A method and system for detecting heavy metal pollution in crop soil

Abstract

The present invention relates to the technical field of soil heavy metal detection, and discloses a method and system for detecting heavy metal pollution in crop soil, wherein the method steps include the following: collecting samples including soil around crop roots and rhizosphere microbial communities from farmland; extracting microbial DNA from the collected samples using molecular biology techniques; sequencing microbial DNA using high-throughput sequencing technology to obtain composition information of the microbial community; analyzing microbial symbiotic networks using computer simulation methods to identify microbial populations and symbiotic relationships related to heavy metal pollution; establishing a heavy metal pollution detection model based on the analysis results of the microbial symbiotic network, and evaluating and detecting heavy metal pollution in crop soil. The method and system for detecting heavy metal pollution in crop soil can quickly and accurately detect heavy metal pollution in crop soil, and can be widely used in agricultural production to improve soil health and agricultural product quality.

Description

Technical Field

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

[0002] Soil is mainly composed of inorganic matter (small particles of rock that have weathered into sand, silt, and clay), organic matter (decomposed plants, animal remains, and fertilizers are collectively referred to as humus), water, and air. Soil samples used for testing generally contain only inorganic and organic components. The digestion method adopted by the national standard is to use various acids to destroy the complex soil structure under high temperature, and finally make a clear, transparent, and instrument-friendly aqueous solution. As the main carrier of agricultural production and an important part of the ecological environment, the quality of soil is closely related to people's lives. At present, the problem of heavy metal pollution in soil has become an increasingly serious ecological problem. The eight most common heavy metals in soil heavy metal detection are Cu, Zn, Pb, Cd, Cr, Ni, As, and Hg. With the increasing development of industrialization, the increase in various coal-fired and garbage incineration sites, the increase in sewage sources, and the increase in traffic vehicles have had a great impact on crop soil. Some farmlands close to roads, coal-fired and garbage incineration sites, and with sewage sources flowing into them have caused great damage, and the detection of heavy metals is varied.

[0003] At present, heavy metal pollution in crop soil poses a serious threat to the environment and human health. Existing soil heavy metal pollution detection methods mainly rely on physical and chemical analysis, including soil sampling and chemical analysis. However, these methods have some limitations, such as cumbersome operation, high cost, and the need for professional personnel, making it difficult to quickly and accurately detect heavy metal pollution in crop soil. Therefore, it is necessary to design corresponding technical solutions to solve this problem. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a method and system for detecting heavy metal pollution in crop soil, which solves the technical problems of complicated operation, high cost, need for professional personnel, and difficulty in rapid and accurate detection of heavy metal pollution in crop soil.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented by the following technical scheme: a method for detecting heavy metal pollution in crop soil, the method steps include the following:

[0008] S1. Sample collection: Collect samples from farmland, including soil around crop roots and rhizosphere microbial communities;

[0009] S2. Microbial DNA extraction: Use molecular biology techniques to extract microbial DNA from collected samples;

[0010] S3. Analysis of microbial symbiotic relationships: Sequencing microbial DNA using high-throughput sequencing technology to obtain information on the composition of the microbial community;

[0011] S4. Data processing and simulation: Use computer simulation methods to analyze the microbial symbiotic network and identify the microbial populations and symbiotic relationships related to heavy metal pollution;

[0012] S5. Heavy metal pollution detection and assessment: Based on the analysis results of the microbial symbiotic network, a heavy metal pollution detection model is established, and heavy metal pollution in crop soil is evaluated and detected;

[0013] S6. Result output: Determine the degree of heavy metal pollution and potential risks in the soil, output the test results to the user based on the analysis results, evaluate and interpret the results, and provide corresponding suggestions and measures, including adjusting crop planting plans and taking soil remediation measures.

[0014] Preferably, in step S1, the specific method steps for collecting soil samples around the root system of crops and rhizosphere microbial community samples include the following:

[0015] S101, design a microbial trap device that is installed in the soil around the roots of crops to adsorb and collect microorganisms while keeping them in situ. The device includes a membrane shell that is permeable to water and gas, and an adsorption medium of activated carbon. It is placed in multiple locations in the field to cover different soil types and crop combinations;

[0016] S102, the microbial samples collected in the device are protected and identified to ensure the retention and tracking of their original characteristics. Tiny soluble packaging materials are used in the device, which can gradually dissolve over time and release the samples. The packaging materials are made of biodegradable polymers or food-grade materials. When collecting samples, each device is accompanied by a unique identification code, using RFID tags for tracking and identifying samples in subsequent processing and analysis;

[0017] S103, design an automated collection system, including multiple microbial trap devices and a central control unit. The central control unit monitors soil environmental conditions, including humidity, temperature, and light, through sensors, and controls the opening and closing of the trap devices according to the set collection strategy. The collected samples are sent to the laboratory or analysis equipment for subsequent DNA extraction and sequencing.

[0018] Preferably, in step S2, the specific method steps of extracting microbial DNA from the collected sample using molecular biology technology include the following:

[0019] S201, microbial cell lysis: the collected sample contains microbial cells, and the microbial cells are lysed to release DNA;

[0020] Use sonication or enzymatic lysis methods. Sonication uses the mechanical action of high-frequency sound waves to break the cell wall, thereby releasing DNA.

[0021] Enzymatic lysis uses specific enzymes, including proteinase K and lyase, to degrade cell walls and membranes and release DNA;

[0022] S202, DNA purification: DNA purification in the presence of other cellular components, proteins, and impurities in the lysed sample, using affinity matrices including specific antibodies or oligonucleotide sequences to selectively capture microbial DNA, or using ionic liquids or nanomaterials to achieve efficient DNA purification;

[0023] S203, quality and concentration testing: The extracted microbial DNA is tested for quality and concentration to ensure the integrity and suitability of the sample. Fluorescence probe method or mass spectrometry technology is used to perform quality and concentration testing to ensure the integrity and suitability of the sample.

[0024] Preferably, in step S3, the specific method steps for sequencing the microbial DNA by high-throughput sequencing technology include the following:

[0025] S301, microbial DNA sample preparation: DNA samples obtained from microbial DNA are labeled with aptamers, and primers with special sequences that are compatible with the sequencing platform are used to connect the aptamers to the DNA samples to form aptamer-DNA complexes, which are achieved using specially designed aptamers and ligases;

[0026] S302, amplification of microbial DNA samples: using a cyclic linear amplification strategy to amplify the aptamer-DNA complex, cyclic linear amplification can achieve efficient DNA amplification without PCR, and a combination of primers and enzymes can be used to cyclically amplify the aptamer-DNA complex in vitro to produce a large amount of amplified products;

[0027] S303, high-throughput sequencing: The amplified microbial DNA samples are sent to a high-throughput sequencing platform for sequencing. During the sequencing process, sequencing primers and sequencing enzymes are used as well as high-throughput sequencing technology to sequence the samples, which can achieve high-throughput and high-precision sequencing and generate a large amount of sequencing data;

[0028] S304, Data Processing and Analysis: Use bioinformatics tools and algorithms to process and analyze sequencing data, including quality control, sequence assembly, removal of low-quality sequences, sequence alignment, and species annotation to identify microbial populations and symbiotic relationships associated with heavy metal pollution.

[0029] Preferably, in step S4, the method steps for analyzing the microbial symbiotic network include the following:

[0030] S401, constructing a symbiotic network: taking microbial species as nodes of the network and the interaction relationships between microorganisms as connections of the network, the network is constructed based on the microbial composition information obtained from microbial DNA sequencing data;

[0031] S402, network feature analysis: feature analysis of the constructed symbiotic network to reveal the structure and characteristics of the network, and to understand the interaction patterns and relationships between microorganisms by analyzing the network features;

[0032] S403, community discovery: using community discovery algorithms to divide closely interacting microbial groups in the symbiotic network into different communities or modules;

[0033] S404, Identification of Important Nodes: Identify microbial species that play a key role in heavy metal pollution by calculating node importance indicators, including degree centrality and surrounding betweenness centrality of nodes;

[0034] S405, prediction and simulation: Based on the existing microbial symbiotic network and heavy metal pollution data, use computer models for prediction and simulation, including simulating the interaction process of microbial species in the symbiotic network and predicting the changes in microbial community composition under different heavy metal concentrations.

[0035] Preferably, in step S5, the specific methods of evaluation and detection include the following:

[0036] S501, collecting the microbial community composition information obtained in step S3 as input data, and collecting the heavy metal content data related to the crop soil sample as a label;

[0037] S502, analyzing the microbial symbiotic network using the computer simulation method in step S4 to identify microbial populations and symbiotic relationships associated with heavy metal pollution;

[0038] S503, selecting features related to heavy metal pollution, microbial species, functional genes or metabolic pathways related to heavy metal tolerance from the results of microbial symbiosis network analysis, and performing data preprocessing on the selected features, including data cleaning and standardization;

[0039] S504, using machine learning or statistical models, including support vector machines, random forests, and neural networks, to train and optimize the models based on the preprocessed data, with the goal of establishing the relationship between heavy metal content and microbial symbiotic network characteristics;

[0040] S505, verifying and evaluating the model using cross validation or holdout method, and evaluating the performance indicators of the model including accuracy, recall rate and precision;

[0041] S506, applying the trained model to new unknown samples, predicting the degree of heavy metal pollution and potential risks in crop soils based on the characteristics of the microbial symbiosis network, and classifying heavy metal pollution into different levels or conducting quantitative assessments based on the evaluation results.

[0042] Preferably, in step S6, the specific method steps for determining the degree of heavy metal pollution and potential risk in the soil include the following:

[0043] S601, Determination of heavy metal content: Use chemical analysis methods, including atomic absorption spectrometry and inductively coupled plasma emission spectrometry, to determine the content of heavy metal elements in the soil and determine whether the concentration of heavy metals in the soil exceeds the specified safety standard;

[0044] S602, Soil Environmental Quality Evaluation Standard: Based on the laws and standards of different regions, the heavy metal content in the soil is evaluated with reference to the soil environmental quality evaluation standard. The standard provides indicators of background values, soil quality classification and limit values ​​for different heavy metal elements to judge the degree of soil pollution and potential risks;

[0045] S603, Pollution Index Calculation: The pollution index is used to evaluate the degree of heavy metal pollution in the soil. The index is based on the concentration data of different heavy metal elements, and takes into account their toxicity and environmental effects, and a comprehensive evaluation index is obtained through mathematical calculation;

[0046] S604, Biological monitoring: assess the toxicity and potential risks of heavy metals in soil by measuring biological indicators of microorganisms, plants or animals in the soil, measure the diversity of soil microorganisms, indicators of soil enzyme activity, as well as crop growth conditions and heavy metal accumulation in plant tissues;

[0047] S605, Soil remediation needs assessment: Based on the assessment results of the degree of heavy metal pollution and potential risks, the soil remediation needs are assessed. The remediation methods include soil improvement and phytoremediation, and corresponding suggestions and measures are provided according to the specific circumstances.

[0048] Preferably, in step S6, the specific method steps for outputting the heavy metal pollution detection result to the user include the following:

[0049] S6001, deploys a soil monitoring system based on an intelligent sensor network. The sensors are directly buried in the soil to monitor the content of heavy metal elements in the soil in real time. Each sensor is equipped with a microprocessor and communication module to collect, process and transmit data in real time;

[0050] S6002, the sensor transmits the collected data to the central server through wireless communication technology, and the coverage of the sensor network is flexibly expanded according to the specific situation to cover various areas in the farmland;

[0051] S6003, after receiving the data transmitted by the sensor, the central server processes and analyzes the data, using machine learning and data mining techniques, combined with a large-scale soil heavy metal data set, to establish a high-precision heavy metal pollution detection model to automatically identify the characteristics and patterns related to heavy metal pollution;

[0052] S6004, develop a user mobile application through which users can obtain the test results of soil heavy metal pollution in real time. The application provides an intuitive interface to display the degree of heavy metal pollution and potential risks in different farmland areas. Users can view the test results of different locations through maps or lists;

[0053] S6005, the application provides personalized suggestions and measures based on the user's geographic location and test results. The suggestions include adjusting crop planting plans, taking soil remediation measures, and choosing appropriate agricultural management methods. The application continuously updates and optimizes the suggestions based on real-time data based on user feedback and interaction.

[0054] A detection system for heavy metal pollution in crop soil, comprising:

[0055] The microbial sample collection unit is used to collect samples containing microbial DNA from crop soil;

[0056] The microbial DNA extraction unit is used to extract microbial DNA from the collected samples;

[0057] The high-throughput sequencing unit is used to perform high-throughput sequencing on the extracted microbial DNA;

[0058] The data processing unit is used to process the sequencing results to obtain the composition information of the microbial community;

[0059] The symbiotic network analysis unit is used to construct a microbial symbiotic network based on the microbial community composition information and identify microbial characteristics associated with heavy metal pollution;

[0060] The model building unit is used to train the detection model based on the characteristics of the microbial symbiosis network and heavy metal data;

[0061] The detection unit is used to make heavy metal detection judgments based on the detection model and new sample characteristics;

[0062] The output unit is used to output the detection results.

[0063] (III) Beneficial effects

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] (1) By analyzing the symbiotic relationship between microorganisms around crop roots and combining existing molecular biology techniques and computer simulation methods, rapid and accurate detection of heavy metal pollution in crop soil can be achieved. This can be widely used in agricultural production to improve soil health and agricultural product quality.

[0066] (2) Analyze the microbial communities around the roots of crops by using microbial symbiosis, thereby indirectly detecting heavy metal pollution in crop soils. The construction and analysis of microbial symbiosis networks can reveal the interactions among microbial populations and their associations with environmental factors (including heavy metal pollution). Through analysis based on microbial symbiosis, non-destructive, rapid, and accurate detection of heavy metal pollution in crop soils can be achieved.

[0067] (3) Microbial symbiosis is used as an indicator for heavy metal pollution detection, which has higher sensitivity and accuracy than traditional methods; molecular biology technology and computer simulation methods are introduced, combined with microbial symbiosis analysis to achieve rapid and accurate detection of heavy metal pollution in crop soils; non-destructive detection methods are used, and there is no need to sample and process the soil, reducing interference and damage to the soil environment; by establishing a heavy metal pollution detection model, it is possible to accurately evaluate and detect heavy metal pollution in crop soils, providing a scientific basis for agricultural production; it has the ability to integrate high-throughput sequencing technology and computer simulation methods, can process large amounts of microbial data and perform complex computational analysis, and improve detection efficiency and accuracy.

[0068] (4) Based on the microbial symbiotic relationship, it can not only detect heavy metal pollution in crop soil, but also reveal the structural and functional information of microbial communities, providing valuable data for further research on soil ecosystems; compared with traditional physical and chemical analysis methods, it has the advantages of simple operation, low cost, rapidity and high efficiency, and can be widely used in actual agricultural production; it uses microbial symbiotic relationship as an indicator of heavy metal pollution, opens up new ideas and methods, and helps promote the development of the field of crop soil pollution detection.

[0069] (5) It can be used for monitoring heavy metal pollution in farmland soil and evaluating crop production safety; it can be used for soil environmental monitoring and soil remediation program evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a schematic diagram of the steps of the overall detection method of the present invention;

[0071] Figure 2 A schematic diagram of the specific method steps for collecting soil and rhizosphere microbial community samples around crop roots of the present invention;

[0072] Figure 3 A schematic diagram of the specific method steps of extracting microbial DNA from a collected sample using molecular biological technology according to the present invention;

[0073] Figure 4 This is a schematic diagram of the specific method steps for sequencing microbial DNA using high-throughput sequencing technology of the present invention;

[0074] Figure 5 A schematic diagram of the method steps for analyzing a microbial symbiotic network of the present invention;

[0075] Figure 6 A schematic diagram of a specific method for evaluation and detection of the present invention;

[0076] Figure 7 A schematic diagram of the specific method steps for determining the degree of heavy metal pollution and potential risks in soil according to the present invention;

[0077] Figure 8 A schematic diagram of the specific method steps for outputting the heavy metal pollution detection results to the user according to the present invention;

[0078] Fig. 9 It is a system schematic diagram of the present invention. DETAILED DESCRIPTION

[0079] 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.

[0080] See also Figure 1-Figure 9 The embodiment of the present invention provides a technical solution: a method for detecting heavy metal pollution in crop soil, the method steps include the following:

[0081] S1. Sample collection: Collect samples from farmland, including soil around crop roots and rhizosphere microbial communities;

[0082] S2. Microbial DNA extraction: Use molecular biology techniques to extract microbial DNA from collected samples;

[0083] S3. Analysis of microbial symbiotic relationships: Sequencing microbial DNA using high-throughput sequencing technology to obtain information on the composition of the microbial community;

[0084] S4. Data processing and simulation: Use computer simulation methods to analyze the microbial symbiotic network and identify the microbial populations and symbiotic relationships related to heavy metal pollution;

[0085] S5. Heavy metal pollution detection and assessment: Based on the analysis results of the microbial symbiotic network, a heavy metal pollution detection model is established, and heavy metal pollution in crop soil is evaluated and detected;

[0086] S6. Result output: Determine the degree of heavy metal pollution and potential risks in the soil, output the test results to the user based on the analysis results, evaluate and interpret the results, and provide corresponding suggestions and measures, including adjusting crop planting plans and taking soil remediation measures.

[0087] In a further improvement, in step S1, the specific method steps for collecting soil samples around the root system of crops and rhizosphere microbial community samples include the following:

[0088] S101, design a microbial trap device that is installed in the soil around the roots of crops to adsorb and collect microorganisms while keeping them in situ. The device includes a membrane shell that is permeable to water and gas, and an adsorption medium of activated carbon. It is placed in multiple locations in the field to cover different soil types and crop combinations;

[0089] S102, the microbial samples collected in the device need to be protected and identified to ensure the retention and tracking of their original characteristics. Tiny soluble packaging materials are used in the device, which can gradually dissolve over time and release the samples. The packaging materials are made of biodegradable polymers or food-grade materials. When collecting samples, each device is accompanied by a unique identification code, using RFID tags for tracking and identification of samples in subsequent processing and analysis;

[0090] S103, improve collection efficiency and reduce manual operations, design an automated collection system, including multiple microbial trap devices and a central control unit. The central control unit monitors soil environmental conditions, including humidity, temperature, and light, through sensors, and controls the opening and closing of the trap devices according to the set collection strategy. The collected samples are sent to the laboratory or analysis equipment for subsequent DNA extraction and sequencing.

[0091] The combination of a microbial trap device, sample protection and identification, and an automated collection system enables efficient and accurate collection of soil and rhizosphere microbial communities around crop roots.

[0092] Further improved, in step S2, the specific method steps of extracting microbial DNA from the collected sample using molecular biology technology include the following:

[0093] S201, microbial cell lysis: the collected sample contains microbial cells, and the microbial cells are lysed to release DNA;

[0094] Traditional cell lysis methods include mechanical disruption, chemical lysis, and thermal lysis, using sonication or enzymatic lysis. Sonication uses the mechanical action of high-frequency sound waves to break the cell wall, thereby releasing DNA.

[0095] Enzymatic lysis uses specific enzymes, including proteinase K and lyase, to degrade cell walls and membranes and release DNA;

[0096] S202, DNA purification: There are other cell components, proteins and impurities in the lysed sample. In order to obtain pure microbial DNA, DNA purification is performed. Traditional DNA purification methods include phenol / chloroform extraction, silica gel column chromatography and magnetic bead purification, etc. Affinity matrices including specific antibodies or oligonucleotide sequences are used to selectively capture microbial DNA, or ionic liquids or nanomaterials are used to achieve efficient DNA purification;

[0097] S203, quality and concentration testing: The extracted microbial DNA needs to be tested for quality and concentration to ensure the integrity and suitability of the sample. Traditional DNA quality and concentration testing methods include colorimetry, gel electrophoresis and spectrophotometry. Quality and concentration testing using fluorescent probes or mass spectrometry techniques are used to ensure the integrity and suitability of the sample and provide faster, more accurate and sensitive DNA quality and concentration test results.

[0098] In order to achieve efficient and accurate DNA extraction and ensure the integrity and purity of the samples, it is possible to efficiently and accurately extract microbial DNA from the collected samples, providing a non-traditional and more efficient DNA extraction solution for the development of heavy metal pollution detection methods in crop soils.

[0099] Further improved, in step S3, the specific method steps of sequencing the microbial DNA by high-throughput sequencing technology include the following:

[0100] S301, microbial DNA sample preparation: DNA samples obtained from microbial DNA are labeled with aptamers. Aptamers are primers with special sequences that are compatible with sequencing platforms. These aptamers are connected to DNA samples to form aptamer-DNA complexes, which are achieved using specially designed aptamers and ligases.

[0101] S302, amplification of microbial DNA samples: using a cyclic linear amplification strategy to amplify the aptamer-DNA complex, cyclic linear amplification can achieve efficient DNA amplification without PCR, and a combination of primers and enzymes can be used to cyclically amplify the aptamer-DNA complex in vitro to produce a large amount of amplified products;

[0102] S303, high-throughput sequencing: The amplified microbial DNA samples are sent to a high-throughput sequencing platform for sequencing. During the sequencing process, sequencing primers and sequencing enzymes are used as well as high-throughput sequencing technology to sequence the samples, which can achieve high-throughput and high-precision sequencing and generate a large amount of sequencing data;

[0103] S304, Data Processing and Analysis: Use bioinformatics tools and algorithms to process and analyze sequencing data, including quality control, sequence assembly, removal of low-quality sequences, sequence alignment, and species annotation to identify microbial populations and symbiotic relationships associated with heavy metal pollution.

[0104] The combination of adapter labeling, cyclic linear amplification and existing high-throughput sequencing technology can achieve high-throughput sequencing of microbial DNA, providing a non-traditional, more efficient and more accurate microbial DNA sequencing solution for the detection of heavy metal pollution in crop soil.

[0105] Traditional high-throughput sequencing technologies mainly include Illumina's sequencing technology (such as Illumina HiSeq and MiSeq series) and Ion Torrent's sequencing technology. These technologies usually rely on connecting DNA fragments to a vector and sequencing through PCR amplification and sequencing reactions.

[0106] Further improved, in step S4, the method steps for analyzing the microbial symbiotic network include the following:

[0107] S401, constructing a symbiotic network: taking microbial species as nodes of the network and the interaction relationships between microorganisms as connections of the network, the network is constructed based on the microbial composition information obtained from microbial DNA sequencing data;

[0108] S402, network feature analysis: feature analysis of the constructed symbiotic network to reveal the structure and characteristics of the network. Network features include degree centrality (indicating the number of connections of a node), betweenness centrality (indicating the degree of mediation of a node in the network) and clustering coefficient (indicating the degree of aggregation between nodes). By analyzing network features, the interaction patterns and relationships between microorganisms can be understood;

[0109] S403, community discovery: using community discovery algorithms, the closely interacting microbial populations in the symbiotic network were divided into different communities or modules, which consisted of closely interacting microorganisms and had a common response ability to heavy metal pollution;

[0110] S404, Identification of important nodes: Identify microbial species that play a key role in heavy metal pollution by calculating the importance indicators of nodes, including the degree centrality and surrounding betweenness centrality of nodes. These microbial species are associated with the biotransformation, resistance or detoxification process of heavy metals.

[0111] S405, prediction and simulation: Based on the existing microbial symbiotic network and heavy metal pollution data, use computer models for prediction and simulation, including simulating the interaction process of microbial species in the symbiotic network and predicting the changes in microbial community composition under different heavy metal concentrations.

[0112] Through the above steps, computer simulation methods can be used to analyze the microbial symbiotic network, revealing the microbial populations and symbiotic relationships related to heavy metal pollution, providing an in-depth understanding of heavy metal pollution in crop soils, and providing a scientific basis for the detection and evaluation of heavy metal pollution.

[0113] Further improved, in step S5, the established heavy metal pollution detection model evaluates and detects heavy metal pollution in crop soil based on the analysis results of the microbial symbiotic network, and the specific methods of evaluation and detection include the following:

[0114] S501, collecting the microbial community composition information obtained in step S3 as input data, and collecting the heavy metal content data related to the crop soil sample as a label;

[0115] S502, analyzing the microbial symbiotic network using the computer simulation method in step S4, identifying microbial populations and symbiotic relationships related to heavy metal pollution, wherein the related microbial populations and symbiotic relationships show sensitivity or tolerance to heavy metals or are related to the transformation and migration process of heavy metals;

[0116] S503, selecting features related to heavy metal pollution, microbial species, functional genes or metabolic pathways related to heavy metal tolerance from the results of microbial symbiosis network analysis, and performing data preprocessing on the selected features, including data cleaning and standardization;

[0117] S504, using machine learning or statistical models, including support vector machines, random forests, and neural networks, to train and optimize the models based on the preprocessed data, with the goal of establishing the relationship between heavy metal content and microbial symbiotic network characteristics;

[0118] S505, verifying and evaluating the model using cross validation or holdout method, and evaluating the performance indicators of the model including accuracy, recall rate and precision;

[0119] S506, applying the trained model to new unknown samples, predicting the degree of heavy metal pollution and potential risks in crop soils based on the characteristics of the microbial symbiosis network, and classifying heavy metal pollution into different levels or conducting quantitative assessments based on the evaluation results.

[0120] Further improved, in step S6, the specific method steps for determining the heavy metal pollution degree and potential risk in the soil include the following:

[0121] S601, Determination of heavy metal content: Use chemical analysis methods, including atomic absorption spectrometry and inductively coupled plasma emission spectrometry, to determine the content of heavy metal elements in the soil. Heavy metals include lead (Pb), cadmium (Cd), mercury (Hg), and chromium (Cr). According to relevant national and regional standards, determine whether the concentration of heavy metals in the soil exceeds the specified safety standards;

[0122] S602, Soil Environmental Quality Evaluation Standard: Based on the laws and standards of different regions, the heavy metal content in the soil is evaluated with reference to the soil environmental quality evaluation standard. The standard provides indicators of background values, soil quality classification and limit values ​​for different heavy metal elements to judge the degree of soil pollution and potential risks;

[0123] S603, Pollution Index Calculation: Use pollution index (single pollution index, comprehensive pollution index) to evaluate the pollution degree of heavy metals in soil. The index is based on the concentration data of different heavy metal elements, and takes into account their toxicity and environmental effects, and a comprehensive evaluation index is obtained through mathematical calculation;

[0124] S604, Biological monitoring: assess the toxicity and potential risks of heavy metals in soil by measuring biological indicators of microorganisms, plants or animals in the soil, measure the diversity of soil microorganisms, indicators of soil enzyme activity, as well as crop growth conditions and heavy metal accumulation in plant tissues;

[0125] S605, Soil remediation needs assessment: Based on the assessment results of the degree of heavy metal pollution and potential risks, the soil remediation needs are assessed. The remediation methods include soil improvement and phytoremediation, and corresponding suggestions and measures are provided according to the specific circumstances.

[0126] The assessment of the degree of heavy metal pollution and potential risks in soil requires comprehensive consideration of multiple indicators and methods to obtain accurate and reliable results. In practical applications, specific assessments and interpretations should be conducted in accordance with local regulations and standards, as well as the guidance of professional environmental monitoring and assessment agencies.

[0127] Specifically, in step S6, the specific method steps for outputting the heavy metal pollution detection result to the user include the following:

[0128] S6001, deploys a soil monitoring system based on an intelligent sensor network. The sensors are directly buried in the soil to monitor the content of heavy metal elements in the soil in real time. Each sensor is equipped with a microprocessor and communication module to collect, process and transmit data in real time;

[0129] S6002, the sensor transmits the collected data to the central server through wireless communication technology (such as LoRa, NB-IoT), and the coverage of the sensor network is flexibly expanded according to the specific situation to cover various areas in the farmland;

[0130] S6003, after receiving the data transmitted by the sensor, the central server processes and analyzes the data, using machine learning and data mining techniques, combined with a large-scale soil heavy metal data set, to establish a high-precision heavy metal pollution detection model to automatically identify the characteristics and patterns related to heavy metal pollution;

[0131] S6004, develop a user mobile application through which users can obtain the test results of soil heavy metal pollution in real time. The application provides an intuitive interface to display the degree of heavy metal pollution and potential risks in different farmland areas. Users can view the test results of different locations through maps or lists;

[0132] S6005, the application provides personalized suggestions and measures based on the user's geographic location and test results. The suggestions include adjusting crop planting plans, taking soil remediation measures, and choosing appropriate agricultural management methods. The application continuously updates and optimizes the suggestions based on real-time data based on user feedback and interaction.

[0133] By combining sensor networks, data processing and analysis, mobile applications and other technologies, real-time and accurate heavy metal pollution detection and user information transmission are achieved, helping farmers, agricultural experts and decision makers to better understand the heavy metal pollution in the soil, and provide personalized suggestions and measures to deal with potential risks, playing an important role in real situations.

[0134] A detection system for heavy metal pollution in crop soil, comprising:

[0135] The microbial sample collection unit is used to collect samples containing microbial DNA from crop soil;

[0136] The microbial DNA extraction unit is used to extract microbial DNA from the collected samples;

[0137] The high-throughput sequencing unit is used to perform high-throughput sequencing on the extracted microbial DNA;

[0138] The data processing unit is used to process the sequencing results to obtain the composition information of the microbial community;

[0139] The symbiotic network analysis unit is used to construct a microbial symbiotic network based on the microbial community composition information and identify microbial characteristics associated with heavy metal pollution;

[0140] The model building unit is used to train the detection model based on the characteristics of the microbial symbiosis network and heavy metal data;

[0141] The detection unit is used to make heavy metal detection judgments based on the detection model and new sample characteristics;

[0142] The output unit is used to output the detection results.

[0143] By analyzing the symbiotic relationship of microorganisms around crop roots and combining existing molecular biology techniques and computer simulation methods, rapid and accurate detection of heavy metal pollution in crop soil can be achieved. This can be widely used in agricultural production to improve soil health and agricultural product quality.

[0144] Microbial symbiosis is used to analyze the microbial communities around the roots of crops, thereby indirectly detecting heavy metal pollution in crop soils; the construction and analysis of microbial symbiosis networks can reveal the interactions between microbial populations and their associations with environmental factors (including heavy metal pollution); through analysis based on microbial symbiosis, non-destructive, rapid and accurate detection of heavy metal pollution in crop soils can be achieved.

[0145] Microbial symbiosis is used as an indicator for heavy metal pollution detection, which has higher sensitivity and accuracy than traditional methods. Molecular biology technology and computer simulation methods are introduced, combined with microbial symbiosis analysis to achieve rapid and accurate detection of heavy metal pollution in crop soils. Non-destructive detection methods are used, and there is no need to sample and process the soil, reducing interference and damage to the soil environment. By establishing a heavy metal pollution detection model, accurate assessment and detection of heavy metal pollution in crop soils can be achieved, providing a scientific basis for agricultural production. The system has the integration capability of high-throughput sequencing technology and computer simulation methods, can process large amounts of microbial data and perform complex computational analysis, thus improving detection efficiency and accuracy.

[0146] Based on the microbial symbiotic relationship, it can not only detect heavy metal pollution in crop soil, but also reveal the structural and functional information of microbial communities, providing valuable data for further research on soil ecosystems; compared with traditional physical and chemical analysis methods, it has the advantages of simple operation, low cost, fast and high efficiency, and can be widely used in actual agricultural production; it uses microbial symbiotic relationship as an indicator of heavy metal pollution, opens up new ideas and methods, and helps promote the development of the field of crop soil pollution detection.

[0147] It can be used for monitoring heavy metal pollution in farmland soil and evaluating crop production safety; it can be used for soil environment monitoring and soil remediation program evaluation.

[0148] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0149] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for detecting heavy metal pollution in crop soil, characterized in that: The method steps include the following: S1. Sample collection: Collect samples from farmland, including soil around crop roots and rhizosphere microbial communities; S2. Microbial DNA extraction: Use molecular biology techniques to extract microbial DNA from collected samples; S3. Analysis of microbial symbiotic relationships: Sequencing microbial DNA using high-throughput sequencing technology to obtain information on the composition of the microbial community; S4. Data processing and simulation: Use computer simulation methods to analyze the microbial symbiotic network and identify the microbial populations and symbiotic relationships related to heavy metal pollution; The method steps for analyzing the microbial symbiotic network include the following: S401, constructing a symbiotic network: taking microbial species as nodes of the network and the interaction relationships between microorganisms as connections of the network, the network is constructed based on the microbial composition information obtained from microbial DNA sequencing data; S402, network feature analysis: feature analysis of the constructed symbiotic network to reveal the structure and characteristics of the network, and to understand the interaction patterns and relationships between microorganisms by analyzing the network features; S403, community discovery: using community discovery algorithms to divide closely interacting microbial groups in the symbiotic network into different communities or modules; S404, Identification of Important Nodes: Identify microbial species that play a key role in heavy metal pollution by calculating node importance indicators, including degree centrality and surrounding betweenness centrality of nodes; S405, prediction and simulation: Based on the existing microbial symbiotic network and heavy metal pollution data, use computer models to make predictions and simulations, including simulating the interaction process of microbial species in the symbiotic network and predicting the changes in microbial community composition under different heavy metal concentrations; S5. Heavy metal pollution detection and assessment: Based on the analysis results of the microbial symbiotic network, a heavy metal pollution detection model is established, and heavy metal pollution in crop soil is evaluated and detected; S6. Result output: Determine the degree of heavy metal pollution and potential risks in the soil, output the test results to the user based on the analysis results, evaluate and interpret the results, and provide corresponding suggestions and measures, including adjusting crop planting plans and taking soil remediation measures.

2. The method for detecting heavy metal pollution in crop soil according to claim 1, characterized in that: In step S1, the specific method steps for collecting soil samples around the root system of crops and rhizosphere microbial community samples include the following: S101, design a microbial trap device that is installed in the soil around the roots of crops to adsorb and collect microorganisms while keeping them in situ. The device includes a membrane shell that is permeable to water and gas, and an adsorption medium of activated carbon. It is placed in multiple locations in the field to cover different soil types and crop combinations; S102, the microbial samples collected in the device are protected and identified to ensure the retention and tracking of their original characteristics. Tiny soluble packaging materials are used in the device, which can gradually dissolve over time and release the samples. The packaging materials are made of biodegradable polymers or food-grade materials. When collecting samples, each device is accompanied by a unique identification code, using RFID tags for tracking and identifying samples in subsequent processing and analysis; S103, design an automated collection system, including multiple microbial trap devices and a central control unit. The central control unit monitors soil environmental conditions, including humidity, temperature, and light, through sensors, and controls the opening and closing of the trap devices according to the set collection strategy. The collected samples are sent to the laboratory or analysis equipment for subsequent DNA extraction and sequencing.

3. The method for detecting heavy metal pollution in crop soil according to claim 1, characterized in that: In step S2, the specific method steps of extracting microbial DNA from the collected sample using molecular biology technology include the following: S201, microbial cell lysis: the collected sample contains microbial cells, and the microbial cells are lysed to release DNA; Use sonication or enzymatic lysis methods. Sonication uses the mechanical action of high-frequency sound waves to break the cell wall, thereby releasing DNA. Enzymatic lysis uses specific enzymes, including proteinase K and lyase, to degrade cell walls and membranes and release DNA; S202, DNA purification: DNA purification in the presence of other cellular components, proteins, and impurities in the lysed sample, using affinity matrices including specific antibodies or oligonucleotide sequences to selectively capture microbial DNA, or using ionic liquids or nanomaterials to achieve efficient DNA purification; S203, quality and concentration testing: The extracted microbial DNA is tested for quality and concentration to ensure the integrity and suitability of the sample. Fluorescence probe method or mass spectrometry technology is used to perform quality and concentration testing to ensure the integrity and suitability of the sample.

4. The method for detecting heavy metal pollution in crop soil according to claim 1, characterized in that: In step S3, the specific method steps for sequencing the microbial DNA by high-throughput sequencing technology include the following: S301, microbial DNA sample preparation: DNA samples obtained from microbial DNA are labeled with aptamers, and primers with special sequences that are compatible with the sequencing platform are used to connect the aptamers to the DNA samples to form aptamer-DNA complexes, which are achieved using specially designed aptamers and ligases; S302, amplification of microbial DNA samples: using a cyclic linear amplification strategy to amplify the aptamer-DNA complex, cyclic linear amplification can achieve efficient DNA amplification without PCR, and a combination of primers and enzymes can be used to cyclically amplify the aptamer-DNA complex in vitro to produce a large amount of amplified products; S303, high-throughput sequencing: The amplified microbial DNA samples are sent to a high-throughput sequencing platform for sequencing. During the sequencing process, sequencing primers and sequencing enzymes are used as well as high-throughput sequencing technology to sequence the samples, which can achieve high-throughput and high-precision sequencing and generate a large amount of sequencing data; S304, Data Processing and Analysis: Use bioinformatics tools and algorithms to process and analyze sequencing data, including quality control, sequence assembly, removal of low-quality sequences, sequence alignment, and species annotation to identify microbial populations and symbiotic relationships associated with heavy metal pollution.

5. The method for detecting heavy metal pollution in crop soil according to claim 1, characterized in that: In step S5, the specific methods of evaluation and detection include the following: S501, collecting the microbial community composition information obtained in step S3 as input data, and collecting the heavy metal content data related to the crop soil sample as a label; S502, analyzing the microbial symbiotic network using the computer simulation method in step S4 to identify microbial populations and symbiotic relationships associated with heavy metal pollution; S503, selecting features related to heavy metal pollution, microbial species, functional genes or metabolic pathways related to heavy metal tolerance from the results of microbial symbiosis network analysis, and performing data preprocessing on the selected features, including data cleaning and standardization; S504, using machine learning or statistical models, including support vector machines, random forests, and neural networks, to train and optimize the models based on the preprocessed data, with the goal of establishing the relationship between heavy metal content and microbial symbiotic network characteristics; S505, verifying and evaluating the model using cross validation or holdout method, and evaluating the performance indicators of the model including accuracy, recall rate and precision; S506, applying the trained model to new unknown samples, predicting the degree of heavy metal pollution and potential risks in crop soils based on the characteristics of the microbial symbiosis network, and classifying heavy metal pollution into different levels or conducting quantitative assessments based on the evaluation results.

6. The method for detecting heavy metal pollution in crop soil according to claim 1, characterized in that: In step S6, the specific method steps for determining the heavy metal pollution degree and potential risk in the soil include the following: S601, Determination of heavy metal content: Use chemical analysis methods, including atomic absorption spectrometry and inductively coupled plasma emission spectrometry, to determine the content of heavy metal elements in the soil and determine whether the concentration of heavy metals in the soil exceeds the specified safety standard; S602, Soil Environmental Quality Evaluation Standard: Based on the laws and standards of different regions, the heavy metal content in the soil is evaluated with reference to the soil environmental quality evaluation standard. The standard provides indicators of background values, soil quality classification and limit values ​​for different heavy metal elements to judge the degree of soil pollution and potential risks; S603, Pollution Index Calculation: The pollution index is used to evaluate the degree of heavy metal pollution in the soil. The index is based on the concentration data of different heavy metal elements, and takes into account their toxicity and environmental effects, and a comprehensive evaluation index is obtained through mathematical calculation; S604, Biological monitoring: assess the toxicity and potential risks of heavy metals in soil by measuring biological indicators of microorganisms, plants or animals in the soil, measure the diversity of soil microorganisms, indicators of soil enzyme activity, as well as crop growth conditions and heavy metal accumulation in plant tissues; S605, Soil remediation needs assessment: Based on the assessment results of the degree of heavy metal pollution and potential risks, the soil remediation needs are assessed. The remediation methods include soil improvement and phytoremediation, and corresponding suggestions and measures are provided according to the specific circumstances.

7. The method for detecting heavy metal pollution in crop soil according to claim 1, characterized in that: In step S6, the specific method steps for outputting the heavy metal pollution detection result to the user include the following: S6001, deploys a soil monitoring system based on an intelligent sensor network. The sensors are directly buried in the soil to monitor the content of heavy metal elements in the soil in real time. Each sensor is equipped with a microprocessor and communication module to collect, process and transmit data in real time; S6002, the sensor transmits the collected data to the central server through wireless communication technology, and the coverage of the sensor network is flexibly expanded according to the specific situation to cover various areas in the farmland; S6003, after receiving the data transmitted by the sensor, the central server processes and analyzes the data, using machine learning and data mining techniques, combined with a large-scale soil heavy metal data set, to establish a high-precision heavy metal pollution detection model to automatically identify the characteristics and patterns related to heavy metal pollution; S6004, develop a user mobile application through which users can obtain the test results of soil heavy metal pollution in real time. The application provides an intuitive interface to display the degree of heavy metal pollution and potential risks in different farmland areas. Users can view the test results of different locations through maps or lists; S6005, the application provides personalized suggestions and measures based on the user's geographic location and test results. The suggestions include adjusting crop planting plans, taking soil remediation measures, and choosing appropriate agricultural management methods. The application continuously updates and optimizes the suggestions based on real-time data based on user feedback and interaction.

8. A detection system for heavy metal pollution in crop soil, characterized in that: include: The microbial sample collection unit is used to collect samples containing microbial DNA from crop soil; The microbial DNA extraction unit is used to extract microbial DNA from the collected samples; The high-throughput sequencing unit is used to perform high-throughput sequencing on the extracted microbial DNA; The data processing unit is used to process the sequencing results to obtain the composition information of the microbial community; The symbiotic network analysis unit is used to construct a microbial symbiotic network based on the microbial community composition information and identify microbial characteristics associated with heavy metal pollution; The model building unit is used to train the detection model based on the characteristics of the microbial symbiosis network and heavy metal data; The detection unit is used to make heavy metal detection judgments based on the detection model and new sample characteristics; The output unit is used to output the detection results.

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