Karst area mine slope ecological management optimization method based on variational self-coding

Abstract

The invention discloses a karst area mine slope ecological management optimization method based on variational self-coding, and the method comprises the following steps: S1, collecting and preprocessing multi-source data, and constructing a multi-source data set; s2, initializing an improved variational auto-encoder based on a multi-source data set, constructing a geological and ecological double latent variable network structure, and generating initial latent variable distribution; s3, optimizing initial latent variable distribution through a meta-cognitive adjustment mechanism, and dynamically updating a latent variable generation rule; s4, inputting the optimized initial latent variable distribution into a domain transfer generation module, and generating adaptive latent variable distribution of the target region; s5, generating a hypothesis restoration scene of the karst region in combination with an anti-factual reasoning mechanism, and generating a restoration strategy; and S6, real-time monitoring is carried out, a monitoring result is fed back to the improved variational auto-encoder, latent variable distribution is updated, and a repair strategy is dynamically optimized. Based on an improved variational auto-encoder and an anti-factual reasoning mechanism, the ecological management of the mine slope in the karst area is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological management and environmental restoration, and in particular to an ecological management optimization method for mine slopes in karst areas based on variational autoencoding. Background Art

[0002] With the continuous development of human activities, the exploitation of mineral resources has increased day by day, especially in karst areas, which has attracted widespread attention. Karst areas have complex geological conditions, obvious karst characteristics, and typical geological and ecological dual fragility. The ecological damage and geological instability caused by mining are particularly prominent. The ecological governance of mine slopes in karst areas has become an important topic for environmental protection, ecological restoration and sustainable development. However, due to the complexity of the geological and ecological environment in these areas and the problems left over from the history of mining, the existing ecological restoration technologies and methods still have many shortcomings, and an efficient, dynamic and systematic governance method is urgently needed.

[0003] The current methods for managing mine slopes in karst areas are mainly based on traditional engineering technologies, such as slope reinforcement, vegetation restoration, soil improvement, etc. Although these traditional methods can solve some obvious geological stability problems or ecological restoration problems in the short term, in complex karst areas, these methods often have defects such as poor adaptability, unsustainable implementation effects, and difficulty in coping with long-term environmental changes. Traditional engineering methods ignore the complex geological and ecological interactions in karst areas, cannot flexibly respond to the personalized needs of different karst areas, and lack a dynamic adjustment mechanism. Therefore, how to establish an intelligent management system that can make full use of multi-source data and consider geological and ecological complexity has become a key issue in the current field of ecological restoration of mine slopes in karst areas.

[0004] With the development of artificial intelligence and big data technology, data-driven ecological restoration methods have gradually become a research hotspot in recent years. This type of method can analyze and predict environmental change trends by collecting a large amount of multidimensional data such as geology, ecology, and climate, combined with machine learning, deep learning and other algorithms, so as to formulate more scientific and reasonable restoration strategies. However, existing data-driven methods are mostly focused on data modeling and analysis in a single field, such as only focusing on the restoration of geological or ecological characteristics, and lack a multi-dimensional and multi-level comprehensive analysis of the complex geological and ecological characteristics of karst areas. Restoration methods based solely on geological data or ecological data often cannot provide effective solutions when faced with special environmental changes in karst areas.

[0005] Existing ecological restoration methods based on variational autoencoders (VAE) still face many challenges in their application in karst areas. As a deep learning method, variational autoencoders have been widely used in tasks such as data dimensionality reduction and feature extraction, but their application in complex environments is still insufficient. Traditional VAE models mainly rely on static data training, ignoring the potential dynamic changes in the data and regional differences, resulting in poor adaptability of the model. In karst areas, the complex interactions between geological and ecological characteristics need to be modeled through more sophisticated models, while traditional VAE models have limited capabilities in processing multi-source data fusion and multi-level feature extraction, and cannot fully capture the complexity of karst areas.

[0006] In addition, although some deep learning-based restoration methods have taken into account the geological and ecological characteristics of karst areas, these methods are mostly optimized through a single model and lack joint modeling of geological and ecological latent variables. There is a high degree of interaction between the geological and ecological characteristics of karst areas. This interaction is not limited to static feature data, but also involves their dynamic evolution in time and space. Existing restoration methods mostly process data in a static way, lacking in-depth exploration of dynamic interactions and iterative optimization between latent variables, resulting in poor adaptability of restoration solutions at different time points and in different regions.

[0007] Therefore, how to provide an optimization method for ecological management of mine slopes in karst areas based on variational autoencoding is an urgent problem that technicians in this field need to solve. Summary of the invention

[0008] One purpose of the present invention is to propose an optimization method for ecological management of mine slopes in karst areas based on variational autoencoders. Based on improved variational autoencoders and counterfactual reasoning mechanisms, the present invention proposes an efficient optimization method for the ecological management of mine slopes in karst areas. By constructing a geological and ecological dual latent variable network, and combining a metacognitive adjustment mechanism, a domain transfer generation module, and a counterfactual reasoning mechanism, it is possible to accurately simulate restoration scenarios and dynamically optimize restoration strategies. This method not only has high adaptability and accuracy, but also can continuously optimize restoration plans based on real-time monitoring data, significantly improve ecological restoration effects and geological stability, and has strong practical application value.

[0009] The method for optimizing ecological management of mine slopes in karst areas based on variational autoencoding according to an embodiment of the present invention comprises the following steps:

[0010] S1, collect multi-source data of mine slopes in karst areas and pre-process them to construct a multi-source data set;

[0011] S2. Based on the improved variational autoencoder initialized by multi-source data sets, a geological and ecological dual latent variable network structure is constructed to characterize the karst landform structure characteristics and ecological dynamic characteristics respectively, and generate the initial latent variable distribution;

[0012] S3, combining geological and ecological environmental data in karst areas, optimizing the initial latent variable distribution through the metacognitive adjustment mechanism, and dynamically updating the latent variable generation rules;

[0013] S4, inputting the optimized initial latent variable distribution into the domain transfer generation module to generate an adaptive latent variable distribution for the target area based on the geological characteristics of different karst areas;

[0014] S5. Generate hypothetical restoration scenarios for karst areas using adaptive latent variable distribution in the target area combined with counterfactual reasoning mechanism, and generate restoration strategies through comparative analysis;

[0015] S6. By real-time monitoring of the ecological and geological change data of the karst mine slopes, the monitoring results are fed back to the improved variational autoencoder to update the latent variable distribution and dynamically optimize the restoration strategy.

[0016] Optionally, the multi-source data collection of S1 specifically includes using geological monitoring equipment to collect slope inclination, crack distribution, cave characteristics and rock and soil structure parameters, using ecological data sensors to collect vegetation coverage, biodiversity indicators, soil nutrient content and meteorological data, and installing slope deformation monitoring equipment, rainfall monitors and soil moisture sensors to collect real-time dynamic detection data; pre-processing the collected multi-source data, including data cleaning, processing missing values ​​and outliers, using normalization methods for standardization, and constructing a multi-source data set.

[0017] Optionally, the S2 specifically includes:

[0018] S21. Define the network structure of the improved variational autoencoder, construct the geological and ecological dual latent variable network structure, where the geological latent variable network is used to capture the large-scale structural characteristics of karst landforms, and the ecological latent variable network is used to model the microscopic dynamic characteristics of the ecosystem, and define the joint spatial initial distribution of the geological and ecological dual latent variables:

[0019]

[0020] in, represents the joint latent variable distribution, X represents multi-source input data, represents the mixing weight of the kth component, z g represents the geological potential variable, z e represents the ecological latent variable, represents the mean of the kth component, represents the covariance matrix of the kth component, K represents the total number of components, and N represents the normal distribution;

[0021] S22. Construct an encoder network to extract high-dimensional features from multiple source data sets and extract geological features through a multi-resolution convolutional neural network:

[0022]

[0023] in, represents the geological characteristics of the lth layer, σ represents the activation function, represents the convolution kernel of the lth layer, Indicates the geological characteristics of the l-1 layer, represents the bias term of the lth layer, * represents the convolution operator, Represents the multi-scale pyramid pooling operation at layer l;

[0024] Extract ecological features based on graph neural network and define propagation rules:

[0025]

[0026] in, represents the ecological characteristics of the tth step, represents the ecological characteristics of the t-1th step, ReLU represents the rectified linear unit activation function, W e represents the weight matrix of the graph neural network, b e represents the bias term, N(i) represents the neighbor set of node i, deg(i) represents the degree of node i, and deg(j) represents the degree of node j;

[0027] S23. The geological and ecological dual latent variable network is optimized by an improved regularization decoupling method. The regularization goal is to minimize the mutual information and overlap characteristics between the geological latent variables and the ecological latent variables at the same time:

[0028]

[0029] Among them, L disentangle represents the regularized objective function, I(z g ;z e ) represents the geological potential variable z g and ecological latent variable z e , α represents the sparse regularization weight, P(z i ) represents the latent variable z i The projection operator of , ∥·∥ 2 represents the L2 norm;

[0030] S24, design a joint decoder to decode the geological latent variables z g and ecological latent variable z eDecoding and reconstruction are performed with the goal of maximizing the ability to generate multi-dimensional restoration features:

[0031]

[0032] in, represents the reconstructed output of the joint decoder, f g (z g ) represents the geological latent variable decoding function, f e (z e ) represents the ecological latent variable decoding function.

[0033] Optionally, the S3 specifically includes:

[0034] S31. Based on the geological characteristics and ecological environment data of the karst area, the initial latent variable distribution is optimized by introducing a metacognitive adjustment mechanism, and the initial latent variable is defined as z0=[z g ,z e ], where z g represents the geological potential variable, z e represents ecological latent variables;

[0035] S32. Use a multi-level adaptive adjustment function to adjust the initial latent variables according to the geological characteristics and ecological environment data of the karst area:

[0036]

[0037] Among them, f adjust represents the adaptive adjustment function, α g represents the adjustment coefficient of geological latent variables, α e represents the adjustment coefficient of the ecological latent variable, represents the gradient of the geological loss function, represents the gradient of the ecological loss function;

[0038] S33. At each iteration, the learning rate is dynamically adjusted by calculating the gradient information of the geological latent variables and the ecological latent variables:

[0039]

[0040] Among them, η t represents the dynamically adjusted learning rate, η0 represents the initial learning rate, and λ adjust represents the adjustment factor, represents the L2 norm of the current gradient, Represents the L2 norm of the initial gradient;

[0041] S34. Introduce an incremental learning mechanism to adaptively update the latent variable generation rules based on the dynamic relationship between geological latent variables and ecological latent variables:

[0042]

[0043] Among them, z t represents the latent variable of the current iteration, z t-1 represents the latent variable of the previous iteration;

[0044] S35. When dynamically updating the latent variable generation rules, a regularization mechanism is added to prevent overfitting and constrain the range of change of the latent variables:

[0045]

[0046] Among them, R t represents the regularization term, λ g represents the geological regularization coefficient, λ e represents the ecological regularization coefficient, z gt represents the current geological potential variable, z et represents the current ecological latent variable, represents the target geological potential variable, Represents the target ecological latent variable.

[0047] Optionally, the S4 specifically includes:

[0048] S41, inputting the optimized initial latent variable distribution into a domain transfer generation module, wherein the domain transfer generation module includes two independent adaptation units: a geological feature adaptation unit and an ecological feature adaptation unit;

[0049] S42, the geological characteristic adaptation unit is responsible for analyzing the geological characteristic data of the target karst area, including slope inclination, crack distribution and cave characteristics, and after feature extraction, matching and adjusting with the geological potential variables of the source area to generate adaptive geological potential variables;

[0050] S43, the ecological feature adaptation unit is responsible for collecting and analyzing ecological data of the target karst area, including vegetation coverage, biodiversity indicators and soil nutrient content, and after feature extraction, matching and adjusting with the ecological latent variables of the source area to generate adaptive ecological latent variables;

[0051] S44, the domain transfer generation module integrates the adaptive geological latent variables and the adaptive ecological latent variables through a multi-layer neural network to generate the overall adaptive latent variable distribution;

[0052] S45. Introduce an adaptive adjustment mechanism to dynamically adjust the weights of adaptive geological latent variables and adaptive ecological latent variables according to the differences in geological and ecological characteristics between the target area and the source area;

[0053] S46, the domain transfer generation module finally outputs the adaptive latent variable distribution of the target area.

[0054] Optionally, the S5 specifically includes:

[0055] S51, inputting the adaptive latent variable distribution of the target area into the counterfactual reasoning mechanism, wherein the counterfactual reasoning mechanism generates a plurality of hypothetical restoration scenarios by simulating the changes of restoration parameters; each hypothetical restoration scenario represents a different combination of restoration schemes, including geological restoration, ecological restoration and soil improvement contents;

[0056] S52. In the counterfactual reasoning mechanism, for each hypothetical restoration scenario, the restoration parameters are adjusted using the adaptive latent variable distribution of the target area, wherein the restoration parameters include vegetation coverage, soil structure, and slope stability, and are adjusted according to the geological and ecological characteristics of the target area;

[0057] S53, combining geological data, ecological data and historical restoration cases of the target area, evaluate the effect of each hypothetical restoration scenario through simulation;

[0058] S54, performing comparative analysis on all generated hypothetical repair scenarios, and comparing the repair effects of different hypothetical repair scenarios;

[0059] S55. During the comparative analysis process, multiple simulation experiments are used to make repeated adjustments based on the changes in the target area and the repair parameters to ensure that the generated hypothetical repair scenario adapts to the characteristics and change trends of the target area, and the repair strategy is generated and output through comparative analysis.

[0060] The beneficial effects of the present invention are:

[0061] First, by constructing a network structure of geological and ecological dual latent variables, the present invention can effectively characterize the complex geological characteristics and ecological dynamic characteristics of karst areas, thereby realizing a comprehensive and systematic analysis of mine slopes. Compared with the prior art, the present invention can make full use of multi-source data for multi-dimensional and multi-level feature extraction, so that the model has a more accurate characterization of the deep-level interaction between geology and ecological environment, avoiding the limitations of traditional methods in a single field, and providing a more comprehensive solution.

[0062] Secondly, the present invention uses a metacognitive adjustment mechanism to optimize the initial latent variable distribution, so that the model can dynamically adjust the generation rules of latent variables according to the characteristics of different geological and ecological environments. This mechanism can update the distribution of latent variables in real time, overcome the shortcomings of static training methods in traditional models, and enable the restoration strategy to be adjusted synchronously with environmental changes, thereby ensuring the adaptability and sustainability of the restoration plan during long-term implementation. Through this dynamic optimization process, the restoration effect can not only better adapt to the characteristics of different karst areas, but also cope with possible environmental changes in the future, ensuring the long-term effectiveness of ecological governance measures.

[0063] In addition, the present invention adaptively adjusts the geological and ecological characteristics of different karst areas through the domain transfer generation module, and can generate a more accurate distribution of latent variables based on the specific characteristics of the target area. This module can flexibly adjust the distribution of latent variables according to the differences between the source area and the target area through a multi-layer neural network and an adaptive adjustment mechanism, and then tailor the most appropriate restoration strategy for different regions. Especially in karst areas, due to the high differences in geological and ecological characteristics, traditional restoration methods are often difficult to achieve effective transfer, while the present invention provides an accurate and efficient solution, which greatly improves the adaptability and accuracy of the restoration strategy.

[0064] At the same time, the present invention introduces a counterfactual reasoning mechanism, which uses the generated adaptive latent variable distribution and is based on the generation and comparative analysis of hypothetical restoration scenarios to provide a more scientific and operational restoration strategy for the ecological management of mine slopes. The counterfactual reasoning mechanism can simulate the effects of different restoration plans and evaluate the pros and cons of each restoration plan through comparative analysis, thereby helping decision makers choose the most effective restoration measures. Through repeated simulation experiments, the present invention can continuously adjust and optimize the restoration strategy in practical applications to ensure that the plan can be adjusted in a timely manner according to the dynamic changes in the target area.

[0065] Finally, the present invention monitors the ecological and geological change data of karst mine slopes in real time, continuously feeds back to the variational autoencoder, further updates the latent variable distribution, and dynamically optimizes the restoration strategy. This feedback mechanism can not only promptly discover problems and challenges in the restoration process, but also adjust the restoration plan according to the new data to ensure the accuracy and long-term effectiveness of the restoration measures. Through this real-time monitoring and dynamic adjustment, the present invention can effectively respond to the complex changes in the environment in karst areas and improve the flexibility and sustainability of mine slope ecological management. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0067] Figure 1 This is an overall flow chart of the method for optimizing ecological management of mine slopes in karst areas based on variational autoencoding proposed by the present invention;

[0068] Figure 2 This is a schematic diagram of the processing operation of the domain transfer generation module of the karst area mine slope ecological management optimization method based on variational autoencoding proposed in the present invention. DETAILED DESCRIPTION

[0069] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0070] refer to Figure 1 and Figure 2 , an optimization method for ecological management of mine slopes in karst areas based on variational autoencoders includes the following steps:

[0071] S1, collect multi-source data of mine slopes in karst areas and pre-process them to construct a multi-source data set;

[0072] S2. Based on the improved variational autoencoder initialized by multi-source data sets, a geological and ecological dual latent variable network structure is constructed to characterize the karst landform structure characteristics and ecological dynamic characteristics respectively, and generate the initial latent variable distribution;

[0073] S3, combining geological and ecological environmental data in karst areas, optimizing the initial latent variable distribution through the metacognitive adjustment mechanism, and dynamically updating the latent variable generation rules;

[0074] S4, inputting the optimized initial latent variable distribution into the domain transfer generation module to generate an adaptive latent variable distribution for the target area based on the geological characteristics of different karst areas;

[0075] S5. Generate hypothetical restoration scenarios for karst areas using adaptive latent variable distribution in the target area combined with counterfactual reasoning mechanism, and generate restoration strategies through comparative analysis;

[0076] S6. By real-time monitoring of the ecological and geological change data of the karst mine slopes, the monitoring results are fed back to the improved variational autoencoder to update the latent variable distribution and dynamically optimize the restoration strategy.

[0077] In this embodiment, the multi-source data collection of S1 specifically includes using geological monitoring equipment to collect slope inclination, crack distribution, cave characteristics and rock and soil structure parameters, using ecological data sensors to collect vegetation coverage, biodiversity indicators, soil nutrient content and meteorological data, and installing slope deformation monitoring equipment, rainfall monitors and soil moisture sensors to collect real-time dynamic detection data; pre-processing the collected multi-source data, including data cleaning, processing missing values ​​and outliers, using normalization methods for standardization, and constructing a multi-source data set.

[0078] In this implementation, S2 specifically includes:

[0079] S21. Define the network structure of the improved variational autoencoder, construct the geological and ecological dual latent variable network structure, where the geological latent variable network is used to capture the large-scale structural characteristics of karst landforms, and the ecological latent variable network is used to model the microscopic dynamic characteristics of the ecosystem, and define the joint spatial initial distribution of the geological and ecological dual latent variables:

[0080]

[0081] in, represents the joint latent variable distribution, X represents multi-source input data, represents the mixing weight of the kth component, z g represents the geological potential variable, z e represents the ecological latent variable, represents the mean of the kth component, represents the covariance matrix of the kth component, K represents the total number of components, and N represents the normal distribution;

[0082] S22. Construct an encoder network to extract high-dimensional features from multiple source data sets and extract geological features through a multi-resolution convolutional neural network:

[0083]

[0084] in, represents the geological characteristics of the lth layer, σ represents the activation function, represents the convolution kernel of the lth layer, Indicates the geological characteristics of the l-1 layer, represents the bias term of the lth layer, * represents the convolution operator, Represents the multi-scale pyramid pooling operation at layer l;

[0085] Extract ecological features based on graph neural network and define propagation rules:

[0086]

[0087] in, represents the ecological characteristics of the tth step, represents the ecological characteristics of the t-1th step, ReLU represents the rectified linear unit activation function, W e represents the weight matrix of the graph neural network, b e represents the bias term, N(i) represents the neighbor set of node i, deg(i) represents the degree of node i, and deg(j) represents the degree of node j;

[0088] S23. The geological and ecological dual latent variable network is optimized by an improved regularization decoupling method. The regularization goal is to minimize the mutual information and overlap characteristics between the geological latent variables and the ecological latent variables at the same time:

[0089]

[0090] Among them, L disentangle represents the regularized objective function, I(z g ;z e ) represents the geological potential variable z g and ecological latent variable z e , α represents the sparse regularization weight, P(z i ) represents the latent variable z i The projection operator of , ∥·∥ 2 represents the L2 norm;

[0091] S24, design a joint decoder to decode the geological latent variables z g and ecological latent variable z e Decoding and reconstruction are performed with the goal of maximizing the ability to generate multi-dimensional restoration features:

[0092]

[0093] in, represents the reconstructed output of the joint decoder, f g (z g ) represents the geological latent variable decoding function, f e (z e ) represents the ecological latent variable decoding function.

[0094] In this implementation, S3 specifically includes:

[0095] S31. Based on the geological characteristics and ecological environment data of the karst area, the initial latent variable distribution is optimized by introducing a metacognitive adjustment mechanism, and the initial latent variable is defined as z0=[z g ,z e ], where z g represents the geological potential variable, z e represents ecological latent variables;

[0096] S32. Use a multi-level adaptive adjustment function to adjust the initial latent variables according to the geological characteristics and ecological environment data of the karst area:

[0097]

[0098] Among them, f adjust represents the adaptive adjustment function, α g represents the adjustment coefficient of geological latent variables, α e represents the adjustment coefficient of the ecological latent variable, represents the gradient of the geological loss function, represents the gradient of the ecological loss function;

[0099] S33. At each iteration, the learning rate is dynamically adjusted by calculating the gradient information of the geological latent variables and the ecological latent variables:

[0100]

[0101] Among them, η t represents the dynamically adjusted learning rate, η0 represents the initial learning rate, and λ adjust represents the adjustment factor, represents the L2 norm of the current gradient, Represents the L2 norm of the initial gradient;

[0102] S34. Introduce an incremental learning mechanism to adaptively update the latent variable generation rules based on the dynamic relationship between geological latent variables and ecological latent variables:

[0103]

[0104] Among them, z t represents the latent variable of the current iteration, z t-1 represents the latent variable of the previous iteration;

[0105] S35. When dynamically updating the latent variable generation rules, a regularization mechanism is added to prevent overfitting and constrain the range of change of the latent variables:

[0106]

[0107] Among them, R t represents the regularization term, λ g represents the geological regularization coefficient, λ e represents the ecological regularization coefficient, z gt represents the current geological potential variable, z et represents the current ecological latent variable, represents the target geological potential variable, Represents the target ecological latent variable.

[0108] In this implementation manner, the S4 specifically includes:

[0109] S41, inputting the optimized initial latent variable distribution into a domain transfer generation module, wherein the domain transfer generation module includes two independent adaptation units: a geological feature adaptation unit and an ecological feature adaptation unit;

[0110] S42, the geological characteristic adaptation unit is responsible for analyzing the geological characteristic data of the target karst area, including slope inclination, crack distribution and cave characteristics, and after feature extraction, matching and adjusting with the geological potential variables of the source area to generate adaptive geological potential variables;

[0111] S43, the ecological feature adaptation unit is responsible for collecting and analyzing ecological data of the target karst area, including vegetation coverage, biodiversity indicators and soil nutrient content, and after feature extraction, matching and adjusting with the ecological latent variables of the source area to generate adaptive ecological latent variables;

[0112] S44, the domain transfer generation module integrates the adaptive geological latent variables and the adaptive ecological latent variables through a multi-layer neural network to generate the overall adaptive latent variable distribution;

[0113] S45. Introduce an adaptive adjustment mechanism to dynamically adjust the weights of adaptive geological latent variables and adaptive ecological latent variables according to the differences in geological and ecological characteristics between the target area and the source area;

[0114] S46, the domain transfer generation module finally outputs the adaptive latent variable distribution of the target area.

[0115] In this implementation manner, S5 specifically includes:

[0116] S51, inputting the adaptive latent variable distribution of the target area into the counterfactual reasoning mechanism, wherein the counterfactual reasoning mechanism generates a plurality of hypothetical restoration scenarios by simulating the changes of restoration parameters; each hypothetical restoration scenario represents a different combination of restoration schemes, including geological restoration, ecological restoration and soil improvement contents;

[0117] S52. In the counterfactual reasoning mechanism, for each hypothetical restoration scenario, the restoration parameters are adjusted using the adaptive latent variable distribution of the target area, wherein the restoration parameters include vegetation coverage, soil structure, and slope stability, and are adjusted according to the geological and ecological characteristics of the target area;

[0118] S53, combining geological data, ecological data and historical restoration cases of the target area, evaluate the effect of each hypothetical restoration scenario through simulation;

[0119] S54, performing comparative analysis on all generated hypothetical repair scenarios, and comparing the repair effects of different hypothetical repair scenarios;

[0120] S55. During the comparative analysis process, multiple simulation experiments are used to make repeated adjustments based on the changes in the target area and the repair parameters to ensure that the generated hypothetical repair scenario adapts to the characteristics and change trends of the target area, and the repair strategy is generated and output through comparative analysis.

[0121] Embodiment 1:

[0122] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a mine slope ecological restoration project in a karst area, with the goal of optimizing the ecological management of the mine slope through the method of the present invention and improving the stability and ecological function of the slope. The area is located in a mountainous area with typical karst landforms, facing problems such as severe ecological degradation, sparse vegetation, and poor soil quality. In addition, the slope is affected by rainfall and natural erosion all year round, and there is a high risk of landslides.

[0123] Before the project was implemented, we collected multi-source data in the area, including the geological characteristics of the slope, ecological conditions, and meteorological data. We used geological monitoring equipment to obtain data such as slope inclination, crack distribution, and cave characteristics. We also used ecological monitoring sensors to obtain indicators such as vegetation coverage, biodiversity index, and soil nutrient content. In addition, the meteorological monitoring system before implementation provided data such as rainfall, temperature, and humidity. In order to ensure the integrity and accuracy of the data, all data were standardized to form a complete multi-source data set.

[0124] The present invention is applied to the ecological management of the mine slope in the area through the following steps:

[0125] Data collection and preprocessing: Through the multi-source data collection system, detailed geological data of the slope (such as slope inclination, crack distribution, cave characteristics, etc.) and ecological data (such as vegetation coverage, biodiversity, soil nutrients, etc.) were obtained. After data cleaning, normalization and other preprocessing, a multi-source data set was constructed.

[0126] Constructing a variational autoencoder: Based on this dataset, an improved variational autoencoder was used to construct a geological and ecological dual latent variable network structure. The geological latent variable network is used to capture the large-scale structural characteristics of the slope, and the ecological latent variable network is used to model the microscopic dynamic characteristics of the ecological environment.

[0127] Optimize the initial latent variable distribution: Through the metacognitive adjustment mechanism, the initial latent variable distribution is dynamically optimized in combination with the geological and ecological characteristics of the karst area. This process ensures that the latent variables can more accurately reflect the actual situation of the target area.

[0128] Domain transfer generation module: Based on the optimized latent variable distribution, it is input into the domain transfer generation module. This module generates adaptive latent variable distribution by analyzing the geological and ecological data of the target area.

[0129] Counterfactual reasoning and repair strategy generation: The adaptive latent variable distribution is input into the counterfactual reasoning mechanism, and the optimal repair strategy is generated by comparing the effects of different repair scenarios.

[0130] Verification and dynamic adjustment of restoration effects: During the restoration process, by real-time monitoring of slope changes and ecological indicators, data is fed back into the model to continuously optimize the restoration strategy.

[0131] In order to verify the effectiveness of the method of the present invention, we carried out ecological restoration and slope management in the area for one year. The specific implementation data are shown in the following table.

[0132] Table 1 Comparison of ecological management data of mine slopes in karst areas before and after

[0133]

[0134]

[0135] It can be seen from Table 1 above that after implementing the ecological management optimization method of the present invention, various ecological and geological indicators in the target area have been significantly improved.

[0136] First, the stability of the slope was significantly improved. The slope inclination angle was reduced from 28.5° before implementation to 15.2°, a decrease of 13.3°, which significantly reduced the risk of slope landslides and improved the stability of the slope. Secondly, the vegetation coverage rate increased from 22% to 70%, an increase of 48%, which fully demonstrated the effectiveness of this method in ecological restoration. The significant improvement in soil nutrient content shows that soil improvement has achieved remarkable results, especially the three main soil nutrients of nitrogen, phosphorus and potassium, which increased by 88.9%, 150% and 100% respectively, providing more sufficient nutrients for vegetation growth.

[0137] In addition, the improvement of the biodiversity index shows that the recovery process of the ecosystem has been effectively promoted. The index increased from 0.12 to 0.38, an increase of 0.26, indicating that the diversity of the ecosystem has been significantly enhanced. The area of ​​karst caves and the density of cracks that are unique to karst areas have also been effectively reduced. The area of ​​karst caves has been reduced from 325 square meters to 140 square meters, a reduction of 57%; the density of cracks has been reduced from 225 to 120, a reduction of 46.7%. This shows that the method of the present invention has a good effect on the geological restoration of karst areas.

[0138] Despite a 4.5% increase in rainfall, the slope stability and ecological restoration capacity of the region were still significantly improved after the implementation of the method of the present invention, further verifying the effectiveness and robustness of the method of the present invention.

[0139] This example verifies the effectiveness of the optimization method for ecological management of mine slopes in karst areas based on variational autoencoders. By collecting and preprocessing multi-source data in the target area, combined with the improved variational autoencoder model and counterfactual reasoning mechanism, the stability of the slopes in the karst area and the restoration of the ecosystem are successfully achieved. The experimental results show that this method can significantly increase the vegetation coverage rate, improve soil quality, restore ecosystem diversity, and effectively enhance the stability of the slope. All data changes show that the present invention can play a huge restoration effect in the complex geological and ecological environment of karst areas, and provides an effective technical solution for the ecological restoration of mines in similar areas.

[0140] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An optimization method for ecological management of mine slopes in karst areas based on variational autoencoders, characterized in that: The steps include: S1, collect multi-source data of mine slopes in karst areas and pre-process them to construct a multi-source data set; S2. Based on the improved variational autoencoder initialized by multi-source data sets, a geological and ecological dual latent variable network structure is constructed to characterize the karst landform structure characteristics and ecological dynamic characteristics respectively, and generate the initial latent variable distribution; S3, combining geological and ecological environmental data in karst areas, optimizing the initial latent variable distribution through metacognitive adjustment mechanism, and dynamically updating the latent variable generation rules; S4, inputting the optimized initial latent variable distribution into the domain transfer generation module to generate an adaptive latent variable distribution for the target area based on the geological characteristics of different karst areas; S5. Generate hypothetical restoration scenarios for karst areas using adaptive latent variable distribution in the target area combined with counterfactual reasoning mechanism, and generate restoration strategies through comparative analysis; S6. By real-time monitoring of the ecological and geological change data of the karst mine slopes, the monitoring results are fed back to the improved variational autoencoder to update the latent variable distribution and dynamically optimize the restoration strategy.

2. The method for optimizing ecological management of mine slopes in karst areas based on variational autoencoder according to claim 1 is characterized in that: The multi-source data collection of S1 specifically includes using geological monitoring equipment to collect slope inclination, crack distribution, cave characteristics and rock and soil structure parameters, using ecological data sensors to collect vegetation coverage, biodiversity indicators, soil nutrient content and meteorological data, and installing slope deformation monitoring equipment, rainfall monitors and soil moisture sensors to collect real-time dynamic detection data; pre-processing the collected multi-source data, including data cleaning, processing missing values ​​and outliers, using normalization methods for standardization, and constructing a multi-source data set.

3. The method for optimizing ecological management of mine slopes in karst areas based on variational autoencoder according to claim 1 is characterized in that: The S2 specifically includes: S21. Define the network structure of the improved variational autoencoder, construct the geological and ecological dual latent variable network structure, where the geological latent variable network is used to capture the large-scale structural characteristics of karst landforms, and the ecological latent variable network is used to model the microscopic dynamic characteristics of the ecosystem, and define the joint spatial initial distribution of the geological and ecological dual latent variables: in, represents the joint latent variable distribution, X represents multi-source input data, represents the mixing weight of the kth component, z g represents the geological potential variable, z e represents the ecological latent variable, represents the mean of the kth component, represents the covariance matrix of the kth component, K represents the total number of components, and N represents the normal distribution; S22. Construct an encoder network to extract high-dimensional features from multiple source data sets and extract geological features through a multi-resolution convolutional neural network: in, represents the geological characteristics of the lth layer, σ represents the activation function, represents the convolution kernel of the lth layer, Indicates the geological characteristics of the l-1 layer, represents the bias term of the lth layer, * represents the convolution operator, Represents the multi-scale pyramid pooling operation at layer l; Extract ecological features based on graph neural network and define propagation rules: in, represents the ecological characteristics of the tth step, represents the ecological characteristics of the t-1th step, ReLU represents the rectified linear unit activation function, W e represents the weight matrix of the graph neural network, b e represents the bias term, N(i represents the neighbor set of node i, deg(i) represents the degree of node i, and deg(j) represents the degree of node j; S23. The geological and ecological dual latent variable network is optimized by an improved regularization decoupling method. The regularization objective is to simultaneously minimize the mutual information and overlap characteristics between the geological latent variables and the ecological latent variables: Among them, L disentangle represents the regularized objective function, I(z g ; z e ) represents the geological potential variable z g and ecological latent variable z e , α represents the sparse regularization weight, P(z i ) represents the latent variable z i The projection operator of , ∥·∥ 2 represents the L2 norm; S24, design a joint decoder to decode the geological latent variables z g and ecological latent variable z e Decoding and reconstruction are performed with the goal of maximizing the ability to generate multi-dimensional restoration features: in, represents the reconstructed output of the joint decoder, f g (z g ) represents the geological latent variable decoding function, f e (z e ) represents the ecological latent variable decoding function.

4. The method for optimizing ecological management of mine slopes in karst areas based on variational autoencoder according to claim 1 is characterized in that: The S3 specifically includes: S31. Based on the geological characteristics and ecological environment data of the karst area, the initial latent variable distribution is optimized by introducing a metacognitive adjustment mechanism, and the initial latent variable is defined as z0=[z g ,z e ], where z g represents the geological potential variable, z e represents ecological latent variables; S32. Use a multi-level adaptive adjustment function to adjust the initial latent variables according to the geological characteristics and ecological environment data of the karst area: Among them, f adjust represents the adaptive adjustment function, α g represents the adjustment coefficient of geological latent variables, α e represents the adjustment coefficient of the ecological latent variable, represents the gradient of the geological loss function, represents the gradient of the ecological loss function; S33. At each iteration, the learning rate is dynamically adjusted by calculating the gradient information of the geological latent variables and the ecological latent variables: Among them, η t represents the dynamically adjusted learning rate, η0 represents the initial learning rate, and λ adjust represents the adjustment factor, represents the L2 norm of the current gradient, Represents the L2 norm of the initial gradient; S34. Introduce an incremental learning mechanism to adaptively update the latent variable generation rules based on the dynamic relationship between geological latent variables and ecological latent variables: Among them, z t represents the latent variable of the current iteration, z t-1 represents the latent variable of the previous iteration; S35. When dynamically updating the latent variable generation rules, a regularization mechanism is added to prevent overfitting and constrain the range of change of the latent variables: Among them, R t represents the regularization term, λ g represents the geological regularization coefficient, λ e represents the ecological regularization coefficient, z gt represents the current geological potential variable, z et represents the current ecological latent variable, represents the target geological potential variable, Represents the target ecological latent variable.

5. The method for optimizing ecological management of mine slopes in karst areas based on variational autoencoder according to claim 1 is characterized in that: The S4 specifically includes: S41, inputting the optimized initial latent variable distribution into a domain transfer generation module, wherein the domain transfer generation module includes two independent adaptation units: a geological feature adaptation unit and an ecological feature adaptation unit; S42, the geological characteristic adaptation unit is responsible for analyzing the geological characteristic data of the target karst area, including slope inclination, crack distribution and cave characteristics, and after feature extraction, matching and adjusting with the geological potential variables of the source area to generate adaptive geological potential variables; S43, the ecological feature adaptation unit is responsible for collecting and analyzing ecological data of the target karst area, including vegetation coverage, biodiversity indicators and soil nutrient content, and after feature extraction, matching and adjusting with the ecological latent variables of the source area to generate adaptive ecological latent variables; S44, the domain transfer generation module integrates the adaptive geological latent variables and the adaptive ecological latent variables through a multi-layer neural network to generate the overall adaptive latent variable distribution; S45. Introduce an adaptive adjustment mechanism to dynamically adjust the weights of adaptive geological latent variables and adaptive ecological latent variables according to the differences in geological and ecological characteristics between the target area and the source area; S46, the domain transfer generation module finally outputs the adaptive latent variable distribution of the target area.

6. The method for optimizing ecological management of mine slopes in karst areas based on variational autoencoder according to claim 1 is characterized in that: The S5 specifically includes: S51, inputting the adaptive latent variable distribution of the target area into the counterfactual reasoning mechanism, wherein the counterfactual reasoning mechanism generates a plurality of hypothetical restoration scenarios by simulating the changes of restoration parameters; each hypothetical restoration scenario represents a different combination of restoration schemes, including geological restoration, ecological restoration and soil improvement contents; S52. In the counterfactual reasoning mechanism, for each hypothetical restoration scenario, the restoration parameters are adjusted using the adaptive latent variable distribution of the target area, wherein the restoration parameters include vegetation coverage, soil structure, and slope stability, and are adjusted according to the geological and ecological characteristics of the target area; S53, combining geological data, ecological data and historical restoration cases of the target area, evaluate the effect of each hypothetical restoration scenario through simulation; S54, performing comparative analysis on all generated hypothetical repair scenarios, and comparing the repair effects of different hypothetical repair scenarios; S55. During the comparative analysis process, multiple simulation experiments are used to make repeated adjustments based on the changes in the target area and the repair parameters to ensure that the generated hypothetical repair scenario adapts to the characteristics and change trends of the target area, and the repair strategy is generated and output through comparative analysis.

Images