CN117171677B - Microbial restoration effect evaluation method, system and medium based on decision tree model - Google Patents
Microbial restoration effect evaluation method, system and medium based on decision tree model Download PDFInfo
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Abstract
The invention relates to a microbial remediation effect evaluation method, a system and a medium based on a decision tree model, and belongs to the field of soil remediation evaluation. According to the invention, the restoration state of the microbial restoration soil is monitored by fusing the decision tree model, so that a large amount of data can be rapidly processed, and the intelligent monitoring of the restoration effect in the soil restoration process is realized. The invention integrates a principal component analysis method and an ABOD algorithm to optimize the decision tree model, and improves the precision of batch processing of data and the precision of evaluating the soil restoration state effect.
Description
Technical Field
The invention relates to the technical field of soil remediation evaluation, in particular to a microbial remediation effect evaluation method, system and medium based on a decision tree model.
Background
Soil is a material basis for agricultural production and is an important component of the ecological environment. In recent years, with the development of industrialization and town, various pollutants in industrial and agricultural production and household garbage flow into soil, and the soil environment is deteriorated. It not only affects the yield of crops, but also directly threatens the health of residents. At present, the microbial technology is continuously developed, and the adoption of the microbial technology to optimize the heavy metal pollution of soil is the basis for reasonably controlling the heavy metal pollution problem, and is also the key for ensuring the correct restoration of the soil. The type of microorganism determines the resistance to heavy metal contamination and the type varies with the general order actinomycetes < bacteria < fungi. The microorganism has high-efficiency degradation activity to clean the soil environment on site, and the special detoxification effect is used for improving the soil biology, but the microorganism is easy to be influenced by various environmental factors such as moisture, temperature, oxygen, pH and the like, and the activity of the microorganism is influenced, so that the soil restoration effect can be directly influenced. The microbial remediation technology is to absorb, enrich and reduce the pollution factors in the dissolved and deposited water through the metabolic function of microorganisms, and the microorganisms cannot directly degrade heavy metals, so that other technologies are usually assisted. At present, in the repairing process of batch monitoring microorganisms, the monitoring is mainly performed through a wireless sensor, however, a large amount of data processing is needed during the monitoring, the soil state of the microorganism repairing can be obtained, most of the monitoring data in the prior art are manually identified, a large amount of manpower and material resources are consumed, and the monitoring cost is too high.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a method, a system and a medium for evaluating the microbial restoration effect based on a decision tree model.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a method for evaluating a microbial restoration effect based on a decision tree model, which comprises the following steps:
the method comprises the steps of arranging wireless sensors in a soil pollution area, constructing a wireless information transmission network according to the wireless sensors, and acquiring pollution data information in the process of repairing soil by microorganisms through the wireless information transmission network;
introducing a decision tree model, classifying and evaluating the polluted data information through the decision tree model, obtaining a restoration evaluation result, inputting a principal component analysis algorithm, and performing data processing through data in the restoration evaluation result to obtain a dimension reduction matrix of each leaf node;
introducing an ABOD algorithm, calculating a feature vector of the dimension reduction matrix through the ABOD algorithm, obtaining a variance value of the included angle, and processing a repair evaluation result according to the variance value of the included angle to obtain a processed evaluation result;
and acquiring a repair evaluation result of each soil pollution area within a preset time, constructing a soil repair state transition probability matrix according to the repair evaluation result of each soil pollution area within the preset time through a Markov chain, and generating a related soil repair suggestion according to the soil repair state transition probability matrix.
Further, in the method, a wireless information transmission network is constructed according to the wireless sensor, and the method specifically comprises the following steps:
setting an information transmission rate threshold of the wireless sensor, initializing the quantity information of the communication facilities, acquiring the quantity information of the wireless sensor, and constructing a wireless information transmission network according to the quantity information of the communication facilities;
calculating information transmission rate information of all wireless sensors when the wireless sensors work according to the wireless information transmission network and the quantity information of the wireless sensors, and judging whether the information transmission rate information of all the wireless sensors when the wireless sensors work is larger than an information transmission rate threshold of the wireless sensors or not;
introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, and increasing the number information of the communication facilities according to the genetic algebra when the information transmission rate information of all the wireless sensors during working is not more than the information transmission rate threshold value of the wireless sensors;
when the information transmission rate information of all the wireless sensors is greater than the information transmission rate threshold value of the wireless sensors during working, iteration is stopped, the quantity information of the communication facilities is output, and the wireless information transmission network is adjusted according to the quantity information of the communication facilities.
Further, in the method, a decision tree model is introduced, the polluted data information is classified and evaluated through the decision tree model, a repair evaluation result is obtained, a principal component analysis algorithm is input, data processing is carried out through data in the repair evaluation result, and a dimension reduction matrix of each leaf node is obtained, and the method specifically comprises the following steps:
introducing a decision tree model, constructing a root node according to pollution data information, setting a plurality of microbial soil restoration state threshold ranges, splitting the root node according to the microbial soil restoration state threshold ranges, and generating a new node;
when all data in the new nodes are within the same microbial soil restoration state threshold range, no new nodes are generated, a plurality of leaf nodes are output, restoration evaluation results are generated according to the leaf nodes, and a principal component analysis algorithm is introduced;
obtaining pollution data information of each leaf node in the repair evaluation result, constructing a low-dimensional space by linear transformation of the pollution data information in the leaf nodes, projecting the pollution data information into the low-dimensional space, and generating a covariance matrix;
and decomposing the eigenvalue of the covariance matrix to obtain eigenvectors corresponding to the main eigenvalue, and constructing a dimension reduction matrix according to the eigenvectors.
Further, in the method, an ABOD algorithm is introduced, the eigenvector of the dimension reduction matrix is calculated through the ABOD algorithm, the variance value of the included angle is obtained, the repair evaluation result is processed according to the variance value of the included angle, and the processed evaluation result is obtained, which specifically comprises:
introducing an ABOD algorithm, calculating eigenvectors of the dimension reduction matrix through the ABOD algorithm, obtaining variance values of formed included angles among the eigenvectors, and judging whether the variance values are larger than a preset variance threshold or not;
when the variance value is larger than the preset variance threshold, obtaining leaf nodes with the variance value larger than the preset variance threshold, and splitting the leaf nodes again;
and when the variance value is no longer larger than the preset variance threshold value, outputting a new leaf node, correcting the repair evaluation result according to the new leaf node, and acquiring the processed evaluation result.
Further, in the method, a repair evaluation result of each soil pollution area within a preset time is obtained, and a soil repair state transition probability matrix is constructed according to the repair evaluation result of each soil pollution area within the preset time through a Markov chain, and the method specifically comprises the following steps:
Constructing a time stamp, acquiring a repair evaluation result of each soil pollution area in each time stamp, and constructing a repair evaluation result of each soil pollution area within a preset time according to the repair evaluation result of each soil pollution area in each time stamp;
calculating a state transition probability value of each time stamp from one microbial soil restoration state to another microbial soil restoration state through a Markov chain, and constructing a soil restoration state transition probability matrix according to the state transition probability value.
Further, in the method, a related soil restoration suggestion is generated according to the soil restoration state transition probability matrix, and the method specifically comprises the following steps:
acquiring an initial microbial soil restoration state of each soil pollution area within the previous preset time, and acquiring a state transition probability value of each soil pollution area at a current time stamp from a state transition matrix;
judging whether the state transition probability value is larger than a preset state transition probability value, and taking the next microbial soil restoration state of the initial microbial soil restoration state as the microbial soil restoration state of the soil pollution area when the state transition probability value is larger than the preset state transition probability value;
When the state transition probability value is not larger than the preset state transition probability value, taking the initial microorganism repairing state as the microorganism soil repairing state of the soil pollution area;
and when the microbial soil restoration state of the soil pollution area is not the preset state, generating a continuous restoration instruction, and generating a related soil restoration suggestion according to the stop restoration instruction and the continuous restoration instruction.
The second aspect of the present invention provides a system for evaluating a microbial restoration effect based on a decision tree model, the system comprising a memory and a processor, the memory including a microbial restoration effect evaluation method program based on the decision tree model, the microbial restoration effect evaluation method program based on the decision tree model being executed by the processor to implement the steps of:
the method comprises the steps of arranging wireless sensors in a soil pollution area, constructing a wireless information transmission network according to the wireless sensors, and acquiring pollution data information in the process of repairing soil by microorganisms through the wireless information transmission network;
introducing a decision tree model, classifying and evaluating the polluted data information through the decision tree model, obtaining a restoration evaluation result, inputting a principal component analysis algorithm, and performing data processing through data in the restoration evaluation result to obtain a dimension reduction matrix of each leaf node;
Introducing an ABOD algorithm, calculating a feature vector of the dimension reduction matrix through the ABOD algorithm, obtaining a variance value of the included angle, and processing a repair evaluation result according to the variance value of the included angle to obtain a processed evaluation result;
and acquiring a repair evaluation result of each soil pollution area within a preset time, constructing a soil repair state transition probability matrix according to the repair evaluation result of each soil pollution area within the preset time through a Markov chain, and generating a related soil repair suggestion according to the soil repair state transition probability matrix.
Furthermore, in the system, a decision tree model is introduced, the pollution data information is classified and evaluated through the decision tree model, a repair evaluation result is obtained, a principal component analysis algorithm is input, and data processing is carried out through data in the repair evaluation result, so that a dimension reduction matrix of each leaf node is obtained, and the method specifically comprises the following steps:
introducing a decision tree model, constructing a root node according to pollution data information, setting a plurality of microbial soil restoration state threshold ranges, splitting the root node according to the microbial soil restoration state threshold ranges, and generating a new node;
when all data in the new nodes are within the same microbial soil restoration state threshold range, no new nodes are generated, a plurality of leaf nodes are output, restoration evaluation results are generated according to the leaf nodes, and a principal component analysis algorithm is introduced;
Obtaining pollution data information of each leaf node in the repair evaluation result, constructing a low-dimensional space by linear transformation of the pollution data information in the leaf nodes, projecting the pollution data information into the low-dimensional space, and generating a covariance matrix;
and decomposing the eigenvalue of the covariance matrix to obtain eigenvectors corresponding to the main eigenvalue, and constructing a dimension reduction matrix according to the eigenvectors.
Further, in the system, the related soil restoration advice is generated according to the soil restoration state transition probability matrix, and specifically includes:
acquiring an initial microbial soil restoration state of each soil pollution area within the previous preset time, and acquiring a state transition probability value of each soil pollution area at a current time stamp from a state transition matrix;
judging whether the state transition probability value is larger than a preset state transition probability value, and taking the next microbial soil restoration state of the initial microbial soil restoration state as the microbial soil restoration state of the soil pollution area when the state transition probability value is larger than the preset state transition probability value;
when the state transition probability value is not larger than the preset state transition probability value, taking the initial microorganism repairing state as the microorganism soil repairing state of the soil pollution area;
And when the microbial soil restoration state of the soil pollution area is not the preset state, generating a continuous restoration instruction, and generating a related soil restoration suggestion according to the stop restoration instruction and the continuous restoration instruction.
The third aspect of the present invention provides a computer-readable storage medium, in which a decision tree model-based microbial restoration effect evaluation method program is included, which when executed by a processor, implements the steps of any one of the decision tree model-based microbial restoration effect evaluation methods.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the method, a wireless sensor is arranged in a soil pollution area, a wireless information transmission network is constructed according to the wireless sensor, pollution data information in the process of repairing the soil by microorganisms is obtained through the wireless information transmission network, a decision tree model is introduced, classification evaluation is carried out on the pollution data information through the decision tree model, a repairing evaluation result is obtained, a principal component analysis algorithm is input, data in the repairing evaluation result is processed to obtain a dimension reduction matrix of each leaf node, an ABOD algorithm is introduced, eigenvectors of the dimension reduction matrix are calculated through the ABOD algorithm, a variance value of an included angle is obtained, the repairing evaluation result is processed according to the variance value of the included angle, the processed evaluation result is obtained, finally, a repairing evaluation result of each soil pollution area within preset time is obtained, a soil repairing state transition probability matrix is constructed according to the repairing evaluation result of each soil pollution area within the preset time through a Markov chain, and relevant soil repairing advice is generated according to the soil repairing state transition probability matrix. According to the invention, the restoration state of the microbial restoration soil is monitored by fusing the decision tree model, so that a large amount of data can be rapidly processed, and the intelligent monitoring of the restoration effect in the soil restoration process is realized. On the other hand, the method integrates a principal component analysis method and an ABOD algorithm to optimize the decision tree model, and can improve the precision of batch processing of data, thereby improving the precision of evaluating the soil restoration state effect.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flow diagram of a method for evaluating the effect of microbial remediation based on a decision tree model;
FIG. 2 shows a first method sub-flowchart of a method for evaluating the effect of microbial remediation based on a decision tree model;
FIG. 3 shows a second method flow diagram of a method for evaluating the effect of microbial remediation based on a decision tree model;
FIG. 4 shows a system block diagram of a microbial repair effect evaluation system based on a decision tree model.
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.
As shown in fig. 1, the first aspect of the present invention provides a method for evaluating the effect of microbial restoration based on a decision tree model, comprising the following steps:
s102, distributing wireless sensors in a soil pollution area, constructing a wireless information transmission network according to the wireless sensors, and acquiring pollution data information in the process of repairing soil by microorganisms through the wireless information transmission network;
as shown in fig. 2, in step S102, the wireless information transmission network is constructed according to the wireless sensor, which specifically includes:
s202, setting an information transmission rate threshold of a wireless sensor, initializing the quantity information of communication facilities, acquiring the quantity information of the wireless sensor, and constructing a wireless information transmission network according to the quantity information of the communication facilities;
s204, calculating information transmission rate information of all the wireless sensors when the wireless sensors work according to the wireless information transmission network and the quantity information of the wireless sensors, and judging whether the information transmission rate information of all the wireless sensors when the wireless sensors work is larger than an information transmission rate threshold of the wireless sensors or not;
S206, introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, and increasing the number information of the communication facilities according to the genetic algebra when the information transmission rate information of all the wireless sensors during working is not more than the information transmission rate threshold value of the wireless sensors;
and S208, when the information transmission rate information of all the wireless sensors are greater than the information transmission rate threshold value of the wireless sensors during operation, stopping iteration, outputting the quantity information of the communication facilities, and adjusting the wireless information transmission network according to the quantity information of the communication facilities.
It should be noted that, the number information of different communication facilities will form different MIMO networks, and different MIMO networks will generate different information transmission speeds for the wireless sensor, and the genetic algorithm selects the best number information of the communication facilities according to the information transmission speed information when the wireless sensor works, so as to implement timely receiving of the monitoring data.
S104, introducing a decision tree model, classifying and evaluating the pollution data information through the decision tree model, obtaining a restoration evaluation result, inputting a principal component analysis algorithm, and performing data processing through data in the restoration evaluation result to obtain a dimension reduction matrix of each leaf node;
As shown in fig. 3, in step S104, the step specifically includes:
s302, introducing a decision tree model, constructing a root node according to pollution data information, setting a plurality of microbial soil restoration state threshold ranges, and splitting the root node according to the microbial soil restoration state threshold ranges to generate a new node;
s304, when all data in the new nodes are within the same microorganism soil restoration state threshold range, no new nodes are generated, a plurality of leaf nodes are output, restoration evaluation results are generated according to the leaf nodes, and a principal component analysis algorithm is introduced;
s306, obtaining pollution data information of each leaf node in the repair evaluation result, constructing a low-dimensional space by linear transformation of the pollution data information in the leaf node, and projecting the pollution data information into the low-dimensional space to generate a covariance matrix;
and S308, carrying out eigenvalue decomposition on the covariance matrix to obtain eigenvectors corresponding to main eigenvalues, and constructing a dimension reduction matrix according to the eigenvectors.
The method is characterized in that the restoration state of the microbial restoration soil is monitored through the decision tree model, so that a large amount of data can be rapidly processed, and the intelligent monitoring of the restoration effect in the soil restoration process is realized. The microbial soil restoration state threshold value range can be divided into a high pollution state, a medium pollution state, a low pollution state and a pollution-free state, in the restoration process, one restoration state is often slowly changed into the other restoration state, if the soil state of a certain area is the high pollution state, the restoration effect route is the high pollution state, the medium pollution state, the low pollution state and the pollution-free state, and finally the restoration effect is changed into the pollution-free state, the pollution-free state represents good restoration effect, and different soil threshold value ranges represent different restoration states, for example, the pollution of 1mol/L-2 mol/L pollution concentration belongs to the medium pollution state. The pollution data information includes pollution concentration, pollution type and other data.
S106, introducing an ABOD algorithm, calculating a feature vector of the dimension reduction matrix through the ABOD algorithm to obtain a variance value of the included angle, and processing a repair evaluation result according to the variance value of the included angle to obtain a processed evaluation result;
in step S106, the method specifically includes:
introducing an ABOD algorithm, calculating eigenvectors of the dimension reduction matrix through the ABOD algorithm, obtaining variance values of formed included angles among the eigenvectors, and judging whether the variance values are larger than a preset variance threshold or not;
when the variance value is larger than the preset variance threshold, obtaining leaf nodes with the variance value larger than the preset variance threshold, and splitting the leaf nodes again;
and when the variance value is no longer larger than the preset variance threshold value, outputting a new leaf node, correcting the repair evaluation result according to the new leaf node, and acquiring the processed evaluation result.
It should be noted that the invention integrates the principal component analysis method and the ABOD algorithm to optimize the decision tree model, can further judge the phenomenon of local optimal solution of the decision tree algorithm, and can improve the precision of batch processing of data, thereby improving the precision of evaluating the soil restoration state effect and realizing intelligent monitoring.
It should be noted that, the ABOD (Angle-based Outlier Detection) algorithm is an anomaly detection algorithm in the field of data mining, and most anomaly detection models require a user to specify parameters that have an important influence on the output result.
S108, acquiring a restoration evaluation result of each soil pollution area within a preset time, constructing a soil restoration state transition probability matrix according to the restoration evaluation result of each soil pollution area within the preset time through a Markov chain, and generating a related soil restoration suggestion according to the soil restoration state transition probability matrix.
In the method, further, a repair evaluation result of each soil pollution area within a preset time is obtained, and a soil repair state transition probability matrix is constructed according to the repair evaluation result of each soil pollution area within the preset time through a markov chain, and the method specifically comprises the following steps:
constructing a time stamp, acquiring a repair evaluation result of each soil pollution area in each time stamp, and constructing a repair evaluation result of each soil pollution area within a preset time according to the repair evaluation result of each soil pollution area in each time stamp;
calculating a state transition probability value of each time stamp from one microbial soil restoration state to another microbial soil restoration state through a Markov chain, and constructing a soil restoration state transition probability matrix according to the state transition probability value.
The state transition probability value represents a probability value of transition from one microbial soil restoration state to another microbial soil restoration state, such as a probability value of transition from a high pollution state to a low pollution state during restoration.
Further, in the method, a related soil restoration suggestion is generated according to the soil restoration state transition probability matrix, and the method specifically comprises the following steps:
acquiring an initial microbial soil restoration state of each soil pollution area within the previous preset time, and acquiring a state transition probability value of each soil pollution area at a current time stamp from a state transition matrix;
judging whether the state transition probability value is larger than a preset state transition probability value, and taking the next microbial soil restoration state of the initial microbial soil restoration state as the microbial soil restoration state of the soil pollution area when the state transition probability value is larger than the preset state transition probability value;
when the state transition probability value is not larger than the preset state transition probability value, taking the initial microorganism repairing state as the microorganism soil repairing state of the soil pollution area;
and when the microbial soil restoration state of the soil pollution area is not the preset state, generating a continuous restoration instruction, and generating a related soil restoration suggestion according to the stop restoration instruction and the continuous restoration instruction.
When the state transition probability value is larger than the preset state transition probability value, the pollution state in the microbial soil restoration process is transferred from one state to the other state, and the preset state is a pollution-free state. The repair process can be monitored by the method, so that the repair stage can be clearly known.
In conclusion, the method monitors the repairing state of the microbial repairing soil by fusing the decision tree model, can rapidly process a large amount of data, and achieves intelligent monitoring of the repairing effect in the soil repairing process. On the other hand, the method integrates a principal component analysis method and an ABOD algorithm to optimize the decision tree model, and can improve the precision of batch processing of data, thereby improving the precision of evaluating the soil restoration state effect.
In addition, the invention can also comprise the following steps:
acquiring historical environmental factor data information of a soil pollution area, calculating the correlation between the historical environmental factor data information and survival of each microorganism type through a gray correlation analysis method, and acquiring environmental factor data information of the current soil pollution area;
acquiring the fitness of the current environmental factor data for the survival of the current microorganism type according to the correlation of the historical environmental factor data information and the survival of each microorganism type and the environmental factor data information of the current soil pollution area;
Judging whether the fitness is not more than a preset fitness, judging whether the environmental factors can be regulated or not when the fitness is not more than the preset fitness, acquiring survival environmental factor data of the most appropriate pre-microorganism type when the environmental factors are the regulated factors, generating relevant regulation and control references according to the survival environmental factor data of the most appropriate current microorganism type, and regulating and controlling the current environmental factor data according to the regulation and control references;
and when the environmental factor is an unregulated factor, retrieving through big data, acquiring the microorganism type of the most appropriate pre-environmental factor, and generating relevant regulation information according to the microorganism type of the most appropriate pre-environmental factor.
It should be noted that, different environmental factors are inconsistent with each other in terms of the fitness of microorganisms, such as temperature, humidity, salinity, soil air permeability and the like, while in the natural environment, some environmental factors are not adjustable or are difficult to adjust and control, such as temperature is high in adjusting and controlling cost and difficult to adjust and control, and the soil restoration effect can be improved by adjusting and controlling according to the environmental factors.
In addition, the method can further comprise the following steps:
acquiring historical temperature factor data information of each soil restoration area in each time period, constructing a temperature prediction model based on a deep learning network, and constructing a feature matrix according to the historical temperature factor data information of each soil restoration area in each time period;
inputting the feature matrix into the temperature prediction model for training, obtaining a trained temperature prediction model, and predicting temperature data information of each period according to the trained temperature prediction model;
acquiring environmental factor data of each soil pollution area, and acquiring temperature data information of each period and the optimal survival microorganism type under the environmental factor data of the soil pollution area through big data;
and making a microorganism type adjustment plan of each time period according to the most suitable survival microorganism type under the temperature data information of each time period, and adjusting the microorganism type of the soil pollution area according to the microorganism type adjustment plan of each time period.
The method can be used for further fusing temperature prediction characteristics to improve the soil restoration effect.
As shown in fig. 4, a second aspect of the present invention provides a system 4 for evaluating a microbial restoration effect based on a decision tree model, where the system 4 includes a memory 41 and a processor 42, and the memory 41 includes a microbial restoration effect evaluation method program based on the decision tree model, and when the microbial restoration effect evaluation method program based on the decision tree model is executed by the processor 42, the following steps are implemented:
the method comprises the steps of arranging wireless sensors in a soil pollution area, constructing a wireless information transmission network according to the wireless sensors, and acquiring pollution data information in the process of repairing soil by microorganisms through the wireless information transmission network;
introducing a decision tree model, classifying and evaluating the polluted data information through the decision tree model, obtaining a restoration evaluation result, inputting a principal component analysis algorithm, and performing data processing through data in the restoration evaluation result to obtain a dimension reduction matrix of each leaf node;
introducing an ABOD algorithm, calculating a feature vector of the dimension reduction matrix through the ABOD algorithm, obtaining a variance value of the included angle, and processing a repair evaluation result according to the variance value of the included angle to obtain a processed evaluation result;
and acquiring a repair evaluation result of each soil pollution area within a preset time, constructing a soil repair state transition probability matrix according to the repair evaluation result of each soil pollution area within the preset time through a Markov chain, and generating a related soil repair suggestion according to the soil repair state transition probability matrix.
Furthermore, in the system, a decision tree model is introduced, the pollution data information is classified and evaluated through the decision tree model, a repair evaluation result is obtained, a principal component analysis algorithm is input, and data processing is carried out through data in the repair evaluation result, so that a dimension reduction matrix of each leaf node is obtained, and the method specifically comprises the following steps:
introducing a decision tree model, constructing a root node according to pollution data information, setting a plurality of microbial soil restoration state threshold ranges, splitting the root node according to the microbial soil restoration state threshold ranges, and generating a new node;
when all data in the new nodes are within the same microbial soil restoration state threshold range, no new nodes are generated, a plurality of leaf nodes are output, restoration evaluation results are generated according to the leaf nodes, and a principal component analysis algorithm is introduced;
obtaining pollution data information of each leaf node in the repair evaluation result, constructing a low-dimensional space by linear transformation of the pollution data information in the leaf nodes, projecting the pollution data information into the low-dimensional space, and generating a covariance matrix;
and decomposing the eigenvalue of the covariance matrix to obtain eigenvectors corresponding to the main eigenvalue, and constructing a dimension reduction matrix according to the eigenvectors.
Further, in the system, the related soil restoration advice is generated according to the soil restoration state transition probability matrix, and specifically includes:
acquiring an initial microbial soil restoration state of each soil pollution area within the previous preset time, and acquiring a state transition probability value of each soil pollution area at a current time stamp from a state transition matrix;
judging whether the state transition probability value is larger than a preset state transition probability value, and taking the next microbial soil restoration state of the initial microbial soil restoration state as the microbial soil restoration state of the soil pollution area when the state transition probability value is larger than the preset state transition probability value;
when the state transition probability value is not larger than the preset state transition probability value, taking the initial microorganism repairing state as the microorganism soil repairing state of the soil pollution area;
and when the microbial soil restoration state of the soil pollution area is not the preset state, generating a continuous restoration instruction, and generating a related soil restoration suggestion according to the stop restoration instruction and the continuous restoration instruction.
The third aspect of the present invention provides a computer-readable storage medium, in which a decision tree model-based microbial restoration effect evaluation method program is included, which when executed by a processor, implements the steps of any one of the decision tree model-based microbial restoration effect evaluation methods.
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 of 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 embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (6)
1. The microbial repair effect evaluation method based on the decision tree model is characterized by comprising the following steps of:
the method comprises the steps of arranging wireless sensors in a soil pollution area, constructing a wireless information transmission network according to the wireless sensors, and acquiring pollution data information in the process of repairing soil by microorganisms through the wireless information transmission network;
introducing a decision tree model, classifying and evaluating pollution data information through the decision tree model, obtaining a restoration evaluation result, inputting a principal component analysis algorithm, and performing data processing through data in the restoration evaluation result to obtain a dimension reduction matrix of each leaf node;
introducing an ABOD algorithm, calculating a feature vector of the dimension reduction matrix through the ABOD algorithm, obtaining a variance value of an included angle, processing the repair evaluation result according to the variance value of the included angle, and obtaining a processed evaluation result;
Obtaining a repair evaluation result of each soil pollution area within a preset time, constructing a soil repair state transition probability matrix according to the repair evaluation result of each soil pollution area within the preset time through a Markov chain, and generating a related soil repair suggestion according to the soil repair state transition probability matrix;
introducing an ABOD algorithm, calculating a feature vector of the dimension reduction matrix through the ABOD algorithm, obtaining a variance value of an included angle, processing the repair evaluation result according to the variance value of the included angle, and obtaining a processed evaluation result, wherein the method specifically comprises the following steps of:
introducing an ABOD algorithm, calculating the eigenvectors of the dimension reduction matrix through the ABOD algorithm, obtaining the variance value of the formed included angle between the eigenvectors, and judging whether the variance value is larger than a preset variance threshold or not;
when the variance value is larger than a preset variance threshold, obtaining leaf nodes with the variance value larger than the preset variance threshold, and splitting the leaf nodes again;
when the situation that the variance value is larger than a preset variance threshold value no longer occurs, outputting a new leaf node, correcting the repair evaluation result according to the new leaf node, and acquiring a processed evaluation result;
Obtaining a repair evaluation result of each soil pollution area within a preset time, and constructing a soil repair state transition probability matrix according to the repair evaluation result of each soil pollution area within the preset time through a Markov chain, wherein the method specifically comprises the following steps:
constructing a time stamp, acquiring a repair evaluation result of each soil pollution area in each time stamp, and constructing a repair evaluation result of each soil pollution area within a preset time according to the repair evaluation result of each soil pollution area in each time stamp;
calculating a state transition probability value of each time stamp from one microbial soil restoration state to another microbial soil restoration state through a Markov chain, and constructing a soil restoration state transition probability matrix according to the state transition probability value;
the repair evaluation result of each soil pollution area within the preset time is the processed evaluation result;
generating a related soil restoration suggestion according to the soil restoration state transition probability matrix, wherein the method specifically comprises the following steps:
acquiring an initial microbial soil restoration state of each soil pollution area within the previous preset time, and acquiring a state transition probability value of each soil pollution area at a current time stamp from the state transition matrix;
Judging whether the state transition probability value is larger than a preset state transition probability value, and taking the next microbial soil restoration state of the initial microbial soil restoration state as a microbial soil restoration state of a soil pollution area when the state transition probability value is larger than the preset state transition probability value;
when the state transition probability value is not larger than a preset state transition probability value, taking the initial microorganism repairing state as a microorganism soil repairing state of the soil pollution area;
and when the microbial soil restoration state of the soil pollution area is not the preset state, generating a continuous restoration instruction, and generating a related soil restoration suggestion according to the continuous restoration instruction and the continuous restoration instruction.
2. The method for evaluating the microbial remediation effect based on the decision tree model according to claim 1, wherein the wireless information transmission network is constructed according to the wireless sensor, and specifically comprises the following steps:
setting an information transmission rate threshold of the wireless sensor, initializing the quantity information of the communication facilities, acquiring the quantity information of the wireless sensor, and constructing a wireless information transmission network according to the quantity information of the communication facilities;
Calculating information transmission rate information of all wireless sensors when the wireless sensors work according to the wireless information transmission network and the quantity information of the wireless sensors, and judging whether the information transmission rate information of all the wireless sensors when the wireless sensors work is larger than an information transmission rate threshold of the wireless sensors or not;
introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, and increasing the number information of communication facilities according to the genetic algebra when the information transmission rate information of all the wireless sensors during working is not more than the information transmission rate threshold value of the wireless sensors;
and when the information transmission rate information of all the wireless sensors is larger than the information transmission rate threshold value of the wireless sensors during working, stopping iteration, outputting the quantity information of the communication facilities, and adjusting the wireless information transmission network according to the quantity information of the communication facilities.
3. The method for evaluating the microbial remediation effect based on the decision tree model according to claim 1, wherein a decision tree model is introduced, the pollution data information is subjected to classification evaluation through the decision tree model, a remediation evaluation result is obtained, a principal component analysis algorithm is input, data processing is performed through data in the remediation evaluation result, and a dimension reduction matrix of each leaf node is obtained, and the method specifically comprises the following steps:
Introducing a decision tree model, constructing a root node according to the pollution data information, setting a plurality of microbial soil restoration state threshold ranges, splitting the root node according to the microbial soil restoration state threshold ranges, and generating a new node;
when all data in the new nodes are within the same microbial soil restoration state threshold range, no new nodes are generated, a plurality of leaf nodes are output, restoration evaluation results are generated according to the leaf nodes, and a principal component analysis algorithm is introduced;
obtaining pollution data information of each leaf node in the repair evaluation result, constructing a low-dimensional space by linear transformation of the pollution data information in the leaf node, and projecting the pollution data information into the low-dimensional space to generate a covariance matrix;
and decomposing the eigenvalue of the covariance matrix to obtain an eigenvector corresponding to the main eigenvalue, and constructing a dimension reduction matrix according to the eigenvector.
4. The microbial repair effect evaluation system based on the decision tree model is characterized by comprising a memory and a processor, wherein the memory comprises a microbial repair effect evaluation method program based on the decision tree model, and when the microbial repair effect evaluation method program based on the decision tree model is executed by the processor, the following steps are realized:
The method comprises the steps of arranging wireless sensors in a soil pollution area, constructing a wireless information transmission network according to the wireless sensors, and acquiring pollution data information in the process of repairing soil by microorganisms through the wireless information transmission network;
introducing a decision tree model, classifying and evaluating pollution data information through the decision tree model, obtaining a restoration evaluation result, inputting a principal component analysis algorithm, and performing data processing through data in the restoration evaluation result to obtain a dimension reduction matrix of each leaf node;
introducing an ABOD algorithm, calculating a feature vector of the dimension reduction matrix through the ABOD algorithm, obtaining a variance value of an included angle, processing the repair evaluation result according to the variance value of the included angle, and obtaining a processed evaluation result;
obtaining a repair evaluation result of each soil pollution area within a preset time, constructing a soil repair state transition probability matrix according to the repair evaluation result of each soil pollution area within the preset time through a Markov chain, and generating a related soil repair suggestion according to the soil repair state transition probability matrix;
introducing an ABOD algorithm, calculating a feature vector of the dimension reduction matrix through the ABOD algorithm, obtaining a variance value of an included angle, processing the repair evaluation result according to the variance value of the included angle, and obtaining a processed evaluation result, wherein the method specifically comprises the following steps of:
Introducing an ABOD algorithm, calculating the eigenvectors of the dimension reduction matrix through the ABOD algorithm, obtaining the variance value of the formed included angle between the eigenvectors, and judging whether the variance value is larger than a preset variance threshold or not;
when the variance value is larger than a preset variance threshold, obtaining leaf nodes with the variance value larger than the preset variance threshold, and splitting the leaf nodes again;
when the situation that the variance value is larger than a preset variance threshold value no longer occurs, outputting a new leaf node, correcting the repair evaluation result according to the new leaf node, and acquiring a processed evaluation result;
obtaining a repair evaluation result of each soil pollution area within a preset time, and constructing a soil repair state transition probability matrix according to the repair evaluation result of each soil pollution area within the preset time through a Markov chain, wherein the method specifically comprises the following steps:
constructing a time stamp, acquiring a repair evaluation result of each soil pollution area in each time stamp, and constructing a repair evaluation result of each soil pollution area within a preset time according to the repair evaluation result of each soil pollution area in each time stamp;
Calculating a state transition probability value of each time stamp from one microbial soil restoration state to another microbial soil restoration state through a Markov chain, and constructing a soil restoration state transition probability matrix according to the state transition probability value;
the repair evaluation result of each soil pollution area within the preset time is the processed evaluation result;
generating a related soil restoration suggestion according to the soil restoration state transition probability matrix, wherein the method specifically comprises the following steps:
acquiring an initial microbial soil restoration state of each soil pollution area within the previous preset time, and acquiring a state transition probability value of each soil pollution area at a current time stamp from the state transition matrix;
judging whether the state transition probability value is larger than a preset state transition probability value, and taking the next microbial soil restoration state of the initial microbial soil restoration state as a microbial soil restoration state of a soil pollution area when the state transition probability value is larger than the preset state transition probability value;
when the state transition probability value is not larger than a preset state transition probability value, taking the initial microorganism repairing state as a microorganism soil repairing state of the soil pollution area;
And when the microbial soil restoration state of the soil pollution area is not the preset state, generating a continuous restoration instruction, and generating a related soil restoration suggestion according to the continuous restoration instruction and the continuous restoration instruction.
5. The decision tree model-based microbial remediation effect evaluation system of claim 4, wherein a decision tree model is introduced, the pollution data information is subjected to classification evaluation through the decision tree model, a remediation evaluation result is obtained, a principal component analysis algorithm is input, data processing is performed through data in the remediation evaluation result, and a dimension reduction matrix of each leaf node is obtained, and the method specifically comprises the following steps:
introducing a decision tree model, constructing a root node according to the pollution data information, setting a plurality of microbial soil restoration state threshold ranges, splitting the root node according to the microbial soil restoration state threshold ranges, and generating a new node;
when all data in the new nodes are within the same microbial soil restoration state threshold range, no new nodes are generated, a plurality of leaf nodes are output, restoration evaluation results are generated according to the leaf nodes, and a principal component analysis algorithm is introduced;
Obtaining pollution data information of each leaf node in the repair evaluation result, constructing a low-dimensional space by linear transformation of the pollution data information in the leaf node, and projecting the pollution data information into the low-dimensional space to generate a covariance matrix;
and decomposing the eigenvalue of the covariance matrix to obtain an eigenvector corresponding to the main eigenvalue, and constructing a dimension reduction matrix according to the eigenvector.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium contains therein a decision tree model-based microorganism-repair-effect evaluation method program, which, when executed by a processor, implements the steps of the decision tree model-based microorganism-repair-effect evaluation method according to any one of claims 1 to 3.
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