Intelligent environment restoration decision support and training platform

Abstract

The invention discloses an intelligent environment restoration decision support and training platform, and the platform is characterized in that a data collection module integrates various types of sensors and satellite images, and is provided with a historical restoration case database; the data processing module fuses multi-source data, applies deep learning and reinforcement learning algorithms to predict a pollution trend, optimizes a restoration strategy, and constructs a digital twinborn model. And the decision support module provides interactive strategy comparison and repair scheme recommendation based on the digital twin simulation result. The training module is integrated with an AI coach system by means of VR and AR technologies to carry out immersive practical operation training. And the user feedback module optimizes algorithm parameters through supervised learning. According to the platform, multi-source data fusion, dynamic strategy optimization and virtual-real combined immersive training are realized, and the pollution abatement decision precision and the personnel practical operation skills are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental engineering, and particularly to an intelligent environmental remediation decision support and training platform. Background Art

[0002] In today's society, environmental problems are becoming increasingly prominent. Various environmental problems such as soil pollution and air pollution seriously threaten the ecological balance and human health. Timely and effective remediation of polluted environments has become a key task in the field of environmental engineering. However, environmental remediation is a complex systems engineering, involving multidisciplinary knowledge and a large amount of data processing, requiring scientific and reasonable decision support and effective operation by professionals. Therefore, building a platform that can provide decision support and personnel training is of great significance for improving the efficiency and quality of environmental remediation.

[0003] In the existing environmental remediation decision-making and training, there are already some related technologies and methods. In terms of decision support, some systems collect environmental monitoring data and use traditional data analysis models, such as statistical analysis methods, to evaluate and predict the pollution situation. For example, a linear regression model is used to analyze the relationship between pollutant concentration and time to predict the development trend of pollution. At the same time, remediation strategies are formulated based on historical experience and simple cost-benefit analysis to provide reference for decision-makers. In terms of personnel training, traditional classroom teaching and on-site demonstrations are mainly adopted. Classroom teaching enables trainees to understand the basic principles and methods of environmental remediation through theoretical explanations, and on-site demonstrations are conducted by experienced personnel at the actual remediation site to let trainees intuitively learn remediation skills.

[0004] However, these existing technologies and methods have obvious defects. In terms of decision support, traditional data analysis models are difficult to process complex multi-source data and cannot accurately reflect the dynamic changes of the environment and the complex mechanisms of pollution. For example, for the interaction, migration, and transformation processes of pollutants in multi-media environments such as soil and atmosphere, traditional models are difficult to effectively simulate and predict. Moreover, the remediation strategies formulated solely based on historical experience and simple cost-benefit analysis lack flexibility and pertinence, cannot be dynamically adjusted according to real-time environmental changes, resulting in poor remediation effects and high costs. In terms of personnel training, traditional classroom teaching and on-site demonstration methods have limitations. Classroom teaching is often too theoretical, and trainees lack practical operation experience, making it difficult to transform theoretical knowledge into practical skills. On-site demonstrations are limited by site, time, and resources, unable to meet the needs of large-scale training, and trainees may not be able to fully learn and master remediation skills due to factors such as nervousness during on-site operations. In addition, the existing training methods cannot conduct real-time evaluation and guidance on the operations of trainees, making it difficult to ensure the quality and effect of training. Therefore, there is an urgent practical need to develop an intelligent environmental remediation decision support and training platform to solve the above problems. Summary of the Invention

[0005] Object of the Invention: In order to overcome the deficiencies of the prior art, the present invention provides an intelligent environmental remediation decision support and training platform, which breaks information silos through multi-source data fusion technology, improves remediation accuracy based on a dynamic policy optimization mechanism of deep reinforcement learning, and a virtual-reality combined immersive training system to bridge the practical operation gap, realizing full-chain intelligent decision-making for pollution control and efficient improvement of personnel skills.

[0006] An intelligent environmental remediation decision support and training platform, comprising the following modules: a data acquisition module, including: environmental sensor data, collecting soil pollutant concentration through an electrochemical sensor, PM2.5 concentration through a laser scattering sensor, and VOCs concentration through a gas chromatograph; satellite image data, including the thermal map distribution of the polluted area obtained through multi-spectral remote sensing; a historical remediation case database, storing historical remediation data, including remediation methods, remediation efficiency, costs, and environmental restoration indicators; the electrochemical sensor is installed on the soil surface of the polluted area to collect soil pollutant concentration, and the data in the historical remediation case database is sourced from public reports and internal records of various environmental remediation agencies, and the historical remediation data in this database is updated monthly.

[0007] A data processing module, which receives the multi-source data of the data acquisition module in real time through an API interface, and is used for: fusing real-time data with historical remediation data, predicting pollution trends through deep learning algorithms, and optimizing remediation strategies through reinforcement learning algorithms; integrating digital twin technology to construct a virtual twin model that synchronously updates with the real environment, and updating the pollution diffusion simulation results every 10 minutes according to sensor data;

[0008] A decision support module, connected to the data processing module via the TCP / IP protocol, is used for: receiving repair strategies and presenting them through an interactive user interface, supporting users to compare different strategies according to the dimensions of cost, effect, and environmental impact; based on the dynamic simulation results of the digital twin model, recommending real-time adjusted repair plans, and predicting the probability of pollution diffusion and early warnings of ecological threshold breakthroughs;

[0009] A training module, connected to the decision support module, includes:

[0010] A VR simulation component, generating a virtual reality scene based on the repair strategy, simulating the repair operation process, and using scene modeling and rendering algorithms to generate a virtual reality scene including the polluted area, repair equipment, and operation process according to the repair strategy; an AR component, realizing environmental space positioning through the SLAM algorithm during on-site repair, and superimposing virtual operation guides on the real polluted area in the user's field of view. The ORB-SLAM2 algorithm based on vision is used, and the key parameter is set as the feature point extraction threshold of 100.

[0011] A user feedback module, used for receiving feedback data from users on the actual effects of repair strategies, and optimizing the algorithm parameters of the data processing module through supervised learning.

[0012] For the intelligent environmental repair decision support and training platform of the present invention, in the data processing module: the deep learning algorithm uses a convolutional neural network CNN. The input is the normalized satellite image RGB pixel matrix, and the output is a binary segmentation map identifying the spatial boundary of the polluted area. Preferably, the convolutional neural network CNN uses 3 convolutional layers and 2 fully connected layers, and the convolutional kernel size is 3x3; the reinforcement learning algorithm uses Q-learning. The input includes the current environmental state, historical repair data, and predicted pollution trends, and the output is the repair strategy. Its reward function is: R = α·pollution reduction rate + β·(1 / cost) - γ·ecological risk value, where α, β, and γ are weight coefficients. Preferably, the value ranges of the weight coefficients α, β, and γ are [0,1], which are determined through experiments.

[0013] An intelligent environmental restoration decision support and training platform according to the present invention. The visualization function of the decision support module includes: multi-dimensional data charts: a time series chart shows the change in pollutant concentration, the time series chart shows the change in pollutant concentration at hourly intervals, a heat map shows the spatial distribution of pollution, and the heat map uses a red-yellow-green color mapping rule to show the spatial distribution of pollution; a three-dimensional pollution diffusion simulation animation, generated based on a computational fluid dynamics model, generated based on the Reynolds-averaged Navier-Stokes equation computational fluid dynamics model, and the model parameters are calibrated according to the actual environmental conditions; an ecological restoration degree assessment, the vegetation coverage rate is calculated by the NDVI index of satellite images, and the biodiversity index is counted by a species recognition model.

[0014] An intelligent environmental restoration decision support and training platform according to the present invention. The VR simulation component of the training module integrates an AI coach system, including: a motion capture unit: real-time tracking of the user's operation trajectory through an inertial sensor; an error recognition unit: taking the time series data of the user's motion trajectory as input, and outputting a similarity score with the standard operation process through a pre-trained LSTM neural network. When the threshold is <0.8, a voice feedback is triggered. The pre-trained LSTM neural network has 2 hidden layers, each layer containing 50 neurons, and the training data comes from the operation trajectory data of professional restoration personnel; a voice feedback unit: generating a correction instruction according to the deviation type, and the instruction priority is dynamically adjusted according to the environmental risk level.

[0015] An intelligent environmental restoration decision support and training platform according to the present invention. The federated learning framework of the data processing module performs the following steps:

[0016] S1 The local modules of each environmental restoration agency use local data to calculate the model gradient and encrypt and upload it to the central server through the Paillier homomorphic encryption algorithm;

[0017] S2 The central server aggregates the global gradient using the FedAvg algorithm The update formula is where η = 0.01;

[0018] S3 Distribute the updated model parameters to each agency and synchronize once every 24 hours.

[0019] An intelligent environmental restoration decision support and training platform according to the present invention. The supervised learning optimization method of the user feedback module is: using the user feedback data as the training set, and minimizing the mean square error between the predicted pollution trend and the actual result through the gradient descent method When the continuous 3 - iteration decline rate of the MSE < 1%, the model retraining is automatically triggered.

[0020] As can be seen from the above technical solutions, the present invention has the following beneficial effects:

[0021] An intelligent environmental restoration decision support and training platform according to the present invention integrates multi-source heterogeneous data of electrochemical sensors, satellite remote sensing, and historical case databases, adopts pixel-level segmentation of CNN and a dynamic decision-making mechanism of Q-learning, greatly improves the accuracy of pollution boundary recognition, and controls the pollution trend prediction error within ±5% based on three-dimensional diffusion simulation of the Reynolds equation, which is significantly improved compared with traditional statistical models, effectively solves the complex modeling problem of multi-media pollutant migration and transformation, and the reinforcement learning framework realizes minute-level dynamic adjustment of restoration strategies through real-time environmental state evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a diagram showing the relationship between modules of an intelligent environmental restoration decision support and training platform according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Embodiment 1

[0024] As Figure 1 shown is a diagram showing the relationship between modules of an intelligent environmental restoration decision support and training platform according to the present invention. In this embodiment, the application scenario is the treatment of soil and atmosphere compound pollution in an industrial park, and the specific implementation steps are as follows:

[0025] (1) Implementation of the data acquisition module

[0026] Sensor deployment

[0027] Collection of soil pollutant concentration: Five electrochemical sensors are arranged on the surface layer (depth 0 - 15 cm) of the polluted area in a grid distribution with a spacing of 10 m × 10 m, and the collection frequency is once every 5 minutes to monitor the concentration of heavy metals (such as cadmium and lead), and the data accuracy is ±0.5 mg / kg.

[0028] Collection of PM2.5 concentration: A laser scattering sensor is installed 300 m upwind of the polluted area, with a sampling frequency of 1 Hz and a data accuracy of ±5%.

[0029] Collection of VOCs concentration: Use a gas chromatograph (model: GC - 2014, Shimadzu, Japan) to collect gas samples in the polluted area once an hour, analyze the concentration of volatile organic compounds such as benzene and toluene, and the detection limit is ≤0.1 ppm.

[0030] Obtaining satellite images: Obtain images of the polluted area through a multi-spectral remote sensing satellite (Sentinel - 2) with a resolution of 10 m × 10 m, collect at a fixed time at 10:00 am every day, and generate pollution heat map distribution data.

[0031] Data source of the historical remediation case database: Collect the remediation reports of 3 similar polluted sites in this industrial park and its surrounding areas in the past 5 years, including the implementation data of bioremediation, chemical leaching, thermal desorption and other methods, and store them in a MySQL database (version 8.0). The data update cycle is the 1st day of each month.

[0032] (2) Implementation of the data processing module

[0033] Multi-source data fusion receives sensor data in real time through an API interface (RESTful protocol). The satellite image is processed into an RGB pixel matrix by the GDAL library (version 3.4.1), normalized to the [0,1] interval, and then input into the CNN model.

[0034] Deep learning for pollution trend prediction

[0035] CNN model parameters:

[0036] Convolutional layer: 3 layers, convolutional kernel 3×3, stride 1, activation function ReLU;

[0037] Fully connected layer: 2 layers, the number of nodes is 128 and 64 respectively, and the output layer has a Sigmoid activation function;

[0038] Training data: Historical satellite image dataset (2000 images with marked pollution area boundaries), batch_size = 32, learning rate 0.001, epoch = 100.

[0039] Output result: Binary segmentation map of the polluted area, with a boundary recognition accuracy rate of ≥92%.

[0040] Reinforcement learning strategy optimization

[0041] Q-learning parameter settings:

[0042] State space: Current pollutant concentration (PM2.5 ≤ 150 μg / m 3 , VOCs ≤ 50 ppm), historical remediation efficiency (≥80%), ecological risk value (≤ level 3);

[0043] Action space: Select bioremediation (cost 100,000 yuan / mu, ecological risk level 1) or chemical leaching (cost 200,000 yuan / mu, ecological risk level 2);

[0044] Reward function: Output strategy: Recommend a chemical leaching plan, predict a 95% pollution reduction rate, cost 200,000 yuan / mu, and an ecological risk value of 1.2 levels.

[0045] Digital twin model update

[0046] A three-dimensional pollution diffusion model was constructed using ANSYS Fluent software (version 2023R1). Based on the Reynolds-averaged Navier-Stokes equations, the k-ε model was selected for the turbulence model. The grid division accuracy was 0.5m × 0.5m × 1m, and the simulation results were updated by synchronizing sensor data every 10 minutes.

[0047] (III) Implementation of the Decision Support Module

[0048] Visualization Function

[0049] Time series graph: The PM2.5 concentration change curve was plotted at hourly intervals in Matplotlib (Python library). The X-axis represents time, and the Y-axis ranges from 0 to 300 μg / m 3 ;

[0050] Heat map: The pollution distribution map was generated using the "Heatmap" plugin in QGIS (version 3.22). The color mapping rules are as follows: red (concentration ≥ 150 μg / m 3 ), yellow (50 - 150 μg / m 3 ), green (< 50 μg / m 3 ); 3D animation: The pollution diffusion process was rendered through ParaView (version 5.10), the diffusion path for the next 72 hours was simulated, and a video file with a resolution of 1920 × 1080 was output.

[0051] Strategy Recommendation and Early Warning

[0052] By comparing the bioremediation and chemical leaching schemes, a three-dimensional radar chart showing cost, effect, and risk was displayed on the interactive interface, and the chemical leaching scheme was recommended;

[0053] When it is predicted that the pollution will spread to the ecological protection area, a threshold warning is triggered (when the NDVI index < 0.3, it is determined that the vegetation is damaged), and a text message is sent to the administrator's mobile phone.

[0054] (IV) Implementation of the Training Module

[0055] VR simulation component hardware configuration: HTC Vive Pro 2 headset (resolution 2160 × 2160 / eye), inertial sensor (model: Xsens MVN, sampling rate 100Hz);

[0056] Scene generation: The Unity engine (version 2021.3) was used to build a virtual polluted site, which includes a polluted soil area (marked in red), a leaching equipment model (1:1 scale), and operation process prompts (text + voice);

[0057] AI Coaching System:

[0058] LSTM model parameters: The length of the input time series data window is 10 seconds, there are 2 hidden layers (each layer has 50 neurons), and the training data comes from the operation trajectories of 10 experts (a total of 5,000 pieces).

[0059] When the user operation similarity score < 0.8, the voice feedback unit prompts "It is necessary to speed up the injection speed of the eluent" through the Bluetooth headset.

[0060] AR component

[0061] SLAM algorithm: Use ORB-SLAM2 (open source code v0.3) to achieve spatial positioning, the feature point extraction threshold is set to 100, and the environmental reconstruction error ≤ 0.5m.

[0062] Operation guide overlay: Through the AR glasses (model: Microsoft HoloLens 2), virtual arrows are overlaid on the real pollution area to guide the user to move the repair device to the example coordinates (32.714°N, 117.123°E).

[0063] (V) User feedback and model optimization

[0064] User feedback data (such as the actual pollution reduction rate of 93% vs the prediction of 95%) is input into the TensorFlow (version 2.10) model, and the MSE is minimized by the gradient descent method: When the MSE decrease rate < 1% for 3 consecutive iterations (such as from 0.05 to 0.049), trigger the model to be retrained (the training period is 24 hours).

[0065] The central server (AWS EC2 instance, model c5.2xlarge) executes every 24 hours:

[0066] Calculate the gradients of the local models of each institution Upload using Paillier homomorphic encryption (key length 2048bit).

[0067] In this embodiment, each module realizes data interaction through the TCP / IP protocol (port 8080), and HTTPS encryption transmission is used between the data acquisition module and the central server to ensure data security.

[0068] The above embodiments are exemplary, and their purpose is to illustrate the technical concept and characteristics of the present invention, so that those skilled in this field can understand the content of the present invention and implement it accordingly, and it cannot be used to limit the protection scope of the present invention. All changes or modifications made according to the spirit and essence of the present invention should be covered within the protection scope of the present invention.

Claims

1. An intelligent environmental remediation decision support and training platform, characterized in that, It includes the following modules: Data acquisition module, including: environmental sensor data, such as soil pollutant concentration collected by electrochemical sensors, PM2.5 concentration collected by laser scattering sensors, and VOCs concentration collected by gas chromatographs; satellite image data, including the thermal map distribution of polluted areas obtained by multispectral remote sensing; historical remediation case database, storing historical remediation data, including remediation methods, remediation efficiency, costs, and environmental restoration indicators; Data processing module, which receives multi-source data from the data acquisition module in real time through the API interface and is used for: fusing real-time data with historical remediation data, predicting pollution trends through deep learning algorithms, and optimizing remediation strategies through reinforcement learning algorithms; integrating digital twin technology to build a virtual twin model that synchronously updates with the real environment, and updating the pollution diffusion simulation results every 10 minutes according to sensor data; Decision support module, connected to the data processing module through the TCP / IP protocol and used for: receiving remediation strategies and presenting them through an interactive user interface, supporting users to compare different strategies based on dimensions such as costs, effects, and environmental impacts; recommending real-time adjusted remediation plans based on the dynamic simulation results of the digital twin model, and predicting pollution diffusion probabilities and ecological threshold breakthrough warnings; Training module, connected to the decision support module, including: VR simulation component, generating a virtual reality scene based on the remediation strategy and simulating the remediation operation process; AR component, realizing environmental space positioning through the SLAM algorithm during on-site remediation and superimposing virtual operation guides on the real polluted areas in the user's field of view; User feedback module, used to receive feedback data from users on the actual effects of remediation strategies and optimize the algorithm parameters of the data processing module through supervised learning.

2. An intelligent environmental restoration decision support and training platform according to claim 1, characterized in that In the data processing module: The deep learning algorithm uses a convolutional neural network CNN. The input is the normalized satellite image RGB pixel matrix, and the output is a binary segmentation map identifying the spatial boundaries of polluted areas; The reinforcement learning algorithm uses Q-learning. The input includes the current environmental state, historical remediation data, and predicted pollution trends, and the output is the remediation strategy. Its reward function is: R = α·pollution reduction rate + β·(1 / cost) - γ·ecological risk value where α, β, and γ are weight coefficients.

3. The intelligent environmental restoration decision support and training platform according to claim 1, characterized in that The visualization functions of the decision support module include: Multi-dimensional data charts: time series charts show the changes in pollutant concentrations, and heat maps show the spatial distribution of pollution; 3D pollution diffusion simulation animation, generated based on the computational fluid dynamics model; Ecological restoration degree assessment. The vegetation coverage rate is calculated through the NDVI index of satellite images, and the biodiversity index is statistically analyzed through a species identification model.

4. An intelligent environmental restoration decision support and training platform according to claim 1, characterized in that The VR simulation component of the training module integrates an AI coach system, including: Motion capture unit: real-time tracking of the user's operation trajectory through inertial sensors; Error recognition unit: taking the time series data of the user's motion trajectory as input, and outputting the similarity score with the standard operation process through a pre-trained LSTM neural network. When the threshold < 0.8, voice feedback is triggered; Voice feedback unit: Generates correction instructions according to the deviation type, and the instruction priority is dynamically adjusted according to the environmental risk level.

5. An intelligent environmental restoration decision support and training platform according to claim 1, characterized in that, The federated learning framework of the data processing module performs the following steps: The local modules of each environmental remediation agency use local data to calculate model gradients and upload them to the central server through homomorphic encryption; The central server S2 uses the FedAvg algorithm to aggregate the global gradients The update formula is where η = 0.01; S3 Distributes the updated model parameters to each institution and synchronizes them every 24 hours.

6. An intelligent environmental restoration decision support and training platform according to claim 1, characterized in that The supervised learning optimization method of the user feedback module is as follows: using the user feedback data as the training set, minimizing the mean square error between the predicted pollution trend and the actual result through the gradient descent method When the MSE consecutive 3 - iteration decline rate < 1%, the model retraining is automatically triggered.

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