CN117172137B - Digital twinning-based soil microorganism control analysis method and system - Google Patents
Digital twinning-based soil microorganism control analysis method and system Download PDFInfo
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Abstract
The invention discloses a soil microorganism control analysis method and system based on digital twinning, and aims to solve the maintenance and recovery problems of the balance condition of soil microorganism population. According to the method, firstly, microbial information is acquired, and the influence degree of each microbe on balance is analyzed to form an influence degree list. Further, a plant growing plan for the target area is determined. Then, a digital twin technology is adopted to construct a plant simulated growth model, and the growth condition and the distribution position of plant rhizosphere are predicted. Further judging the distribution range and the quantity of plant rhizosphere microorganisms, and evaluating the recovery effect of the balance condition of the soil microorganism population. And finally, updating the plant planting scheme according to the evaluation result. The invention provides a high-efficiency and accurate method for monitoring, maintaining and repairing the microbial population balance of soil by combining a digital twin technology with microbial control, and has important significance for improving soil health and agricultural production.
Description
Technical Field
The invention relates to the technical field of soil microorganism control, in particular to a soil microorganism control analysis method and system based on digital twin.
Background
Soil microorganisms play a key role in maintaining the health of the soil ecosystem and in agricultural production. The balance of the soil microbial population directly affects soil quality, plant growth and ecological balance. However, due to factors such as climate change, soil pollution and unreasonable land management, the balance of soil microbial populations is often compromised and destroyed, leading to soil health problems and instability in agricultural production.
Traditional soil microbial control methods rely primarily on laboratory analysis and empirical soil management strategies, which have limitations including time consuming, expensive, imprecise, and difficult application to large-scale land. Therefore, there is an urgent need for an efficient, accurate, sustainable soil microorganism control method to maintain and restore the balance of soil microorganism populations, improve soil health and sustainability of agricultural production.
Digital twinning technology is an emerging method based on computer simulation and data analysis, which has been successfully applied in a plurality of fields. The digital twin technology is introduced into the field of soil microorganism control, so that the accurate monitoring, simulation and control of the soil microorganism population balance can be realized. The technology can comprehensively consider a plurality of factors such as soil properties, meteorological conditions, plant growth and the like, provide scientific basis for decision makers, optimize soil management strategies, and therefore improve soil health and stability of agricultural production.
Therefore, the invention aims to provide a soil microorganism control analysis method and system based on digital twinning, which realize the monitoring, maintenance and recovery of the soil microorganism population balance by periodically collecting soil samples, analyzing microorganism information, optimizing a plant planting scheme and predicting a digital twinning model, and provide a new and efficient solution for improving soil health and agricultural sustainability.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a soil microorganism control analysis method and system based on digital twinning.
The first aspect of the invention provides a soil microorganism control analysis method based on digital twinning, which comprises the following steps:
acquiring a soil sample of a target area according to a preset period, and analyzing the soil sample to obtain microbial information of the soil, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information;
judging the soil microorganism population balance condition based on microorganism information, and if the soil microorganism population balance condition is lower than a preset balance value, analyzing the influence degree of each microorganism in the soil on the soil microorganism population balance condition to obtain a soil microorganism influence degree list;
Screening a plant list of microorganisms in the soil microorganism influence degree list in plant rhizosphere microorganisms, and selecting proper plants in a target area from the plant list to obtain a target area plant planting scheme;
generating a plant simulated growth model based on a digital twin technology, introducing a target area plant planting scheme into the plant simulated growth model, and predicting plant rhizosphere growth conditions and distribution positions according to the plant simulated growth model to obtain a prediction result;
judging the distribution range and the quantity of plant rhizosphere microorganisms according to the prediction result, and evaluating the soil microorganism population balance condition recovery effect of the target area through the distribution range and the quantity of the plant rhizosphere microorganisms;
and updating a target area plant planting scheme according to the soil microorganism population balance recovery effect.
In this scheme, obtain the soil sample in target area according to predetermineeing the cycle, obtain the microorganism information of soil through analyzing the soil sample, microorganism information includes microorganism name, microorganism type change information, microorganism abundance change information, specifically does:
obtaining soil samples of a target area according to a preset period, diluting the soil samples, and placing a plurality of diluted soil samples into a selective culture medium rich in different nutrients for culture to obtain microbial colonies;
Acquiring historical morphological characteristic data of microbial colonies in soil, wherein the morphological characteristic data comprise colors, shapes, sizes and edge characteristics of the microbial colonies;
shooting the microbial colony in each culture medium to obtain a microbial colony image set;
extracting features of the image set of the microbial colony based on an image processing technology to obtain morphological feature data of the microbial colony;
comparing the morphological characteristic data of the microorganisms with the historical morphological characteristic data of the microorganisms to obtain microorganism names of various microorganism colonies in the culture medium;
analyzing the microorganism name obtained in each period, and judging the change information of the microorganism type;
and evaluating the microorganism abundance information based on the microorganism type change information to obtain the microorganism abundance change information.
In this scheme, judge soil microorganism population balance situation based on microorganism information, if soil microorganism population balance situation is less than predetermine the equilibrium value, the influence degree of each microorganism in the analysis soil to soil microorganism population balance situation obtains soil microorganism influence degree list, specifically does:
counting the number of each microbial colony to obtain the number information of each microbial colony;
Calculating the relative abundance percentage of each microorganism population based on the number information of each microorganism colony;
drawing a graph of the relative abundance percentage of each microorganism population in each period to obtain a microorganism relative abundance change graph;
setting a relative abundance percentage change threshold range of each microbial population, and integrating the relative abundance percentage change threshold range of each microbial population to form a preset balance value;
assigning an impact weight to the microbial abundance of each microbial population affecting the target area based on the relative abundance percentage of each microbial population;
and judging the microbial population balance condition based on the microbial relative abundance change curve graph, and analyzing the influence degree of each microorganism in the soil on the microbial population balance condition based on the influence weight if the microbial population balance condition of the soil is lower than a preset balance value, so as to obtain a soil microbial influence degree list.
In this scheme, screening out a plant list of microorganisms in the soil microorganism influence degree list from plant rhizosphere microorganisms, and selecting suitable plants in a target area from the plant list to obtain a plant planting scheme in the target area, wherein the plant planting scheme specifically comprises:
Constructing a plant information database;
digging plant names of microorganisms with the soil microorganism influence degree list greater than a preset influence degree in plant rhizosphere microorganisms in the Internet based on a big data technology, integrating the plant names into a plant list, and storing the plant list into a plant information database;
acquiring plant suitable environment information based on plant names, wherein the suitable environment information comprises soil properties, illumination conditions, air temperature and humidity, and the plant suitable environment information is imported into a plant information database and forms a one-to-one mapping relation with the plant names;
acquiring environment information of a target area, comparing the environment information of the target area with the suitable environment information to obtain the matching degree of each plant in a plant information database, extracting 5 plants with the highest matching degree, and forming a recommended planting list of the target area;
and generating a target area plant planting scheme based on the target area recommended planting list and the environmental information of the target area, wherein the target area plant planting scheme comprises plant planting density and planting time.
In this scheme, based on the digital twin technology generates a plant simulated growth model, a target area plant planting scheme is guided into the plant simulated growth model, and plant rhizosphere growth conditions and distribution positions are predicted according to the plant simulated growth model to obtain a prediction result, specifically:
Establishing a plant simulated growth model based on a digital twin technology, and introducing a target area plant planting scheme into the plant simulated growth model;
acquiring root system structure of plants in a plant planting scheme of a target area, soil property of the target area and soil layered structure data;
according to a target area plant planting scheme, planting plants in a target area, and monitoring actual growth conditions of the planted plants in a preset time period, wherein the actual growth conditions comprise plant growth rate, rhizosphere growth length and actual rhizosphere distribution position;
the root system structure, the actual growth condition, the soil property and the soil layered structure data of the plant are imported into a plant simulated growth model for learning, so that a complete plant simulated growth model is obtained;
and predicting the growth condition and distribution position of the plant rhizosphere in a preset time period in the future based on the complete plant simulated growth model to obtain a prediction result.
In this scheme, judge the distribution scope and the quantity of plant rhizosphere microorganism according to the prediction result, evaluate the soil microorganism population balance situation recovery effect in target area through the distribution scope and the quantity of plant rhizosphere microorganism, specifically be:
Randomly acquiring rhizosphere samples of plant plants planted in a target area, and evaluating the microorganism quantity of different parts in the rhizosphere samples to obtain microorganism quantity information of different parts of the rhizosphere;
analyzing based on the microbial quantity information and the prediction results of different rhizosphere positions, and judging the distribution range and the quantity of plant rhizosphere microorganisms;
judging the relative abundance percentage of the microbial population in the soil of the current target area according to the distribution range and the quantity of the plant rhizosphere microorganisms;
and comparing the relative abundance percentage of the microbial population in the soil of the current target area with a preset balance value, and evaluating the balance condition recovery effect of the microbial population in the soil of the target area.
In this scheme, the plant planting scheme in the target area is updated according to the soil microorganism population balance state recovery effect, specifically:
if the recovery effect of the balance condition of the soil microorganism population reaches the expected recovery effect, maintaining the plant planting scheme of the current target area;
if the restoring effect of the balance condition of the soil microorganism population does not reach the expected restoring effect, judging whether the plant reaches the theoretical growth rate or not based on the growth rate and the rhizosphere growth condition of the plant;
If the theoretical growth rate is not reached, judging influence factors influencing the plant growth rate to obtain influence factors;
updating the target area plant planting scheme based on the impact factor.
The second aspect of the invention also provides a soil microorganism control analysis system based on digital twinning, which comprises: the device comprises a memory and a processor, wherein the memory comprises a soil microorganism control analysis method program based on digital twinning, and when the soil microorganism control analysis method program based on digital twinning is executed by the processor, the following steps are realized:
acquiring a soil sample of a target area according to a preset period, and analyzing the soil sample to obtain microbial information of the soil, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information;
judging the soil microorganism population balance condition based on microorganism information, and if the soil microorganism population balance condition is lower than a preset balance value, analyzing the influence degree of each microorganism in the soil on the soil microorganism population balance condition to obtain a soil microorganism influence degree list;
screening a plant list of microorganisms in the soil microorganism influence degree list in plant rhizosphere microorganisms, and selecting proper plants in a target area from the plant list to obtain a target area plant planting scheme;
Generating a plant simulated growth model based on a digital twin technology, introducing a target area plant planting scheme into the plant simulated growth model, and predicting plant rhizosphere growth conditions and distribution positions according to the plant simulated growth model to obtain a prediction result;
judging the distribution range and the quantity of plant rhizosphere microorganisms according to the prediction result, and evaluating the soil microorganism population balance condition recovery effect of the target area through the distribution range and the quantity of the plant rhizosphere microorganisms;
and updating a target area plant planting scheme according to the soil microorganism population balance recovery effect.
In this scheme, based on the digital twin technology generates a plant simulated growth model, a target area plant planting scheme is guided into the plant simulated growth model, and plant rhizosphere growth conditions and distribution positions are predicted according to the plant simulated growth model to obtain a prediction result, specifically:
establishing a plant growth simulation model based on a digital twin technology;
acquiring root system structure of plants in a plant planting scheme of a target area, soil property of the target area and soil layered structure data;
according to a target area plant planting scheme, planting plants in a target area, and monitoring actual growth conditions of the planted plants in a preset time period, wherein the actual growth conditions comprise plant growth rate, rhizosphere growth length and actual rhizosphere distribution position;
The root system structure, the actual growth condition, the soil property and the soil layered structure data of the plant are imported into a plant simulated growth model for learning, so that a complete plant simulated growth model is obtained;
and predicting the growth condition and distribution position of the plant rhizosphere in a preset time period in the future based on the complete plant simulated growth model to obtain a prediction result.
In this scheme, the plant planting scheme in the target area is updated according to the soil microorganism population balance state recovery effect, specifically:
if the recovery effect of the balance condition of the soil microorganism population reaches the expected recovery effect, maintaining the plant planting scheme of the current target area;
if the restoring effect of the balance condition of the soil microorganism population does not reach the expected restoring effect, judging whether the plant reaches the theoretical growth rate or not based on the growth rate and the rhizosphere growth condition of the plant;
if the theoretical growth rate is not reached, judging influence factors influencing the plant growth rate to obtain influence factors;
updating the target area plant planting scheme based on the impact factor.
The invention discloses a soil microorganism control analysis method and system based on digital twinning, and aims to solve the maintenance and recovery problems of the balance condition of soil microorganism population. According to the method, firstly, microbial information is acquired, and the influence degree of each microbe on balance is analyzed to form an influence degree list. Further, a plant growing plan for the target area is determined. Then, a digital twin technology is adopted to construct a plant simulated growth model, and the growth condition and the distribution position of plant rhizosphere are predicted. Further judging the distribution range and the quantity of plant rhizosphere microorganisms, and evaluating the recovery effect of the balance condition of the soil microorganism population. And finally, updating the plant planting scheme according to the evaluation result. The invention provides a high-efficiency and accurate method for monitoring, maintaining and repairing the microbial population balance of soil by combining a digital twin technology with microbial control, and has important significance for improving soil health and agricultural production.
Drawings
FIG. 1 shows a flow chart of a digital twinning-based soil microorganism control analysis method of the present invention;
FIG. 2 shows a flow chart of the invention for obtaining a list of soil microorganism impact levels;
FIG. 3 shows a flow chart of the present invention for obtaining a target area plant growing solution;
FIG. 4 shows a block diagram of a digital twinning-based soil microbial control analysis system of the present invention.
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 shows a flow chart of a digital twinning-based soil microorganism control analysis method of the present invention.
As shown in fig. 1, the first aspect of the present invention provides a method for analyzing soil microorganism control based on digital twinning, comprising:
S102, acquiring a soil sample of a target area according to a preset period, and analyzing the soil sample to obtain microbial information of the soil, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information;
s104, judging the population balance condition of the soil microorganisms based on the microorganism information, and if the population balance condition of the soil microorganisms is lower than a preset balance value, analyzing the influence degree of each microorganism in the soil on the population balance condition of the soil microorganisms to obtain a soil microorganism influence degree list;
s106, screening a plant list of microorganisms in the soil microorganism influence degree list in plant rhizosphere microorganisms, and selecting proper plants in a target area from the plant list to obtain a plant planting scheme in the target area;
s108, generating a plant simulated growth model based on a digital twin technology, introducing a target area plant planting scheme into the plant simulated growth model, and predicting plant rhizosphere growth conditions and distribution positions according to the plant simulated growth model to obtain a prediction result;
s110, judging the distribution range and the quantity of plant rhizosphere microorganisms according to the prediction result, and evaluating the soil microorganism population balance condition recovery effect of the target area through the distribution range and the quantity of the plant rhizosphere microorganisms;
And S112, updating a plant planting scheme in the target area according to the soil microorganism population balance recovery effect.
The balance of the microbial population of the soil is estimated by evaluating the balance of the microbial population of the soil, and the balance of the microbial population of the soil is restored by planting plants in a target area according to the balance of the microbial population, so that the monitoring and improvement of the soil ecosystem of the target area are realized by combining the soil microbial ecology, the digital twin technology and the plant growth simulation; by analyzing microbial information, plant interaction and simulation prediction, soil health can be managed more effectively, and the sustainability of agriculture and land management is improved.
According to the embodiment of the invention, the soil sample of the target area is obtained according to the preset period, and the microbial information of the soil is obtained by analyzing the soil sample, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information, and specifically comprises the following steps:
obtaining soil samples of a target area according to a preset period, diluting the soil samples, and placing a plurality of diluted soil samples into a selective culture medium rich in different nutrients for culture to obtain microbial colonies;
Acquiring historical morphological characteristic data of microbial colonies in soil, wherein the morphological characteristic data comprise colors, shapes, sizes and edge characteristics of the microbial colonies;
shooting the microbial colony in each culture medium to obtain a microbial colony image set;
extracting features of the image set of the microbial colony based on an image processing technology to obtain morphological feature data of the microbial colony;
comparing the morphological characteristic data of the microorganisms with the historical morphological characteristic data of the microorganisms to obtain microorganism names of various microorganism colonies in the culture medium;
analyzing the microorganism name obtained in each period, and judging the change information of the microorganism type;
and evaluating the microorganism abundance information based on the microorganism type change information to obtain the microorganism abundance change information.
The diluted multiple soil samples are placed into selective culture mediums rich in different nutrients for culture, and as the nutrients in each selective culture medium are different, colonies of soil microorganisms can be initially screened in the selective culture medium, so that the growth of too many different microbial colonies in the culture medium is avoided, and the difficulty in observing and identifying the microbial colonies is reduced; the microorganism names obtained in each period are analyzed, and the microorganism type change information is judged, namely whether the microorganism names exist in each period or not is judged, so that the type change of the microorganisms is judged; the microbial abundance refers to how many species are within a microbial community.
FIG. 2 shows a flow chart of the present invention for obtaining a list of soil microorganism impact levels.
According to the embodiment of the invention, the microbial information is based on judging the microbial population balance condition of the soil, and if the microbial population balance condition of the soil is lower than a preset balance value, analyzing the influence degree of each microorganism in the soil on the microbial population balance condition of the soil to obtain a soil microbial influence degree list, wherein the soil microbial influence degree list comprises the following concrete steps:
s202, counting the number of each microbial colony to obtain the number information of each microbial colony;
s204, calculating the relative abundance percentage of each microorganism population based on the quantity information of each microorganism colony;
s206, drawing a graph of the relative abundance percentage of each microorganism population in each period to obtain a microorganism relative abundance change graph;
s208, setting a relative abundance percentage change threshold range of each microbial population, and integrating the relative abundance percentage change threshold ranges of each microbial population to form a preset balance value;
s210, assigning an influence weight to the microorganism abundance of each microorganism population affecting the target area based on the relative abundance percentage of each microorganism population;
S212, judging the microbial population balance condition based on the microbial relative abundance change curve graph, and if the microbial population balance condition of the soil is lower than a preset balance value, analyzing the influence degree of each microorganism in the soil on the microbial population balance condition of the soil based on the influence weight to obtain a soil microorganism influence degree list.
The relative abundance percentage of the microbial population is the ratio of one microbial population number to the total microbial population number; the microorganism relative abundance change curve graph is used for displaying the change trend of the abundance of the microorganism population along with time, and is beneficial to monitoring the dynamic change of the microorganism community; the preset balance value is the combination of the relative abundance percentage change threshold range of each microorganism population, and whether the relative abundance percentage is in the relative abundance percentage change threshold range is judged by comparing the relative abundance percentage of the microorganisms with the preset balance value; the influence weight is determined by the influence degree of the soil microbial community on the balance state; the embodiment of the invention identifies the problem of microorganism unbalance by analyzing the number of microorganisms, the abundance change and the influence weight, is beneficial to a soil manager to quickly determine the balance condition of soil microorganisms, reduces the intervention of human resources and saves the cost of identifying the soil.
FIG. 3 shows a flow chart of the present invention for achieving a target area plant growing strategy.
According to the embodiment of the invention, a plant list of microorganisms in the soil microorganism influence degree list in plant rhizosphere microorganisms is screened, and suitable plants in a target area are selected from the plant list to obtain a plant planting scheme in the target area, wherein the plant planting scheme specifically comprises the following steps:
s302, constructing a plant information database;
s304, digging plant names of microorganisms with the influence degree greater than a preset influence degree in the soil microorganism influence degree list in plant rhizosphere microorganisms in the Internet based on a big data technology, integrating the plant names into a plant list, and storing the plant list into a plant information database;
s306, acquiring plant suitable environment information based on plant names, wherein the suitable environment information comprises soil property, illumination condition, air temperature and humidity, and the plant suitable environment information is imported into a plant information database and forms a one-to-one mapping relation with the plant names;
s308, acquiring environment information of a target area, comparing the environment information of the target area with the suitable environment information to obtain the matching degree of each plant in a plant information database, and extracting 5 plants with the highest matching degree to form a recommended planting list of the target area;
S310, generating a target area plant planting scheme based on the target area recommended planting list and the environment information of the target area, wherein the target area plant planting scheme comprises plant planting density and planting time.
The plant information database is used for storing detailed information of plants, is beneficial to quickly screening a recommended planting list of a target area, reduces manual screening and improves screening efficiency; according to the target area plant planting scheme, plants matched with the environmental conditions of the target area are selected, the planting scheme is beneficial to utilizing factors such as soil, climate and illumination to the greatest extent, so that the growth speed of the plants is improved, the microbial population balance condition in the soil is improved by planting the suitable plants, and the health degree of the soil is improved.
According to the embodiment of the invention, a plant simulated growth model is generated based on a digital twin technology, a target area plant planting scheme is guided into the plant simulated growth model, and plant rhizosphere growth conditions and distribution positions are predicted according to the plant simulated growth model to obtain a prediction result, specifically:
establishing a plant simulated growth model based on a digital twin technology, and introducing a target area plant planting scheme into the plant simulated growth model;
Acquiring root system structure of plants in a plant planting scheme of a target area, soil property of the target area and soil layered structure data;
according to a target area plant planting scheme, planting plants in a target area, and monitoring actual growth conditions of the planted plants in a preset time period, wherein the actual growth conditions comprise plant growth rate, rhizosphere growth length and actual rhizosphere distribution position;
the root system structure, the actual growth condition, the soil property and the soil layered structure data of the plant are imported into a plant simulated growth model for learning, so that a complete plant simulated growth model is obtained;
and predicting the growth condition and distribution position of the plant rhizosphere in a preset time period in the future based on the complete plant simulated growth model to obtain a prediction result.
It should be noted that the plant simulated growth model is a virtual plant simulated growth environment created based on a digital twin technology, and the environment reflects physical and ecological conditions of actual plant growth; the root system structure, the actual growth condition, the soil property and the soil layered structure data of the plant are used for training a plant simulated growth model, so that the prediction capability and the prediction accuracy of the plant simulated growth model are improved; the prediction result comprises plant rhizosphere growth conditions and distribution positions; the embodiment of the invention combines the digital twin technology, data collection and machine learning to realize more accurate plant growth simulation and prediction, is beneficial to improving the efficiency of plant management, optimizing the resource utilization and predicting the result of plant growth in advance, thereby making related intervention measures on soil microorganisms in advance.
According to the embodiment of the invention, the distribution range and the number of the plant rhizosphere microorganisms are judged according to the prediction result, and the soil microorganism population balance condition recovery effect of the target area is estimated according to the distribution range and the number of the plant rhizosphere microorganisms, specifically:
randomly acquiring rhizosphere samples of plant plants planted in a target area, and evaluating the microorganism quantity of different parts in the rhizosphere samples to obtain microorganism quantity information of different parts of the rhizosphere;
analyzing based on the quantity information of different rhizosphere parts and the prediction result, and judging the distribution range and quantity of plant rhizosphere microorganisms;
judging the relative abundance percentage of the microbial population in the soil of the current target area according to the distribution range and the quantity of the plant rhizosphere microorganisms;
and comparing the relative abundance percentage of the microbial population in the soil of the current target area with a preset balance value, and evaluating the balance condition recovery effect of the microbial population in the soil of the target area.
The method is characterized in that the relative abundance percentage of the microbial population in the soil of the target area and the recovery effect of the balance condition of the microbial population in the soil of the target area are both predicted results; by evaluating the effect of restoring the balance of the population of the soil microorganisms in the target area, the interaction between the plant rhizosphere microorganisms and the population of the soil microorganisms can be better understood, and the health condition of the soil ecosystem can be evaluated so as to take necessary measures to maintain or improve the effect of the balance of the population of the soil microorganisms.
According to the embodiment of the invention, the target area plant planting scheme is updated according to the soil microorganism population balance state recovery effect, and specifically comprises the following steps:
if the recovery effect of the balance condition of the soil microorganism population reaches the expected recovery effect, maintaining the plant planting scheme of the current target area;
if the restoring effect of the balance condition of the soil microorganism population does not reach the expected restoring effect, judging whether the plant reaches the theoretical growth rate or not based on the growth rate and the rhizosphere growth condition of the plant;
if the theoretical growth rate is not reached, judging influence factors influencing the plant growth rate to obtain influence factors;
updating the target area plant planting scheme based on the impact factor.
It should be noted that, by judging the effect of the population balance condition of the soil microorganisms and the growth rate of the plants in each period, correcting the plant planting scheme of the target area in the next period by the influence factors of the previous period, continuously updating the plant planting scheme of the target area, improving the recovery effect of the population balance condition of the soil microorganisms in the target area, and improving the improvement efficiency of the soil health condition; in the embodiment of the invention, the plant planting scheme is adjusted in real time according to the soil microorganism condition and the plant growth condition, so that the plant growth efficiency is improved, the soil ecological balance is maintained, and more sustainable agriculture or land management practice is finally realized.
According to an embodiment of the present invention, further comprising:
performing actual planting based on a plant planting scheme of a target area, monitoring the growth condition of plants in real time, analyzing the growth condition of the plants, and judging whether the plants have abnormal growth or not;
if the growth abnormality exists, marking a planting area of the plant to obtain a marked area;
acquiring the information of the number of soil microorganisms and the type information in the marked area, and judging the microbial number adjusting effect based on the information of the number of the soil microorganisms and the type information;
if the microbial quantity adjusting effect is lower than the preset effect, marking the microbes to obtain marked microbes;
acquiring data of the influence of different pollutants on microorganisms, and obtaining a pollutant-microorganism influence data table;
performing joint analysis based on a pollutant-microorganism influence data table and plant growth conditions, performing probability analysis on pollutants affecting plant growth in a target area, and predicting to obtain the type of the pollutants affecting microorganism growth in the target area;
a targeted contaminant monitoring scheme is generated based on the contaminant type.
It should be noted that, by judging the growth condition of the plants in the target area and judging the adjustment effect of the microorganism number in the planting area, judging whether the growth of the plants and the microorganism number are affected by pollution, marking the microorganisms with the microorganism number adjustment effect lower than the preset effect, after the marked microorganisms pass through the planting of the plants, recovering the bad microorganisms, predicting the pollutant type affecting the growth of the microorganisms in the target area according to the growth condition of the plants and the pollutant-microorganism influence data table, forming a pollutant monitoring scheme for the pollutant type, and by the pollutant monitoring scheme, being beneficial to timely taking proper pollution treatment measures for pollutants; the preset effect refers to the number of microorganisms in an equilibrium state.
According to an embodiment of the present invention, further comprising:
performing actual planting according to a plant planting scheme of a target area to obtain the growth condition of plants, wherein the growth condition of the plants comprises the growth rate of the plants and the number of plant leaves;
comparing and calculating the growth rate of the plant with the theoretical growth rate to obtain a plant growth inhibition coefficient;
acquiring microbial conditions of a polluted area in a target area, wherein the microbial conditions comprise microbial types, numbers, microbial population balance conditions and abundance;
acquiring pollution conditions of a pollution area in a target area, wherein the pollution conditions comprise pollutant types, pollution degrees and pollution ranges;
generating a plant-microorganism combined repair scheme based on the growth conditions, microorganism conditions, and pollution conditions of the plants;
generating a pollution control simulation model based on a digital twin technology, introducing the growth condition, the microorganism condition and the pollution condition of the plant into the pollution control simulation model, and predicting the growth condition of the plant, the growth condition of the microorganism and the change data of pollutants to obtain a pollution control prediction result;
judging pollution treatment effect according to the pollution treatment prediction result, and predicting the feasibility of the plant-microorganism combined restoration scheme according to the pollution treatment effect.
It should be noted that, in the target area, there may be a contaminated area, which may affect the growth of microorganisms and the growth of plants, and the pollution is treated by a plant-microorganism combined repair scheme, which is to plant plants capable of absorbing pollutants in the contaminated area and deliver beneficial microorganisms to repair the contaminated area, so as to improve the treatment effect and treatment efficiency of the pollution; and the pollution treatment effect is judged by establishing a pollution treatment simulation model, and the feasibility of a plant-microorganism combined restoration scheme is speculated, so that the comprehensive effect and efficiency of environmental pollution treatment are improved, and the ecological environment of a polluted area is improved more effectively.
FIG. 4 shows a block diagram of a digital twinning-based soil microbial control analysis system of the present invention.
The second aspect of the present invention also provides a digital twin-based soil microorganism control analysis system 4, comprising: the memory 41 and the processor 42, wherein the memory comprises a soil microorganism control analysis method program based on digital twin, and when the soil microorganism control analysis method program based on digital twin is executed by the processor, the following steps are realized:
Acquiring a soil sample of a target area according to a preset period, and analyzing the soil sample to obtain microbial information of the soil, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information;
judging the soil microorganism population balance condition based on microorganism information, and if the soil microorganism population balance condition is lower than a preset balance value, analyzing the influence degree of each microorganism in the soil on the soil microorganism population balance condition to obtain a soil microorganism influence degree list;
screening a plant list of microorganisms in the soil microorganism influence degree list in plant rhizosphere microorganisms, and selecting proper plants in a target area from the plant list to obtain a target area plant planting scheme;
generating a plant simulated growth model based on a digital twin technology, introducing a target area plant planting scheme into the plant simulated growth model, and predicting plant rhizosphere growth conditions and distribution positions according to the plant simulated growth model to obtain a prediction result;
judging the distribution range and the quantity of plant rhizosphere microorganisms according to the prediction result, and evaluating the soil microorganism population balance condition recovery effect of the target area through the distribution range and the quantity of the plant rhizosphere microorganisms;
And updating a target area plant planting scheme according to the soil microorganism population balance recovery effect.
The balance of the microbial population of the soil is estimated by evaluating the balance of the microbial population of the soil, and the balance of the microbial population of the soil is restored by planting plants in a target area according to the balance of the microbial population, so that the monitoring and improvement of the soil ecosystem of the target area are realized by combining the soil microbial ecology, the digital twin technology and the plant growth simulation; by analyzing microbial information, plant interaction and simulation prediction, soil health can be managed more effectively, and the sustainability of agriculture and land management is improved.
According to the embodiment of the invention, the soil sample of the target area is obtained according to the preset period, and the microbial information of the soil is obtained by analyzing the soil sample, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information, and specifically comprises the following steps:
obtaining soil samples of a target area according to a preset period, diluting the soil samples, and placing a plurality of diluted soil samples into a selective culture medium rich in different nutrients for culture to obtain microbial colonies;
Acquiring historical morphological characteristic data of microbial colonies in soil, wherein the morphological characteristic data comprise colors, shapes, sizes and edge characteristics of the microbial colonies;
shooting the microbial colony in each culture medium to obtain a microbial colony image set;
extracting features of the image set of the microbial colony based on an image processing technology to obtain morphological feature data of the microbial colony;
comparing the morphological characteristic data of the microorganisms with the historical morphological characteristic data of the microorganisms to obtain microorganism names of various microorganism colonies in the culture medium;
analyzing the microorganism name obtained in each period, and judging the change information of the microorganism type;
and evaluating the microorganism abundance information based on the microorganism type change information to obtain the microorganism abundance change information.
The diluted multiple soil samples are placed into selective culture mediums rich in different nutrients for culture, and as the nutrients in each selective culture medium are different, colonies of soil microorganisms can be initially screened in the selective culture medium, so that the growth of too many different microbial colonies in the culture medium is avoided, and the difficulty in observing and identifying the microbial colonies is reduced; the microorganism names obtained in each period are analyzed, and the microorganism type change information is judged, namely whether the microorganism names exist in each period or not is judged, so that the type change of the microorganisms is judged; the microbial abundance refers to how many species are within a microbial community.
According to the embodiment of the invention, the microbial information is based on judging the microbial population balance condition of the soil, and if the microbial population balance condition of the soil is lower than a preset balance value, analyzing the influence degree of each microorganism in the soil on the microbial population balance condition of the soil to obtain a soil microbial influence degree list, wherein the soil microbial influence degree list comprises the following concrete steps:
counting the number of each microbial colony to obtain the number information of each microbial colony;
calculating the relative abundance percentage of each microorganism population based on the number information of each microorganism colony;
drawing a graph of the relative abundance percentage of each microorganism population in each period to obtain a microorganism relative abundance change graph;
setting a relative abundance percentage change threshold range of each microbial population, and integrating the relative abundance percentage change threshold range of each microbial population to form a preset balance value;
assigning an impact weight to the microbial abundance of each microbial population affecting the target area based on the relative abundance percentage of each microbial population;
and judging the microbial population balance condition based on the microbial relative abundance change curve graph, and analyzing the influence degree of each microorganism in the soil on the microbial population balance condition based on the influence weight if the microbial population balance condition of the soil is lower than a preset balance value, so as to obtain a soil microbial influence degree list.
The relative abundance percentage of the microbial population is the ratio of one microbial population number to the total microbial population number; the microorganism relative abundance change curve graph is used for displaying the change trend of the abundance of the microorganism population along with time, and is beneficial to monitoring the dynamic change of the microorganism community; the preset balance value is the combination of the relative abundance percentage change threshold range of each microorganism population, and whether the relative abundance percentage is in the relative abundance percentage change threshold range is judged by comparing the relative abundance percentage of the microorganisms with the preset balance value; the influence weight is determined by the influence degree of the soil microbial community on the balance state; the embodiment of the invention identifies the problem of microorganism unbalance by analyzing the number of microorganisms, the abundance change and the influence weight, is beneficial to a soil manager to quickly determine the balance condition of soil microorganisms, reduces the intervention of human resources and saves the cost of identifying the soil.
According to the embodiment of the invention, a plant list of microorganisms in the soil microorganism influence degree list in plant rhizosphere microorganisms is screened, and suitable plants in a target area are selected from the plant list to obtain a plant planting scheme in the target area, wherein the plant planting scheme specifically comprises the following steps:
Constructing a plant information database;
digging plant names of microorganisms with the soil microorganism influence degree list greater than a preset influence degree in plant rhizosphere microorganisms in the Internet based on a big data technology, integrating the plant names into a plant list, and storing the plant list into a plant information database;
acquiring plant suitable environment information based on plant names, wherein the suitable environment information comprises soil properties, illumination conditions, air temperature and humidity, and the plant suitable environment information is imported into a plant information database and forms a one-to-one mapping relation with the plant names;
acquiring environment information of a target area, comparing the environment information of the target area with the suitable environment information to obtain the matching degree of each plant in a plant information database, extracting 5 plants with the highest matching degree, and forming a recommended planting list of the target area;
and generating a target area plant planting scheme based on the target area recommended planting list and the environmental information of the target area, wherein the target area plant planting scheme comprises plant planting density and planting time.
The plant information database is used for storing detailed information of plants, is beneficial to quickly screening a recommended planting list of a target area, reduces manual screening and improves screening efficiency; according to the target area plant planting scheme, plants matched with the environmental conditions of the target area are selected, the planting scheme is beneficial to utilizing factors such as soil, climate and illumination to the greatest extent, so that the growth speed of the plants is improved, the microbial population balance condition in the soil is improved by planting the suitable plants, and the health degree of the soil is improved.
According to the embodiment of the invention, a plant simulated growth model is generated based on a digital twin technology, a target area plant planting scheme is guided into the plant simulated growth model, and plant rhizosphere growth conditions and distribution positions are predicted according to the plant simulated growth model to obtain a prediction result, specifically:
establishing a plant simulated growth model based on a digital twin technology, and introducing a target area plant planting scheme into the plant simulated growth model;
acquiring root system structure of plants in a plant planting scheme of a target area, soil property of the target area and soil layered structure data;
according to a target area plant planting scheme, planting plants in a target area, and monitoring actual growth conditions of the planted plants in a preset time period, wherein the actual growth conditions comprise plant growth rate, rhizosphere growth length and actual rhizosphere distribution position;
the root system structure, the actual growth condition, the soil property and the soil layered structure data of the plant are imported into a plant simulated growth model for learning, so that a complete plant simulated growth model is obtained;
and predicting the growth condition and distribution position of the plant rhizosphere in a preset time period in the future based on the complete plant simulated growth model to obtain a prediction result.
It should be noted that the plant simulated growth model is a virtual plant simulated growth environment created based on a digital twin technology, and the environment reflects physical and ecological conditions of actual plant growth; the root system structure, the actual growth condition, the soil property and the soil layered structure data of the plant are used for training a plant simulated growth model, so that the prediction capability and the prediction accuracy of the plant simulated growth model are improved; the prediction result comprises plant rhizosphere growth conditions and distribution positions; the embodiment of the invention combines the digital twin technology, data collection and machine learning to realize more accurate plant growth simulation and prediction, is beneficial to improving the efficiency of plant management, optimizing the resource utilization and predicting the result of plant growth in advance, thereby making related intervention measures on soil microorganisms in advance.
According to the embodiment of the invention, the distribution range and the number of the plant rhizosphere microorganisms are judged according to the prediction result, and the soil microorganism population balance condition recovery effect of the target area is estimated according to the distribution range and the number of the plant rhizosphere microorganisms, specifically:
randomly acquiring rhizosphere samples of plant plants planted in a target area, and evaluating the microorganism quantity of different parts in the rhizosphere samples to obtain microorganism quantity information of different parts of the rhizosphere;
Analyzing based on the quantity information of different rhizosphere parts and the prediction result, and judging the distribution range and quantity of plant rhizosphere microorganisms;
judging the relative abundance percentage of the microbial population in the soil of the current target area according to the distribution range and the quantity of the plant rhizosphere microorganisms;
and comparing the relative abundance percentage of the microbial population in the soil of the current target area with a preset balance value, and evaluating the balance condition recovery effect of the microbial population in the soil of the target area.
The method is characterized in that the relative abundance percentage of the microbial population in the soil of the target area and the recovery effect of the balance condition of the microbial population in the soil of the target area are both predicted results; by evaluating the effect of restoring the balance of the population of the soil microorganisms in the target area, the interaction between the plant rhizosphere microorganisms and the population of the soil microorganisms can be better understood, and the health condition of the soil ecosystem can be evaluated so as to take necessary measures to maintain or improve the effect of the balance of the population of the soil microorganisms.
According to the embodiment of the invention, the target area plant planting scheme is updated according to the soil microorganism population balance state recovery effect, and specifically comprises the following steps:
If the recovery effect of the balance condition of the soil microorganism population reaches the expected recovery effect, maintaining the plant planting scheme of the current target area;
if the restoring effect of the balance condition of the soil microorganism population does not reach the expected restoring effect, judging whether the plant reaches the theoretical growth rate or not based on the growth rate and the rhizosphere growth condition of the plant;
if the theoretical growth rate is not reached, judging influence factors influencing the plant growth rate to obtain influence factors;
updating the target area plant planting scheme based on the impact factor.
It should be noted that, by judging the effect of the population balance condition of the soil microorganisms and the growth rate of the plants in each period, correcting the plant planting scheme of the target area in the next period by the influence factors of the previous period, continuously updating the plant planting scheme of the target area, improving the recovery effect of the population balance condition of the soil microorganisms in the target area, and improving the improvement efficiency of the soil health condition; in the embodiment of the invention, the plant planting scheme is adjusted in real time according to the soil microorganism condition and the plant growth condition, so that the plant growth efficiency is improved, the soil ecological balance is maintained, and more sustainable agriculture or land management practice is finally realized.
According to an embodiment of the present invention, further comprising:
performing actual planting based on a plant planting scheme of a target area, monitoring the growth condition of plants in real time, analyzing the growth condition of the plants, and judging whether the plants have abnormal growth or not;
if the growth abnormality exists, marking a planting area of the plant to obtain a marked area;
acquiring the information of the number of soil microorganisms and the type information in the marked area, and judging the microbial number adjusting effect based on the information of the number of the soil microorganisms and the type information;
if the microbial quantity adjusting effect is lower than the preset effect, marking the microbes to obtain marked microbes;
acquiring data of the influence of different pollutants on microorganisms, and obtaining a pollutant-microorganism influence data table;
performing joint analysis based on a pollutant-microorganism influence data table and plant growth conditions, performing probability analysis on pollutants affecting plant growth in a target area, and predicting to obtain the type of the pollutants affecting microorganism growth in the target area;
a targeted contaminant monitoring scheme is generated based on the contaminant type.
It should be noted that, by judging the growth condition of the plants in the target area and judging the adjustment effect of the microorganism number in the planting area, judging whether the growth of the plants and the microorganism number are affected by pollution, marking the microorganisms with the microorganism number adjustment effect lower than the preset effect, after the marked microorganisms pass through the planting of the plants, recovering the bad microorganisms, predicting the pollutant type affecting the growth of the microorganisms in the target area according to the growth condition of the plants and the pollutant-microorganism influence data table, forming a pollutant monitoring scheme for the pollutant type, and by the pollutant monitoring scheme, being beneficial to timely taking proper pollution treatment measures for pollutants; the preset effect refers to the number of microorganisms in an equilibrium state.
According to an embodiment of the present invention, further comprising:
performing actual planting according to a plant planting scheme of a target area to obtain the growth condition of plants, wherein the growth condition of the plants comprises the growth rate of the plants and the number of plant leaves;
comparing and calculating the growth rate of the plant with the theoretical growth rate to obtain a plant growth inhibition coefficient;
acquiring microbial conditions of a polluted area in a target area, wherein the microbial conditions comprise microbial types, numbers, microbial population balance conditions and abundance;
acquiring pollution conditions of a pollution area in a target area, wherein the pollution conditions comprise pollutant types, pollution degrees and pollution ranges;
generating a plant-microorganism combined repair scheme based on the growth conditions, microorganism conditions, and pollution conditions of the plants;
generating a pollution control simulation model based on a digital twin technology, introducing the growth condition, the microorganism condition and the pollution condition of the plant into the pollution control simulation model, and predicting the growth condition of the plant, the growth condition of the microorganism and the change data of pollutants to obtain a pollution control prediction result;
judging pollution treatment effect according to the pollution treatment prediction result, and predicting the feasibility of the plant-microorganism combined restoration scheme according to the pollution treatment effect.
It should be noted that, in the target area, there may be a contaminated area, which may affect the growth of microorganisms and the growth of plants, and the pollution is treated by a plant-microorganism combined repair scheme, which is to plant plants capable of absorbing pollutants in the contaminated area and deliver beneficial microorganisms to repair the contaminated area, so as to improve the treatment effect and treatment efficiency of the pollution; and the pollution treatment effect is judged by establishing a pollution treatment simulation model, and the feasibility of a plant-microorganism combined restoration scheme is speculated, so that the comprehensive effect and efficiency of environmental pollution treatment are improved, and the ecological environment of a polluted area is improved more effectively.
The invention discloses a soil microorganism control analysis method and system based on digital twinning, and aims to solve the maintenance and recovery problems of the balance condition of soil microorganism population. According to the method, firstly, microbial information is acquired, and the influence degree of each microbe on balance is analyzed to form an influence degree list. Further, a plant growing plan for the target area is determined. Then, a digital twin technology is adopted to construct a plant simulated growth model, and the growth condition and the distribution position of plant rhizosphere are predicted. Further judging the distribution range and the quantity of plant rhizosphere microorganisms, and evaluating the recovery effect of the balance condition of the soil microorganism population. And finally, updating the plant planting scheme according to the evaluation result. The invention provides a high-efficiency and accurate method for monitoring, maintaining and repairing the microbial population balance of soil by combining a digital twin technology with microbial control, and has important significance for improving soil health and agricultural production.
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 (8)
1. The soil microorganism control analysis method based on digital twinning is characterized by comprising the following steps of:
acquiring a soil sample of a target area according to a preset period, and analyzing the soil sample to obtain microbial information of the soil, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information;
judging the soil microorganism population balance condition based on microorganism information, and if the soil microorganism population balance condition is lower than a preset balance value, analyzing the influence degree of each microorganism in the soil on the soil microorganism population balance condition to obtain a soil microorganism influence degree list;
screening a plant list of microorganisms in the soil microorganism influence degree list in plant rhizosphere microorganisms, and selecting proper plants in a target area from the plant list to obtain a target area plant planting scheme;
Generating a plant simulated growth model based on a digital twin technology, introducing a target area plant planting scheme into the plant simulated growth model, and predicting plant rhizosphere growth conditions and distribution positions according to the plant simulated growth model to obtain a prediction result;
judging the distribution range and the quantity of plant rhizosphere microorganisms according to the prediction result, and evaluating the soil microorganism population balance condition recovery effect of the target area through the distribution range and the quantity of the plant rhizosphere microorganisms;
updating a target area plant planting scheme according to the soil microorganism population balance recovery effect;
the method comprises the steps of obtaining a soil sample of a target area according to a preset period, and obtaining microbial information of soil by analyzing the soil sample, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information, and specifically comprises the following steps:
obtaining soil samples of a target area according to a preset period, diluting the soil samples, and placing a plurality of diluted soil samples into a selective culture medium rich in different nutrients for culture to obtain microbial colonies;
acquiring historical morphological characteristic data of microbial colonies in soil, wherein the morphological characteristic data comprise colors, shapes, sizes and edge characteristics of the microbial colonies;
Shooting the microbial colony in each culture medium to obtain a microbial colony image set;
extracting features of the image set of the microbial colony based on an image processing technology to obtain morphological feature data of the microbial colony;
comparing the morphological characteristic data of the microorganisms with the historical morphological characteristic data of the microorganisms to obtain microorganism names of various microorganism colonies in the culture medium;
analyzing the microorganism name obtained in each period, and judging the change information of the microorganism type;
the method comprises the steps of evaluating microorganism abundance information based on microorganism type change information to obtain microorganism abundance change information;
judging the soil microorganism population balance condition based on microorganism information, and analyzing the influence degree of each microorganism in the soil on the soil microorganism population balance condition if the soil microorganism population balance condition is lower than a preset balance value to obtain a soil microorganism influence degree list, wherein the soil microorganism influence degree list specifically comprises:
counting the number of each microbial colony to obtain the number information of each microbial colony;
calculating the relative abundance percentage of each microorganism population based on the number information of each microorganism colony;
drawing a graph of the relative abundance percentage of each microorganism population in each period to obtain a microorganism relative abundance change graph;
Setting a relative abundance percentage change threshold range of each microbial population, and integrating the relative abundance percentage change threshold range of each microbial population to form a preset balance value;
assigning an impact weight to the microbial abundance of each microbial population affecting the target area based on the relative abundance percentage of each microbial population;
and judging the microbial population balance condition based on the microbial relative abundance change curve graph, and analyzing the influence degree of each microorganism in the soil on the microbial population balance condition based on the influence weight if the microbial population balance condition of the soil is lower than a preset balance value, so as to obtain a soil microbial influence degree list.
2. The method for analyzing soil microorganism control based on digital twin according to claim 1, wherein the screening out a plant list of microorganisms in the soil microorganism influence degree list from plant rhizosphere microorganisms, selecting suitable plants in a target area from the plant list, and obtaining a plant planting scheme of the target area, wherein the plant planting scheme comprises the following steps:
constructing a plant information database;
digging plant names of microorganisms with the soil microorganism influence degree list greater than a preset influence degree in plant rhizosphere microorganisms in the Internet based on a big data technology, integrating the plant names into a plant list, and storing the plant list into a plant information database;
Acquiring plant suitable environment information based on plant names, wherein the suitable environment information comprises soil properties, illumination conditions, air temperature and humidity, and the plant suitable environment information is imported into a plant information database and forms a one-to-one mapping relation with the plant names;
acquiring environment information of a target area, comparing the environment information of the target area with the suitable environment information to obtain the matching degree of each plant in a plant information database, extracting 5 plants with the highest matching degree, and forming a recommended planting list of the target area;
and generating a target area plant planting scheme based on the target area recommended planting list and the environmental information of the target area, wherein the target area plant planting scheme comprises plant planting density and planting time.
3. The method for analyzing soil microorganism control based on digital twin according to claim 1, wherein the method is characterized in that a plant simulated growth model is generated based on digital twin technology, a plant planting scheme of a target area is led into the plant simulated growth model, and plant rhizosphere growth conditions and distribution positions are predicted according to the plant simulated growth model to obtain a prediction result, specifically:
establishing a plant simulated growth model based on a digital twin technology, and introducing a target area plant planting scheme into the plant simulated growth model;
Acquiring root system structure of plants in a plant planting scheme of a target area, soil property of the target area and soil layered structure data;
according to a target area plant planting scheme, planting plants in a target area, and monitoring actual growth conditions of the planted plants in a preset time period, wherein the actual growth conditions comprise plant growth rate, rhizosphere growth length and actual rhizosphere distribution position;
the root system structure, the actual growth condition, the soil property and the soil layered structure data of the plant are imported into a plant simulated growth model for learning, so that a complete plant simulated growth model is obtained;
and predicting the growth condition and distribution position of the plant rhizosphere in a preset time period in the future based on the complete plant simulated growth model to obtain a prediction result.
4. The method for analyzing soil microorganism control based on digital twin according to claim 1, wherein the method for judging the distribution range and the number of plant rhizosphere microorganisms according to the prediction result, and evaluating the effect of restoring the soil microorganism population balance condition of the target area according to the distribution range and the number of the plant rhizosphere microorganisms is specifically as follows:
randomly acquiring rhizosphere samples of plant plants planted in a target area, and evaluating the microorganism quantity of different parts in the rhizosphere samples to obtain microorganism quantity information of different parts of the rhizosphere;
Analyzing based on the microbial quantity information and the prediction results of different rhizosphere positions, and judging the distribution range and the quantity of plant rhizosphere microorganisms;
judging the relative abundance percentage of the microbial population in the soil of the current target area according to the distribution range and the quantity of the plant rhizosphere microorganisms;
and comparing the relative abundance percentage of the microbial population in the soil of the current target area with a preset balance value, and evaluating the balance condition recovery effect of the microbial population in the soil of the target area.
5. The method for analyzing soil microorganism control based on digital twin according to claim 1, wherein the updating of the target area plant planting scheme according to the recovery effect of the balance of the soil microorganism population is specifically:
if the recovery effect of the balance condition of the soil microorganism population reaches the expected recovery effect, maintaining the plant planting scheme of the current target area;
if the restoring effect of the balance condition of the soil microorganism population does not reach the expected restoring effect, judging whether the plant reaches the theoretical growth rate or not based on the growth rate and the rhizosphere growth condition of the plant;
if the theoretical growth rate is not reached, judging influence factors influencing the plant growth rate to obtain influence factors;
Updating the target area plant planting scheme based on the impact factor.
6. A digital twinning-based soil microorganism control analysis system, comprising a memory and a processor, wherein the memory comprises a digital twinning-based soil microorganism control analysis method program, and the digital twinning-based soil microorganism control analysis method program is executed by the processor to realize the following steps:
acquiring a soil sample of a target area according to a preset period, and analyzing the soil sample to obtain microbial information of the soil, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information;
judging the soil microorganism population balance condition based on microorganism information, and if the soil microorganism population balance condition is lower than a preset balance value, analyzing the influence degree of each microorganism in the soil on the soil microorganism population balance condition to obtain a soil microorganism influence degree list;
screening a plant list of microorganisms in the soil microorganism influence degree list in plant rhizosphere microorganisms, and selecting proper plants in a target area from the plant list to obtain a target area plant planting scheme;
Generating a plant simulated growth model based on a digital twin technology, introducing a target area plant planting scheme into the plant simulated growth model, and predicting plant rhizosphere growth conditions and distribution positions according to the plant simulated growth model to obtain a prediction result;
judging the distribution range and the quantity of plant rhizosphere microorganisms according to the prediction result, and evaluating the soil microorganism population balance condition recovery effect of the target area through the distribution range and the quantity of the plant rhizosphere microorganisms;
updating a target area plant planting scheme according to the soil microorganism population balance recovery effect;
the method comprises the steps of obtaining a soil sample of a target area according to a preset period, and obtaining microbial information of soil by analyzing the soil sample, wherein the microbial information comprises a microbial name, microbial type change information and microbial abundance change information, and specifically comprises the following steps:
obtaining soil samples of a target area according to a preset period, diluting the soil samples, and placing a plurality of diluted soil samples into a selective culture medium rich in different nutrients for culture to obtain microbial colonies;
acquiring historical morphological characteristic data of microbial colonies in soil, wherein the morphological characteristic data comprise colors, shapes, sizes and edge characteristics of the microbial colonies;
Shooting the microbial colony in each culture medium to obtain a microbial colony image set;
extracting features of the image set of the microbial colony based on an image processing technology to obtain morphological feature data of the microbial colony;
comparing the morphological characteristic data of the microorganisms with the historical morphological characteristic data of the microorganisms to obtain microorganism names of various microorganism colonies in the culture medium;
analyzing the microorganism name obtained in each period, and judging the change information of the microorganism type;
the method comprises the steps of evaluating microorganism abundance information based on microorganism type change information to obtain microorganism abundance change information;
judging the soil microorganism population balance condition based on microorganism information, and analyzing the influence degree of each microorganism in the soil on the soil microorganism population balance condition if the soil microorganism population balance condition is lower than a preset balance value to obtain a soil microorganism influence degree list, wherein the soil microorganism influence degree list specifically comprises:
counting the number of each microbial colony to obtain the number information of each microbial colony;
calculating the relative abundance percentage of each microorganism population based on the number information of each microorganism colony;
drawing a graph of the relative abundance percentage of each microorganism population in each period to obtain a microorganism relative abundance change graph;
Setting a relative abundance percentage change threshold range of each microbial population, and integrating the relative abundance percentage change threshold range of each microbial population to form a preset balance value;
assigning an impact weight to the microbial abundance of each microbial population affecting the target area based on the relative abundance percentage of each microbial population;
and judging the microbial population balance condition based on the microbial relative abundance change curve graph, and analyzing the influence degree of each microorganism in the soil on the microbial population balance condition based on the influence weight if the microbial population balance condition of the soil is lower than a preset balance value, so as to obtain a soil microbial influence degree list.
7. The system for controlling and analyzing soil microorganisms based on digital twin according to claim 6, wherein the plant simulated growth model is generated based on digital twin technology, the plant planting scheme of the target area is introduced into the plant simulated growth model, and the plant rhizosphere growth condition and distribution position are predicted according to the plant simulated growth model to obtain the prediction result, specifically:
establishing a plant growth simulation model based on a digital twin technology;
acquiring root system structure of plants in a plant planting scheme of a target area, soil property of the target area and soil layered structure data;
According to a target area plant planting scheme, planting plants in a target area, and monitoring actual growth conditions of the planted plants in a preset time period, wherein the actual growth conditions comprise plant growth rate, rhizosphere growth length and actual rhizosphere distribution position;
the root system structure, the actual growth condition, the soil property and the soil layered structure data of the plant are imported into a plant simulated growth model for learning, so that a complete plant simulated growth model is obtained;
and predicting the growth condition and distribution position of the plant rhizosphere in a preset time period in the future based on the complete plant simulated growth model to obtain a prediction result.
8. The digital twin-based soil microorganism control analysis system according to claim 6, wherein the updating of the target area plant cultivation scheme according to the restoration effect of the balance of the soil microorganism population is specifically:
if the recovery effect of the balance condition of the soil microorganism population reaches the expected recovery effect, maintaining the plant planting scheme of the current target area;
if the restoring effect of the balance condition of the soil microorganism population does not reach the expected restoring effect, judging whether the plant reaches the theoretical growth rate or not based on the growth rate and the rhizosphere growth condition of the plant;
If the theoretical growth rate is not reached, judging influence factors influencing the plant growth rate to obtain influence factors;
updating the target area plant planting scheme based on the impact factor.
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| CN114511157A (en) * | 2022-04-18 | 2022-05-17 | 广东省农业科学院植物保护研究所 | Microecological regulation and control method and system based on vegetable bacterial wilt prevention and control |
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