Soil remediation effect dynamic evaluation system and method based on deep learning

Abstract

The invention discloses a soil remediation effect dynamic evaluation system and method based on deep learning. The system comprises a data acquisition module, a data preprocessing module, a deep learning model module, a dynamic evaluation module, an optimization suggestion module, a visual interaction module, a data storage module and a communication module. The data acquisition module is used for acquiring multi-source monitoring data in a soil remediation process in real time, and the multi-source monitoring data comprises pollutant concentration data, soil physicochemical property data, microbial activity data and meteorological environment data. Dynamic evaluation of the soil remediation effect is achieved through the deep learning technology, the defects that a traditional method is poor in real-time performance and depends on manpower are overcome, the system can automatically collect and analyze multi-source data during implementation, high-precision remediation effect evaluation and trend prediction are provided, and optimization of a remediation strategy is helped; in addition, the remediation effect can be reflected more comprehensively, and a scientific basis is provided for environmental governance.

Description

Technical Field

[0001] The present application relates to the field of soil remediation technology, and specifically to a dynamic evaluation system and method for soil remediation effects based on deep learning. Background Art

[0002] Soil pollution has become a global environmental challenge. Currently, traditional soil remediation effectiveness assessment methods rely primarily on laboratory testing and manual sampling and analysis. However, this model has numerous drawbacks, including lengthy evaluation cycles, which hinder timely monitoring of soil remediation progress; high costs, which consume significant human, material, and financial resources; and poor real-time performance, which makes it difficult to monitor dynamic changes in soil remediation.

[0003] Some existing approaches attempt to incorporate sensor data to assist in monitoring soil remediation progress. However, these methods remain limited to data collection and lack the support of intelligent analytical tools. This makes it difficult to accurately predict soil remediation trends based on the collected data, let alone optimize remediation strategies.

[0004] Furthermore, traditional assessment methods suffer from a significant flaw: they often overlook the dynamic nature of soil ecosystems. Soil ecosystems are complex and constantly evolving, and remediation outcomes vary over time, depending on various factors, including environmental factors. Traditional methods fail to fully account for this, resulting in assessment results that fail to fully and accurately reflect the actual effectiveness of soil remediation.

[0005] In view of this, this application proposes a soil remediation effect dynamic evaluation system and method based on deep learning. Summary of the Invention

[0006] To this end, this application provides a soil remediation effect dynamic evaluation system and method based on deep learning to solve the problems of the existing technology in lacking intelligent analysis and ignoring the dynamic change characteristics of the soil ecosystem.

[0007] In order to achieve the above objectives, this application provides the following technical solutions:

[0008] First, a deep learning-based dynamic evaluation system for soil remediation effects includes a data acquisition module, a data preprocessing module, a deep learning model module, a dynamic evaluation module, an optimization suggestion module, a visual interaction module, a data storage module, and a communication module.

[0009] The data acquisition module is used to obtain multi-source monitoring data in the soil remediation process in real time, including pollutant concentration data, soil physical and chemical properties data, microbial activity data and meteorological environment data;

[0010] The data preprocessing module is used to clean, normalize and extract features from the multi-source monitoring data to generate a standardized data set;

[0011] The deep learning model module includes a pollutant removal effect prediction sub-model and an ecological restoration assessment sub-model. The pollutant removal effect prediction sub-model is constructed based on a convolutional neural network and is used to analyze the changing trend of pollutant concentration over time and predict the restoration effect. The ecological restoration assessment sub-model is constructed based on a recurrent neural network and is used to evaluate the recovery of soil microbial community structure and ecological functions.

[0012] The dynamic evaluation module is used to generate a dynamic evaluation report on the soil remediation effect based on the output results of the deep learning model module. The evaluation report includes the pollutant removal rate, ecological restoration index and remediation stage division;

[0013] The optimization suggestion module is used to generate repair parameter adjustment suggestions based on the dynamic evaluation report and the preset repair goals;

[0014] The visualization interaction module is used to display the dynamic evaluation report and optimization suggestions in the form of charts and maps, and provide a user interaction interface;

[0015] The data storage module is used to store the multi-source monitoring data, the standardized data set and the training parameters of the deep learning model;

[0016] The communication module is used to realize wireless data transmission between the system and external monitoring equipment and user terminals.

[0017] Preferably, the data acquisition module includes a pollutant concentration sensor, a soil physical and chemical property sensor, a microbial activity detection device, and a meteorological environment monitoring device;

[0018] Pollutant concentration sensors are used to detect the concentrations of heavy metals, organic pollutants, and inorganic pollutants in the soil;

[0019] Soil physical and chemical property sensors are used to detect soil pH, moisture content, organic matter content and porosity;

[0020] The microbial activity detection device is used to collect soil microbial community structure and metabolic activity data;

[0021] Meteorological environment monitoring equipment is used to collect temperature, humidity, rainfall and light intensity data.

[0022] Preferably, the data preprocessing module includes a data cleaning unit, a data normalization unit and a feature extraction unit;

[0023] The data cleaning unit is used to remove outliers and missing values;

[0024] The data normalization unit is used to convert data of different dimensions into a unified standard;

[0025] The feature extraction unit is used to extract key features through principal component analysis or wavelet transform.

[0026] Preferably, the pollutant removal effect prediction sub-model is trained using historical pollutant concentration data and remediation parameters, and outputs a pollutant concentration change curve and a predicted removal rate; the ecological restoration assessment sub-model is trained using historical microbial activity data and soil physical and chemical property data, and outputs an ecological restoration index and a stability score.

[0027] Preferably, the dynamic evaluation module divides the soil remediation process into an initial stage, a progress stage and a stable stage according to the pollutant removal rate and the ecological restoration index, and generates a stage-by-stage remediation effect score.

[0028] Preferably, the optimization suggestion module recommends adjustment plans for the dosage of the repair agent, the irrigation frequency or the aeration intensity based on the deviation between the repair target and the actual repair effect.

[0029] First, a deep learning-based dynamic evaluation method for soil remediation effects includes the following steps:

[0030] Step 1: Use the data acquisition module to obtain multi-source monitoring data during the soil remediation process in real time;

[0031] Step 2: Clean, normalize and extract features of the multi-source monitoring data through a data preprocessing module to generate a standardized data set;

[0032] Step 3: Input the standardized data set into the deep learning model module, and analyze it through the pollutant removal effect prediction sub-model and the ecological restoration assessment sub-model respectively to obtain the pollutant removal trend and ecological restoration assessment results;

[0033] Step 4: Generate a dynamic assessment report based on the pollutant removal trend and ecological restoration assessment results;

[0034] Step 5: Generate repair parameter optimization suggestions based on the dynamic evaluation report;

[0035] Step six: display the dynamic evaluation report and optimization suggestions through a visual interactive module.

[0036] Preferably, the training process of the deep learning model module includes:

[0037] Collect historical soil remediation data as a training set;

[0038] Performing data enhancement processing on the training set;

[0039] Stochastic gradient descent algorithm is used to optimize model parameters;

[0040] Model performance was evaluated through cross-validation until a preset accuracy threshold was reached.

[0041] Compared with the prior art, this application has at least the following beneficial effects:

[0042] The present invention uses deep learning technology to achieve dynamic evaluation of soil remediation effects, overcoming the shortcomings of traditional methods such as poor real-time performance and reliance on manual labor. During implementation, the system can automatically collect and analyze multi-source data, provide high-precision remediation effect evaluation and trend prediction, and help optimize remediation strategies. In addition, the present invention can also more comprehensively reflect the remediation effect and provide a scientific basis for environmental governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more intuitively illustrate the prior art and the present application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be considered as limiting conditions for implementing the present application; for example, based on the technical concepts disclosed in this application and the exemplary drawings, those skilled in the art are capable of easily making routine adjustments or further optimizations to the addition / reduction / attribution division, specific shapes, positional relationships, connection methods, dimensional ratios, etc. of certain units (components).

[0044] Figure 1 This is a module diagram of the deep learning-based soil remediation effect dynamic evaluation system for this application. DETAILED DESCRIPTION

[0045] The present application will be further described below in detail through specific embodiments in conjunction with the accompanying drawings.

[0046] Example 1

[0047] like Figure 1 As shown, the present application discloses a soil remediation effect dynamic evaluation system based on deep learning, including a data acquisition module, a data preprocessing module, a deep learning model module, a dynamic evaluation module, an optimization suggestion module, a visual interaction module, a data storage module and a communication module;

[0048] The data acquisition module is used to acquire multi-source monitoring data during the soil remediation process in real time. The multi-source monitoring data includes pollutant concentration data, soil physical and chemical properties data, microbial activity data, and meteorological environment data. The data acquisition module breaks away from the lag of traditional laboratory testing, realizes continuous tracking of the remediation process, and improves response speed;

[0049] The data preprocessing module is used to clean, normalize and extract features from the multi-source monitoring data to generate a standardized data set;

[0050] The deep learning model module includes a pollutant removal effect prediction sub-model and an ecological restoration assessment sub-model. The pollutant removal effect prediction sub-model is constructed based on a convolutional neural network and is used to analyze the changing trend of pollutant concentration over time and predict the restoration effect. The ecological restoration assessment sub-model is constructed based on a recurrent neural network and is used to evaluate the recovery of soil microbial community structure and ecological functions. During implementation, the deep learning model automatically analyzes complex data, reduces manual intervention, and improves the accuracy and objectivity of the assessment.

[0051] The dynamic assessment module is used to generate a dynamic assessment report on soil remediation effectiveness based on the output of the deep learning model module. The assessment report includes pollutant removal rate, ecological restoration index, and remediation stage division. It considers both pollutant removal and ecological restoration, avoiding the limitations of traditional methods that only focus on a single indicator.

[0052] The optimization suggestion module is used to generate repair parameter adjustment suggestions based on the dynamic evaluation report and the preset repair goals. The optimization suggestion module recommends adjustment solutions based on the dynamic evaluation results to reduce repair costs and shorten repair cycles.

[0053] The visualization interaction module is used to display the dynamic evaluation report and optimization suggestions in the form of charts and maps, and provides a user interaction interface to intuitively display data and analysis results, making it easier for non-professionals to understand and use them, thereby improving decision-making efficiency;

[0054] The data storage module is used to store the multi-source monitoring data, the standardized data set and the training parameters of the deep learning model;

[0055] The communication module is used to realize wireless data transmission between the system and external monitoring equipment and user terminals.

[0056] When this application is implemented, first, the data acquisition module uses various sensors (such as pollutant concentration sensors, soil physical and chemical property sensors, etc.) to collect multi-source data in the soil remediation process in real time, including pollutant concentration, soil pH value, microbial activity and meteorological environment data. These data are cleaned, normalized and feature extracted by the data preprocessing module to eliminate noise and unify the data format to form a standardized data set. Subsequently, the deep learning model module analyzes the processed data, in which the pollutant removal effect prediction sub-model (for example, based on CNN) learns the time series variation law of pollutant concentration and predicts future remediation trends; and the ecological restoration assessment sub-model (for example, based on RNN) evaluates the recovery of microbial communities and soil functions. The dynamic evaluation module integrates the outputs of the two sub-models to generate an evaluation report including pollutant removal rate, ecological restoration index and remediation stage division. The optimization suggestion module automatically generates a remediation parameter adjustment plan (such as remediation agent dosage optimization) in combination with the preset remediation goals. Finally, the visual interaction module intuitively displays the evaluation results in the form of charts and maps to facilitate user decision-making. All data are stored in the data storage module, and remote data transmission is achieved through the communication module.

[0057] The data acquisition module includes a pollutant concentration sensor, a soil physical and chemical property sensor, a microbial activity detection device, and a meteorological environment monitoring device;

[0058] Pollutant concentration sensors are used to detect the concentrations of heavy metals, organic pollutants, and inorganic pollutants in the soil;

[0059] Soil physical and chemical property sensors are used to detect soil pH, moisture content, organic matter content and porosity;

[0060] The microbial activity detection device is used to collect soil microbial community structure and metabolic activity data;

[0061] Meteorological environment monitoring equipment is used to collect temperature, humidity, rainfall and light intensity data.

[0062] When implementing the above technical solution, the data acquisition module uses multiple sensors to work together to ensure comprehensive data. Pollutant concentration sensors (such as X-ray fluorescence spectrometers) can accurately detect heavy metals and organic pollutants; soil physical and chemical property sensors (such as pH electrodes and moisture sensors) monitor the basic soil state in real time; microbial activity detection devices (such as high-throughput sequencers) analyze microbial community dynamics; and meteorological and environmental monitoring equipment (such as temperature and humidity sensors) records external environmental influences. The multi-source data fusion method overcomes the one-sidedness of a single data source and provides a reliable foundation for subsequent analysis.

[0063] The data preprocessing module includes a data cleaning unit, a data normalization unit and a feature extraction unit;

[0064] The data cleaning unit is used to remove outliers and missing values;

[0065] The data normalization unit is used to convert data of different dimensions into a unified standard;

[0066] The feature extraction unit is used to extract key features through principal component analysis or wavelet transform.

[0067] The data preprocessing module uses standardized processes to improve data quality. The data cleaning unit uses outlier detection algorithms (such as boxplots) to remove abnormal data; the normalization unit uses Min-Max or Z-score methods to eliminate dimensional differences; and the feature extraction unit uses principal component analysis (PCA) or wavelet transforms to reduce dimensionality and retain key features. The use of the data preprocessing module significantly reduces noise interference and improves the training efficiency and prediction accuracy of deep learning models.

[0068] The pollutant removal effect prediction sub-model is trained using historical pollutant concentration data and remediation parameters, and outputs a pollutant concentration change curve and a predicted removal rate; the ecological restoration assessment sub-model is trained using historical microbial activity data and soil physical and chemical property data, and outputs an ecological restoration index and a stability score.

[0069] The pollutant removal effect prediction sub-model can capture the spatial and temporal characteristics of pollutant concentrations and thus predict future trends; the ecological restoration assessment sub-model analyzes the succession patterns of microbial communities. The two models run in parallel, which can not only output results independently but also achieve collaborative optimization through the feature fusion layer to ensure the comprehensiveness of the assessment results.

[0070] The dynamic assessment module divides the soil remediation process into three quantifiable stages based on the pollutant removal rate (e.g., reaching 70% is the progress stage) and the ecological restoration index (e.g., 50% recovery of microbial diversity is the stability stage), and provides a stage-by-stage score. This design allows users to intuitively grasp the progress of remediation and develop differentiated strategies for different stages.

[0071] The optimization suggestion module recommends adjustments to the remediation agent dosage, irrigation frequency, or aeration intensity based on the deviation between the restoration target and the actual restoration effect. The optimization suggestion module combines the target and the actual deviation to generate actionable solutions. For example, if the pollutant removal rate is lower than expected, the optimization suggestion module may recommend increasing the remediation agent dosage or adjusting the irrigation frequency; if ecological recovery is lagging, it may recommend introducing microbial agents.

[0072] This application also discloses a soil remediation effect dynamic evaluation method based on deep learning, which includes the following steps:

[0073] Step 1: Use the data acquisition module to obtain multi-source monitoring data during the soil remediation process in real time;

[0074] Step 2: Clean, normalize and extract features of the multi-source monitoring data through a data preprocessing module to generate a standardized data set;

[0075] Step 3: Input the standardized data set into the deep learning model module, and analyze it through the pollutant removal effect prediction sub-model and the ecological restoration assessment sub-model respectively to obtain the pollutant removal trend and ecological restoration assessment results;

[0076] Step 4: Generate a dynamic assessment report based on the pollutant removal trend and ecological restoration assessment results;

[0077] Step 5: Generate repair parameter optimization suggestions based on the dynamic evaluation report;

[0078] Step six: display the dynamic evaluation report and optimization suggestions through a visual interactive module.

[0079] The training process of the deep learning model module includes:

[0080] Collect historical soil remediation data as a training set;

[0081] Performing data enhancement processing on the training set;

[0082] Stochastic gradient descent algorithm is used to optimize model parameters;

[0083] Model performance was evaluated through cross-validation until a preset accuracy threshold was reached.

[0084] The technical features of the above embodiments can be combined arbitrarily (as long as there is no contradiction in the combination of these technical features). In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described; these embodiments that are not explicitly written should also be considered to be within the scope of this specification.

Claims

1. A dynamic evaluation system for soil remediation effects based on deep learning, characterized by: It includes data acquisition module, data preprocessing module, deep learning model module, dynamic evaluation module, optimization suggestion module, visual interaction module, data storage module and communication module; The data acquisition module is used to obtain multi-source monitoring data in the soil remediation process in real time, including pollutant concentration data, soil physical and chemical properties data, microbial activity data and meteorological environment data; The data preprocessing module is used to clean, normalize and extract features from the multi-source monitoring data to generate a standardized data set; The deep learning model module includes a pollutant removal effect prediction sub-model and an ecological restoration assessment sub-model. The pollutant removal effect prediction sub-model is constructed based on a convolutional neural network and is used to analyze the changing trend of pollutant concentration over time and predict the restoration effect. The ecological restoration assessment sub-model is constructed based on a recurrent neural network and is used to evaluate the recovery of soil microbial community structure and ecological functions. The dynamic evaluation module is used to generate a dynamic evaluation report on the soil remediation effect based on the output results of the deep learning model module. The evaluation report includes the pollutant removal rate, ecological restoration index and remediation stage division; The optimization suggestion module is used to generate repair parameter adjustment suggestions based on the dynamic evaluation report and the preset repair goals; The visualization interaction module is used to display the dynamic evaluation report and optimization suggestions in the form of charts and maps, and provide a user interaction interface; The data storage module is used to store the multi-source monitoring data, the standardized data set and the training parameters of the deep learning model; The communication module is used to realize wireless data transmission between the system and external monitoring equipment and user terminals.

2. The soil remediation effect dynamic evaluation system based on deep learning according to claim 1 is characterized in that: The data acquisition module includes a pollutant concentration sensor, a soil physical and chemical property sensor, a microbial activity detection device, and a meteorological environment monitoring device; Pollutant concentration sensors are used to detect the concentrations of heavy metals, organic pollutants, and inorganic pollutants in the soil; Soil physical and chemical property sensors are used to detect soil pH, moisture content, organic matter content and porosity; The microbial activity detection device is used to collect soil microbial community structure and metabolic activity data; Meteorological environment monitoring equipment is used to collect temperature, humidity, rainfall and light intensity data.

3. The soil remediation effect dynamic evaluation system based on deep learning according to claim 2 is characterized in that: The data preprocessing module includes a data cleaning unit, a data normalization unit and a feature extraction unit; The data cleaning unit is used to remove outliers and missing values; The data normalization unit is used to convert data of different dimensions into a unified standard; The feature extraction unit is used to extract key features through principal component analysis or wavelet transform.

4. The soil remediation effect dynamic evaluation system based on deep learning according to claim 1 is characterized in that: The pollutant removal effect prediction sub-model is trained using historical pollutant concentration data and remediation parameters, and outputs a pollutant concentration change curve and a predicted removal rate; the ecological restoration assessment sub-model is trained using historical microbial activity data and soil physical and chemical property data, and outputs an ecological restoration index and a stability score.

5. The soil remediation effect dynamic evaluation system based on deep learning according to claim 1 is characterized in that: The dynamic evaluation module divides the soil remediation process into the initial stage, the progress stage and the stable stage according to the pollutant removal rate and the ecological restoration index, and generates a stage-by-stage remediation effect score.

6. The soil remediation effect dynamic evaluation system based on deep learning according to claim 1 is characterized in that: The optimization suggestion module recommends adjustment plans for the dosage of the repair agent, irrigation frequency or aeration intensity based on the deviation between the repair target and the actual repair effect.

7. A dynamic evaluation method for soil remediation effects based on deep learning, characterized in that: The following steps are involved: Step 1: Use the data acquisition module to obtain multi-source monitoring data during the soil remediation process in real time; Step 2: Clean, normalize and extract features of the multi-source monitoring data through a data preprocessing module to generate a standardized data set; Step 3: Input the standardized data set into the deep learning model module, and analyze it through the pollutant removal effect prediction sub-model and the ecological restoration assessment sub-model respectively to obtain the pollutant removal trend and ecological restoration assessment results; Step 4: Generate a dynamic assessment report based on the pollutant removal trend and ecological restoration assessment results; Step 5: Generate repair parameter optimization suggestions based on the dynamic evaluation report; Step six: display the dynamic evaluation report and optimization suggestions through a visual interactive module.

8. The soil remediation effect dynamic evaluation method based on deep learning according to claim 7 is characterized in that: The training process of the deep learning model module includes: Collect historical soil remediation data as a training set; Performing data enhancement processing on the training set; Stochastic gradient descent algorithm is used to optimize model parameters; Model performance was evaluated through cross-validation until a preset accuracy threshold was reached.

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