is also a, and mark the volume of all surface samples in the i-th target area as {tj 1 、tj 2 、tj 3 ,...,tj a}, tj1 To tj a are the volumes of the first to ath surface samples respectively, and the dry weight of all surface samples in the i-th target area is marked as {gz 1 , gz 2 , gz 3 ,...,gz a}, gz 1 to gz a are the dry weights of the first to ath surface samples respectively;

[0028] S12. Calculate the surface density BM of the kth surface sample in the i-th target area k , and its calculation formula is as follows:

[0029]

[0030] In the formula, tj k represents the volume of the kth surface sample, gz k represents the dry weight of the kth surface sample;

[0031] S13. According to S12, calculate the surface density BM of all surface samples in the i-th target area. i , and its calculation formula is as follows:

[0032]

[0033] The process of judging whether all monitoring points in a single area are homogeneous is as follows:

[0034] S21, based on the surface density BM of all surface samples in the i-th target area i , calculate the fluctuation rate Bdl of the surface density in the i-th target area i , and its calculation formula is as follows:

[0035]

[0036]

[0037] In the formula, represents the average surface density of surface samples in the i-th target area, It means that according to the variance formula, the fluctuation rate of the surface density in the i-th target area is obtained;

[0038] S22, according to the surface density BM of all surface samples in the i-th target area i , calculate the range JC of the surface density in the i-th target area i , and its calculation formula is as follows:

[0039] JCi =maxBM-minBM

[0040] In the formula, maxBM represents the maximum value of the surface density in the i-th target area, minBM represents the minimum value of the surface density in the i-th target area, and the average value and fluctuation rate of the surface density reveal the uniformity characteristics of the soil;

[0041] S23, according to the fluctuation rate Bdl of the surface density in the i-th target area i and the range JC of the surface density in the jth target area i , determine whether all monitoring points in the i-th target area are homogeneous;

[0042] If the fluctuation rate of surface density in the i-th target area is Bdl i Exceeding the fluctuation threshold BDY or the range JC of the surface density in the i-th target area i If the range threshold JCY is exceeded, it means that the monitoring points in the target area are not homogeneous, and it is necessary to reset the monitoring points and collect surface samples for each monitoring point until the fluctuation rate Bdl of the surface density in the target area is included in the fluctuation threshold BDY, and the range JC of the surface density in the target area is included in the range threshold JCY. Resetting the monitoring points for areas that are not homogeneous is helpful to optimize the sampling plan, improve monitoring accuracy, and cover soil characteristics in multiple dimensions.

[0043] Step 3: Conduct stratified collection of deep samples at homogeneous monitoring points, and obtain the indicator data of all deep samples through monitoring devices to form a deep sample data set;

[0044] Deep samples are collected in layers for a number of homogeneous monitoring points in the ith target area. In the ith target area, the monitoring points are numbered {1, 2, 3, ..., a}, and each monitoring point corresponds to three deep samples. For a single monitoring point, the collection depth of each deep sample increases in sequence as a multiple of the surface sample collection depth, which can obtain soil characteristics at different depths and facilitate stratified research. The deep sample data set corresponding to the ith target area is expressed as {S1 d 、S2 d 、S3 d 、...、Sm d},S1 s To Sn sare the indicator data of the first to mth deep samples, m=3a, including layer thickness, collection time, layer volume, layer dry weight, PAH mass concentration, nutrient content and total number of microbial species. d represents the monitoring point number corresponding to each deep sample, d∈{1, 2, 3, ..., a}, where the layer thickness and collection time of deep samples of all monitoring points in the i-th target area are the same;

[0045] Step 4: According to the deep sample data set, analyze the density difference Mdc and index difference Zbc between different deep samples, then set the fixed range of density difference threshold MDY and index difference threshold ZBY to determine whether there is an abnormality in the deep samples of the monitoring point, and output the corresponding judgment result;

[0046] The density difference Mdc calculation process is as follows:

[0047] S31. According to the deep sample data set corresponding to the i-th target area, the layered volume of the g-th deep sample at the f-th monitoring point is marked as The layered dry weight of the g-th deep sample at the f-th monitoring point is marked as

[0048] S32, calculate the deep density of the g-th deep sample at the f-th monitoring point The calculation formula is as follows:

[0049]

[0050] S33. According to S32, the deep density of the g+1th deep sample at the fth monitoring point is calculated. and the deep density of the g+2th deep sample at the fth monitoring point

[0051]

[0052] In the formula, It represents the average surface density of surface samples in the i-th target area. According to the formula, the density difference between each layer is calculated, which provides a scientific basis for identifying abnormal areas.

[0053] The calculation process of the indicator difference Zbc is as follows:

[0054] S41. According to the deep sample data set corresponding to the i-th target area, the PAH mass concentration of the g-th deep sample at the f-th monitoring point is marked as The nutrient content of the g-th deep sample at the f-th monitoring point is marked as The total number of microbial species in the g-th deep sample at the f-th monitoring point is marked as

[0055]

[0056] In the formula, represents the mass concentration of PAHs in the g-1th deep sample at the fth monitoring point, represents the difference in PAH mass concentration between two deep samples, represents the nutrient content of the g-1th deep sample at the fth monitoring point, Indicates the difference in nutrient content between two deep samples. represents the total number of microbial species in the g-1th deep sample at the fth monitoring point, It represents the difference in the total number of microbial species between two deep samples. The indicator difference Zbc directly reflects the soil pollution status and its potential impact;

[0057] If any value in the density difference Mdc exceeds the density difference threshold MDY or the mass concentration difference of polycyclic aromatic hydrocarbons in the index difference Zbc exceeds the index difference threshold ZBY, it means that there is an abnormality in the deep sample of the monitoring point, accurately locating the problem area and comprehensively reflecting the health status of the regional soil environment, providing a scientific basis and operational plan for soil pollution monitoring, environmental assessment and ecological restoration.

[0058] See also Figure 2 ,The soil environment monitoring system based on stratified sampling includes a data acquisition module and an intelligent monitoring module;

[0059] The data collection module is composed of a surface data unit and a deep data unit. The surface data unit is used to collect surface sample data sets, which include the collected data of surface samples of all monitoring points. The deep data unit is used to collect deep sample data sets, which include the indicator data of all deep samples.

[0060] The intelligent monitoring module consists of a surface analysis unit, a deep analysis unit and an environmental management unit. The surface analysis unit analyzes and generates the corresponding surface density BM based on the surface sample data set, and then sets a fixed range of fluctuation threshold BDY and extreme difference threshold JCY to determine whether all monitoring points are homogeneous, and transmits the judgment results to the deep analysis unit through the network, covering soil characteristics in multiple dimensions. The deep analysis unit analyzes the density difference Mdc and index difference Zbc between samples of different depths for the monitoring points with homogeneity in the deep sample data set. The deep analysis unit sets a fixed range of density difference threshold MDY and index difference threshold ZBY, and then combines the density difference Mdc and index difference Zbc to determine whether there are abnormalities in the deep samples of the monitoring points, and outputs the corresponding judgment results. The environmental monitoring has high accuracy.

[0061] Example 1: In this experiment, a target area with a collection depth of 10 cm was selected as the experimental object. After testing, the target area was set with three monitoring points A, B, and C. The collection depth, collection time, and volume of the surface samples at each monitoring point were the same. The volume of the three monitoring points was 100 cm 3 , the dry weight of the surface sample at monitoring point A is 100g, the dry weight of the surface sample at monitoring point B is 110g, and the dry weight of the surface sample at monitoring point C is 120g. The calculation formula for the surface density BM of all surface samples in the target area is as follows:

[0062]

[0063] In the formula, 1g / cm 3 Indicates the surface density BM of the surface sample at monitoring point A A , 1.1g / cm 3 Indicates the surface density BM of the surface sample at monitoring point B B , 1.2g / cm 3 Indicates the surface density BM of the surface sample at monitoring point C C .

[0064] Example 2: In this experiment, target areas with collection depths of 20 cm, 40 cm, 60 cm and 80 cm were selected as experimental objects. The collection depth of 20 cm corresponds to the surface sample, the collection depth of 40 cm corresponds to the deep sample 1, the collection depth of 60 cm corresponds to the deep sample 2, and the collection depth of 80 cm corresponds to the deep sample 3. After testing, the average surface density of the surface samples is 1.5 g / cm 3 , the surface density of deep sample 1 is 2.5g / cm 3 The surface density of deep sample 2 is 3.6 g / cm 3 The surface density of deep sample 3 is 4.2 g / cm 3 , density difference threshold MDY is 0.8-1.0g / cm 3 , the calculation formula of the target area density difference Mdc is as follows:

[0065]

[0066] In the formula, 1.0g / cm 3 Represents the density difference between the surface sample and the deep sample, 1.1 g / cm 3 Indicates the density difference between deep sample 1 and deep sample 2, 0.6 g / cm 3 Represents the density difference between deep sample two and deep sample three.

[0067] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A soil environment monitoring method based on stratified sampling, characterized in that: The following steps are involved: Step 1: N target areas are set, and several monitoring points are set in each target area. Surface samples are collected from each monitoring point to form a surface sample data set; Step 2: According to the surface sample data set, analyze the surface density BM of the surface samples in each target area, and then set the fixed range fluctuation threshold BDY and the range threshold JCY to determine whether all monitoring points in a single area are homogeneous. If not, return to step 1. If so, continue to step 3. Step 3: Conduct stratified collection of deep samples at homogeneous monitoring points, and obtain the indicator data of all deep samples through monitoring devices to form a deep sample data set; Step 4: According to the deep sample data set, analyze the density difference Mdc and index difference Zbc between different deep samples, then set the fixed range of density difference threshold MDY and index difference threshold ZBY to determine whether there are any abnormalities in the deep samples of the monitoring point, and output the corresponding judgment results.

2. The soil environment monitoring method based on stratified sampling according to claim 1 is characterized in that: In step 1, the expression of the surface sample data set is {Y1 q 、Y2 q 、Y3 q 、...、Yn q }, Y1 s To Yn s are the collection data of surface samples from the first to the nth monitoring points, respectively. The collection data include collection depth, collection time, volume and dry weight. q represents the area number corresponding to each surface sample. In a single area, the collection depth, collection time and volume of surface samples of all monitoring points are the same.

3. The soil environment monitoring method based on stratified sampling according to claim 2 is characterized in that: In step 2, the calculation process of surface density BM is as follows: S11. According to the sample data set, count the collected data of surface samples of all monitoring points in the i-th target area, i∈N, the total number of monitoring points in the i-th target area is a, and the corresponding number of surface samples is also a, and mark the volume of all surface samples in the i-th target area as {tj1, tj2, tj3, ..., tj a }, tj1 to tj a are the volumes of the first to ath surface samples respectively, and the dry weights of all surface samples in the i-th target area are marked as {gz1, gz2, gz3, ..., gz a }, gz1 to gz a are the dry weights of the first to ath surface samples respectively; S12. Calculate the surface density BM of the kth surface sample in the i-th target area k ; S13. According to S12, calculate the surface density BM of all surface samples in the i-th target area. i .

4. The soil environment monitoring method based on stratified sampling according to claim 3 is characterized in that: In the step 2, the judgment process is as follows: S21, based on the surface density BM of all surface samples in the i-th target area i , calculate the fluctuation rate Bdl of the surface density in the i-th target area i ; S22, according to the surface density BM of all surface samples in the i-th target area i , calculate the range JC of the surface density in the i-th target area i ; S23, according to the fluctuation rate Bdl of the surface density in the i-th target area i and the range JC of the surface density in the i-th target area i , determine whether all monitoring points in the i-th target area are homogeneous; If the fluctuation rate of surface density in the i-th target area is Bdl i Exceeding the fluctuation threshold BDY or the range JC of the surface density in the i-th target area i Exceeding the extreme value threshold JCY indicates that the monitoring points in the target area are not homogeneous, and it is necessary to reset the monitoring points and collect surface samples at each monitoring point until the fluctuation rate Bdl of the surface density in the target area is included in the fluctuation threshold BDY, and the extreme value JC of the surface density in the target area is included in the extreme value threshold JCY.

5. The soil environment monitoring method based on stratified sampling according to claim 4 is characterized in that: In the step three, deep samples are collected in layers for a number of homogeneous monitoring points in the ith target area. In the ith target area, the monitoring points are numbered {1, 2, 3, ..., a}, and each monitoring point corresponds to three deep samples. For a single monitoring point, the collection depth of each deep sample increases in sequence as a multiple of the surface sample collection depth.

6. The soil environment monitoring method based on stratified sampling according to claim 5 is characterized in that: In step 3, the deep sample data set corresponding to the i-th target area is expressed as {S1 d 、S2 d 、S3 d 、...、Sm d },S1 s To Sn s are the indicator data of the first to mth deep samples, m=3a, including layer thickness, collection time, layer volume, layer dry weight, PAH mass concentration, nutrient content and total number of microbial species. d represents the monitoring point number corresponding to each deep sample, d∈{1, 2, 3, ..., a}, where the layer thickness and collection time of deep samples at all monitoring points in the ith target area are the same.

7. The soil environment monitoring method based on stratified sampling according to claim 6 is characterized in that: In step 4, the density difference Mdc calculation process is as follows: S31. According to the deep sample data set corresponding to the i-th target area, the layered volume of the g-th deep sample at the f-th monitoring point is marked as The layered dry weight of the g-th deep sample at the f-th monitoring point is marked as S32, calculate the deep density of the g-th deep sample at the f-th monitoring point S33. According to S32, the deep density of the g+1th deep sample at the fth monitoring point is calculated. and the deep density of the g+2th deep sample at the fth monitoring point 8. The soil environment monitoring method based on stratified sampling according to claim 7 is characterized in that: In step 4, the calculation process of the index difference Zbc is as follows: S41. According to the deep sample data set corresponding to the i-th target area, the PAH mass concentration of the g-th deep sample at the f-th monitoring point is marked as The nutrient content of the g-th deep sample at the f-th monitoring point is marked as The total number of microbial species in the g-th deep sample at the f-th monitoring point is marked as 9. The soil environment monitoring method based on stratified sampling according to claim 8 is characterized in that: In the step 4, if any value in the density difference Mdc exceeds the density difference threshold MDY or the PAH mass concentration difference in the index difference Zbc exceeds the index difference threshold ZBY, it indicates that the deep sample at the monitoring point is abnormal.

10. A soil environment monitoring system based on stratified sampling, applied to the soil environment monitoring method based on stratified sampling according to any one of claims 1 to 9, characterized in that: Including data acquisition module and intelligent monitoring module; The data acquisition module is composed of a surface data unit and a deep data unit. The surface data unit is used to collect a surface sample data set, which includes the collected data of surface samples of all monitoring points. The deep data unit is used to collect a deep sample data set, which includes the indicator data of all deep samples. The intelligent monitoring module consists of a surface analysis unit, a deep analysis unit and an environmental management unit. The surface analysis unit analyzes and generates the corresponding surface density BM based on the surface sample data set, and then sets a fixed range of fluctuation threshold BDY and extreme difference threshold JCY to determine whether all monitoring points are homogeneous, and transmits the judgment result to the deep analysis unit through the network. The deep analysis unit analyzes the density difference Mdc and index difference Zbc between different deep samples for the monitoring points with homogeneity in the deep sample data set. The deep analysis unit sets a fixed range of density difference threshold MDY and index difference threshold ZBY, and then combines the density difference Mdc and index difference Zbc to determine whether there is an abnormality in the deep sample of the monitoring point, and outputs the corresponding judgment result.

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