CN117172578B - Soil ecological environment restoration method and system based on microorganism identification - Google Patents
Soil ecological environment restoration method and system based on microorganism identification Download PDFInfo
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Abstract
The invention discloses a soil ecological environment restoration method and system based on microorganism identification, comprising the following steps: acquiring soil sample information of a target area, and carrying out soil microorganism identification according to the soil sample information to obtain microorganism identification information; carrying out pollution analysis on soil in a target area according to the microorganism identification information to obtain soil pollution analysis information; performing soil capacity analysis and soil quality evaluation on the soil in the target area to obtain soil capacity analysis information and soil quality evaluation information; acquiring expected use information of soil, performing gap analysis by combining the soil quality evaluation information, and performing repair demand analysis; and acquiring analysis information of the repair requirement, making a repair scheme, and selecting an optimal repair scheme. The ecological restoration effect of the soil is improved, and the friendliness and pertinence of ecological restoration are ensured, so that ecological balance and sustainable development are promoted.
Description
Technical Field
The invention relates to the technical field of microorganism identification and soil ecological environment restoration, in particular to a soil ecological environment restoration method and system based on microorganism identification.
Background
Soil is an important component of the earth's ecosystem, and its quality and ecological environmental status have important effects on agriculture, ecological protection, and human health. However, modern agricultural, industrial and urban processes result in soil suffering from various contaminations, including accumulation of chemicals and heavy metals, as well as damage to soil fertility and microbial diversity. These problems have a negative impact on the soil ecosystem, leading to plant growth limitations, soil degradation, ecological balance disruption, water source pollution, etc. Microorganisms play a vital role in the soil ecosystem, including the potential for beneficial microorganisms to maintain soil health and contaminant degradation. Therefore, a microorganism-based soil ecological restoration method is attracting attention. The development of microbial identification technology provides new opportunities in this area. By analyzing the diversity and functions of microorganisms in the soil, the soil condition can be better known, the formulation of a repairing scheme is guided, and the recovery of the ecological environment of the soil is promoted.
Conventional soil remediation methods typically involve physical and chemical methods such as soil addition, chemical treatment, or physical excavation. However, these methods tend to be expensive, destructive, and difficult to restore the natural state of the soil. Therefore, it is urgent to find a sustainable, environmentally friendly soil remediation method.
Disclosure of Invention
The invention overcomes the defects of the prior art, and provides a soil ecological environment restoration method and system based on microorganism identification, which have the important purposes of improving the effect of soil ecological environment restoration and ensuring the friendliness of ecological restoration.
In order to achieve the above object, the first aspect of the present invention provides a soil ecological environment restoration method based on microbial identification, comprising:
acquiring soil sample information of a target area, and carrying out soil microorganism identification according to the soil sample information to obtain microorganism identification information;
carrying out pollution analysis on soil in a target area according to the microorganism identification information to obtain soil pollution analysis information;
performing soil capacity analysis and soil quality evaluation on the soil in the target area to obtain soil capacity analysis information and soil quality evaluation information;
acquiring expected use information of soil, performing gap analysis by combining the soil quality evaluation information, and performing repair demand analysis;
and acquiring analysis information of the repair requirement, making a repair scheme, and selecting an optimal repair scheme.
In this scheme, obtain the soil sample information of target area, carry out soil microorganism identification according to soil sample information, specifically do:
Acquiring soil sample information of a target area based on different acquisition positions and depths, and extracting DNA from the soil sample information to obtain DNA extraction information;
performing metagenome sequencing on the DNA extraction information based on a high-throughput metagenome sequencing technology to obtain DNA sequence data of soil microorganisms in a target area and performing quality control to obtain microorganism sequence information;
constructing a microorganism identification model, acquiring DNA sequence information of various microorganisms based on big data retrieval, forming a training data set, and performing deep learning and training on the microorganism identification model;
the microorganism sequence information is imported into the microorganism identification model for identification analysis, so as to obtain microorganism identification information;
generating a species classification matrix according to the microorganism identification information, wherein each row represents one sample, each column represents different microorganism classification units, and each element in the matrix represents the relative abundance of each classification unit in the sample;
calculating Shannon indexes in the target soil sample according to the species classification matrix, and analyzing the diversity of microbial communities in a single sample to obtain first analysis information;
calculating UniFrac distances between different samples, and carrying out Beta diversity analysis by combining the first analysis information and the species classification matrix with the microorganism identification information to obtain second analysis information;
And fusing the first analysis information and the second analysis information to form ecological analysis information.
In this scheme, according to the microorganism identification information carries out pollution analysis to target area soil, specifically does:
acquiring the living environments and living quantity of microorganisms with different types and different pollution degrees based on big data retrieval to form a comparison data set;
performing preferential living environment analysis on each kind of microorganism according to the comparison data set, extracting living numbers of each kind of microorganism in different environments, and judging with a preset threshold value to obtain preferential living environment analysis information;
carrying out relevance analysis according to the analysis information of the preferential living environment and the comparison data set, and extracting the living numbers of microorganisms with different pollution degrees under the preferential living environment to be used as relevance support;
presetting an association judgment threshold, judging the association support degree and the association judgment threshold, and associating the pollution degree of various living environments with the types and the number of microorganisms according to the judgment result to obtain association analysis information;
acquiring soil history use information, and extracting attribute characteristics of the soil history use information to obtain soil attribute characteristic information;
Constructing a pollution analysis model, constructing a training data set based on the relevance analysis information, and performing deep learning and training on the pollution analysis model to obtain a pollution analysis model which meets expectations;
and acquiring microorganism identification information and ecological analysis information, and carrying out pollution analysis on the soil in the target area through a pollution analysis model by combining the soil attribute characteristic information to obtain pollution analysis information.
In this scheme, carry out soil ability analysis and soil quality evaluation to target area soil, specifically do:
acquiring soil sample information, microorganism identification information, ecological analysis information and pollution analysis information;
detecting soil in a target area according to the soil sample information to obtain soil index information of different depths in different positions;
presetting a plurality of index judgment thresholds, judging soil index information and the index judgment thresholds, and analyzing the soil capacity of a target area to obtain soil capacity analysis information;
according to the ecological analysis information and the microorganism identification information, carrying out ecological advantage analysis, extracting the relative abundance of various microorganisms in soil as the ecological dominance, and judging with a preset threshold value to obtain dominant microorganism analysis information;
Constructing a benefit analysis model, inputting the dominant microorganism analysis information and the soil index information into the benefit analysis model, and analyzing the benefit of the dominant microorganism on the target soil to obtain benefit analysis information;
and constructing a soil quality assessment system based on an expert knowledge method, setting corresponding assessment weights for various assessment indexes, and carrying out soil quality assessment by combining benefit analysis information, soil index information, soil capacity analysis information and pollution analysis information to obtain soil quality assessment information.
In this scheme, obtain the soil and expect the purpose information, combine the soil quality evaluation information to carry out the gap analysis to carry out restoration demand analysis, specifically do:
acquiring expected soil use information and soil quality evaluation information, and searching according to the expected soil use information to acquire proper soil quality information;
performing gap analysis on the suitable soil quality information and the soil quality evaluation information, and calculating the deviation between the current soil environment and the suitable soil environment to obtain gap analysis information;
calculating Euclidean distance between the suitable soil quality information and the soil expected use information, and analyzing the relation between various soil indexes and the soil expected use through the Euclidean distance to obtain main soil index information;
Performing repair direction priority analysis according to the main soil index information and the gap analysis information, extracting the difference value of each index, sorting the indexes, and classifying the indexes by combining the sorting result based on a clustering algorithm to obtain repair direction priority information;
and combining the gap analysis information, the main soil index information and the repair direction priority information to form repair requirement analysis information.
In the scheme, the repairing scheme is formulated, and an optimal repairing scheme is selected, specifically:
constructing a repair scheme making model based on a particle swarm optimization algorithm and a simulated annealing algorithm, acquiring various soil repair scheme examples based on big data retrieval, constructing a training data set, and performing deep learning and training on the repair scheme making model;
acquiring repair demand analysis information, pollution analysis information, ecological analysis information and benefit analysis information, constructing an objective function and setting constraint conditions according to the repair demand analysis information, and presetting a stopping criterion;
importing the repair demand analysis information, the pollution analysis information, the ecological analysis information and the benefit analysis information into a repair scheme making model to make a repair scheme;
Generating an initial particle swarm randomly according to the imported information through a particle swarm optimization algorithm, calculating an initial fitness value of each particle, judging with a preset threshold value, and selecting particles larger than the preset threshold value to update the position and the speed to obtain initial solution information;
extracting the fitness value of each particle in the initial solution information, sorting, selecting the particle with the highest fitness as a candidate optimal solution, generating a new solution by combining the candidate optimal solution based on a simulated annealing algorithm, and calculating the fitness value of the new solution;
presetting an acceptance criterion, judging the fitness value of the new solution and the acceptance criterion, judging whether the new solution is accepted or not, and performing iterative optimization according to a judging result until the new solution meets a stopping criterion or reaches the maximum iterative times to obtain candidate repair scheme information;
extracting the attribute of the candidate repair scheme information, and extracting the aging attribute, the cost attribute and the effect attribute of each scheme to obtain repair scheme attribute information;
and acquiring restoration expected information, setting attribute weight according to the restoration expected information, carrying out weighted calculation on candidate restoration schemes by combining the restoration scheme attribute information, and selecting an optimal restoration scheme according to a weighted calculation result.
The second aspect of the invention provides a soil ecological environment restoration system based on microorganism identification, which comprises: the soil ecological environment restoration method comprises a memory and a processor, wherein the memory contains a soil ecological environment restoration method program based on microorganism identification, and the soil ecological environment restoration method program based on microorganism identification realizes the following steps when being executed by the processor:
acquiring soil sample information of a target area, and carrying out soil microorganism identification according to the soil sample information to obtain microorganism identification information;
carrying out pollution analysis on soil in a target area according to the microorganism identification information to obtain soil pollution analysis information;
performing soil capacity analysis and soil quality evaluation on the soil in the target area to obtain soil capacity analysis information and soil quality evaluation information;
acquiring expected use information of soil, performing gap analysis by combining the soil quality evaluation information, and performing repair demand analysis;
and acquiring analysis information of the repair requirement, making a repair scheme, and selecting an optimal repair scheme.
The invention discloses a soil ecological environment restoration method and system based on microorganism identification, comprising the following steps: acquiring soil sample information of a target area, and carrying out soil microorganism identification according to the soil sample information to obtain microorganism identification information; carrying out pollution analysis on soil in a target area according to the microorganism identification information to obtain soil pollution analysis information; performing soil capacity analysis and soil quality evaluation on the soil in the target area to obtain soil capacity analysis information and soil quality evaluation information; acquiring expected use information of soil, performing gap analysis by combining the soil quality evaluation information, and performing repair demand analysis; and acquiring analysis information of the repair requirement, making a repair scheme, and selecting an optimal repair scheme. The ecological restoration effect of the soil is improved, and the friendliness and pertinence of ecological restoration are ensured, so that ecological balance and sustainable development are promoted.
Drawings
In order to more clearly illustrate the technical solutions of embodiments or examples of the present invention, the drawings that are required to be used in the embodiments or examples of the present invention will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive efforts for those skilled in the art.
FIG. 1 is a flowchart of a soil ecological environment restoration method based on microorganism identification according to an embodiment of the present invention;
FIG. 2 is a flowchart of an embodiment of the present invention for obtaining an optimal repair solution;
FIG. 3 is a block diagram of a soil ecological restoration system based on microorganism identification according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
FIG. 1 is a flowchart of a soil ecological environment restoration method based on microorganism identification according to an embodiment of the present invention;
as shown in FIG. 1, the invention provides a soil ecological environment restoration method flow chart based on microorganism identification, which comprises the following steps:
s102, acquiring soil sample information of a target area, and carrying out soil microorganism identification according to the soil sample information to obtain microorganism identification information;
acquiring soil sample information of a target area based on different acquisition positions and depths, and extracting DNA from the soil sample information to obtain DNA extraction information;
performing metagenome sequencing on the DNA extraction information based on a high-throughput metagenome sequencing technology to obtain DNA sequence data of soil microorganisms in a target area and performing quality control to obtain microorganism sequence information;
constructing a microorganism identification model, acquiring DNA sequence information of various microorganisms based on big data retrieval, forming a training data set, and performing deep learning and training on the microorganism identification model;
The microorganism sequence information is imported into the microorganism identification model for identification analysis, so as to obtain microorganism identification information;
generating a species classification matrix according to the microorganism identification information, wherein each row represents one sample, each column represents different microorganism classification units, and each element in the matrix represents the relative abundance of each classification unit in the sample;
calculating Shannon indexes in the target soil sample according to the species classification matrix, and analyzing the diversity of microbial communities in a single sample to obtain first analysis information;
calculating UniFrac distances between different samples, and carrying out Beta diversity analysis by combining the first analysis information and the species classification matrix with the microorganism identification information to obtain second analysis information;
and fusing the first analysis information and the second analysis information to form ecological analysis information.
The method is characterized in that the target area is subjected to microorganism identification based on a high-throughput metagenome sequencing technology, and the microorganism survival condition of the soil of the target area can be clearly known by analyzing the types and the numbers of microorganisms in the collected soil at different positions and depths; and then, further carrying out ecological analysis according to the microbial identification information, and analyzing the microbial ecological structures in a single sample and the microbial ecological structures of a plurality of samples, so that the diversity and the diversity of soil microbial communities are more comprehensively known, and the overall condition of the soil ecological system is mapped.
S104, carrying out pollution analysis on the soil of the target area according to the microorganism identification information to obtain soil pollution analysis information;
acquiring the living environments and living quantity of microorganisms with different types and different pollution degrees based on big data retrieval to form a comparison data set;
performing preferential living environment analysis on each kind of microorganism according to the comparison data set, extracting living numbers of each kind of microorganism in different environments, and judging with a preset threshold value to obtain preferential living environment analysis information;
carrying out relevance analysis according to the analysis information of the preferential living environment and the comparison data set, and extracting the living numbers of microorganisms with different pollution degrees under the preferential living environment to be used as relevance support;
presetting an association judgment threshold, judging the association support degree and the association judgment threshold, and associating the pollution degree of various living environments with the types and the number of microorganisms according to the judgment result to obtain association analysis information;
acquiring soil history use information, and extracting attribute characteristics of the soil history use information to obtain soil attribute characteristic information;
constructing a pollution analysis model, constructing a training data set based on the relevance analysis information, and performing deep learning and training on the pollution analysis model to obtain a pollution analysis model which meets expectations;
And acquiring microorganism identification information and ecological analysis information, and carrying out pollution analysis on the soil in the target area through a pollution analysis model by combining the soil attribute characteristic information to obtain pollution analysis information.
It should be noted that, by analyzing the preferential living environment of various microorganisms, the types and the numbers of the living microorganisms in different pollution levels are analyzed according to the preferential living environment obtained by analysis, and the preferential living environment, the pollution levels and the types are related to each other, so that when the pollution analysis is performed on the soil in the target area, the pollution levels and the types of the area can be mapped from the angle of the microorganisms, meanwhile, the soil pollution condition can be further analyzed according to the microorganism identification information of different depths in different positions, the pollution condition of different soil depths can be analyzed, and more information can be provided for the subsequent ecological restoration of the soil.
S106, carrying out soil capacity analysis and soil quality evaluation on the soil in the target area to obtain soil capacity analysis information and soil quality evaluation information;
acquiring soil sample information, microorganism identification information, ecological analysis information and pollution analysis information;
detecting soil in a target area according to the soil sample information to obtain soil index information of different depths in different positions;
Presetting a plurality of index judgment thresholds, judging soil index information and the index judgment thresholds, and analyzing the soil capacity of a target area to obtain soil capacity analysis information;
according to the ecological analysis information and the microorganism identification information, carrying out ecological advantage analysis, extracting the relative abundance of various microorganisms in soil as the ecological dominance, and judging with a preset threshold value to obtain dominant microorganism analysis information;
constructing a benefit analysis model, inputting the dominant microorganism analysis information and the soil index information into the benefit analysis model, and analyzing the benefit of the dominant microorganism on the target soil to obtain benefit analysis information;
and constructing a soil quality assessment system based on an expert knowledge method, setting corresponding assessment weights for various assessment indexes, and carrying out soil quality assessment by combining benefit analysis information, soil index information, soil capacity analysis information and pollution analysis information to obtain soil quality assessment information.
S108, acquiring the expected use information of the soil, performing gap analysis by combining the soil quality evaluation information, and performing repair demand analysis;
acquiring expected soil use information and soil quality evaluation information, and searching according to the expected soil use information to acquire proper soil quality information;
Performing gap analysis on the suitable soil quality information and the soil quality evaluation information, and calculating the deviation between the current soil environment and the suitable soil environment to obtain gap analysis information;
calculating Euclidean distance between the suitable soil quality information and the soil expected use information, and analyzing the relation between various soil indexes and the soil expected use through the Euclidean distance to obtain main soil index information;
performing repair direction priority analysis according to the main soil index information and the gap analysis information, extracting the difference value of each index, sorting the indexes, and classifying the indexes by combining the sorting result based on a clustering algorithm to obtain repair direction priority information;
and combining the gap analysis information, the main soil index information and the repair direction priority information to form repair requirement analysis information.
S110, acquiring analysis information of repair requirements, making a repair scheme, and selecting an optimal repair scheme;
constructing a repair scheme making model based on a particle swarm optimization algorithm and a simulated annealing algorithm, acquiring various soil repair scheme examples based on big data retrieval, constructing a training data set, and performing deep learning and training on the repair scheme making model;
Acquiring repair demand analysis information, pollution analysis information, ecological analysis information and benefit analysis information, constructing an objective function and setting constraint conditions according to the repair demand analysis information, and presetting a stopping criterion;
importing the repair demand analysis information, the pollution analysis information, the ecological analysis information and the benefit analysis information into a repair scheme making model to make a repair scheme;
generating an initial particle swarm randomly according to the imported information through a particle swarm optimization algorithm, calculating an initial fitness value of each particle, judging with a preset threshold value, and selecting particles larger than the preset threshold value to update the position and the speed to obtain initial solution information;
extracting the fitness value of each particle in the initial solution information, sorting, selecting the particle with the highest fitness as a candidate optimal solution, generating a new solution by combining the candidate optimal solution based on a simulated annealing algorithm, and calculating the fitness value of the new solution;
presetting an acceptance criterion, judging the fitness value of the new solution and the acceptance criterion, judging whether the new solution is accepted or not, and performing iterative optimization according to a judging result until the new solution meets a stopping criterion or reaches the maximum iterative times to obtain candidate repair scheme information;
Extracting the attribute of the candidate repair scheme information, and extracting the aging attribute, the cost attribute and the effect attribute of each scheme to obtain repair scheme attribute information;
and acquiring restoration expected information, setting attribute weight according to the restoration expected information, carrying out weighted calculation on candidate restoration schemes by combining the restoration scheme attribute information, and selecting an optimal restoration scheme according to a weighted calculation result.
It should be noted that, a repair scheme making model is constructed based on a particle swarm optimization algorithm and a simulated annealing algorithm, and the two algorithms are combined for use, so that the global searching capability of the repair scheme making process is enlarged, meanwhile, the characteristics of various schemes can be combined, and scheme combination can be performed according to repair requirement analysis information, so that a repair scheme meeting expectations is obtained, and the accuracy and efficiency of decision making are improved; determining an optimization target, such as one or more optimization targets of efficient repair, environmental protection or ecological benefit, according to the repair demand analysis information, so as to construct an objective function, and construct a repair scheme conforming to the optimization target, so that the method is greatly close to repair expectations; and then, analyzing the attributes of the candidate restoration schemes, wherein the restoration expected information is the time, cost and effect of the expected restoration, and setting weight according to the restoration expected information, so that the final scheme with good restoration effect, moderate cost and proper restoration time is screened out, and the soil pollution treatment and sustainable land utilization are realized.
Further, pollution analysis information is obtained, and the type and the capability of pollution degradation microorganisms are obtained according to the pollution analysis information, so that degradation microorganism information is obtained; acquiring microorganism identification information and soil quality evaluation information, and analyzing the content of degrading microorganisms in the soil of the target area by combining the degradation microorganism information to obtain degradation microorganism content information; presetting a content judgment threshold, and judging the content information of the degrading microorganisms and the content judgment threshold to obtain judgment result information; if the judging result information is that the content is insufficient, reinforcing the soil of the target area; acquiring microbial agents of various degrading microorganisms and applicable soil information based on big data retrieval to form a comparison data set; recommending the microbial inoculum according to the soil quality evaluation information and the comparison data set to obtain microbial inoculum recommendation information; formulating a microorganism strengthening scheme according to the recommended information of the microbial inoculum, enhancing the degradation capability of soil microorganisms and repairing the soil pollution of a target area; if the judgment result shows that the microbial degradation agent does not contain the degrading microbes, recommending the degrading microbes according to the microbial identification information, the soil quality evaluation information and the degrading microbial information to obtain recommended degrading microbial information; and (3) combining the degradation microorganism information and the comparison data set to formulate a microorganism throwing scheme, and carrying out ecological restoration on the soil in the target area according to the microorganism throwing scheme, so that the soil quality is improved, the pollution is reduced, and the ecological balance is promoted.
FIG. 2 is a flowchart of an embodiment of the present invention for obtaining an optimal repair solution;
as shown in fig. 2, the present invention provides a flowchart for obtaining an optimal repair scheme, including:
s202, acquiring soil sample information of a target area, and carrying out soil microorganism identification and ecological analysis;
s204, acquiring soil history use information, and carrying out pollution analysis on the soil in the target area;
s206, analyzing the soil capacity and the soil quality of the soil in the target area, and analyzing the restoration requirement;
s208, constructing an objective function and preset conditions based on the repair demand analysis information, and carrying out scheme formulation through a repair scheme formulation model;
s210, extracting attributes of candidate repair schemes, and carrying out weighted calculation on the candidate repair schemes by combining with repair expected information;
s212, selecting an optimal repair scheme according to the weighted calculation result.
Further, based on big data retrieval, obtaining repair benefit examples of various repair schemes in different repair stages or times, and forming a benefit training data set; constructing a repair benefit prediction model based on a random forest algorithm, and performing deep learning and training through the benefit training data set; acquiring an optimal repair scheme, and extracting features of the optimal repair scheme to obtain feature information of the optimal repair scheme; importing the characteristic information of the optimal repair scheme and the optimal repair scheme into a repair benefit prediction model to perform benefit prediction, so as to obtain repair benefit prediction information; performing soil quality assessment according to the repair benefit prediction information, and analyzing the soil quality of different repair stages to obtain repair soil quality assessment information; acquiring historical proper planting information of a target area, and analyzing proper planting plants in each repair stage by combining the repair soil quality evaluation information to obtain proper planting information; constructing a planting influence analysis model, and inputting the optimal repair scheme information and the proper planting information into the planting influence analysis model for analysis to obtain planting influence analysis information; according to the planting influence analysis information and the proper planting information, planting recommendation of each repair stage is carried out, and planting recommendation information is obtained; presetting a restoration benefit judgment threshold, judging the restoration benefit prediction information and the preset threshold, dividing the soil of the target area according to a judgment result, and carrying out planting planning by combining with planting recommendation information to obtain planting planning information; and planting suggestions are carried out according to the planting planning information and the planting recommendation information, so that the soil restoration effect and the health of an ecological system are improved, economic benefits are brought, and good soil utilization is promoted.
Fig. 3 is a block diagram 3 of a soil ecological environment restoration system based on microorganism identification according to an embodiment of the present invention, where the system includes: the device comprises a memory 31 and a processor 32, wherein the memory 31 contains a soil ecological environment restoration method program based on microorganism identification, and the soil ecological environment restoration method program based on microorganism identification realizes the following steps when being executed by the processor 32:
acquiring soil sample information of a target area, and carrying out soil microorganism identification according to the soil sample information to obtain microorganism identification information;
carrying out pollution analysis on soil in a target area according to the microorganism identification information to obtain soil pollution analysis information;
performing soil capacity analysis and soil quality evaluation on the soil in the target area to obtain soil capacity analysis information and soil quality evaluation information;
acquiring expected use information of soil, performing gap analysis by combining the soil quality evaluation information, and performing repair demand analysis;
and acquiring analysis information of the repair requirement, making a repair scheme, and selecting an optimal repair scheme.
The invention provides a soil ecological environment restoration method and a system based on microorganism identification, which are used for carrying out microorganism identification and ecological analysis on soil in a target area by adopting a metagenome technology so as to carry out detailed analysis on microbial communities in the target area; the relationship between the microorganisms and the pollution types and degrees is further analyzed through the microbial identification information and the ecological analysis information obtained through analysis, so that the pollution condition of the target soil is mapped; then, carrying out soil capacity analysis and soil quality evaluation, judging the current condition and quality of the soil in more detail, and carrying out gap analysis in combination with the expected use so as to analyze the repair requirement; and finally, making a repair scheme according to the repair requirement, and taking feasibility, timeliness and benefit of the repair scheme into consideration to make an optimal soil repair scheme, so as to ensure sustainable use and ecological restoration of the land and promote ecological balance development.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The soil ecological environment restoration method based on microorganism identification is characterized by comprising the following steps of:
acquiring soil sample information of a target area, and carrying out soil microorganism identification according to the soil sample information to obtain microorganism identification information;
carrying out pollution analysis on soil in a target area according to the microorganism identification information to obtain soil pollution analysis information;
performing soil capacity analysis and soil quality evaluation on the soil in the target area to obtain soil capacity analysis information and soil quality evaluation information;
acquiring expected use information of soil, performing gap analysis by combining the soil quality evaluation information, and performing repair demand analysis;
acquiring analysis information of the repair requirement, making a repair scheme, and selecting an optimal repair scheme;
the method for carrying out pollution analysis on the soil in the target area according to the microorganism identification information specifically comprises the following steps:
Acquiring the living environments and living quantity of microorganisms with different types and different pollution degrees based on big data retrieval to form a comparison data set;
according to the comparison data set, carrying out preferential living environment analysis on each kind of microorganism, extracting living numbers of each kind of microorganism in different environments, and judging with a preset threshold value to obtain preferential living environment analysis information;
carrying out relevance analysis according to the analysis information of the preferential living environment and the comparison data set, and extracting the living numbers of microorganisms with different pollution degrees under the preferential living environment to be used as relevance support;
presetting an association judgment threshold, judging the association support degree and the association judgment threshold, and associating the pollution degree of various living environments with the types and the number of microorganisms according to the judgment result to obtain association analysis information;
acquiring soil history use information, and extracting attribute characteristics of the soil history use information to obtain soil attribute characteristic information;
constructing a pollution analysis model, constructing a training data set based on the relevance analysis information, and performing deep learning and training on the pollution analysis model to obtain a pollution analysis model which meets expectations;
Acquiring microorganism identification information and ecological analysis information, and carrying out pollution analysis on soil in a target area through a pollution analysis model by combining soil attribute characteristic information to obtain pollution analysis information;
the soil capacity analysis and soil quality evaluation of the soil in the target area specifically comprise the following steps:
acquiring soil sample information, microorganism identification information, ecological analysis information and pollution analysis information;
detecting soil in a target area according to the soil sample information to obtain soil index information of different depths in different positions;
presetting a plurality of index judgment thresholds, judging soil index information and the index judgment thresholds, and analyzing the soil capacity of a target area to obtain soil capacity analysis information;
according to the ecological analysis information and the microorganism identification information, carrying out ecological advantage analysis, extracting the relative abundance of various microorganisms in soil as the ecological dominance, and judging with a preset threshold value to obtain dominant microorganism analysis information;
constructing a benefit analysis model, inputting the dominant microorganism analysis information and the soil index information into the benefit analysis model, and analyzing the benefit of the dominant microorganism on the target soil to obtain benefit analysis information;
And constructing a soil quality assessment system based on an expert knowledge method, setting corresponding assessment weights for various assessment indexes, and carrying out soil quality assessment by combining benefit analysis information, soil index information, soil capacity analysis information and pollution analysis information to obtain soil quality assessment information.
2. The method for restoring soil ecological environment based on microbial identification according to claim 1, wherein the method for acquiring soil sample information of a target area and carrying out soil microbial identification according to the soil sample information comprises the following steps:
acquiring soil sample information of a target area based on different acquisition positions and depths, and extracting DNA from the soil sample information to obtain DNA extraction information;
performing metagenome sequencing on the DNA extraction information based on a high-throughput metagenome sequencing technology to obtain DNA sequence data of soil microorganisms in a target area and performing quality control to obtain microorganism sequence information;
constructing a microorganism identification model, acquiring DNA sequence information of various microorganisms based on big data retrieval, forming a training data set, and performing deep learning and training on the microorganism identification model;
the microorganism sequence information is imported into the microorganism identification model for identification analysis, so as to obtain microorganism identification information;
Generating a species classification matrix according to the microorganism identification information, wherein each row represents one sample, each column represents different microorganism classification units, and each element in the matrix represents the relative abundance of each classification unit in the sample;
calculating Shannon indexes in the target soil sample according to the species classification matrix, and analyzing the diversity of microbial communities in a single sample to obtain first analysis information;
calculating UniFrac distances between different samples, and carrying out Beta diversity analysis by combining the first analysis information and the species classification matrix with the microorganism identification information to obtain second analysis information;
and fusing the first analysis information and the second analysis information to form ecological analysis information.
3. The method for restoring soil ecological environment based on microbial identification according to claim 1, wherein the steps of obtaining the information of expected use of soil, performing gap analysis in combination with the soil quality evaluation information, and performing restoration demand analysis comprise:
acquiring expected soil use information and soil quality evaluation information, and searching according to the expected soil use information to acquire proper soil quality information;
performing gap analysis on the suitable soil quality information and the soil quality evaluation information, and calculating the deviation between the current soil environment and the suitable soil environment to obtain gap analysis information;
Calculating Euclidean distance between the suitable soil quality information and the soil expected use information, and analyzing the relation between various soil indexes and the soil expected use through the Euclidean distance to obtain main soil index information;
performing repair direction priority analysis according to the main soil index information and the gap analysis information, extracting the difference value of each index, sorting the indexes, and classifying the indexes by combining the sorting result based on a clustering algorithm to obtain repair direction priority information;
and combining the gap analysis information, the main soil index information and the repair direction priority information to form repair requirement analysis information.
4. The method for restoring the soil ecological environment based on the microbial identification according to claim 1, wherein the steps of making a restoring scheme and selecting an optimal restoring scheme are as follows:
constructing a repair scheme making model based on a particle swarm optimization algorithm and a simulated annealing algorithm, acquiring various soil repair scheme examples based on big data retrieval, constructing a training data set, and performing deep learning and training on the repair scheme making model;
acquiring repair demand analysis information, pollution analysis information, ecological analysis information and benefit analysis information, constructing an objective function and setting constraint conditions according to the repair demand analysis information, and presetting a stopping criterion;
Importing the repair demand analysis information, the pollution analysis information, the ecological analysis information and the benefit analysis information into a repair scheme making model to make a repair scheme;
generating an initial particle swarm randomly according to the imported information through a particle swarm optimization algorithm, calculating an initial fitness value of each particle, judging with a preset threshold value, and selecting particles larger than the preset threshold value to update the position and the speed to obtain initial solution information;
extracting the fitness value of each particle in the initial solution information, sorting, selecting the particle with the highest fitness as a candidate optimal solution, generating a new solution by combining the candidate optimal solution based on a simulated annealing algorithm, and calculating the fitness value of the new solution;
presetting an acceptance criterion, judging the fitness value of the new solution and the acceptance criterion, judging whether the new solution is accepted or not, and performing iterative optimization according to a judging result until the new solution meets a stopping criterion or reaches the maximum iterative times to obtain candidate repair scheme information;
extracting the attribute of the candidate repair scheme information, and extracting the aging attribute, the cost attribute and the effect attribute of each scheme to obtain repair scheme attribute information;
and acquiring restoration expected information, setting attribute weight according to the restoration expected information, carrying out weighted calculation on candidate restoration schemes by combining the restoration scheme attribute information, and selecting an optimal restoration scheme according to a weighted calculation result.
5. A soil ecological restoration system based on microbial identification, the system comprising: the soil ecological environment restoration method comprises a memory and a processor, wherein the memory contains a soil ecological environment restoration method program based on microorganism identification, and the soil ecological environment restoration method program based on microorganism identification realizes the following steps when being executed by the processor:
acquiring soil sample information of a target area, and carrying out soil microorganism identification according to the soil sample information to obtain microorganism identification information;
carrying out pollution analysis on soil in a target area according to the microorganism identification information to obtain soil pollution analysis information;
performing soil capacity analysis and soil quality evaluation on the soil in the target area to obtain soil capacity analysis information and soil quality evaluation information;
acquiring expected use information of soil, performing gap analysis by combining the soil quality evaluation information, and performing repair demand analysis;
acquiring analysis information of the repair requirement, making a repair scheme, and selecting an optimal repair scheme;
the method for carrying out pollution analysis on the soil in the target area according to the microorganism identification information specifically comprises the following steps:
acquiring the living environments and living quantity of microorganisms with different types and different pollution degrees based on big data retrieval to form a comparison data set;
According to the comparison data set, carrying out preferential living environment analysis on each kind of microorganism, extracting living numbers of each kind of microorganism in different environments, and judging with a preset threshold value to obtain preferential living environment analysis information;
carrying out relevance analysis according to the analysis information of the preferential living environment and the comparison data set, and extracting the living numbers of microorganisms with different pollution degrees under the preferential living environment to be used as relevance support;
presetting an association judgment threshold, judging the association support degree and the association judgment threshold, and associating the pollution degree of various living environments with the types and the number of microorganisms according to the judgment result to obtain association analysis information;
acquiring soil history use information, and extracting attribute characteristics of the soil history use information to obtain soil attribute characteristic information;
constructing a pollution analysis model, constructing a training data set based on the relevance analysis information, and performing deep learning and training on the pollution analysis model to obtain a pollution analysis model which meets expectations;
acquiring microorganism identification information and ecological analysis information, and carrying out pollution analysis on soil in a target area through a pollution analysis model by combining soil attribute characteristic information to obtain pollution analysis information;
The soil capacity analysis and soil quality evaluation of the soil in the target area specifically comprise the following steps:
acquiring soil sample information, microorganism identification information, ecological analysis information and pollution analysis information;
detecting soil in a target area according to the soil sample information to obtain soil index information of different depths in different positions;
presetting a plurality of index judgment thresholds, judging soil index information and the index judgment thresholds, and analyzing the soil capacity of a target area to obtain soil capacity analysis information;
according to the ecological analysis information and the microorganism identification information, carrying out ecological advantage analysis, extracting the relative abundance of various microorganisms in soil as the ecological dominance, and judging with a preset threshold value to obtain dominant microorganism analysis information;
constructing a benefit analysis model, inputting the dominant microorganism analysis information and the soil index information into the benefit analysis model, and analyzing the benefit of the dominant microorganism on the target soil to obtain benefit analysis information;
and constructing a soil quality assessment system based on an expert knowledge method, setting corresponding assessment weights for various assessment indexes, and carrying out soil quality assessment by combining benefit analysis information, soil index information, soil capacity analysis information and pollution analysis information to obtain soil quality assessment information.
6. The soil ecological environment restoration system based on the microbial identification according to claim 5, wherein the obtaining of the soil sample information of the target area, and the performing of the soil microbial identification according to the soil sample information, specifically comprise:
acquiring soil sample information of a target area based on different acquisition positions and depths, and extracting DNA from the soil sample information to obtain DNA extraction information;
performing metagenome sequencing on the DNA extraction information based on a high-throughput metagenome sequencing technology to obtain DNA sequence data of soil microorganisms in a target area and performing quality control to obtain microorganism sequence information;
constructing a microorganism identification model, acquiring DNA sequence information of various microorganisms based on big data retrieval, forming a training data set, and performing deep learning and training on the microorganism identification model;
the microorganism sequence information is imported into the microorganism identification model for identification analysis, so as to obtain microorganism identification information;
generating a species classification matrix according to the microorganism identification information, wherein each row represents one sample, each column represents different microorganism classification units, and each element in the matrix represents the relative abundance of each classification unit in the sample;
Calculating Shannon indexes in the target soil sample according to the species classification matrix, and analyzing the diversity of microbial communities in a single sample to obtain first analysis information;
calculating UniFrac distances between different samples, and carrying out Beta diversity analysis by combining the first analysis information and the species classification matrix with the microorganism identification information to obtain second analysis information;
and fusing the first analysis information and the second analysis information to form ecological analysis information.
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