Method for optimizing plant growth conditions based on analysis of degradation effect of antibiotic contaminants

Abstract

The present application relates to the field of antibiotic degradation, and discloses a plant growth condition optimization method based on antibiotic pollutant degradation effect analysis, comprising the following steps: through sampling soil contaminated by antibiotic pollutants and detecting the types and concentrations, determining plants that can degrade antibiotic pollutants, i.e., target degradation plants, then based on the target degradation plants, performing experimental simulation on different types of antibiotic pollutants, and evaluating the experimental results, generating and adjusting the growth conditions of the target degradation plants according to the degradation rate optimization. The present application can optimize the growth conditions of plants, such as temperature, illumination, humidity, etc., improve the degradation efficiency of plants on antibiotics, and thus increase the economic benefits of plants in antibiotic pollution control.

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 analysis of degradation effects of antibiotic pollutants. Background Art

[0002] Antibiotics are secondary metabolites produced by microorganisms (including bacteria, fungi, and actinomycetes) or higher plants and animals during their life processes, possessing antipathogenic or other activities. They are chemical substances that can interfere with the developmental functions of other living cells. Antibiotic pollution primarily originates from the antibiotic production industry, medical and veterinary antibiotics, and aquaculture. These sources continuously release low concentrations of antibiotics into the environment, making antibiotics "pseudo-persistent organic pollutants."

[0003] In the soil, antibiotic use is increasing bacterial resistance, potentially leading to the emergence of "superbugs." These resistant bacteria not only pose a threat to human health but can also be transmitted to animals and humans through the food chain. Furthermore, antibiotics can disrupt the microbial balance in the soil, affecting soil fertility and plant growth. Long-term, high-volume antibiotic use can reduce soil microbial diversity and soil quality. Antibiotic contamination can also inhibit plant growth, reducing the yield and quality of agricultural products. Furthermore, antibiotic residues may remain in agricultural products, posing a potential threat to human health.

[0004] Degrading antibiotic contaminants can reduce soil, water, and air pollution, protecting the ecological environment and biodiversity. It can also lower the risk of human ingestion of antibiotic residues, reduce the emergence and spread of drug-resistant bacteria, and safeguard human health. Some plants, including Vallisneria, Duckweed, and Phragmites australis, have the potential to degrade antibiotic contaminants. These plants, by absorbing, transforming, and degrading antibiotics, help reduce antibiotic contamination in water bodies. By optimizing plant growth conditions, such as temperature, light, and humidity, the efficiency of plant antibiotic degradation can be improved. This helps accelerate the degradation of antibiotic contaminants and reduce environmental pollution. Optimizing plant growth conditions can increase plant biomass and degradation capacity, thereby increasing the economic benefits of plant-based antibiotic pollution control. This will help promote the industrialization and commercialization of plant-based antibiotic degradation technology. Therefore, a method for optimizing plant growth conditions based on analysis of antibiotic contaminant degradation efficiency 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 analysis of the degradation effects of antibiotic pollutants.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

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

[0008] 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;

[0009] Experimental simulations of different types of antibiotic pollutants were carried out using target degradation plants, and the concentration degradation rates of antibiotic pollutants after the experimental simulations were calculated;

[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 contaminated soil areas, 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.

[0012] Furthermore, in a preferred embodiment of the present invention, based on the type and concentration of antibiotic pollutants, plants that can degrade antibiotic pollutants are screened from all plants and named as target degradation plants, specifically:

[0013] determining an area where soil is contaminated by antibiotic contaminants, marking it as a contaminated soil area, and sampling contaminated soil in the contaminated soil area to obtain a contaminated soil sample;

[0014] 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;

[0015] 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;

[0016] Concentrating the contaminated soil extract to obtain a concentrated contaminated soil extract, introducing the concentrated contaminated soil extract into a high performance liquid chromatograph, obtaining a big data network, and retrieving a standard mobile phase composition and a standard flow rate for separating antibiotic contaminants in a high performance liquid chromatograph from the big data network, and calibrating the mobile phase composition and the target flow rate to the target mobile phase composition and the target flow rate;

[0017] The concentrated extract of the contaminated soil is poured into a high performance liquid chromatograph, a target mobile phase composition and a target flow rate are set in the high performance liquid chromatograph, and the high performance liquid chromatograph is run to simultaneously generate a chromatogram of the concentrated extract of the contaminated soil, which is calibrated as a chromatogram of antibiotic contaminants;

[0018] On the antibiotic pollutant chromatogram, calculating the peak areas of different types of antibiotic pollutants, and generating the concentrations of the 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, 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 are then marked at different locations in the contaminated soil area.

[0020] Furthermore, in a preferred embodiment of the present invention, the target degradation plants are used to perform experimental simulations on different types of antibiotic pollutants, and the concentration degradation rates of the antibiotic pollutants after the experimental simulations are calculated, specifically:

[0021] Based on different types of antibiotic pollutants and all corresponding 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 antibiotic pollutant types;

[0022] Retrieve the environmental conditions of the contaminated soil area through the big data network and calibrate them as the contaminated soil environmental conditions;

[0023] Conduct antibiotic pollutant degradation experimental 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 the contaminated soil environment conditions. 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;

[0024] The experimental simulation time is preset. During the experimental simulation time, the experimental conditions of the experimental groups are adjusted in different experimental sets. The concentration degradation rate of the antibiotic pollutants in the experimental groups under different experimental conditions is calculated and calibrated as the experimental concentration degradation rate. At the same time, the concentration degradation rate of the antibiotic pollutants in the control group is calculated and calibrated as the control concentration degradation rate. Finally, the experimental simulation results of all experimental sets are recorded.

[0025] Furthermore, 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:

[0026] In different experimental sets, the difference between the degradation rate of the experimental concentration and the degradation rate of the control concentration is calculated and calibrated as the degradation rate difference, and the standard degradation rate difference is preset;

[0027] 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;

[0028] In a class of experimental sets, the experimental conditions within the experimental group are extracted and calibrated as a class of experimental conditions, and the degradation combinations within the experimental group are calibrated as a class of degradation combinations;

[0029] Analyzing the first type of experimental conditions to determine feasibility of using the first type of experimental conditions, wherein the feasibility of using the first type of experimental conditions is determined by searching a big data network to determine whether environmental conditions in the contaminated soil area have ever been equal to the first type of experimental conditions;

[0030] If so, the feasibility of the application of a type of experimental conditions is judged to be qualified, and the corresponding type of experimental set is marked as a qualified experimental set. If not, the big data network is searched to see whether there is a solution that can make the environmental conditions of the contaminated soil area equal to the type of experimental conditions;

[0031] 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;

[0032] 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.

[0033] Furthermore, in a preferred embodiment of the present invention, a 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:

[0034] In all qualified experimental sets, the degradation rate differences are analyzed respectively, and the experimental conditions corresponding to the maximum degradation rate difference are determined and calibrated as the target experimental conditions;

[0035] Retrieving a solution output from the big data network that can make the environmental conditions in the contaminated soil area equal to the target experimental conditions, marking it as an experimental condition improvement solution, and outputting the experimental condition improvement solution in the contaminated soil area;

[0036] 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.

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

[0038] In the contaminated soil area, according to the type of antibiotic pollutants, the target degradation plant associated with the type of antibiotic pollutants is planted, and the growth conditions of the plant in the contaminated soil area are adjusted to be equal to the target growth conditions of the plant;

[0039] A preset antibiotic pollutant degradation estimation time is counted from the completion of planting the target degradation plant associated with the type of antibiotic pollutants, and when the time exceeds the antibiotic pollutant degradation estimation time, a degraded contaminated soil area is obtained;

[0040] Sampling detection is carried out in the degraded contaminated soil area, wherein the sampling detection in the degraded contaminated soil area is to detect the degradation rate of antibiotic pollutants, and is marked as a true degradation rate;

[0041] A standard true degradation rate is preset, and if the true degradation rate is not less than the standard true degradation rate, the target growth conditions of the plant are marked as qualified target growth conditions of the plant;

[0042] If the true degradation rate is less than the standard true degradation rate, the target growth conditions of the plant in the contaminated soil area are adjusted, wherein the adjustment of the target growth conditions of the plant includes dynamic adjustment of temperature and pH value, and the true degradation rate is continuously calculated after the adjustment of the target growth conditions of the plant, until the true degradation rate is not less than the standard true degradation rate, then the adjustment of the target growth conditions of the plant is stopped, the adjusted target growth conditions of the plant are output, and are marked as qualified target growth conditions of the plant.

[0043] The second aspect of the present application also provides a plant growth condition optimization system based on antibiotic pollutant degradation effect analysis, which comprises a memory and a processor, and the memory stores a plant growth condition optimization method, and the plant growth condition optimization method is executed by the processor to realize the following steps:

[0044] Based on the types and concentrations of antibiotic pollutants, plants capable of degrading antibiotic pollutants are screened from all plants and are named as target degradation plants;

[0045] Different types of antibiotic pollutants are simulated by the target degradation plants, and the concentration degradation rate of the antibiotic pollutants after the simulation is calculated;

[0046] The simulation results of all experimental sets are evaluated, and the growth conditions of the target degradation plants are optimized based on the evaluation results;

[0047] In the contaminated soil area, the degradation rates of different types of antibiotic pollutants are recorded in real time, and the target growth conditions of the plant are adjusted according to the degradation rates.

[0048] The present invention addresses the technical deficiencies in the background art and has the following beneficial effects: by sampling soil contaminated with antibiotic pollutants and testing for species and concentration, plants capable of degrading antibiotic pollutants, i.e., target degradation plants, are identified. Experimental simulations of different types of antibiotic pollutants are then conducted based on the target degradation plants. The experimental results are evaluated to generate and optimize and adjust the growth conditions of the target degradation plants based on the degradation rate. By optimizing plant growth conditions, such as temperature, light, and humidity, the present invention can improve the plant's efficiency in degrading antibiotics, thereby increasing its economic benefits in antibiotic pollution control. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0050] Figure 1 A flow chart showing a method for optimizing plant growth conditions based on analysis of the degradation effect of antibiotic pollutants is shown;

[0051] Figure 2 A flow chart of a method for optimizing growth conditions of target degradation plants is shown;

[0052] Figure 3 Shown is a program view of a plant growth condition optimization system based on analysis of the degradation effect of antibiotic pollutants. DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0055] Figure 1 A flow chart of a method for optimizing plant growth conditions based on analysis of the degradation effect of antibiotic pollutants is shown, comprising the following steps:

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

[0057] S104: Conducting experimental simulations on different types of antibiotic pollutants using target degradation plants, and calculating 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, the degradation rates of different types of antibiotic pollutants are recorded in real time, and the target growth conditions of the plants are adjusted according to the degradation rates.

[0060] Furthermore, in a preferred embodiment of the present invention, based on the type and concentration of antibiotic pollutants, plants that can degrade antibiotic pollutants are screened from all plants and named as target degradation plants, specifically:

[0061] determining an area where soil is contaminated by antibiotic contaminants, marking it as a contaminated soil area, and sampling contaminated soil in the contaminated soil area to obtain a contaminated soil sample;

[0062] 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;

[0063] 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;

[0064] Concentrating the contaminated soil extract to obtain a concentrated contaminated soil extract, introducing the concentrated contaminated soil extract into a high performance liquid chromatograph, obtaining a big data network, and retrieving a standard mobile phase composition and a standard flow rate for separating antibiotic contaminants in a high performance liquid chromatograph from the big data network, and calibrating the mobile phase composition and the target flow rate to the target mobile phase composition and the target flow rate;

[0065] The concentrated extract of the contaminated soil is poured into a high performance liquid chromatograph, a target mobile phase composition and a target flow rate are set in the high performance liquid chromatograph, and the high performance liquid chromatograph is run to simultaneously generate a chromatogram of the concentrated extract of the contaminated soil, which is calibrated as a chromatogram of antibiotic contaminants;

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

[0067] 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 are then marked at different locations in the contaminated soil area.

[0068] It should be noted that due to the wide variety of antibiotic contaminants, different types of antibiotic contaminants may require different degrading plants. Therefore, soil sampling and testing are necessary to determine the type and concentration of antibiotic contaminants in the soil, thereby identifying suitable degrading plants. Liquid chromatography is used to determine the concentration of antibiotic contaminants. First, soil concentrate extraction is performed to release the antibiotics in the soil, remove interfering impurities, and improve detection sensitivity. The HPLC column, mobile phase composition, and flow rate must then be configured. Optimizing the mobile phase composition and flow rate effectively separates the antibiotics, improving detection efficiency and accuracy. The target mobile phase composition and flow rate can be retrieved from the big data network. After generating the antibiotic contaminant chromatogram, the chromatogram is analyzed and the peak area is recorded. Peak area correlates with antibiotic concentration. The surrounding environment of the contaminated soil area determines the type of all antibiotic contaminants present. Since the contaminated soil is caused by wastewater, the antibiotics present in the wastewater can be determined. Finally, the target degrading plants can be identified by combining the concentration and type of antibiotic contaminants within the big data network.

[0069] Furthermore, in a preferred embodiment of the present invention, the target degradation plants are used to perform experimental simulations on different types of antibiotic pollutants, and the concentration degradation rates of the antibiotic pollutants after the experimental simulations are calculated, specifically:

[0070] Based on different types of antibiotic pollutants and all corresponding 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 antibiotic pollutant types;

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

[0072] Conduct antibiotic pollutant degradation experimental 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 the contaminated soil environment conditions. 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;

[0073] The experimental simulation time is preset. During the experimental simulation time, the experimental conditions of the experimental groups are adjusted in different experimental sets. The concentration degradation rate of the antibiotic pollutants in the experimental groups under different experimental conditions is calculated and calibrated as the experimental concentration degradation rate. At the same time, the concentration degradation rate of the antibiotic pollutants in the control group is calculated and calibrated as the control concentration degradation rate. Finally, the experimental simulation results of all experimental sets are recorded.

[0074] It should be noted that there is a corresponding relationship between antibiotic pollutants and degrading plants, so one degradation combination describes all types of target degrading plants corresponding to one antibiotic pollutant. The purpose of determining the environmental conditions of the contaminated soil and conducting experimental simulations is to analyze whether the target degrading plants can really treat the antibiotic pollutants and the degradation rate under different plant growth conditions. The greater the degradation rate, the more suitable the corresponding plant growth conditions are for plant degradation of antibiotic pollutants. For the sake of experimental rigor, experimental and control groups are constructed, and the experimental and control groups form an experimental set. The experimental group is a degradation combination that adjusts the plant growth conditions, and the control group is a degradation combination of the current contaminated 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, the degradation rates of different types of antibiotic pollutants in the contaminated soil area are recorded in real time, and the target plant growth conditions are adjusted according to the degradation rates, specifically:

[0076] Planting target degradation plants associated with the types of antibiotic pollutants in the contaminated soil area, and adjusting the growth conditions of the plants in the contaminated soil area to be equal to the target plant growth conditions;

[0077] The estimated degradation time of the antibiotic pollutant is preset, and the time starts from the completion of the planting of the target degradation plant associated with the type of antibiotic pollutant. When the estimated degradation time of the antibiotic pollutant is exceeded, the degraded contaminated soil area is obtained;

[0078] 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 pollutant and mark it as the actual degradation rate;

[0079] 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;

[0080] If the true degradation rate is less than the standard true 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 true degradation rate after the target plant growth conditions are adjusted until the true degradation rate is not less than the standard true 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.

[0081] It should be noted that after obtaining the target plant growth conditions, the plant growth conditions in the contaminated soil area are controlled to be equal to the target plant growth conditions, as experimental results show that the target plant growth conditions can fully degrade the antibiotic contaminant. After determining the estimated degradation time of the antibiotic contaminant, whether the degradation effect meets the standard is determined. If it does, the experimental simulation results are correct, and plants can be directly planted under the target plant growth conditions. If it does not meet the standard, dynamic adjustment of temperature and pH values ​​is required to achieve a true degradation rate that is no less than the standard true degradation rate.

[0082] Figure 2 A flow chart of a method for optimizing the growth conditions of target degradation plants is shown, comprising 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: Perform secondary experimental analysis on all qualified experimental sets, and optimize the growth conditions of the target degradation plants based on the secondary experimental analysis results.

[0085] Furthermore, 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:

[0086] In different experimental sets, the difference between the degradation rate of the experimental concentration and the degradation rate of the control concentration is calculated and calibrated as the degradation rate difference, and the standard degradation rate difference is preset;

[0087] 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;

[0088] In a class of experimental sets, the experimental conditions within the experimental group are extracted and calibrated as a class of experimental conditions, and the degradation combinations within the experimental group are calibrated as a class of degradation combinations;

[0089] Analyzing the first type of experimental conditions to determine feasibility of using the first type of experimental conditions, wherein the feasibility of using the first type of experimental conditions is determined by searching a big data network to determine whether environmental conditions in the contaminated soil area have ever been equal to the first type of experimental conditions;

[0090] If so, the feasibility of the application of a type of experimental conditions is judged to be qualified, and the corresponding type of experimental set is marked as a qualified experimental set. If not, the big data network is searched to see 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 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;

[0092] 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.

[0093] It should be noted that the degradation rate difference represents the degree and effect of degradation of antibiotic pollutants. The larger the degradation rate difference, the better the degradation effect. The experimental set with a degradation rate difference not less than the standard degradation rate difference is screened and calibrated as a type of experimental set. The plant growth conditions in the type of experimental set need to be analyzed again to determine whether they can be used in the soil, because there may be some plant growth conditions that will make the environmental pollution more serious when acting in the soil and are not in line with the actual situation. After obtaining a type of experimental conditions, the feasibility of the application of the type of experimental conditions is judged. The type of experimental conditions are screened according to the feasibility of application, and based on the screened type of experimental conditions, qualified experimental sets and unqualified experimental sets are generated.

[0094] Furthermore, in a preferred embodiment of the present invention, a 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:

[0095] In all qualified experimental sets, the degradation rate differences are analyzed respectively, and the experimental conditions corresponding to the maximum degradation rate difference are determined and calibrated as the target experimental conditions;

[0096] Retrieving a solution output from the big data network that can make the environmental conditions in the contaminated soil area equal to the target experimental conditions, marking it as an experimental condition improvement solution, and outputting the experimental condition improvement solution in the contaminated soil area;

[0097] 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.

[0098] It should be noted that the experimental condition output with the largest degradation rate difference is selected from all qualified experimental sets, that is, the target experimental condition is output, and the experimental condition improvement scheme output is retrieved in the big data network according to the target experimental condition, so as to generate the plant target growth condition.

[0099] As shown in Figure 3 The second aspect of the present application also provides a plant growth condition optimization system based on analysis of degradation effect of antibiotic pollutants, which comprises a memory 31 and a processor 32, the memory 31 stores a plant growth condition optimization method, and the plant growth condition optimization method is executed by the processor 32 to realize the following steps:

[0100] Based on the types and concentrations of antibiotic pollutants, plants capable of degrading antibiotic pollutants are screened from all plants and named as target degradation plants;

[0101] Experimental simulation of different types of antibiotic pollutants is carried out by the target degradation plants, and the concentration degradation rate of the antibiotic pollutants after the experimental simulation is 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 contaminated 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 degradation rates.

[0104] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for optimizing plant growth conditions based on 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 of different types of antibiotic pollutants were carried out using target degradation plants, and the concentration degradation rates of antibiotic pollutants after the 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 plant growth conditions are adjusted according to the degradation rates.

2. The method for optimizing plant growth conditions based on 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: determining an area where soil is contaminated by antibiotic contaminants, marking it as a contaminated soil area, and sampling 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; Concentrating the contaminated soil extract to obtain a concentrated contaminated soil extract, introducing the concentrated contaminated soil extract into a high performance liquid chromatograph, obtaining a big data network, and retrieving a standard mobile phase composition and a standard flow rate for separating antibiotic contaminants in a high performance liquid chromatograph from the big data network, and calibrating the mobile phase composition and the target flow rate to the target mobile phase composition and the target flow rate; The concentrated extract of the contaminated soil is poured into a high performance liquid chromatograph, a target mobile phase composition and a target flow rate are set in the high performance liquid chromatograph, and the high performance liquid chromatograph is run to simultaneously generate a chromatogram of the concentrated extract of the contaminated soil, which is calibrated as a chromatogram of antibiotic contaminants; On the antibiotic pollutant chromatogram, calculating the peak areas of different types of antibiotic pollutants, and generating the concentrations of the 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 are then marked at different locations in the contaminated soil area.

3. The method for optimizing plant growth conditions based on analysis of the degradation effect of antibiotic pollutants according to claim 1, characterized in that: The target degradation plants are used to simulate 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 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 antibiotic pollutant types; Retrieve the environmental conditions of the contaminated soil area through the big data network and calibrate them as the contaminated soil environmental conditions; Conduct antibiotic pollutant degradation experimental 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 the contaminated soil environment conditions. 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, the experimental conditions of the experimental groups are adjusted in different experimental sets. The concentration degradation rate of the antibiotic pollutants in the experimental groups under different experimental conditions is calculated and calibrated as the experimental concentration degradation rate. At the same time, the concentration degradation rate of the antibiotic pollutants in the control group is calculated and calibrated as the control concentration degradation rate. Finally, the experimental simulation results of all experimental sets are recorded.

4. The method for optimizing plant growth conditions based on 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 and 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 within the experimental group are extracted and calibrated as a class of experimental conditions, and the degradation combinations within the experimental group are calibrated as a class of degradation combinations; Analyzing the first type of experimental conditions to determine feasibility of using the first type of experimental conditions, wherein the feasibility of using the first type of experimental conditions is determined by searching a big data network to determine whether environmental conditions in the contaminated soil area have ever been equal to the first type of experimental conditions; If so, the feasibility of the application of a type of experimental conditions is judged to be qualified, and the corresponding type of experimental set is marked as a qualified experimental set. If not, the big data network is searched to see 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 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, and the experimental conditions corresponding to the maximum degradation rate difference are determined and calibrated as the target experimental conditions; Retrieving a solution output from the big data network that can make the environmental conditions in the contaminated soil area equal to the target experimental conditions, 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 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: Planting target degradation plants associated with the types of antibiotic pollutants in the contaminated soil area, and adjusting the growth conditions of the plants in the contaminated soil area to be equal to the target plant growth conditions; The estimated degradation time of the antibiotic pollutant is preset, and the time starts from the completion of the planting of the target degradation plant associated with the type of antibiotic pollutant. When the estimated degradation time of the antibiotic pollutant is exceeded, 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 pollutant 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 true degradation rate is less than the standard true 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 true degradation rate after the target plant growth conditions are adjusted until the true degradation rate is not less than the standard true 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. When the plant growth condition optimization method program is executed by the processor, the plant growth condition optimization method steps according to any one of claims 1 to 6 are implemented.

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