CN117310124A - Method and related device for measuring soil carbon fixation amount based on biochar carbon negative emission - Google Patents
Method and related device for measuring soil carbon fixation amount based on biochar carbon negative emission Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, and discloses a method and a related device for measuring soil carbon fixation based on biochar carbon negative emission. The method for measuring the carbon fixation amount of the soil based on the biochar carbon negative emission comprises the following steps: measuring the carbon content of a target soil sample in real time through target equipment to obtain real-time measurement data; based on the real-time measurement data, quantitatively evaluating the carbon fixation effect of the biochar in the target soil sample through a preset machine learning algorithm to obtain an evaluation result of the carbon fixation effect, and comparing the evaluation result with a preset evaluation result of a soil sample without the biochar, so as to obtain a carbon fixation difference result of the biochar; and constructing a biochar-based soil carbon fixation quantity prediction model according to the carbon fixation difference result. The invention realizes flexible, rapid and accurate soil carbon fixation analysis, and is beneficial to realizing environmental protection targets and scientific guidance of soil improvement work.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a related device for measuring soil carbon fixation based on biochar carbon emission.
Background
At present, global environmental problems are more and more prominent, and the most serious is global warming. Carbon dioxide is the main greenhouse gas, and the emission amount of the carbon dioxide is directly related to global warming. In order to reduce the carbon dioxide content in the atmosphere, various methods have been sought for sequestration or absorption of carbon dioxide. Among them, the use of biochar for soil carbon fixation, by absorbing a large amount of carbon dioxide, is considered as a very effective solution. The biochar can generate carbon for sealing in the carbon fixing process, so that the biochar can absorb carbon dioxide in the atmosphere, can store carbon in soil for a longer time and realize carbon emission.
Although there are applications and researches on the carbon fixation effect of biochar at present, in the practical application process, there is still a certain difficulty in accurately and quantitatively evaluating the carbon fixation effect of biochar. The method mainly consists of the fact that factors such as temperature, humidity and the like have great differences in soil environment, can have important influence on the carbon fixing effect, and meanwhile, the type, the addition amount and the like of the biochar can also influence the final carbon fixing effect. In addition, a model capable of comprehensively and accurately predicting the carbon fixing capability of biochar in soil under different environmental conditions does not exist at present. Therefore, in the practical application process, the application effect cannot be accurately estimated and predicted, which limits the application of the biochar in the aspects of climate change adaptation and slowing down to a certain extent.
For the above reasons, a new solution needs to be found to meet the need for accurate prediction of biochar soil carbon sequestration under varying environmental conditions.
Disclosure of Invention
The invention provides a method and a related device for measuring soil carbon fixation based on biochar carbon negative emission, which are used for solving the technical problem of how to accurately predict the carbon fixation of biochar soil under changing environmental conditions.
The first aspect of the invention provides a method for measuring soil carbon sequestration based on biochar carbon emission, which comprises the following steps:
measuring the carbon content of a target soil sample in real time through target equipment to obtain real-time measurement data; wherein the target soil sample is a soil sample added with biochar;
based on the real-time measurement data, quantitatively evaluating the carbon fixation effect of the biochar in the target soil sample through a preset machine learning algorithm to obtain an evaluation result of the carbon fixation effect, and comparing the evaluation result with a preset evaluation result of a soil sample without the biochar, so as to obtain a carbon fixation difference result of the biochar;
According to the carbon sequestration difference result, constructing a biochar-based soil carbon sequestration quantity prediction model; the biochar-based soil carbon fixation amount prediction model is used for predicting the carbon fixation capacity of soil under different environmental conditions and different biochar addition amounts.
Optionally, in a first implementation manner of the first aspect of the present invention, before the step of obtaining real-time measurement data by measuring, by the target device, the carbon content of the target soil sample includes:
selecting a biomass material, and performing anoxic pyrolysis treatment on the biomass material in a preset temperature range to prepare biochar; wherein the biomass comprises at least wood material, crop residues, and food waste;
adding the biochar into the selected soil sample, and mixing the biochar with the soil sample through preset mechanical stirring equipment until the uniform state is achieved, so as to obtain a target soil sample;
incubating a target soil sample under preset temperature, humidity and oxygen supply conditions, and simulating a soil carbon sequestration process in an actual application environment.
Optionally, in a second implementation manner of the first aspect of the present invention, the method further includes:
Collecting the soil organic carbon content of a first soil sample to obtain first data, and collecting the soil organic carbon content of a second soil sample to obtain second data;
respectively extracting the characteristics of the first data and the second data based on a preset principal component analysis algorithm to obtain corresponding first characteristic data and second characteristic data, carrying out aggregation treatment on the first characteristic data and the second characteristic data to obtain a soil sample data set,
collecting the environmental factors of the first soil sample to obtain a first environmental factor, collecting the environmental factors of the second soil sample to obtain a second environmental factor, and carrying out weighting treatment on the second environmental factor and the second environmental factor to obtain an environmental factor data set;
respectively analyzing the microorganism composition and abundance in the first soil sample and the second soil sample through a preset microorganism gene sequencing algorithm, and performing data fusion to obtain a microorganism data set;
based on a preset fusion algorithm, carrying out fusion processing on the soil sample data set, the environmental factor data set and the microorganism data set to obtain a fusion data set, and dividing the fusion data set into a training sample set and a test sample set;
Inputting the training sample set into a trained deep learning model, predicting to obtain the relationship among soil organic carbon, environmental factors, microorganism composition and abundance, generating a predicted value of soil organic carbon content based on the relationship among the soil organic carbon, the environmental factors, the microorganism composition and the abundance, and drawing a corresponding initial predicted distribution diagram according to the predicted value of the soil organic carbon content;
calculating a residual value through an actual value of the actually measured organic carbon content of the soil and a predicted value of the organic carbon content of the soil to obtain a first residual value, and performing spatial interpolation on the first residual value through a preset interpolation method to obtain a second residual value;
performing spatial addition operation according to the soil organic carbon content predicted by the deep learning model and the second residual error value to obtain a corrected predicted value, and forming a corrected predicted distribution diagram based on the corrected predicted value and an initial predicted distribution diagram;
and constructing a soil organic carbon content prediction model based on the corrected prediction distribution diagram.
Optionally, in a third implementation manner of the first aspect of the present invention, the environmental factor data at least includes a soil type, a climate condition, a vegetation type, a soil moisture content, a pH value, an amount of biochar added, and an adding time;
The first soil sample is a soil sample added with biochar; the second soil sample is other soil samples without added biochar.
Optionally, in a fourth implementation manner of the first aspect of the present invention, the analyzing, by a preset microorganism gene sequencing algorithm, the microorganism composition and abundance in the first soil sample and the second soil sample to obtain a microorganism data set includes:
microorganism separation treatment is carried out on the first soil sample and the second soil sample through a preset centrifugal machine, so that a treated soil sample is obtained;
extracting microorganism DNA from the treated soil sample by a preset chemical reagent;
performing PCR amplification on the extracted microorganism DNA based on the special foreign substance primer, and obtaining sequencing data through a high-throughput sequencer;
cleaning sequencing data through a preset data cleaning algorithm, and removing error data and noise in the sequencing data;
matching the washed sequencing data with a preset microorganism gene sequence database to determine the types of microorganisms in the soil sample;
based on the types of microorganisms in the soil samples, the composition and abundance of each microorganism in each soil sample are counted and analyzed to obtain microorganism data.
Optionally, in a fifth implementation manner of the first aspect of the present invention, the matching the washed sequencing data with a preset microbial gene sequence database includes:
acquiring sequencing data after cleaning;
generating a unique identification code according to the washed sequencing data;
matching the generated unique identification code with a preset microorganism gene sequence database, and searching whether a data table ID matched with the unique identification code exists in the microorganism gene sequence database;
if the data table ID matched with the unique identification code exists, reading a corresponding microorganism gene sequence from a data table containing the ID;
and determining the types of microorganisms in the soil sample according to the read microorganism gene sequences.
Optionally, in a sixth implementation manner of the first aspect of the present invention, the generating a unique identification code according to the washed sequencing data includes:
setting a character string with a designated bit number as an initial identification code;
defining identifiable data center identifiers and machine ID identifiers in a distributed system environment with a plurality of data centers and a plurality of machines, and generating position identifiers according to the data center identifiers and the machine ID identifiers;
Acquiring a current millisecond-level time stamp, and coding the millisecond-level time stamp into a time code;
initializing a counter; wherein the counter is used to generate different device identification codes for different sequencing data within the same millisecond;
after each time of generating the equipment identification code, checking whether the current time is the same as the time when the equipment identification code is generated last time; if the same, the counter is incremented by one timing unit; if the next millisecond has arrived, the counter will be re-zeroed to re-count in the new millisecond;
the time code, the location identifier, the device identifier, and the counter count are combined in a predetermined order to form a unique identifier for the sequencing data.
The second aspect of the present invention provides a soil carbon sequestration amount measurement apparatus based on biochar carbon negative emission, comprising:
the acquisition module is used for measuring and acquiring the carbon content of the target soil sample in real time through target equipment to obtain real-time measurement data; wherein the target soil sample is a soil sample added with biochar;
the evaluation module is used for quantitatively evaluating the carbon fixation effect of the biochar in the target soil sample through a preset machine learning algorithm based on the real-time measurement data to obtain an evaluation result of the carbon fixation effect, and comparing the evaluation result with a preset evaluation result of the soil sample without the biochar, so as to obtain a carbon fixation difference result of the biochar;
The construction module is used for constructing a soil carbon fixation quantity prediction model based on biochar according to the carbon fixation difference result; the biochar-based soil carbon fixation amount prediction model is used for predicting the carbon fixation capacity of soil under different environmental conditions and different biochar addition amounts.
A third aspect of the present invention provides a soil carbon sequestration measurement apparatus based on biochar carbon emission, comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to cause the biochar carbon negative emission-based soil carbon fixation measurement device to perform the biochar carbon negative emission-based soil carbon fixation measurement method described above.
A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which when run on a computer, cause the computer to perform the above-described biochar carbon emission-based soil carbon sequestration calculation method.
In the technical scheme provided by the invention, the beneficial effects are as follows: the invention provides a soil carbon fixation amount measuring method based on biochar carbon negative emission and a related device, wherein the method is used for measuring the carbon content of a target soil sample in real time through target equipment to obtain real-time measurement data; based on the real-time measurement data, quantitatively evaluating the carbon fixation effect of the biochar in the target soil sample through a preset machine learning algorithm to obtain an evaluation result of the carbon fixation effect, and comparing the evaluation result with a preset evaluation result of a soil sample without the biochar, so as to obtain a carbon fixation difference result of the biochar; according to the carbon sequestration difference result, constructing a biochar-based soil carbon sequestration quantity prediction model; the invention can complete the real-time measurement and analysis of the carbon content of the soil in a short time, quickly obtain the current carbon content data of the soil and provide basic data for subsequent work. And moreover, the carbon fixation effect of the biochar in the soil sample can be quantitatively evaluated through a machine learning algorithm, and a complex carbon fixation process is converted into a numerical value or a rating, so that the research and analysis of the carbon fixation effect are more visual and easier to understand. Meanwhile, the carbon content change of the soil sample added with the biochar and the soil sample not added with the biochar is compared, the carbon fixation effect of the biochar can be clearly seen, and a carbon fixation difference result is obtained. Finally, according to the carbon fixation difference result, a soil carbon fixation amount prediction model based on biochar can be constructed, and the model can be used for predicting the carbon fixation capacity of soil under different environmental conditions and different addition amounts of the biochar, so that scientific prediction and support are provided for actual soil improvement work and environmental protection work.
Drawings
FIG. 1 is a schematic view showing an embodiment of a method for measuring carbon sequestration in soil based on biochar carbon emission in an embodiment of the present invention;
FIG. 2 is a schematic view of an embodiment of a device for measuring carbon sequestration in soil based on biochar carbon emission in an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a soil carbon sequestration amount measuring method based on biochar carbon negative emission and a related device. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
For ease of understanding, a specific flow of an embodiment of the present invention will be described below with reference to fig. 1, and an embodiment of a method for measuring carbon sequestration in soil based on biochar carbon emission in the embodiment of the present invention includes:
step 101, measuring the carbon content of a target soil sample in real time through target equipment to obtain real-time measurement data; wherein the target soil sample is a soil sample added with biochar;
it is to be understood that the execution subject of the present invention may be a device for measuring carbon fixation in soil based on carbon emission of biochar, and may also be a terminal or a server, which is not limited herein. The embodiment of the invention is described by taking a server as an execution main body as an example.
Specifically, the specific implementation process of the embodiment of the invention is as follows:
sample selection: first, a soil sample to which biochar has been added is selected, which is the subject of the experiment. For example, a block of farmland soil to which biochar has been applied may be selected as a sample.
Preparing equipment: the target equipment capable of detecting the carbon content of the soil is prepared. Such equipment typically includes, but is not limited to, soil spectrometers, soil constituent analyzers, and the like, which are capable of measuring and analyzing soil carbon content in real time.
Sample pretreatment: next, the selected soil sample is subjected to necessary pretreatment such as baking, grinding, screening, etc., so as to enable the subsequent carbon content detection.
Carbon content measurement: and (3) placing the pretreated soil sample into target equipment, and measuring the total carbon content in the soil in real time according to the equipment operation guideline. During this process, the apparatus generates real-time measurement data of the carbon content of the soil.
And (3) data recording: the resulting real-time measurement data is recorded or stored for later analysis access. These data are typically represented numerically or graphically and can directly reflect the current carbon content of the soil.
102, quantitatively evaluating the carbon fixation effect of the biochar in a target soil sample through a preset machine learning algorithm based on the real-time measurement data to obtain an evaluation result of the carbon fixation effect, and comparing the evaluation result with a preset evaluation result of a soil sample without the biochar, so as to obtain a carbon fixation difference result of the biochar;
specifically, the specific implementation process of the embodiment of the invention is as follows:
selecting a suitable machine learning algorithm: a suitable machine learning algorithm is determined based on the study requirements. Common algorithms include linear regression, support vector machines, random forests, and the like. For example, a random forest algorithm is selected to address this problem.
Training a machine learning model: the existing soil sample data is used as a training set, wherein the data comprises a series of attributes such as carbon content of the soil sample and corresponding carbon fixation effect results. The model understands the relationships between these data through training.
Carrying out quantitative evaluation on the carbon fixation effect of the biochar: inputting the carbon content data obtained by real-time measurement into a trained model, and predicting a carbon fixation effect quantized value corresponding to the target soil sample by the model according to the learned rule.
And (3) obtaining an evaluation result of the carbon fixation effect: the predicted value output by the model is the quantized evaluation result of the carbon fixation effect of the biochar in the target soil sample.
Comparison of evaluation results: this step requires a preset evaluation result of the soil sample to which no biochar is added. This preset evaluation result may be derived based on historical data, existing studies, or theoretical predictions. And comparing the evaluation result of the carbon fixation effect of the biochar soil with the evaluation result.
Obtaining a carbon fixation difference result: after comparative analysis, the result of difference in carbon fixation of the biochar is obtained, which means the degree of change in carbon fixation of the soil before and after the biochar is added.
Consider an example, where the above steps are explicitly:
Selecting a machine learning algorithm: it is assumed that to solve this problem, a random forest algorithm is chosen because of its advantage in processing complex nonlinear data.
Training a machine learning model: a batch of historical soil sample data containing various characteristics (such as soil type, PH, biochar addition, etc.) and corresponding carbon sequestration effects is provided. These data are used to train a random forest model that learns the relationships between individual features and carbon sequestration effects.
Quantitatively evaluating carbon fixation effect: the real-time measurement of the carbon content of the soil is carried out on a farmland to which the biochar is added, the obtained data are input into a model trained in the last step, and the model returns a predicted quantized value of the carbon fixation effect, which is assumed to be X.
And (3) obtaining an evaluation result of the carbon fixation effect: the quantized value X is the evaluation result of the carbon fixation effect of the biochar in the farmland soil sample.
Comparison of evaluation results: meanwhile, a preset carbon fixation effect evaluation result Y of a similar soil sample without added biochar is provided, and the result can be preset based on historical experimental data.
Obtaining a carbon fixation difference result: by comparison, the difference between the carbon fixation effects before and after adding the biochar, namely the difference between X and Y, is found out, and the difference is the carbon fixation difference result of the biochar.
Step 103, constructing a biochar-based soil carbon sequestration quantity prediction model according to the carbon sequestration difference result; the biochar-based soil carbon fixation amount prediction model is used for predicting the carbon fixation capacity of soil under different environmental conditions and different biochar addition amounts.
Specifically, the specific implementation process of the embodiment of the invention is as follows:
collecting data: a number of existing data about the soil are collected including, but not limited to, soil type, pH, biochar addition, environmental conditions (e.g., climate, temperature, etc.), and known carbon sequestration. Such data may be obtained from published data sources or from experiments that have been performed.
Data cleaning: the collected data is cleaned and preprocessed. This step is mainly to remove invalid, inaccurate or irrelevant data to ensure data quality.
Feature selection and engineering: for each soil sample, a set of key features affecting the carbon sequestration capacity of the soil, such as the amount of biochar added, the soil type, environmental conditions, etc., are determined. These features are subjected to certain engineering processes, such as discretizing continuous variables, encoding unordered class variables, etc.
Model training: the feature set and known carbon sequestration training model described above are utilized using a selected machine learning algorithm (e.g., random forest, regression model, neural network, etc.). The model learns to understand the relationship between these characteristics and the carbon sequestration amount.
Model test and verification: and (3) testing the prediction capability of the model by using a part of data which is not used for training, and comparing the prediction result with the actual situation, thereby verifying the accuracy of the model.
Creation of a prediction model: after test verification shows that the model performs well, the prediction model can be used for predicting the soil carbon fixation capacity under different environmental conditions and different biochar addition amounts, and a user can obtain a prediction result only by inputting corresponding conditions.
This process is illustrated by the following specific example:
collecting data: the farmland in different areas of Asia is taken as a research object, and the characteristic information of soil type, pH value, biochar addition amount, climate, temperature and the like of each farmland and the corresponding known carbon fixation amount are collected.
Data cleaning: the data may contain invalid or erroneous entries, for example, some farmland carbon sequestration may not be recorded or may be incorrectly recorded, and the data should be purged or corrected.
Feature selection and engineering: the key characteristics affecting the carbon fixing ability of the soil are determined, for example, the soil type is coded as 1 representing clay, 2 representing sandy loam, 3 representing loam and the like, the addition amount of biochar is directly in tons, the climate condition is coded as 1 representing tropical zone, 2 representing temperate zone, 3 representing cold zone and the like.
Model training: assuming that a random forest is selected as a machine learning algorithm, inputting the feature set processed in the previous step and the known carbon sequestration amount, and training a model.
Model test and verification: and (3) reserving 30% of farmland data as a test set, predicting by using a trained model, comparing actual values, calculating a prediction error, and checking the accuracy of the model.
Creation of a prediction model: after the five steps, a soil carbon fixation quantity prediction model based on biochar is successfully created. If the existing farmland of sandy loam is in a temperate zone and has 70% of humidity, 10 tons of biochar are planned to be added, the parameters are input into a prediction model, the model outputs the expected carbon fixation amount of soil, and thus a farmland manager can expect the carbon fixation capacity of the farmland after the biochar is applied.
Another embodiment of the method for measuring soil carbon fixation based on biochar carbon emission in the embodiment of the invention comprises the following steps:
The method for measuring the carbon content of the target soil sample in real time through the target equipment comprises the following steps before the step of obtaining real-time measurement data:
selecting a biomass material, and performing anoxic pyrolysis treatment on the biomass material in a preset temperature range to prepare biochar; wherein the biomass comprises at least wood material, crop residues, and food waste;
adding the biochar into the selected soil sample, and mixing the biochar with the soil sample through preset mechanical stirring equipment until the uniform state is achieved, so as to obtain a target soil sample;
incubating a target soil sample under preset temperature, humidity and oxygen supply conditions, and simulating a soil carbon sequestration process in an actual application environment.
Specifically, the specific process for implementing the above steps is as follows:
selecting a biomass material: suitable biomass materials are first selected, which may be from a variety of sources, including wood, crop residues, food waste, and the like.
Preparing biochar: the biomass material is placed in a special apparatus, such as a pyrolysis furnace, which may perform an anoxic pyrolysis treatment on the biomass material within a predetermined temperature range to convert it into biochar. The process is actually further pyrolysis of biomass materials in an anaerobic or hypoxic environment, and the specific temperature range needs to be set according to the research objectives and equipment.
Biochar is mixed with soil samples: the selected soil sample is added with pre-prepared biochar. The biochar is then mixed with the soil sample using a pre-set mechanical stirring device, such as a rotary stirrer. Stirring is continued until the material is uniformly distributed, so that the biochar is completely mixed with the soil sample to obtain a target soil sample.
And (3) incubating soil: the target soil sample is incubated under preset conditions of temperature, humidity and oxygen supply. The hatching process is actually a soil carbon sequestration process simulated in a real environment. This step can be carried out by means of an incubator, and the corresponding growth conditions are set so that the interaction of biochar with the soil approaches the real soil environment.
For example, straw is selected as biomass material, and then subjected to anoxic pyrolysis at 500 ℃ to obtain biochar. A sample of sandy loam was selected, to which 5% by weight of biochar was added, and then mixed with a stirrer until homogeneous. Then the mixed soil sample is put into an incubator, the temperature is set to 25 ℃, the humidity is set to 70%, good oxygen supply is kept, incubation is carried out for 3 months, and finally the target soil sample is obtained.
Another embodiment of the method for measuring soil carbon fixation based on biochar carbon emission in the embodiment of the invention comprises the following steps:
collecting the soil organic carbon content of a first soil sample to obtain first data, and collecting the soil organic carbon content of a second soil sample to obtain second data;
respectively extracting the characteristics of the first data and the second data based on a preset principal component analysis algorithm to obtain corresponding first characteristic data and second characteristic data, carrying out aggregation treatment on the first characteristic data and the second characteristic data to obtain a soil sample data set,
collecting the environmental factors of the first soil sample to obtain a first environmental factor, collecting the environmental factors of the second soil sample to obtain a second environmental factor, and carrying out weighting treatment on the second environmental factor and the second environmental factor to obtain an environmental factor data set;
respectively analyzing the microorganism composition and abundance in the first soil sample and the second soil sample through a preset microorganism gene sequencing algorithm, and performing data fusion to obtain a microorganism data set;
based on a preset fusion algorithm, carrying out fusion processing on the soil sample data set, the environmental factor data set and the microorganism data set to obtain a fusion data set, and dividing the fusion data set into a training sample set and a test sample set;
Inputting the training sample set into a trained deep learning model, predicting to obtain the relationship among soil organic carbon, environmental factors, microorganism composition and abundance, generating a predicted value of soil organic carbon content based on the relationship among the soil organic carbon, the environmental factors, the microorganism composition and the abundance, and drawing a corresponding initial predicted distribution diagram according to the predicted value of the soil organic carbon content;
calculating a residual value through an actual value of the actually measured organic carbon content of the soil and a predicted value of the organic carbon content of the soil to obtain a first residual value, and performing spatial interpolation on the first residual value through a preset interpolation method to obtain a second residual value;
performing spatial addition operation according to the soil organic carbon content predicted by the deep learning model and the second residual error value to obtain a corrected predicted value, and forming a corrected predicted distribution diagram based on the corrected predicted value and an initial predicted distribution diagram;
and constructing a soil organic carbon content prediction model based on the corrected prediction distribution diagram.
In particular, for a better understanding of the embodiments of the present invention, the following specific examples are given as illustrations:
the organic carbon content of the two soils was measured separately using laboratory testing and the data recorded. And then, carrying out feature extraction on the two data by utilizing a principal component analysis algorithm to obtain the feature data of the two data.
At the same time, the environmental factors of the two pieces of soil, such as temperature, humidity, PH value, etc., are collected, and then weighted to generate an environmental factor data set.
And then analyzing the types and the amounts of microorganisms existing in the two soil samples by using a microorganism gene sequencing algorithm to obtain a microorganism data set. These three portions of data are then combined by a fusion algorithm, and the fused data set is finally divided into a training sample set (e.g., 70% of the data) and a detection sample set (e.g., 30% of the data).
Then, the training sample set is input into a trained deep learning model, such as a random forest, a neural network and the like, so that the mutual influence of soil organic carbon, environmental factors and microorganisms is predicted, a predicted value of the soil organic carbon content is generated, and an initial prediction distribution diagram is drawn.
Then, a residual error between the predicted value and the actually measured organic carbon content is calculated to obtain a first residual value. The first residual value is processed by spatial interpolation such as the kriging method to obtain a second residual value.
On the basis of the process, the predicted soil organic carbon content and the second residual error value are subjected to space addition operation, so that a corrected predicted value is obtained. And forming a corrected predicted distribution diagram according to the corrected predicted value and the initial predicted distribution diagram. And finally, constructing a soil organic carbon content prediction model according to the corrected prediction distribution diagram.
Another embodiment of the method for measuring soil carbon fixation based on biochar carbon emission in the embodiment of the invention comprises the following steps:
the environmental factor data at least comprises soil type, climate condition, vegetation type, soil moisture content, pH value, biochar addition amount and addition time;
the first soil sample is a soil sample added with biochar; the second soil sample is other soil samples without added biochar.
Another embodiment of the method for measuring soil carbon fixation based on biochar carbon emission in the embodiment of the invention comprises the following steps:
analyzing the microorganism composition and abundance in the first soil sample and the second soil sample by a preset microorganism gene sequencing algorithm to obtain a microorganism data set, wherein the method comprises the following steps:
microorganism separation treatment is carried out on the first soil sample and the second soil sample through a preset centrifugal machine, so that a treated soil sample is obtained;
extracting microorganism DNA from the treated soil sample by a preset chemical reagent;
performing PCR amplification on the extracted microorganism DNA based on the special foreign substance primer, and obtaining sequencing data through a high-throughput sequencer;
cleaning sequencing data through a preset data cleaning algorithm, and removing error data and noise in the sequencing data;
Matching the washed sequencing data with a preset microorganism gene sequence database to determine the types of microorganisms in the soil sample;
based on the types of microorganisms in the soil samples, the composition and abundance of each microorganism in each soil sample are counted and analyzed to obtain microorganism data.
Specifically, the specific process for implementing the above steps is as follows:
microbial separation: first, a first soil sample and a second soil sample are subjected to a microorganism separation process using a preset centrifuge, and the method may include forming a suspension and separating microorganisms from the soil sample using the centrifuge to obtain a processed soil sample.
Extracting microorganism DNA: then, DNA of the microorganism, which is a key part of genetic information of the microorganism, is extracted from the treated soil sample using a predetermined chemical agent such as phenol-chloroform extraction or the like.
PCR amplification and sequencing: after the microbial DNA was obtained, PCR (Polymerase Chain Reaction) amplification was performed on the microbial DNA based on specific primers, allowing for an increase in the amount of microbial DNA for subsequent sequencing analysis. The amplified sample is subjected to high-throughput sequencer to obtain sequencing data, which is a key for determining the type and quantity of microorganisms.
Data cleaning: raw data provided by a sequencer often contains a large amount of error data and noise, so that a preset data cleaning algorithm is needed to perform preliminary processing on sequencing data, and the purpose is to remove the error data and noise in the sequencing data and obtain data which can be directly researched.
Microbial species determination and abundance statistical analysis: the type of microorganisms in the soil sample can be determined by matching the washed sequencing data with a preset microorganism gene sequence database. After each microorganism type is determined, the microorganism composition and abundance in each soil sample are counted and analyzed, and then microorganism data can be obtained.
Another embodiment of the method for measuring soil carbon fixation based on biochar carbon emission in the embodiment of the invention comprises the following steps:
the step of matching the washed sequencing data with a preset microorganism gene sequence database comprises the following steps:
acquiring sequencing data after cleaning;
generating a unique identification code according to the washed sequencing data;
matching the generated unique identification code with a preset microorganism gene sequence database, and searching whether a data table ID matched with the unique identification code exists in the microorganism gene sequence database;
If the data table ID matched with the unique identification code exists, reading a corresponding microorganism gene sequence from a data table containing the ID;
and determining the types of microorganisms in the soil sample according to the read microorganism gene sequences.
Specifically, the specific process for implementing the above steps is as follows:
acquiring sequencing data after cleaning: first, washed sequencing data is obtained. Data cleansing is the removal of noise and erroneous data to obtain an accurate DNA base pairing sequence.
Generating a unique identification code: then, a unique identification code is generated based on the washed sequencing data. This identification code may be a hash value or other code that uniquely identifies the DNA sequence. For example, an algorithm called MD5 is used which converts any length of data into a string of unique identification codes of fixed length.
Matching database: with this unique identification code, a match is then made with a database of pre-set microbial gene sequences. This database is a known library of DNA sequences for the identification of microorganisms. And matching each unique identification code with the data table ID in the database one by one to find out whether the microorganism gene sequence corresponding to the identification code exists.
Reading the microbial gene sequence: if a data table ID matching the unique identification code exists in the database, we read the corresponding microorganism gene sequence from the data table of the ID. This gene sequence represents a specific class of microorganisms.
Determining the microorganism species: based on the read gene sequences of the microorganisms, the species of the microorganisms in the soil sample can be determined. For example, it is determined that a sample contains a large amount of nitrifying bacteria, actinomycetes decomposing humic substances, and the like.
Another embodiment of the method for measuring soil carbon fixation based on biochar carbon emission in the embodiment of the invention comprises the following steps:
the generating a unique identification code according to the washed sequencing data comprises the following steps:
setting a character string with a designated bit number as an initial identification code;
defining identifiable data center identifiers and machine ID identifiers in a distributed system environment with a plurality of data centers and a plurality of machines, and generating position identifiers according to the data center identifiers and the machine ID identifiers;
acquiring a current millisecond-level time stamp, and coding the millisecond-level time stamp into a time code;
initializing a counter; wherein the counter is used to generate different device identification codes for different sequencing data within the same millisecond;
After each time of generating the equipment identification code, checking whether the current time is the same as the time when the equipment identification code is generated last time; if the same, the counter is incremented by one timing unit; if the next millisecond has arrived, the counter will be re-zeroed to re-count in the new millisecond;
the time code, the location identifier, the device identifier, and the counter count are combined in a predetermined order to form a unique identifier for the sequencing data.
Specifically, the specific process for implementing the above steps is as follows:
setting an initial identification code: an initial string identification code "0000000000" of length 10 is set first.
Generating a position identifier: it is assumed that there are three data centers (labeled 1,2,3, respectively) and two machines (labeled 01,02, respectively) per data center in a distributed system environment, so that a location identification, such as "1-01", can be obtained by the data center identification and the machine ID identification.
Acquiring time codes: the current millisecond time stamp is obtained, say 1631111111111, which will be encoded as a time code.
Initializing a counter: the first time a device identification code is generated, a counter is initialized to 0.
Counter count logic: if new data is generated in less than 1 millisecond, the counter is incremented, e.g., from 0 to 1, generating a new device identification code such as "1-01-1"; if 1 millisecond has elapsed, the counter will be reset to zero again, generating a new device identification code such as "1-01-0".
Forming a unique identification code: and finally, combining the parts to obtain the unique identification code. For example, "0000000000-1-01-1631111111111-1", this identification code contains the location and time of data generation and serial number information within the same millisecond, is unique enough and has a rich information volume.
In the embodiment of the invention, the beneficial effects are as follows: the embodiment of the invention ensures that in the distributed system environment, even under the condition of very large concurrency, a unique identification code can be generated for each sequencing data, thereby being beneficial to the subsequent data management and analysis work. And each part of washed sequencing data has a unique identification code, so that the accuracy and independence of the data can be ensured no matter what environment is, and the subsequent data processing and analysis are convenient.
The method for measuring the amount of carbon in soil based on carbon emission of biochar in the embodiment of the present invention is described above, and the apparatus for measuring the amount of carbon in soil based on carbon emission of biochar in the embodiment of the present invention is described below, referring to fig. 2, one embodiment of the apparatus for measuring the amount of carbon in soil based on carbon emission of biochar in the embodiment of the present invention includes:
the acquisition module is used for measuring and acquiring the carbon content of the target soil sample in real time through target equipment to obtain real-time measurement data; wherein the target soil sample is a soil sample added with biochar;
The evaluation module is used for quantitatively evaluating the carbon fixation effect of the biochar in the target soil sample through a preset machine learning algorithm based on the real-time measurement data to obtain an evaluation result of the carbon fixation effect, and comparing the evaluation result with a preset evaluation result of the soil sample without the biochar, so as to obtain a carbon fixation difference result of the biochar;
the construction module is used for constructing a soil carbon fixation quantity prediction model based on biochar according to the carbon fixation difference result; the biochar-based soil carbon fixation amount prediction model is used for predicting the carbon fixation capacity of soil under different environmental conditions and different biochar addition amounts.
The invention also provides a device for calculating the amount of carbon fixation in soil based on the carbon emission of the biochar, which comprises a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the method for calculating the amount of carbon fixation in soil based on the carbon emission of the biochar in the above embodiments.
The present invention also provides a computer readable storage medium, which may be a non-volatile computer readable storage medium, and may also be a volatile computer readable storage medium, in which instructions are stored, which when executed on a computer, cause the computer to perform the steps of the biochar carbon sequestration measurement method based on biochar carbon sequestration emission.
The beneficial effects are that: the invention provides a soil carbon fixation amount measuring method based on biochar carbon negative emission and a related device, wherein the method is used for measuring the carbon content of a target soil sample in real time through target equipment to obtain real-time measurement data; based on the real-time measurement data, quantitatively evaluating the carbon fixation effect of the biochar in the target soil sample through a preset machine learning algorithm to obtain an evaluation result of the carbon fixation effect, and comparing the evaluation result with a preset evaluation result of a soil sample without the biochar, so as to obtain a carbon fixation difference result of the biochar; according to the carbon sequestration difference result, constructing a biochar-based soil carbon sequestration quantity prediction model; the invention can complete the real-time measurement and analysis of the carbon content of the soil in a short time, quickly obtain the current carbon content data of the soil and provide basic data for subsequent work. And moreover, the carbon fixation effect of the biochar in the soil sample can be quantitatively evaluated through a machine learning algorithm, and a complex carbon fixation process is converted into a numerical value or a rating, so that the research and analysis of the carbon fixation effect are more visual and easier to understand. Meanwhile, the carbon content change of the soil sample added with the biochar and the soil sample not added with the biochar is compared, the carbon fixation effect of the biochar can be clearly seen, and a carbon fixation difference result is obtained. Finally, according to the carbon fixation difference result, a soil carbon fixation amount prediction model based on biochar can be constructed, and the model can be used for predicting the carbon fixation capacity of soil under different environmental conditions and different addition amounts of the biochar, so that scientific prediction and support are provided for actual soil improvement work and environmental protection work.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The method for measuring the carbon fixation amount of the soil based on the carbon negative emission of the biochar is characterized by comprising the following steps of:
measuring the carbon content of a target soil sample in real time through target equipment to obtain real-time measurement data; wherein the target soil sample is a soil sample added with biochar;
based on the real-time measurement data, quantitatively evaluating the carbon fixation effect of the biochar in the target soil sample through a preset machine learning algorithm to obtain an evaluation result of the carbon fixation effect, and comparing the evaluation result with a preset evaluation result of a soil sample without the biochar, so as to obtain a carbon fixation difference result of the biochar;
According to the carbon sequestration difference result, constructing a biochar-based soil carbon sequestration quantity prediction model; the biochar-based soil carbon fixation amount prediction model is used for predicting the carbon fixation capacity of soil under different environmental conditions and different biochar addition amounts.
2. The method for measuring carbon sequestration of soil according to claim 1, wherein before the step of measuring carbon content of the target soil sample in real time by the target device to obtain real-time measurement data, the method comprises:
selecting a biomass material, and performing anoxic pyrolysis treatment on the biomass material in a preset temperature range to prepare biochar; wherein the biomass comprises at least wood material, crop residues, and food waste;
adding the biochar into the selected soil sample, and mixing the biochar with the soil sample through preset mechanical stirring equipment until the uniform state is achieved, so as to obtain a target soil sample;
incubating a target soil sample under preset temperature, humidity and oxygen supply conditions, and simulating a soil carbon sequestration process in an actual application environment.
3. The method of measuring carbon sequestration in soil of claim 1, further comprising:
Collecting the soil organic carbon content of a first soil sample to obtain first data, and collecting the soil organic carbon content of a second soil sample to obtain second data;
respectively extracting the characteristics of the first data and the second data based on a preset principal component analysis algorithm to obtain corresponding first characteristic data and second characteristic data, carrying out aggregation treatment on the first characteristic data and the second characteristic data to obtain a soil sample data set,
collecting the environmental factors of the first soil sample to obtain a first environmental factor, collecting the environmental factors of the second soil sample to obtain a second environmental factor, and carrying out weighting treatment on the second environmental factor and the second environmental factor to obtain an environmental factor data set;
respectively analyzing the microorganism composition and abundance in the first soil sample and the second soil sample through a preset microorganism gene sequencing algorithm, and performing data fusion to obtain a microorganism data set;
based on a preset fusion algorithm, carrying out fusion processing on the soil sample data set, the environmental factor data set and the microorganism data set to obtain a fusion data set, and dividing the fusion data set into a training sample set and a test sample set;
Inputting the training sample set into a trained deep learning model, predicting to obtain the relationship among soil organic carbon, environmental factors, microorganism composition and abundance, generating a predicted value of soil organic carbon content based on the relationship among the soil organic carbon, the environmental factors, the microorganism composition and the abundance, and drawing a corresponding initial predicted distribution diagram according to the predicted value of the soil organic carbon content;
calculating a residual value through an actual value of the actually measured organic carbon content of the soil and a predicted value of the organic carbon content of the soil to obtain a first residual value, and performing spatial interpolation on the first residual value through a preset interpolation method to obtain a second residual value;
performing spatial addition operation according to the soil organic carbon content predicted by the deep learning model and the second residual error value to obtain a corrected predicted value, and forming a corrected predicted distribution diagram based on the corrected predicted value and an initial predicted distribution diagram;
and constructing a soil organic carbon content prediction model based on the corrected prediction distribution diagram.
4. The method for measuring carbon sequestration in soil according to claim 3, wherein the environmental factor data at least comprises soil type, climate condition, vegetation type, soil moisture content, pH, biochar addition amount, addition time;
The first soil sample is a soil sample added with biochar; the second soil sample is other soil samples without added biochar.
5. The method of claim 3, wherein analyzing the microorganism composition and abundance in the first and second soil samples by a predetermined microorganism gene sequencing algorithm to obtain a microorganism data set comprises:
microorganism separation treatment is carried out on the first soil sample and the second soil sample through a preset centrifugal machine, so that a treated soil sample is obtained;
extracting microorganism DNA from the treated soil sample by a preset chemical reagent;
performing PCR amplification on the extracted microorganism DNA based on the special foreign substance primer, and obtaining sequencing data through a high-throughput sequencer;
cleaning sequencing data through a preset data cleaning algorithm, and removing error data and noise in the sequencing data;
matching the washed sequencing data with a preset microorganism gene sequence database to determine the types of microorganisms in the soil sample;
based on the types of microorganisms in the soil samples, the composition and abundance of each microorganism in each soil sample are counted and analyzed to obtain microorganism data.
6. The method of claim 5, wherein the matching the washed sequencing data with a database of predetermined microbial gene sequences comprises:
acquiring sequencing data after cleaning;
generating a unique identification code according to the washed sequencing data;
matching the generated unique identification code with a preset microorganism gene sequence database, and searching whether a data table ID matched with the unique identification code exists in the microorganism gene sequence database;
if the data table ID matched with the unique identification code exists, reading a corresponding microorganism gene sequence from a data table containing the ID;
and determining the types of microorganisms in the soil sample according to the read microorganism gene sequences.
7. The method of claim 6, wherein generating a unique identification code from the washed sequencing data comprises:
setting a character string with a designated bit number as an initial identification code;
defining identifiable data center identifiers and machine ID identifiers in a distributed system environment with a plurality of data centers and a plurality of machines, and generating position identifiers according to the data center identifiers and the machine ID identifiers;
Acquiring a current millisecond-level time stamp, and coding the millisecond-level time stamp into a time code;
initializing a counter; wherein the counter is used to generate different device identification codes for different sequencing data within the same millisecond;
after each time of generating the equipment identification code, checking whether the current time is the same as the time when the equipment identification code is generated last time; if the same, the counter is incremented by one timing unit; if the next millisecond has arrived, the counter will be re-zeroed to re-count in the new millisecond;
the time code, the location identifier, the device identifier, and the counter count are combined in a predetermined order to form a unique identifier for the sequencing data.
8. The utility model provides a soil solid carbon measuring device based on charcoal negative carbon emission which characterized in that, soil solid carbon measuring device based on charcoal negative carbon emission includes:
the acquisition module is used for measuring and acquiring the carbon content of the target soil sample in real time through target equipment to obtain real-time measurement data; wherein the target soil sample is a soil sample added with biochar;
the evaluation module is used for quantitatively evaluating the carbon fixation effect of the biochar in the target soil sample through a preset machine learning algorithm based on the real-time measurement data to obtain an evaluation result of the carbon fixation effect, and comparing the evaluation result with a preset evaluation result of the soil sample without the biochar, so as to obtain a carbon fixation difference result of the biochar;
The construction module is used for constructing a soil carbon fixation quantity prediction model based on biochar according to the carbon fixation difference result; the biochar-based soil carbon fixation amount prediction model is used for predicting the carbon fixation capacity of soil under different environmental conditions and different biochar addition amounts.
9. A biochar carbon emission-based soil carbon fixation amount measurement apparatus, characterized in that the biochar carbon emission-based soil carbon fixation amount measurement apparatus comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invokes the instructions in the memory to cause the biochar carbon negative emission-based soil carbon fixation measurement device to perform the biochar carbon negative emission-based soil carbon fixation measurement method of any one of claims 1-7.
10. A computer readable storage medium having instructions stored thereon, which when executed by a processor, implement the biochar carbon emission based soil carbon sequestration calculation method of any one of claims 1 to 7.
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