Plant growth condition optimization method based on antibiotic pollutant degradation effect analysis

Abstract

The invention relates to the field of antibiotic degradation, and discloses a plant growth condition optimization method based on antibiotic pollutant degradation effect analysis, which comprises the following steps: sampling soil polluted by antibiotic pollutants and carrying out variety concentration detection to determine plants capable of carrying out antibiotic pollutant degradation, namely target degraded plants; and then performing experimental simulation on different types of antibiotic pollutants based on the target degradation plant, evaluating an experimental result, and generating, optimizing and adjusting growth conditions of the target degradation plant according to the degradation rate. According to the method, the antibiotic degradation efficiency of the plants can be improved by optimizing the growth conditions of the plants, such as temperature, illumination and humidity, so that the economic benefits of the plants in antibiotic pollution treatment are increased.

Description

Technical Field

[0001] The present invention relates to the field of antibiotic degradation, in particular to a method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants. Background Art

[0002] Antibiotics are a class of secondary metabolites produced by microorganisms (including bacteria, fungi, actinomycetes) or higher animals and plants during their life processes, which have anti-pathogenic or other activities and are chemical substances that can interfere with the development functions of other living cells. Antibiotic pollution mainly comes from the antibiotic production industry, medical antibiotics, veterinary antibiotics, and aquaculture, etc. These pollution sources continuously discharge low-concentration antibiotics into the environment, making antibiotics become "pseudo-persistent organic pollutants".

[0003] In the soil, with the use of antibiotics, the bacterial drug resistance gradually increases, and "super bacteria" may appear. These drug-resistant bacteria not only pose a threat to human health but may also be transmitted to animals and humans through the food chain. At the same time, antibiotics will disrupt the microbial balance in the soil, affect soil fertility and plant growth. Long-term and large-scale use of antibiotics may lead to a decrease in soil microbial diversity and soil quality, and antibiotic pollution will inhibit plant growth, reduce the yield and quality of agricultural products. At the same time, antibiotics may remain in agricultural products, posing a potential threat to human health.

[0004] Degrading antibiotic pollutants can reduce pollution to soil, water sources, and air, protect the ecological environment and biodiversity, and can also reduce the risk of humans ingesting antibiotic residues, reduce the generation and spread of drug-resistant bacteria, and maintain human health. Some plants have the potential to degrade antibiotic pollutants, including Vallisneria natans, Lemna minor, Phragmites australis, etc., which have the ability to degrade antibiotics. These plants help reduce antibiotic pollution in water bodies by absorbing, transforming, and degrading antibiotics. By optimizing the growth conditions of plants, such as temperature, light, humidity, etc., the degradation efficiency of plants against antibiotics can be improved. This helps to accelerate the degradation rate of antibiotic pollutants and reduce environmental pollution. By optimizing the growth conditions of plants, the biomass and degradation ability of plants can be improved, thereby increasing their economic benefits in the treatment of antibiotic pollution. This helps to promote the industrialization and commercialization process of the technology of plant degradation of antibiotics, so a method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants is proposed. Summary of the Invention

[0005] The present invention overcomes the deficiencies of the prior art and provides a method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] The first aspect of the present invention provides a method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants, including the following steps:

[0008] Based on the types and concentrations of antibiotic pollutants, screen plants that can degrade antibiotic pollutants from all plants, and name them target degradation plants;

[0009] Conduct experimental simulations on different types of antibiotic pollutants using the target degradation plants, and calculate the concentration degradation rate of the antibiotic pollutants after the experimental simulations;

[0010] Evaluate the experimental simulation results of all experimental sets, and optimize the growth conditions of the target degradation plants based on the evaluation results;

[0011] In the polluted soil area, record the degradation rates of different types of antibiotic pollutants in real time, and adjust the target growth conditions of the plants according to the magnitudes of the degradation rates.

[0012] Further, in a preferred embodiment of the present invention, the step of screening plants that can degrade antibiotic pollutants from all plants based on the types and concentrations of antibiotic pollutants and naming them target degradation plants is specifically as follows:

[0013] Determine the area of the soil polluted by antibiotic pollutants, mark it as the polluted soil area, and sample the polluted soil in the polluted soil area to obtain polluted soil samples;

[0014] Record the sampling locations and sampling depths of the polluted soil samples, grind the polluted soil samples to obtain polluted soil sample particles, and pour methanol into the polluted soil sample particles to obtain a polluted soil extract;

[0015] Determine the surrounding environment of the polluted soil area, and determine all the types of antibiotic pollutants in the polluted soil area based on the surrounding environment of the polluted soil area;

[0016] Concentrate the polluted soil extract to obtain a concentrated polluted soil extract, introduce it into a high-performance liquid chromatograph, obtain a big data network, and retrieve the standard mobile phase composition and standard flow rate for separating antibiotic pollutants in the high-performance liquid chromatograph in the big data network, and mark them as the target mobile phase composition and target flow rate;

[0017] Pour the concentrated polluted soil extract into the high-performance liquid chromatograph, set the target mobile phase composition and target flow rate in the high-performance liquid chromatograph, run the high-performance liquid chromatograph and simultaneously generate a chromatogram of the concentrated polluted soil extract, and mark it as the antibiotic pollutant chromatogram;

[0018] On the chromatogram of the antibiotic pollutants, calculate the peak areas of different types of antibiotic pollutants, and generate the concentrations of different types of antibiotic pollutants based on the peak areas.

[0019] In the big data network, based on the types of antibiotic pollutants and the concentrations of different types of antibiotic pollutants, retrieve the plants that can degrade all the current types of antibiotic pollutants, name them target degradation plants, and mark the types and concentrations of antibiotic pollutants at different positions in the polluted soil area.

[0020] Further, in a preferred embodiment of the present invention, the experimental simulation of different types of antibiotic pollutants by the target degradation plants and the calculation of the concentration degradation rate of the antibiotic pollutants after the experimental simulation are specifically as follows:

[0021] Based on different types of antibiotic pollutants and all types of corresponding target degradation plants, construct degradation combinations. Among them, one degradation combination describes all types of target degradation plants corresponding to one type of antibiotic pollutant, and the number of degradation combinations is equal to the number of types of antibiotic pollutants.

[0022] Retrieve the environmental conditions of the polluted soil area through the big data network and label them as the polluted soil environmental conditions.

[0023] Conduct experimental simulations of the degradation of antibiotic pollutants for different degradation combinations respectively, and set up experimental groups and control groups. Among them, the experimental group is the degradation combination with changed experimental conditions, and the control group is the degradation combination with experimental conditions equal to the polluted soil environmental conditions. Among them, one experimental group corresponds to one control group to form an experimental set, and the degradation combinations corresponding to the control group and the experimental group in one experimental set are equal.

[0024] Preset the experimental simulation time. During the experimental simulation time, in different experimental sets, adjust the experimental conditions of the experimental groups, calculate the concentration degradation rate of the antibiotic pollutants in the experimental groups under different experimental conditions, label it as the experimental concentration degradation rate, and at the same time calculate the concentration degradation rate of the antibiotic pollutants in the control groups, label it as the control concentration degradation rate, and finally record the experimental simulation results of all experimental sets.

[0025] Further, in a preferred embodiment of the present invention, the experimental simulation results of all experimental sets are evaluated, and the growth conditions of the target degradation plants are optimized based on the evaluation results, specifically as follows:

[0026] In different experimental sets, calculate the difference between the experimental concentration degradation rate and the control concentration degradation rate, label it as the degradation rate difference, and preset the standard degradation rate difference.

[0027] If the difference in degradation rates is not less than the standard difference in degradation rates, the corresponding experimental set is labeled as a first-class experimental set. If the difference in degradation rates is less than the standard difference in degradation rates, the corresponding experimental set is labeled as a non-conforming experimental set;

[0028] In a first-class experimental set, extract the experimental conditions within the experimental group, label them as first-class experimental conditions, and label the degradation combinations within the experimental group as first-class degradation combinations;

[0029] Analyze the first-class experimental conditions to determine the feasibility of applying the first-class experimental conditions. Among them, the method for judging the feasibility of applying the first-class experimental conditions is to retrieve in the big data network whether the environmental conditions of the polluted soil area have ever been equal to the first-class experimental conditions;

[0030] If so, judge that the feasibility of applying the first-class experimental conditions is qualified, and label the corresponding first-class experimental set as a qualified experimental set. If not, retrieve in the big data network whether there is a solution that can make the environmental conditions of the polluted soil area equal to the first-class experimental conditions;

[0031] If so, continue to judge that the feasibility of applying the first-class experimental conditions is qualified. If still not, judge that the feasibility of applying the first-class experimental conditions is unqualified, and label the corresponding first-class experimental set as a non-conforming experimental set;

[0032] Screen the experimental conditions for all qualified experimental sets, and determine the growth conditions of the target degradation plant based on the screening results.

[0033] Furthermore, in a preferred embodiment of the present invention, the secondary experimental analysis is performed on all qualified experimental sets, and the growth conditions of the target degradation plant are optimized based on the results of the secondary experimental analysis, specifically:

[0034] In all qualified experimental sets, analyze the difference in degradation rates respectively, determine the first-class experimental conditions corresponding to the maximum difference in degradation rates, and label them as target experimental conditions;

[0035] Retrieve and output in the big data network the solution that can make the environmental conditions of the polluted soil area equal to the target experimental conditions, label it as the experimental condition improvement plan, and output the experimental condition improvement plan in the polluted soil area;

[0036] Among them, the target experimental conditions are the growth conditions of the target degradation plant. In the polluted soil area, based on the target experimental conditions, determine the growth conditions of the target degradation plant, and label them as the plant target growth conditions.

[0037] Furthermore, in a preferred embodiment of the present invention, in the polluted soil area, the degradation rates of different types of antibiotic pollutants are recorded in real time, and the plant target growth conditions are adjusted according to the magnitude of the degradation rates, specifically:

[0038] In the contaminated soil area, according to the types of antibiotic pollutants, plant target degradation plants associated with the types of antibiotic pollutants, and at the same time adjust the growth conditions of the plants in the contaminated soil area to be equal to the target growth conditions of the plants;

[0039] Preset the estimated time for antibiotic pollutant degradation, start timing from the completion of planting the target degradation plants associated with the types of antibiotic pollutants, and when the time has elapsed the estimated time for antibiotic pollutant degradation, obtain the degraded contaminated soil area;

[0040] Conduct sampling and testing in the degraded contaminated soil area. Among them, the sampling and testing in the degraded contaminated soil area is to detect the degradation rate of antibiotic pollutants and mark it as the true degradation rate;

[0041] Preset the standard true degradation rate. If the true degradation rate is not less than the standard true degradation rate, then mark the target growth conditions of the plants as qualified target growth conditions of the plants;

[0042] If the true degradation rate is less than the standard true degradation rate, then in the contaminated soil area, adjust the target growth conditions of the plants. Among them, the adjustment of the target growth conditions of the plants includes dynamically adjusting the temperature and pH value, and continue to calculate the true degradation rate after the adjustment of the target growth conditions of the plants until the true degradation rate is not less than the standard true degradation rate, then stop adjusting the target growth conditions of the plants, output the adjusted target growth conditions of the plants, and mark them as qualified target growth conditions of the plants.

[0043] The second aspect of the present invention also provides a plant growth condition optimization system based on the analysis of the degradation effect of antibiotic pollutants. The plant growth condition optimization system includes a memory and a processor. The memory stores a plant growth condition optimization method. When the plant growth condition optimization method is executed by the processor, the following steps are realized:

[0044] Based on the types and concentrations of antibiotic pollutants, screen plants that can degrade antibiotic pollutants from all plants and name them target degradation plants;

[0045] Conduct experimental simulations on different types of antibiotic pollutants through the target degradation plants and calculate the concentration degradation rate of the antibiotic pollutants after the experimental simulations;

[0046] Evaluate the experimental simulation results of all experimental sets and optimize the growth conditions of the target degradation plants based on the evaluation results;

[0047] In the contaminated soil area, record the degradation rates of different types of antibiotic pollutants in real time and adjust the target growth conditions of the plants according to the magnitudes of the degradation rates.

[0048] The technical defects existing in the background art solved by the present invention, and the present invention has the following beneficial effects: By sampling the soil contaminated by antibiotic pollutants and detecting the types and concentrations, determine the plants that can degrade antibiotic pollutants, that is, the target degradation plants, and then conduct experimental simulations on different types of antibiotic pollutants based on the target degradation plants, and evaluate the experimental results, generate and optimize and adjust the growth conditions of the target degradation plants according to the degradation rate. The present invention can improve the degradation efficiency of plants against antibiotics by optimizing the growth conditions of plants, such as temperature, light, humidity, etc., thereby increasing its economic benefits in the treatment of antibiotic pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0050] Figure 1 Shows a flowchart of a method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants;

[0051] Figure 2 Shows a flowchart of a method for optimizing the growth conditions of target degradation plants;

[0052] Figure 3 Shows a program view of a system for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0054] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. However, the present invention can be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0055] Figure 1 Shows a flowchart of a method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants, including the following steps:

[0056] S102: Based on the types and concentrations of antibiotic pollutants, screen plants that can degrade antibiotic pollutants from all plants, and name them target degradation plants;

[0057] S104: Conduct experimental simulations of different types of antibiotic pollutants using the target degradation plants, and calculate the concentration degradation rate of the antibiotic pollutants after the experimental simulations;

[0058] S106: Evaluate the experimental simulation results of all experimental sets, and optimize the growth conditions of the target degradation plants based on the evaluation results;

[0059] S108: In the contaminated soil area, record the degradation rates of different types of antibiotic pollutants in real time, and adjust the target growth conditions of the plants according to the magnitudes of the degradation rates.

[0060] Further, in a preferred embodiment of the present invention, the step of screening plants that can degrade antibiotic pollutants from all plants based on the types and concentrations of antibiotic pollutants and naming them target degradation plants is specifically as follows:

[0061] Determine the area of the soil contaminated by antibiotic pollutants, mark it as the contaminated soil area, sample the contaminated soil in the contaminated soil area to obtain contaminated soil samples;

[0062] Record the sampling location and sampling depth of the contaminated soil samples, grind the contaminated soil samples to obtain contaminated soil sample particles, and pour methanol into the contaminated soil sample particles to obtain a contaminated soil extract;

[0063] Determine the surrounding environment of the contaminated soil area, and determine all the types of antibiotic pollutants in the contaminated soil area based on the surrounding environment of the contaminated soil area;

[0064] Concentrate the contaminated soil extract to obtain a concentrated contaminated soil extract, introduce it into a high-performance liquid chromatograph, obtain a big data network, and retrieve the standard mobile phase composition and standard flow rate for separating antibiotic pollutants in the high-performance liquid chromatograph in the big data network, and mark them as the target mobile phase composition and target flow rate;

[0065] Pour the concentrated contaminated soil extract into the high-performance liquid chromatograph, set the target mobile phase composition and target flow rate in the high-performance liquid chromatograph, run the high-performance liquid chromatograph to generate a chromatogram of the concentrated contaminated soil extract at the same time, and mark it as the antibiotic pollutant chromatogram;

[0066] On the antibiotic pollutant chromatogram, calculate the peak areas of different types of antibiotic pollutants, and generate the concentrations of different types of antibiotic pollutants based on the peak areas;

[0067] In a big data network, based on the types of antibiotic pollutants and the concentrations of different types of antibiotic pollutants, search for plants that can degrade all currently existing types of antibiotic pollutants, name them target degradation plants, and mark the types and concentrations of antibiotic pollutants at different positions in the polluted soil area.

[0068] It should be noted that since there are many types of antibiotic pollutants and different types of antibiotic pollutants may require different plants for degradation, it is necessary to sample and detect the soil to determine the types and concentrations of antibiotic pollutants in the soil, so as to judge the plants that can be used for degradation. For the detection of the types and concentrations of antibiotic pollutants by liquid chromatography, soil concentrate extraction is required first, with the aim of releasing the antibiotics in the soil, removing interfering impurities and improving the detection sensitivity. Subsequently, the chromatographic column, mobile phase composition and flow rate in the high-performance liquid chromatograph need to be set. Among them, setting appropriate mobile phase composition and flow rate can achieve effective separation of antibiotics, improve the detection efficiency and accuracy. The target mobile phase composition and target flow rate can be retrieved in the big data network. After generating the chromatogram of antibiotic pollutants, analyze the chromatogram of antibiotic pollutants and record the peak area. The size of the peak area is related to the antibiotic concentration, and the surrounding environment of the polluted soil area determines all the types of antibiotic pollutants in the polluted soil area, because the polluted soil is caused by sewage and the antibiotics in the sewage can be determined. Finally, in the big data network, combining the concentration and types of antibiotic pollutants, the target degradation plants can be determined.

[0069] Further, in a preferred embodiment of the present invention, the different types of antibiotic pollutants are experimentally simulated by the target degradation plants, and the concentration degradation rate of the antibiotic pollutants after the experimental simulation is calculated, specifically as follows:

[0070] Based on different types of antibiotic pollutants and all types of corresponding target degradation plants, degradation combinations are constructed. Among them, one degradation combination describes all types of target degradation plants corresponding to one type of antibiotic pollutant, and the number of degradation combinations is equal to the number of types of antibiotic pollutants;

[0071] Retrieve the environmental conditions of the polluted soil area through the big data network and label them as the environmental conditions of the polluted soil;

[0072] The antibiotic pollutant degradation experiments are simulated for different degradation combinations respectively, and experimental groups and control groups are set. Among them, the experimental group is the degradation combination with changed experimental conditions, and the control group is the degradation combination with experimental conditions equal to the environmental conditions of the polluted soil. Among them, one experimental group corresponds to one control group to form an experimental set, and the degradation combinations corresponding to the control group and the experimental group in one experimental set are equal;

[0073] Preset the experimental simulation time. During the experimental simulation time, in different experimental sets, adjust the experimental conditions for the experimental group, calculate the concentration degradation rate of antibiotic pollutants in the experimental group under different experimental conditions, label it as the experimental concentration degradation rate, and at the same time calculate the concentration degradation rate of antibiotic pollutants in the control group, label it as the control concentration degradation rate, and finally record the experimental simulation results of all experimental sets.

[0074] It should be noted that there is a corresponding relationship between antibiotic pollutants and degrading plants. Therefore, all types of target degrading plants corresponding to a certain antibiotic pollutant are described in one degradation combination. Determining the environmental conditions of the polluted soil and conducting experimental simulations are aimed at analyzing whether the target degrading plants can truly degrade antibiotic pollutants and the magnitude of the degradation rate under different plant growth conditions. The larger the degradation rate, the more suitable the corresponding plant growth conditions are for degrading antibiotic pollutants by plants. For the sake of experimental rigor, an experimental group and a control group are constructed. The experimental group and the control group form an experimental set. The experimental group is the degradation combination with adjusted plant growth conditions, and the control group is the degradation combination of the current polluted soil environmental conditions. During the experimental simulation time, it is necessary to calculate the concentration degradation rate of antibiotic pollutants in the experimental group and the concentration degradation rate of antibiotic pollutants in the control group under different experimental conditions, and conduct comparative analysis.

[0075] Furthermore, in a preferred embodiment of the present invention, in the polluted soil area, the degradation rates of different types of antibiotic pollutants are recorded in real time, and the plant target growth conditions are adjusted according to the magnitude of the degradation rates, specifically:

[0076] In the polluted soil area, according to the types of antibiotic pollutants, plant the target degrading plants associated with the types of antibiotic pollutants, and at the same time adjust the growth conditions of the plants in the polluted soil area to be equal to the plant target growth conditions;

[0077] Preset the estimated time for antibiotic pollutant degradation. Start timing from the moment when the target degrading plants associated with the types of antibiotic pollutants are planted. When the time has elapsed the estimated time for antibiotic pollutant degradation, obtain the polluted soil area that has been degraded;

[0078] Conduct sampling detection in the polluted soil area that has been degraded. Among them, the sampling detection in the polluted soil area that has been degraded is to detect the degradation rate of antibiotic pollutants and label it as the true degradation rate;

[0079] Preset the standard true degradation rate. If the true degradation rate is not less than the standard true degradation rate, then label the plant target growth conditions as qualified plant target growth conditions;

[0080] If the actual degradation rate is less than the standard actual degradation rate, then in the polluted soil area, the target growth conditions of the plants are adjusted. Among them, the adjustment of the target growth conditions of the plants includes dynamically adjusting the temperature and pH value, and continuing to calculate the actual degradation rate after the adjustment of the target growth conditions of the plants until the actual degradation rate is not less than the standard actual degradation rate, then stop adjusting the target growth conditions of the plants, output the adjusted target growth conditions of the plants, and label them as qualified target growth conditions of the plants.

[0081] It should be noted that after obtaining the target growth conditions of the plants, control the growth conditions of the plants in the polluted soil area to be equal to the target growth conditions of the plants, because the experimental results show that the target growth conditions of the plants can fully degrade the antibiotic pollutants. After judging the estimated degradation time of the antibiotic pollutants, determine whether the degradation effect meets the standard. If it meets the standard, it proves that the results obtained from the experimental simulation are correct, and the target growth conditions of the plants can be directly used to plant plants; if it does not meet the standard, dynamic adjustment of the temperature and pH value is required to achieve an actual degradation rate not less than the standard actual degradation rate.

[0082] Figure 2 The flowchart of the method for optimizing the growth conditions of the target degradation plants is shown, including the following steps:

[0083] S202: Evaluate the experimental simulation results of all experimental sets, and optimize the growth conditions of the target degradation plants based on the evaluation results;

[0084] S204: Conduct secondary experimental analysis on all qualified experimental sets, and optimize the growth conditions of the target degradation plants based on the results of the secondary experimental analysis.

[0085] Furthermore, in a preferred embodiment of the present invention, the evaluation of the experimental simulation results of all experimental sets and the optimization of the growth conditions of the target degradation plants based on the evaluation results are specifically as follows:

[0086] In different experimental sets, calculate the difference between the experimental concentration degradation rate and the control concentration degradation rate, label it as the degradation rate difference, and preset the standard degradation rate difference;

[0087] If the degradation rate difference is not less than the standard degradation rate difference, then label the corresponding experimental set as a first-class experimental set. If the degradation rate difference is less than the standard degradation rate difference, then label the corresponding experimental set as an unqualified experimental set;

[0088] In the first-class experimental sets, extract the experimental conditions within the experimental group, label them as first-class experimental conditions, and label the degradation combinations within the experimental group as first-class degradation combinations;

[0089] Analyze the above-mentioned type of experimental conditions to determine the feasibility of applying the type of experimental conditions. Among them, the method for judging the feasibility of applying the type of experimental conditions is to retrieve in the big data network whether the environmental conditions of the contaminated soil area have ever been equal to the type of experimental conditions;

[0090] If so, it is judged that the feasibility of applying the type of experimental conditions is qualified, and the corresponding type of experimental set is marked as a qualified experimental set. If not, retrieve in the big data network whether there is a solution that can make the environmental conditions of the contaminated soil area equal to the type of experimental conditions;

[0091] If so, continue to judge that the feasibility of applying the type of experimental conditions is qualified. If still not, it is judged that the feasibility of applying the type of experimental conditions is unqualified, and the corresponding type of experimental set is marked as an unqualified experimental set;

[0092] Screen the experimental conditions for all qualified experimental sets, and determine the growth conditions of the target degradation plant based on the screening results.

[0093] It should be noted that the degradation rate difference represents the degree and effect of the degradation of antibiotic pollutants. The larger the degradation rate difference, the better the degradation effect. Screen the experimental sets with a degradation rate difference not less than the standard degradation rate difference and mark them as the type of experimental sets. The plant growth conditions in the type of experimental sets need to be analyzed again to determine whether they can be applied to the soil, because there may be some plant growth conditions that will cause more serious environmental pollution when applied to the soil and do not conform to the actual situation. After obtaining the type of experimental conditions, judge the feasibility of applying the type of experimental conditions, screen the type of experimental conditions according to the feasibility, and generate a qualified experimental set and an unqualified experimental set according to the screened type of experimental conditions.

[0094] Further, in a preferred embodiment of the present invention, the secondary experimental analysis is performed on all qualified experimental sets, and the growth conditions of the target degradation plant are optimized based on the results of the secondary experimental analysis, specifically:

[0095] In all qualified experimental sets, analyze the degradation rate difference respectively, and determine the type of experimental conditions corresponding to the maximum degradation rate difference, which is marked as the target experimental conditions;

[0096] Retrieve and output in the big data network the solution that can make the environmental conditions of the contaminated soil area equal to the target experimental conditions, which is marked as the experimental condition improvement plan, and output the experimental condition improvement plan in the contaminated soil area;

[0097] Among them, the target experimental conditions are the growth conditions of the target degradation plant. In the contaminated soil area, based on the target experimental conditions, determine the growth conditions of the target degradation plant, which are marked as the plant target growth conditions.

[0098] It should be noted that the experimental conditions with the largest difference in degradation rate are selected from all qualified experimental sets for output, that is, the target experimental conditions are output, and improvement solutions for the experimental conditions are retrieved and output in the big data network based on the target experimental conditions to generate the target growth conditions for plants.

[0099] As Figure 3 shown, the second aspect of the present invention also provides a plant growth condition optimization system based on the analysis of the degradation effect of antibiotic pollutants. The plant growth condition optimization system includes a memory 31 and a processor 32. The memory 31 stores a plant growth condition optimization method. When the plant growth condition optimization method is executed by the processor 32, the following steps are implemented:

[0100] Based on the types and concentrations of antibiotic pollutants, among all plants, plants that can degrade antibiotic pollutants are screened and named target degradation plants;

[0101] Experimental simulations of different types of antibiotic pollutants are carried out by the target degradation plants, and the concentration degradation rates of the antibiotic pollutants after the experimental simulations are calculated;

[0102] The experimental simulation results of all experimental sets are evaluated, and the growth conditions of the target degradation plants are optimized based on the evaluation results;

[0103] In the polluted soil area, the degradation rates of different types of antibiotic pollutants are recorded in real time, and the target growth conditions of plants are adjusted according to the magnitudes of the degradation rates.

[0104] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants, characterized in that: The following steps are involved: Based on the types and concentrations of antibiotic pollutants, plants that can degrade antibiotic pollutants are screened among all plants and named as target degradation plants; Experimental simulations were conducted on different types of antibiotic pollutants through target degradation plants, and the concentration degradation rates of antibiotic pollutants after experimental simulations were calculated; Evaluate the experimental simulation results of all experimental sets, and optimize the growth conditions of the target degradation plants based on the evaluation results; In contaminated soil areas, the degradation rates of different types of antibiotic pollutants are recorded in real time, and the target growth conditions of plants are adjusted according to the degradation rates.

2. The method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants according to claim 1, characterized in that: Based on the types and concentrations of antibiotic pollutants, plants that can degrade antibiotic pollutants are screened from all plants and named as target degradation plants, specifically: Determine an area where soil is contaminated by antibiotic contaminants, mark it as a contaminated soil area, and sample the contaminated soil in the contaminated soil area to obtain a contaminated soil sample; Recording the sampling location and sampling depth of the contaminated soil sample, grinding the contaminated soil sample to obtain contaminated soil sample particles, and pouring methanol into the contaminated soil sample particles to obtain a contaminated soil extract; determining the surrounding environment of the contaminated soil area, and determining the types of all antibiotic contaminants in the contaminated soil area based on the surrounding environment of the contaminated soil area; The contaminated soil extract is concentrated to obtain a concentrated contaminated soil extract, which is introduced into a high performance liquid chromatograph to obtain a big data network, and a standard mobile phase composition and a standard flow rate for separating antibiotic pollutants in a high performance liquid chromatograph are retrieved from the big data network, and the standard mobile phase composition and the standard flow rate are calibrated as target mobile phase composition and target flow rate; Pour the concentrated extract of the contaminated soil into a high performance liquid chromatograph, set a target mobile phase composition and a target flow rate in the high performance liquid chromatograph, run the high performance liquid chromatograph and simultaneously generate a chromatogram of the concentrated extract of the contaminated soil, which is calibrated as an antibiotic pollutant chromatogram; On the antibiotic pollutant chromatogram, calculating the peak areas of different types of antibiotic pollutants, and generating the concentrations of different types of antibiotic pollutants based on the peak areas; In the big data network, based on the types of antibiotic pollutants and the concentrations of different types of antibiotic pollutants, plants that can degrade all current types of antibiotic pollutants are retrieved and named as target degradation plants. The types and concentrations of antibiotic pollutants at different locations in the contaminated soil area are marked.

3. The method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants according to claim 1, characterized in that: The target degradation plants are used to perform experimental simulations on different types of antibiotic pollutants, and the concentration degradation rate of the antibiotic pollutants after the experimental simulation is calculated, specifically: Based on different types of antibiotic pollutants and all corresponding types of target degradation plants, a degradation combination is constructed, wherein one degradation combination describes all types of target degradation plants corresponding to one antibiotic pollutant, and the number of degradation combinations is equal to the number of types of antibiotic pollutants; Retrieve environmental conditions of contaminated soil areas through the big data network and calibrate them as contaminated soil environmental conditions; Conduct antibiotic pollutant degradation experiment simulations for different degradation combinations, and set up experimental groups and control groups, wherein the experimental group is a degradation combination with changed experimental conditions, and the control group is a degradation combination with experimental conditions equal to polluted soil environmental conditions, wherein one experimental group corresponds to one control group, forming an experimental set, and the degradation combinations corresponding to the control group and the experimental group in one experimental set are equal; The experimental simulation time is preset. During the experimental simulation time, in different experimental sets, the experimental conditions of the experimental groups are adjusted, and the concentration degradation rates of the antibiotic pollutants in the experimental groups under different experimental conditions are calculated and calibrated as the experimental concentration degradation rates. At the same time, the concentration degradation rates of the antibiotic pollutants in the control group are calculated and calibrated as the control concentration degradation rates. Finally, the experimental simulation results of all experimental sets are recorded.

4. The method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants according to claim 1, characterized in that: The experimental simulation results of all experimental sets are evaluated, and the growth conditions of the target degradation plants are optimized based on the evaluation results, specifically: In different experimental sets, the difference between the degradation rate of the experimental concentration and the degradation rate of the control concentration is calculated, calibrated as the degradation rate difference, and the standard degradation rate difference is preset; If the degradation rate difference is not less than the standard degradation rate difference, the corresponding experimental set is calibrated as a first-class experimental set; if the degradation rate difference is less than the standard degradation rate difference, the corresponding experimental set is calibrated as an unqualified experimental set; In a class of experimental sets, the experimental conditions in the experimental group are extracted and marked as a class of experimental conditions, and the degradation combinations in the experimental group are marked as a class of degradation combinations; Analyze the first type of experimental conditions to determine the feasibility of using the first type of experimental conditions, wherein the feasibility of using the first type of experimental conditions is determined by searching the big data network to determine whether the environmental conditions of the contaminated soil area have ever been equal to the first type of experimental conditions; If yes, the feasibility of using a type of experimental condition is judged to be qualified, and the corresponding type of experimental set is marked as a qualified experimental set. If no, it is searched in the big data network whether there is a solution that can make the environmental conditions of the contaminated soil area equal to the type of experimental conditions; If yes, then continue to judge whether the feasibility of the application of a type of experimental conditions is qualified; if still no, then judge whether the feasibility of the application of a type of experimental conditions is unqualified, and mark the corresponding type of experimental set as an unqualified experimental set; Experimental conditions are screened for all qualified experimental sets, and the growth conditions of the target degradation plants are determined based on the screening results.

5. The method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants according to claim 4, characterized in that: The secondary experimental analysis is performed on all qualified experimental sets, and the growth conditions of the target degradation plants are optimized based on the secondary experimental analysis results, specifically: In all qualified experimental sets, the degradation rate differences are analyzed respectively to determine the experimental conditions corresponding to the maximum degradation rate difference, which are calibrated as the target experimental conditions; Retrieving a solution output that can make the environmental conditions of the contaminated soil area equal to the target experimental conditions in the big data network, marking it as an experimental condition improvement solution, and outputting the experimental condition improvement solution in the contaminated soil area; Among them, the target experimental conditions are the growth conditions of the target degradation plants. In the contaminated soil area, the growth conditions of the target degradation plants are determined based on the target experimental conditions and calibrated as the target growth conditions of the plants.

6. The method for optimizing plant growth conditions based on the analysis of the degradation effect of antibiotic pollutants according to claim 1, characterized in that: In the contaminated soil area, the degradation rates of different types of antibiotic pollutants are recorded in real time, and the target plant growth conditions are adjusted according to the degradation rates, specifically: In the contaminated soil area, according to the type of antibiotic contaminants, target degradation plants associated with the type of antibiotic contaminants are planted, and at the same time, the growth conditions of the plants in the contaminated soil area are adjusted to be equal to the target growth conditions of the plants; The estimated degradation time of antibiotic pollutants is preset, and the time starts from the completion of planting of target degradation plants associated with the type of antibiotic pollutants. When the estimated degradation time of antibiotic pollutants is reached, the degraded contaminated soil area is obtained; Performing sampling and testing in the degraded contaminated soil area, wherein the sampling and testing in the degraded contaminated soil area is to detect the degradation rate of the antibiotic pollutants and mark it as the actual degradation rate; A standard true degradation rate is preset, and if the true degradation rate is not less than the standard true degradation rate, the target plant growth condition is calibrated as a qualified target plant growth condition; If the actual degradation rate is less than the standard actual degradation rate, the target plant growth conditions are adjusted in the contaminated soil area, wherein the adjustment of the target plant growth conditions includes dynamically adjusting the temperature and pH value, and continuing to calculate the actual degradation rate after the target plant growth conditions are adjusted until the actual degradation rate is not less than the standard actual degradation rate, then the adjustment of the target plant growth conditions is stopped, and the adjusted target plant growth conditions are output and calibrated as qualified target plant growth conditions.

7. Plant growth condition optimization system based on analysis of antibiotic pollutant degradation effect, characterized in that: The plant growth condition optimization system includes a memory and a processor, wherein a plant growth condition optimization method program is stored in the memory, and when the plant growth condition optimization method program is executed by the processor, the plant growth condition optimization method steps as described in any one of claims 1-6 are implemented.

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