Ecological restoration method for gangue slope of mountain road

Abstract

The invention provides a mountain road coal gangue slope ecological restoration method, which comprises the following steps: acquiring real-time monitoring data from a coal gangue slope through a sensor network, including weathering degree and compaction density indexes, and obtaining an initial feature set; according to the initial feature set, carrying out preliminary grouping on the side slopes by adopting a clustering analysis method, and determining a classification standard threshold value; feedback data in the restoration implementation process are obtained and compared with the preliminary groups, and the effect deviation degree is judged; if the effect deviation exceeds a preset threshold value, a classification standard threshold value is updated through a regression analysis model, and an adjusted classification system is obtained; for the adjusted classification system, extracting a corresponding repair scheme template from a database, and generating an optimization scheme version; performing virtual testing on the optimization scheme version through an analog simulation module to obtain a prediction effect index; and collecting subsequent monitoring data according to the final execution parameter, and performing iterative comparison with the prediction effect index to obtain a further adjustment signal.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for ecological restoration of coal gangue slopes along mountain roads. Background Technology

[0002] Ecological restoration of coal gangue slopes in mountainous highway construction is a key engineering area for ensuring traffic safety and environmental protection. Coal gangue, a major solid waste from coal mining, is widely used as filler in mountainous highway construction; however, its unique physicochemical properties pose significant challenges to slope stability and ecological restoration. Current slope restoration methods generally suffer from insufficient adaptability, primarily due to a lack of mechanisms for dynamic adjustment based on actual results after the restoration plan is formulated. Existing technologies often establish fixed restoration standards based on initial survey data, but during actual construction and subsequent maintenance, the actual characteristics of the slope often deviate from the initial assessment, leading to unsatisfactory restoration results. The root cause of this problem lies in the contradiction between the rigidity of slope classification standards and the dynamic changes in actual slope characteristics. Slope classification is the basis for selecting restoration schemes, requiring division based on key parameters such as rock weathering degree and fill compaction degree. However, the threshold settings for these parameters are often based on theoretical experience and may not be accurate enough in practical applications. When the restoration effect of a certain type of slope deviates from expectations, the original classification standards reveal their limitations, but traditional methods lack the ability to correct classification standards in real time. For example, when the actual thickness of the weathered layer on a moderately weathered rock slope exceeds the preset standard of 0.5 to 1.5 meters, the restoration plan implemented according to the original standard will lack specificity, thus affecting the overall restoration quality. Therefore, establishing an adaptive mechanism that can dynamically adjust the slope classification standards based on feedback data during the restoration process, and continuously optimize the matching between the classification system and the actual slope characteristics, has become a key issue in improving the accuracy and effectiveness of ecological restoration of coal gangue slopes along mountain roads. Summary of the Invention

[0003] This invention provides a method for ecological restoration of coal gangue slopes along mountain highways, mainly comprising:

[0004] Real-time monitoring data, including weathering degree and compaction density indices, are acquired from coal gangue slopes using a sensor network to obtain an initial feature set. Based on this initial feature set, cluster analysis is used to initially group the slopes and determine classification thresholds. Feedback data during the restoration process is acquired and compared with the initial groupings to assess the degree of deviation. If the deviation exceeds a preset threshold, the classification thresholds are updated using a regression analysis model to obtain an adjusted classification system. For the adjusted classification system, corresponding restoration plan templates are extracted from the database to generate optimized plan versions. The optimized plan versions are virtually tested using a simulation module to obtain predicted performance indicators. If the predicted performance indicators meet the matching optimization requirements, the optimized plan versions are applied to the actual slope restoration process to determine the final execution parameters. Subsequent monitoring data is collected based on the final execution parameters and iteratively compared with the predicted performance indicators to obtain further adjustment signals. Further, the degree of performance deviation is assessed. Further, the adjusted classification system is obtained. Further, the final execution parameters are determined. Further, further adjustment signals are obtained.

[0005] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0006] This invention discloses a coal gangue slope restoration system based on real-time monitoring and adaptive optimization. Addressing the problem of restoration scheme failure caused by dynamic changes in the weathering degree and compaction density of coal gangue slopes, the system uses a sensor network to acquire real-time monitoring data to form an initial feature set. Cluster analysis is then used to initially group slopes and determine classification thresholds. Simultaneously, the system acquires restoration implementation feedback data and compares it with the initial grouping to determine the effect deviation. If the deviation exceeds a preset threshold, a regression analysis model dynamically updates the classification thresholds, generating an adjusted classification system. Corresponding restoration scheme templates are extracted from the database to form an optimized scheme version. Subsequently, the system uses a simulation module to virtually test the optimized scheme, verifying whether the predicted effect indicators meet the requirements. Once qualified, the scheme is applied to actual restoration, and the final execution parameters are determined. Finally, subsequent monitoring data is iteratively compared with predicted indicators to continuously adjust and optimize the restoration strategy. This invention achieves adaptive dynamic optimization of slope restoration schemes, significantly improving the restoration success rate and long-term stability. (See attached figures.)

[0007] Figure 1 This is a flowchart of an ecological restoration method for coal gangue slopes along mountain roads according to the present invention.

[0008] Figure 2 This is a schematic diagram of an ecological restoration method for coal gangue slopes along mountain roads according to the present invention.

[0009] Figure 3 This is a schematic diagram of data indicators for an ecological restoration method for coal gangue slopes along mountain roads according to the present invention. Detailed Implementation

[0010] The technical solutions of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0011] like Figure 1-3 This embodiment of an ecological restoration method for coal gangue slopes along mountain roads may specifically include:

[0012] Step S34: The effect evaluation module performs cluster analysis on the marked abnormal deviation items, classifying them into corresponding effect categories based on deviation pattern similarity, thus obtaining the effect deviation classification results. Specifically, the effect evaluation module uses cluster analysis to process the marked abnormal deviation items to identify the patterns and causes of the deviations. Based on the numerical characteristics of the deviation coefficients and the repair type identifier, the cluster analysis uses the K-means algorithm to classify the abnormal deviation items into several categories, such as material failure, improper construction parameters, or environmental interference. During the clustering process, the system calculates the distance between each abnormal item based on the deviation pattern similarity, grouping those with closer distances into the same category. The clustering results are output as effect deviation classification results, including a description of the deviation characteristics of each category and a list of abnormal items. In one embodiment, in a coal gangue slope repair project, the abnormal deviation items include deviation coefficients from multiple highly weathered, low-density areas. Cluster analysis revealed that the deviation patterns of some abnormal items were related to insufficient shotcrete thickness, while others were related to material loss caused by rainfall interference. The system classifies these abnormal items into two categories: material construction and environmental impact. The effect deviation classification results provide a basis for subsequent deviation scoring, which helps to optimize the remediation strategy in a targeted manner. Step S35: Based on the deviation intensity values ​​in the effect deviation classification results, a weighted calculation method is used to comprehensively score each category of deviation to determine the overall remediation effect deviation level. It should be noted that the deviation intensity values ​​in the effect deviation classification results reflect the severity of each category of anomalies. The weighted calculation method allocates weights according to the source and impact of the deviation category. For example, materials and construction-related deviations have higher weights because they directly affect the remediation effect, while environmental impact-related deviations have lower weights because they can be mitigated through external measures. The comprehensive score is obtained by weighted summation of the deviation intensity of each category, with a score range of 0 to 100; a higher value indicates a more severe deviation. Based on the scoring results, the system classifies the remediation effect deviation into three levels: low, medium, and high. For example, a score below 30 is considered low deviation, 30 to 60 is medium deviation, and above 60 is high deviation. If the deviation level is high, the remediation plan adjustment mechanism is triggered. For example, in a slope repair scenario, the deviation intensity of the material construction category is 0.4, with a weight of 0.6; the deviation intensity of the environmental impact category is 0.2, with a weight of 0.4. After weighted calculation, the comprehensive score is 0.32, belonging to the medium deviation level. Based on this result, the system prompts the construction personnel to check the construction parameters of the shotcrete and suggests adding rain protection measures. This scoring mechanism can quantify the degree of deviation in the repair effect, providing data support for subsequent adjustments. Step S36: Through the matching degree analysis between the deviation level and the preset effect standard, if the matching degree is lower than the set benchmark value, the repair plan adjustment mechanism is triggered to obtain the final effect deviation judgment conclusion. In one possible implementation, the deviation level is analyzed for matching degree with the preset effect standard.The preset effect standards include target values ​​for the degree of weathering and compaction density after repair, such as a weathering degree below 0.5 and a compaction density above 0.9 g / cm³. The matching degree is obtained by calculating the closeness between the deviation level and the target value. If the matching degree is lower than the set benchmark value, such as 0.7, it indicates that the repair effect has not met expectations, and the system automatically triggers the repair plan adjustment mechanism. The adjustment mechanism includes reallocating repair resources, optimizing construction parameters, or replacing repair materials. The final effect deviation judgment conclusion is output in report form, including the deviation level, the matching degree value, and adjustment suggestions. Specifically, in the repair of an open-pit coal mine slope, the deviation level was medium, and the matching degree was 0.65, lower than the benchmark value of 0.7. System analysis found that insufficient shotcrete thickness was the main reason, triggering the adjustment mechanism, which suggested increasing the shotcrete thickness to 15 cm and extending the curing time. After the adjusted repair plan was re-implemented, the matching degree improved to 0.82, achieving the expected effect. This dynamic adjustment mechanism improves the success rate of repair and reduces resource waste. Step S4: If the effect deviation exceeds a preset threshold, the classification standard threshold is updated through a regression analysis model to obtain an adjusted classification system. Specifically, if the effect deviation exceeds the preset threshold, for example, 0.3, it indicates that the current classification system cannot accurately reflect the actual state of the slope and needs to be updated. The regression analysis model uses historical classification result data as input to construct a predictive model for the classification standard threshold. Historical classification result data includes the initial feature set, preliminary grouping results, and restoration feedback data, reflecting the evolution of the slope state. The regression model uses the least squares method to fit the relationship between the classification standard threshold and the slope characteristics by optimizing the regression coefficients and intercepts. After training, the model predicts a new classification standard threshold based on the current deviation value and updates the classification system. In one embodiment, the initial classification standard threshold for a certain slope is set to a weathering degree of 0.6 and a compaction density of 0.85 g / cm³. The restoration feedback shows a deviation coefficient of 0.35, exceeding the threshold of 0.3. The regression model analyzes historical data and finds that the weathering degree threshold should be adjusted to 0.55 to adapt to the restored slope state. The updated classification system re-divides the slope area and generates a new set of classification rules. This update mechanism ensures that the classification system remains consistent with the actual condition of the slope. Step S41 involves acquiring historical classification result data for the current classification system and calculating the effect deviation value between the actual classification result and the standard classification result using a deviation detection algorithm. It should be noted that the historical classification result data is stored in a database, including cluster centers, classification boundary parameters, and feature distributions for each grouping. The deviation detection algorithm calculates the effect deviation value by comparing the feature values ​​of the actual classification result and the standard classification result. The deviation value is expressed as the absolute value of the difference between the weathering degree and compaction density, calculated using a weighted average method. If the deviation value exceeds a preset threshold, such as 0.2, it indicates that the classification system needs further optimization, triggering the threshold update process.For example, in a slope monitoring project, the actual classification result shows that the weathering degree of a certain area is 0.58, while the standard classification result is 0.50, with a deviation of 0.08, which is lower than the threshold of 0.2, indicating that the classification system is basically reasonable. However, the compaction density deviation of another area is 0.25, exceeding the threshold, triggering a threshold update. This deviation detection mechanism can accurately locate the deficiencies of the classification system. In step S42, if the effect deviation value exceeds the preset threshold range, the threshold update process is triggered, and the historical classification data is standardized using the data preprocessing module to obtain a standardized training dataset. In one possible implementation, the effect deviation value exceeding the threshold indicates that the adaptability of the classification system is insufficient, and the threshold needs to be updated. The data preprocessing module standardizes the historical classification data, including normalizing feature values ​​and filling missing data. Normalization maps the weathering degree and compaction density values ​​to the range of 0 to 1, eliminating dimensional differences. Missing data is processed by interpolation or mean imputation methods to obtain a standardized training dataset. The training dataset contains time-series classification results and corresponding slope feature values, providing a basis for training the regression model. Specifically, in a certain restoration project, historical classification data includes records of weathering degree and compaction density over the past year. The data preprocessing module normalizes the weathering degree to 0 to 1 and the compaction density to the range of 0.7 to 1.2 g / cm³. Missing data is imputed using linear interpolation to generate a standardized training dataset. This preprocessing method improves data quality and ensures the reliability of model training. Step S43: Based on the standardized training dataset, a regression model is constructed for model training. The regression coefficients and intercept parameters are determined using the least squares method to obtain the trained regression model. In one embodiment, the regression model is trained using a linear regression method with the standardized training dataset as input. The least squares method optimizes the regression coefficients and intercept parameters by minimizing the sum of squared errors between predicted and actual values. The input features of the model include weathering degree, compaction density, and bias values, and the output is the classification standard threshold. During training, the system uses cross-validation to evaluate model performance and ensure generalization ability. After training, the regression model can predict new classification standard thresholds based on the current slope characteristics. For example, in a slope restoration scenario, the regression coefficient for the weathering threshold obtained after the regression model training is 0.75, and the intercept is 0.1. The model predicts a new weathering threshold of 0.54, which is 0.06 lower than the original threshold of 0.6. This prediction reflects the actual trend of weathering after slope restoration and improves the adaptability of the classification system. Step S44: The trained regression model is used to adjust the parameters of the current classification standard threshold, and a new classification standard threshold value is calculated through regression prediction. Specifically, the trained regression model uses the current slope characteristics and deviation value as input to predict the new classification standard threshold value.The prediction process is calculated using the model's regression equation. For example, normalized values ​​of weathering degree and compaction density are substituted into the equation to obtain new thresholds. These new threshold values ​​are used to replace the boundary conditions in the original classification system, forming an updated set of classification rules. In one possible implementation, the current weathering degree of a slope is 0.57, and the compaction density is 0.88 g / cm³. The regression model predicts the weathering degree threshold to be adjusted to 0.53, and the compaction density threshold to 0.90 g / cm³. The updated set of classification rules re-divides the slope area, generating more accurate classification results. This dynamic adjustment mechanism improves the adaptability of the classification system. Step S45: Based on the new classification standard threshold values, the classification boundary conditions in the original classification system are corrected, and an updated set of classification rules is obtained through a threshold replacement operation. It should be noted that the new classification standard threshold values ​​are directly applied to the original classification system through the threshold replacement operation. The replacement operation replaces the old threshold with the new threshold, updating the classification boundary conditions. For example, the weathering degree threshold is adjusted from 0.6 to 0.53, the classification boundary line is adjusted accordingly, and the slope area is redefined. The updated classification rule set is stored in tabular form, containing the new threshold, boundary parameters, and group labels. For instance, in a slope restoration project, the new weathering degree threshold of 0.53 narrows the range of highly weathered areas, reclassifying some areas as moderately weathered areas. The updated classification rule set improves grouping accuracy and reduces the possibility of misclassification. This update method ensures that the classification system remains consistent with the actual slope condition. Step S46: The updated classification rule set is used to classify and verify the test data, and the performance evaluation index of the corrected classification system is obtained through the accuracy calculation module. In one embodiment, the test data is randomly sampled from historical classification data, containing weathering degree and compaction density feature values. The updated classification rule set classifies the test data, generating predicted grouping results. The accuracy calculation module calculates the classification accuracy by comparing the degree of matching between the predicted group and the actual group. Accuracy is defined as the ratio of the number of correctly classified samples to the total number of samples. If the accuracy reaches the preset performance requirement, such as 0.85, the updated classification rule set is considered effective and determined as the final optimization result. Specifically, in a slope monitoring case, the test data contained 1000 sample points. The updated classification rule set correctly classified 920 samples, with an accuracy of 0.92, higher than the requirement of 0.85. The system confirms the superior performance of the updated classification system and completes the adaptive adjustment process. This verification mechanism ensures the reliability of the classification system. Step S5: For the adjusted classification system, the corresponding repair scheme template is extracted from the database to generate an optimized scheme version. Specifically, the adjusted classification system is stored in the database as identifiers and a classification hierarchy structure. The identifier parsing module extracts classification node information, such as the node number and feature range of the high-weathered, low-density area, by parsing the hierarchical relationship of the classification system.The database query conditions are constructed based on node information, and a keyword matching algorithm is used to retrieve corresponding template records from the repair scheme template library. The template library contains various repair scheme templates, such as shotcrete templates, vegetation cover templates, and anchor reinforcement templates. Each template includes repair steps, parameter configurations, and descriptions of applicable scenarios. In one possible implementation, the classification system of a certain slope indicates that high-weathered, low-density areas should be prioritized for repair. The keyword matching algorithm retrieves shotcrete and vegetation cover templates, which are applicable to high-weathered and low-density scenarios, respectively. The system filters out templates that completely match the classification nodes through template matching rules, generating a candidate template set. This retrieval method improves the efficiency of template selection. Step S51: Construct database query conditions based on classification node information, and use a keyword matching algorithm to retrieve corresponding template records from the repair scheme template library to obtain a candidate template set. It should be noted that the classification node information includes the weathering degree range, compaction density range, and grouping labels. The query conditions are generated in the form of structured query statements, such as "weathering degree > 0.5 and compaction density < 0.9 g / cm³". The keyword matching algorithm, based on cosine similarity, calculates the similarity between the query conditions and the template description. Templates with a similarity greater than 0.8 are selected into the candidate template set. The candidate template set contains multiple template records, each including a template number, applicable scenario, and repair parameters. For example, in a slope repair project, the query conditions are "weathering degree 0.5 to 0.7, compaction density 0.8 to 0.9 g / cm³". The keyword matching algorithm retrieves three candidate templates: shotcrete template, vegetation cover template, and composite repair template. The candidate template set provides diverse options for subsequent screening. In step S52, the candidate template set is screened using template matching rules. If the template's classification label completely matches the classification system node, the template is marked as a valid template, determining the final template matching result. In one embodiment, the template matching rule is based on strict matching of classification labels; for example, the applicable weathering degree range of the template must be consistent with the range of the classification node. If the template's classification label completely matches the node, it is marked as a valid template. The matching results are stored in list form, including the valid template number and priority order. Priority is determined based on the historical application results of the templates; for example, templates with higher success rates have higher priority. Specifically, in a slope restoration scenario, the candidate template set contains three templates. The classification label of the shotcrete template perfectly matches the high-weathered, low-density node and is marked as a valid template. The label of the vegetation cover template partially matches, and its priority is lower. The final matching result selects the shotcrete template as the primary restoration solution. This screening mechanism ensures that the templates are highly compatible with the actual needs of the slope. In step S53, a template content parser is used to perform structured parsing on the valid templates, extracting the restoration steps, parameter configurations, and execution logic from the templates to obtain the core content components of the templates.In one possible implementation, the template content parser decomposes the valid template into structured components, including repair steps, parameter configurations, and execution logic. Repair steps describe the specific construction process, such as the spraying sequence and thickness requirements for shotcrete; parameter configurations include material ratios and construction time; execution logic defines the dependencies between steps, such as waiting 24 hours for curing after spraying. The core content components are stored in JSON format for easy subsequent scheme generation. For example, the parsing result of the shotcrete template includes: Step 1, cleaning the slope surface; Step 2, spraying 10 cm thick concrete; parameter configuration, cement mortar ratio 1:3; execution logic, executing Step 2 immediately after Step 1 is completed. This structured parsing improves the operability of the template content. Step S54 generates a scheme version number based on the core content components of the template, records the creation time and version sequence of the scheme through a version control mechanism, and determines the unique identifier of the scheme version. It should be noted that the scheme version number consists of the template number, classification system identifier, and creation time, for example, "TC001-SYS20251020-V1". The version control mechanism records the creation time, modification history, and version sequence of a solution, ensuring the uniqueness and traceability of solution versions. A unique identifier is used for storage and retrieval in the solution library. In one embodiment, a repair solution is generated based on a shotcrete template, with version number "TC001-SYS20251020-V1" and creation date of October 20, 2025. The version control mechanism records the solution's generation process, facilitating subsequent tracking and updates. This version management method improves the standardization of solution management. Step S55: The core content components of the template are converted into specific optimized solution content using a content generator. A clustering algorithm is used to group and integrate similar solutions to obtain the final optimized solution version. Specifically, the content generator generates specific optimized solution content based on the core content components, including detailed construction steps, parameter settings, and execution plans. The clustering algorithm analyzes historical solutions, identifies similar solutions, and integrates their advantageous features, such as incorporating construction parameters that have been successfully applied multiple times into the new solution. The final optimized solution version is output in document form, containing complete repair steps and parameter configurations. For example, an optimized solution version integrates the advantages of shotcrete and vegetation cover to generate a composite remediation solution. The solution includes spraying an 8 cm thick layer of concrete followed by planting drought-resistant grass seeds, with a construction period of 7 days. This integration improves the overall effectiveness of the solution. Step S56: A verification check is performed based on the completeness of the optimized solution version's content. If the solution content includes complete remediation steps and parameter configurations, the solution version is stored in the solution library, completing the generation process of the optimized solution version. In one possible implementation, the content integrity verification check checks whether the solution includes all necessary remediation steps, parameter configurations, and execution logic. If the solution is complete, the system stores it in the solution library, recording the solution version number and generation time.If the plan is incomplete, such as lacking construction parameters, the system prompts for supplementation and re-verification. Specifically, an optimized plan version that passes verification, including the complete steps of shotcrete and vegetation cover, is stored in the plan library with the number "TC001-SYS20251020-V1". This verification mechanism ensures the feasibility of the plan. Step S6: The optimized plan version is virtually tested through the simulation module to obtain predicted effect indicators. Specifically, the simulation module constructs a virtual test environment based on the configuration parameters and algorithm logic of the optimized plan version. The simulation environment simulates the actual geological conditions of the slope, such as soil looseness and rainfall effects. The system loads the plan configuration, executes the virtual repair process, and records key indicators during the operation, such as changes in weathering degree and compaction density after repair. Predicted effect indicators include throughput prediction values ​​and stability scores, reflecting the potential effects of the plan. In one embodiment, an optimized plan version is tested in the simulation environment. After simulated shotcrete repair, the weathering degree decreases from 0.6 to 0.4, and the compaction density increases from 0.8 g / cm³ to 0.95 g / cm³. The predicted performance index shows a stability score of 85, indicating that the solution is effective. This simulation test improves the reliability assessment before the solution is implemented. Step S61: Obtain the optimized solution version data to be tested, parse the configuration parameters and algorithm logic in the solution, and obtain a standardized solution description file. It should be noted that the optimized solution version data includes repair steps, parameter configuration, and execution logic. The parsing process converts this data into a standardized solution description file, which is stored in XML format and contains structured step descriptions and parameter tables. The standardized file facilitates loading and processing by the simulation module. For example, the description file of a certain solution version includes: Step 1, sprayed concrete, 10 cm thick; Step 2, curing for 24 hours; Parameter, cement mortar ratio 1:3. This standardized format improves the efficiency of data processing. Step S62: Construct a simulation environment model based on the characteristics of the business environment, set the boundary conditions and constraint rules of the virtual running scenario, and determine the range of running parameters for the simulation test. In one possible implementation, the simulation environment model is constructed based on the business environment characteristics of the slope, such as soil type, slope angle, and rainfall. Boundary conditions include maximum weathering degree and minimum compaction density, while constraint rules include construction time limits and material usage limits. The range of operating parameters is determined based on historical data; for example, the weathering degree varies from 0.3 to 0.8. Specifically, in a slope simulation test, the environmental model simulated a scenario with an annual rainfall of 800 mm, and the boundary condition was set to a weathering degree not exceeding 0.7. This environmental setting improves the realism of the simulation. Step S63 involves using a random forest algorithm to train the historical operating data, establishing a performance prediction model, and obtaining performance benchmark values ​​under different operating conditions. In one embodiment, the random forest algorithm uses historical operating data as input to train the performance prediction model.Historical data includes the weathering degree, compaction density, and repair effects of past restoration projects. The model predicts performance benchmark values ​​under different operating conditions, such as post-repair stability scores, by constructing multiple decision trees. After model training, it can predict the effectiveness of the solution based on input parameters. For example, the random forest model predicts a stability score of 80 to 90 for shotcrete restoration, with a benchmark value of 85. This predictive model improves the accuracy of effect evaluation. Step S64 involves loading the optimized solution configuration into a simulation environment, simulating the execution of the business process, recording response time and resource consumption data during operation, and determining the operating status of the solution in the virtual scenario. Specifically, the simulation environment loads the configuration parameters of the optimized solution to simulate the restoration process, such as the implementation of shotcrete and vegetation cover. The system records response time, such as construction completion time, and resource consumption, such as material usage. If the operating status is normal, such as no construction interruption or parameter exceeding limits, the next step of effect evaluation is performed. In one possible implementation, a solution shows a construction time of 5 days and material consumption of 10 tons in the simulation, with normal operating status. The system records these data for subsequent performance evaluation. Step S65: If the operation is normal, the performance prediction model is used to calculate key performance indicators to obtain the predicted throughput and stability score. It should be noted that the performance prediction model calculates key performance indicators based on simulation data, including the predicted throughput (repair coverage area) and the stability score (slope stability). The throughput is obtained by calculating the ratio of the repair area to the construction time, and the stability score is calculated by considering the degree of weathering and compaction density. For example, the simulation results of a certain scheme show a throughput of 100 square meters / day and a stability score of 88, indicating that the repair efficiency and effect are both excellent. This quantitative evaluation provides a basis for the implementation of the scheme. Step S66: Based on the comparative analysis of the predicted results and the performance benchmark value, the performance improvement of the scheme is calculated, and the quantitative evaluation data of the optimization effect is determined. In one embodiment, the predicted results are compared with the performance benchmark value to calculate the performance improvement. The improvement is expressed as a percentage difference between the predicted value and the benchmark value; for example, if the stability score increases from 80 to 88, the improvement is 10%. The quantitative evaluation data is output in the form of a report, including the improvement and key indicators. Specifically, a solution's stability score improves by 10% and throughput by 15%, indicating a significant optimization effect. This comparative analysis improves the objectivity of the solution evaluation. Step S67 uses a weighted scoring mechanism to comprehensively calculate multiple evaluation indicators, generating a final predicted effect indicator report to determine the feasibility level of the optimization solution. In one possible implementation, the weighted scoring mechanism integrates indicators such as throughput, stability score, and resource consumption, assigning weights, for example, a stability score weight of 0.5 and a throughput weight of 0.3. The comprehensive score is obtained through weighted summation, and the feasibility level is determined based on the score range; for example, a score above 80 indicates high feasibility.For example, a certain scheme has a comprehensive score of 85, which belongs to the high feasibility level. The predicted effect index report recommends prioritizing the implementation of this scheme. This scoring mechanism improves the scientific nature of scheme selection. Step S7: If the predicted effect index meets the matching optimization requirements, the optimized scheme version is applied to the actual slope repair process to determine the final execution parameters. Specifically, the predicted effect index is compared with the matching optimization standard threshold, for example, a stability score higher than 80. If the requirements are met, the system extracts the optimized scheme version from the scheme library and obtains the deployment parameters in the configuration file. The deployment parameters include construction equipment settings, material ratios, and construction period. The system starts the slope repair process and generates the final set of execution parameters. In one embodiment, the stability score of a certain scheme is 88, exceeding the threshold of 80. The system extracts the shotcrete scheme and determines the execution parameters: thickness 10 cm, construction period 5 days. This parameter determination method improves the efficiency of repair implementation. Step S71: Obtain the slope repair predicted effect index data, classify and evaluate the predicted effect index using the random forest algorithm, and obtain the effect evaluation results. It should be noted that the random forest algorithm uses the predicted effect index as input to classify and evaluate the repair effect. Input features include stability score, throughput, and resource consumption; output is an effect level, such as excellent, good, or moderate. The classification result reflects the actual effect of the scheme. For example, a scheme with a stability score of 88 and a throughput of 100 square meters / day has an excellent classification result. This evaluation method improves the accuracy of effect judgment. Step S72: If the effect evaluation result exceeds the preset matching optimization standard threshold, the corresponding optimized scheme version is extracted from the scheme library to obtain the scheme version configuration file. In one possible implementation, the effect evaluation result is excellent, exceeding the threshold of good. The system extracts the scheme version from the scheme library and obtains the configuration file, which contains construction steps and parameter settings. This extraction mechanism ensures rapid deployment of the scheme. Specifically, the configuration file of a certain scheme includes a shotcrete thickness of 10 cm and a curing time of 24 hours. The system starts the repair process according to the configuration file. Step S73: According to the deployment parameters in the scheme version configuration file, the system deployment process of the slope repair implementation stage is started to determine the deployment status information. In one embodiment, the deployment parameters are loaded to the repair equipment, such as a shotcrete machine, through the system deployment module. The deployment process includes equipment initialization, parameter setting, and construction scheduling. The deployment status information records the equipment's operating status and construction progress. For example, in a certain restoration project, the shotcrete machine is set to spray a thickness of 10 cm according to parameters. The deployment status shows the equipment is operating normally, and the construction progress is proceeding as planned. Step S74 uses the deployment status information to drive the parameter configuration module, which processes the slope geological data and restoration requirement data using a support vector machine algorithm to generate the final set of execution parameters. Specifically, the support vector machine algorithm uses the deployment status information and slope geological data as input to optimize the restoration parameters.Geological data includes soil type and slope, while remediation requirements include target weathering degree and compaction density. The final set of execution parameters includes specific construction parameters, such as spray thickness and material ratio. In one possible implementation, for a slope with loose soil and a steep slope, a support vector machine algorithm optimizes the spray thickness to 12 cm, generating the final set of parameters. This optimization improves the targeted nature of the remediation. Step S75 controls the operation of the slope remediation equipment using the final set of execution parameters, obtaining real-time monitoring data feedback. It should be noted that the final set of execution parameters is loaded into the remediation equipment to control the construction process. The sensor network collects real-time data on the weathering degree and compaction density after remediation, forming feedback information. The feedback information is wirelessly transmitted to the data processing center. For example, in a remediation project, after spraying concrete, the sensor feedback showed that the weathering degree decreased to 0.45 and the compaction density increased to 0.92 g / cm³, indicating a significant remediation effect. Step S76 calculates the slope stability performance index based on the monitoring data feedback information to determine whether the remediation quality meets the preset standards. In one embodiment, the slope stability performance index is calculated by combining the degree of weathering and compaction density using a weighted summation method. If the index is higher than a preset standard, such as 80, the repair quality is considered qualified. The system records the repair results and updates the database. Specifically, if the stability index of a certain repair project is 85, which is higher than the standard of 80, the repair quality is considered qualified. This evaluation mechanism ensures the reliability of the repair effect. Step S77: If the repair quality meets the standard, the execution status completion information is recorded, and the scheme version application record database is updated. In one possible implementation, after the repair quality is qualified, the system records the execution status, including the construction completion time and effect index. The scheme version application record database is updated to store the implementation record of the repair scheme. For example, after a certain repair project is completed, the database records the scheme number "TC001-SYS20251020-V1", the stability index 85, and the construction time 5 days. This recording method facilitates subsequent tracking. Step S8: Subsequent monitoring data is collected based on the final execution parameters and iteratively compared with the predicted effect index to obtain further adjustment signals. Specifically, the final execution parameter configuration table guides the subsequent monitoring data collection. The data acquisition module captures weathering degree and compaction density data in real time through a sensor network, forming a monitoring data stream. The monitoring data is compared with predicted performance indicators to identify deviations and generate adjustment signals. In one embodiment, after a restoration project is completed, the monitoring data stream shows that the weathering degree stabilizes at 0.4, meeting the predicted indicator. The system continues to collect data to ensure the continuous stability of the slope condition. Step S81: Obtain parameter values ​​from the execution parameter configuration table and capture the monitoring data stream in real time through the data acquisition module. It should be noted that the execution parameter configuration table includes construction parameters, such as spray thickness and material ratio. The data acquisition module collects monitoring data at a high frequency, for example, once per hour, forming a continuous monitoring data stream.For example, in a certain project, the configuration table specifies a spray thickness of 10 cm. The data acquisition module records weathering degree and compaction density data every hour, generating a monitoring data stream. Step S82: If the data integrity check in the monitoring data stream passes, the monitoring data is sorted by time series to obtain an ordered monitoring dataset. In one possible implementation, the data integrity check examines whether the monitoring data contains missing or outlier values. If the check passes, the system sorts the data by timestamp to form an ordered monitoring dataset. The dataset is stored in tabular form, containing time, weathering degree, and compaction density. Specifically, a monitoring data stream passes the integrity check and, after sorting, forms a dataset containing 1000 time points. This sorting method facilitates subsequent trend analysis. Step S83: Based on the ordered monitoring dataset, a baseline value for the predicted indicator is constructed, and a linear regression algorithm is used to calculate the trend line of the predicted indicator to obtain the trajectory of the predicted indicator change. In one embodiment, the ordered monitoring dataset is used to construct the baseline value for the predicted indicator. The linear regression algorithm uses time as the independent variable and weathering degree and compaction density as dependent variables to fit a trend line. The slope of the trend line reflects the rate of change of the indicator, generating the trajectory of the predicted indicator change. For example, the trend line of a dataset shows that the degree of weathering decreases by 0.02 per month, indicating that the restoration effect continues to improve. This trend analysis improves the accuracy of prediction. Step S84: If the slope of the predicted indicator change trajectory exceeds a preset threshold range, a deviation analysis program is triggered to determine the deviation analysis result. It should be noted that the slope of the predicted indicator change trajectory is compared with a preset threshold, for example, the absolute value of the slope is greater than 0.05. If it exceeds the threshold, it indicates an abnormal change in the slope state, triggering the deviation analysis program. The deviation analysis program calculates the difference between the actual value and the predicted value, generating the deviation analysis result. In one possible implementation, the slope of the trend line of a certain slope is 0.06, exceeding the threshold of 0.05. Deviation analysis finds that the rate of weathering decrease is too fast, possibly due to increased rainfall, generating the corresponding result. Step S85: By associating and matching the deviation analysis result with historical execution parameters, a parameter deviation mapping relationship table is established. Specifically, the deviation analysis result is matched with historical execution parameters to identify the source of deviation. The parameter deviation mapping relationship table records the correspondence between deviation values ​​and parameters, for example, the weathering degree deviation is related to the spray thickness. For example, a mapping table shows that a weathering degree deviation of 0.03 is related to insufficient spray thickness, suggesting adjustment of the thickness parameter. This mapping relationship improves the targeting of adjustments. Step S86: Based on the deviation degree in the parameter deviation mapping table, the adjustment signal strength value is calculated to obtain the adjustment signal output vector. In one embodiment, the adjustment signal strength is obtained by weighted calculation of the deviation degree, with the weights allocated according to the importance of the parameters. The adjustment signal output vector contains the adjustment direction and magnitude of multiple parameters, such as increasing the spray thickness by 0.2 cm. Specifically, in a certain project, the adjustment signal strength is 0.4, and the output vector suggests increasing the spray thickness and extending the curing time.This vector output improves the accuracy of the adjustment. Step S87: The adjustment signal output vector-driven feedback mechanism is activated. If the adjustment signal strength value is greater than a critical threshold, a high-priority feedback instruction is generated. It should be noted that the adjustment signal output vector-driven feedback mechanism generates a high-priority feedback instruction if the strength is greater than a critical threshold, for example, 0.3. The instruction contains specific suggestions for the adjustment parameters and is sent to the construction management system. For example, if the adjustment signal strength is 0.5, a high-priority instruction is generated, suggesting increasing the spray thickness to 12 cm. This feedback mechanism speeds up the adjustment response. Step S88: The corresponding parameter item in the execution parameter configuration table is corrected through the high-priority feedback instruction to obtain the updated parameter configuration. In one possible implementation, the high-priority feedback instruction directly modifies the execution parameter configuration table, for example, adjusting the spray thickness from 10 cm to 12 cm. The updated parameter configuration is reloaded to the repair equipment. Specifically, a project adjusts the spray thickness according to the instruction, and the updated configuration table records the new parameters to ensure optimized repair process. Step S89: The monitoring data for the next cycle is re-collected based on the updated parameter configuration, and the new and old monitoring data are classified and compared using a support vector machine algorithm. In one embodiment, the updated parameter configuration guides the next cycle of data collection. A support vector machine algorithm compares the old and new monitoring data to determine if the data category has changed, for example, from high risk to low risk. For example, new data for a project shows a weathering degree decreasing to 0.38, and the classification result changes from high risk to low risk, indicating that the adjustment is effective. Step S90: If the classification comparison result shows a shift in data category, it is determined that the adjustment effect has been achieved, and the optimization strategy's effective status is confirmed. It should be noted that a data category shift indicates that the adjusted parameters have improved the slope condition. The system records the optimization strategy's effective status and updates the database. Specifically, if the data category for a project changes from high risk to low risk, the optimization strategy is effective, and the recorded status is "Adjustment Successful." Step S91: The accuracy of the prediction index is verified by the optimization strategy's effective status. If the accuracy of the prediction index is lower than the set benchmark, the benchmark value of the prediction index is recalibrated. In one possible implementation, the optimization strategy's effective status is compared with the prediction index to calculate the prediction accuracy. If the accuracy is lower than the benchmark, for example, 0.8, the benchmark value of the prediction index is recalibrated, and the trend prediction model is updated. For example, if the prediction accuracy of a certain project is 0.75, lower than the benchmark of 0.8, the system calibrates the benchmark value and optimizes the model parameters. Step S92 involves updating the trend prediction model parameters based on the calibrated prediction index benchmark value to obtain a corrected prediction model. Specifically, the calibrated prediction index benchmark value is used to update the trend prediction model and adjust model parameters, such as regression coefficients. The corrected prediction model is then reapplied to the next period's prediction. In one embodiment, after updating the model parameters, the predicted trend of decreasing weathering is more accurate, and the model performance is improved. This update mechanism enhances the system's adaptability.Step S93 involves establishing a dynamic monitoring and early warning system by modifying the prediction model to predict the subsequent slope condition over the long term. Specifically, the modified prediction model uses calibrated parameters to predict the slope condition over the long term, with a prediction period typically ranging from 6 months to 1 year. The dynamic monitoring and early warning system combines real-time monitoring data and prediction results to establish a multi-level early warning mechanism. The early warning system includes three levels: daily monitoring, periodic assessment, and emergency response, each corresponding to different risk levels and response measures. In one embodiment, a coal gangue slope restoration project established a complete dynamic monitoring and early warning system. The modified prediction model predicts that the weathering degree will stabilize between 0.35 and 0.40 within the next 6 months, and the compaction density will remain above 0.92 g / cm³. The early warning system sets three warning thresholds: Level 1 is for a weathering degree exceeding 0.5, Level 2 is for 0.4 to 0.5, and Level 3 is for 0.35 to 0.4. The system automatically determines the warning level based on real-time monitoring data, and immediately initiates the emergency response procedure if a Level 1 warning is reached. This early warning system can identify slope condition deterioration trends in advance, providing a basis for timely protective measures. Step S94: Optimize the long-term maintenance strategy of the restoration plan based on feedback information from the dynamic monitoring and early warning system. It should be noted that the feedback information generated by the dynamic monitoring and early warning system includes changes in early warning levels, monitoring data trends, and records of abnormal events. The long-term maintenance strategy is optimized based on this feedback information, including adjustments to the maintenance cycle, updates to maintenance content, and optimization of resource allocation. The maintenance strategy adopts an adaptive adjustment mechanism, dynamically adjusting the maintenance frequency and intensity according to changes in the slope condition. For example, in a slope restoration project, the early warning system feedback showed an increasing trend in weathering during spring rainfall. The system optimized the maintenance strategy, adjusting the spring maintenance frequency from once a month to once every two weeks, adding drainage facility inspections and vegetation replanting. The maintenance content was expanded from the original surface cleaning to deep drainage system maintenance to ensure the stability of the slope under severe weather conditions. This optimization strategy improves the targeting and effectiveness of maintenance work. Step S95: Establish a closed-loop feedback mechanism for restoration effect evaluation to achieve continuous improvement of the restoration plan. In one possible implementation, the closed-loop feedback mechanism feeds back the restoration effect evaluation results to the plan design stage, forming a continuous improvement cycle. The feedback mechanism comprises four stages: effect data collection, analysis and evaluation, scheme optimization, and implementation verification. Effect data collection is conducted through a combination of sensor networks and manual inspections to ensure the comprehensiveness and accuracy of the data. Analysis and evaluation employ multivariate statistical analysis methods to identify key factors affecting the restoration effect. Scheme optimization adjusts restoration parameters and construction techniques based on the evaluation results, and implementation verification verifies the optimization effect through comparative experiments. Specifically, a coal gangue slope restoration project established a complete closed-loop feedback mechanism. Six months after project implementation, the effect evaluation revealed cracking of the shotcrete under high-temperature conditions. Analysis and evaluation determined that the cracking was caused by insufficient curing time and improper material ratios.The optimized plan extended the curing time from 24 hours to 48 hours and adjusted the cement mortar mix ratio from 1:3 to 1:2.5. Implementation verification showed that the optimized plan significantly reduced cracking and improved slope stability by 15%. This closed-loop feedback mechanism ensures continuous improvement and optimization of the repair plan. Step S96 involves constructing a knowledge base and experience accumulation system for the repair project, providing a reference for subsequent similar projects. In one embodiment, the knowledge base and experience accumulation system collect and organize data from the entire process of the repair project, including geological conditions, repair plans, construction parameters, effect evaluations, and maintenance records. The knowledge base uses a structured storage method to establish a mapping relationship between geological conditions and repair plans, forming a searchable experience database. The experience accumulation system extracts successful experiences and lessons learned from repair projects through case analysis and statistical modeling, forming standardized operating guidelines and technical specifications. The knowledge base also includes expert experience and best practice cases to provide decision support for new projects. For example, a knowledge base for coal gangue slope repair was established in a certain region, containing detailed data from 50 repair projects. The knowledge base is categorized by geological conditions into three main types: highly weathered loose, moderately weathered dense, and low-weathered stable. Each category includes recommended remediation schemes, key parameter ranges, and expected effect indicators. New projects can quickly retrieve similar cases and obtain remediation scheme suggestions by inputting geological conditions. The knowledge base also records the applicable conditions and performance of various remediation materials, providing a scientific basis for material selection. This knowledge base system improves the success rate and efficiency of remediation projects. Step S97 optimizes the remediation decision-making process based on artificial intelligence algorithms, enhancing the intelligence level of the remediation scheme. Specifically, artificial intelligence algorithms include technologies such as machine learning, deep learning, and expert systems, used to optimize each stage of remediation decision-making. Machine learning algorithms analyze historical remediation data to identify key factors and patterns affecting remediation effectiveness. Deep learning algorithms process complex multidimensional data, such as geological images and monitoring time-series data, to extract deep feature information. Expert systems integrate the knowledge and experience of domain experts to provide intelligent decision-making suggestions. Algorithm optimization covers multiple aspects, including scheme selection, parameter setting, construction scheduling, and effect prediction. In one possible implementation, a restoration project employs a deep neural network algorithm to analyze slope geological images, automatically identifying the degree of weathering and fissure distribution. The algorithm was trained using 1000 labeled images, achieving an accuracy rate exceeding 95%. Based on the image recognition results, the system automatically generates restoration plan suggestions, including shotcrete thickness, anchor bolt placement, and vegetation configuration. Machine learning algorithms analyze the relationship between construction parameters and restoration effects, optimizing cement mortar mix ratios and construction techniques. An expert system, considering geological conditions and climatic factors, provides personalized maintenance recommendations. This intelligent decision-making process reduces the subjectivity of human judgment and improves the scientific rigor and accuracy of decisions. Step S98 establishes a risk management system for the restoration project to ensure the safety and reliability of the restoration process.It should be noted that the risk management system identifies various risks that may arise during the restoration process, including technical risks, environmental risks, safety risks, and economic risks. Technical risks involve the applicability of the restoration plan and construction quality control; environmental risks include the impact of weather changes and geological disasters; safety risks involve the safety of construction personnel and equipment; and economic risks include cost control and resource allocation. The risk management system establishes a complete process for risk identification, assessment, control, and monitoring, and formulates corresponding emergency plans and response measures. Risk assessment uses a combination of quantitative and qualitative methods to establish risk level classification standards. Risk control measures include technical measures, management measures, and emergency measures to ensure that risks are within a controllable range. For example, a coal gangue slope restoration project established a comprehensive risk management system. The project identified 15 major risks, including slope instability, substandard material quality, and the impact of severe weather. The risk assessment classified slope instability as a high risk, and a detailed monitoring plan and emergency plan were developed. Technical measures included increasing the density of monitoring points, setting up an early warning system, and preparing emergency materials. Management measures included establishing a dedicated risk management team, conducting regular risk assessments, and carrying out emergency drills. Emergency measures include personnel evacuation plans, equipment protection measures, and rapid repair procedures. The establishment of a risk management system significantly improves the safety and success rate of the project. Step S99 involves implementing a third-party assessment and certification of the repair effect to ensure the objectivity and authority of the repair quality. In one embodiment, the third-party assessment and certification are undertaken by qualified professional institutions, including geological survey institutions, engineering testing institutions, and environmental assessment institutions. The assessment covers multiple aspects of the repair project, including technical indicators, safety performance, environmental impact, and economic benefits. Technical indicator assessment includes slope stability, repair material performance, and construction quality testing. Safety performance assessment includes slope bearing capacity, anti-sliding stability, and long-term durability analysis. Environmental impact assessment includes evaluation of ecological restoration effects, soil and water conservation effects, and landscape harmony. Economic benefit assessment includes return on investment, maintenance costs, and social benefit analysis. The third-party assessment adopts standardized assessment procedures and indicator systems to ensure the objectivity and comparability of the assessment results. Specifically, a large-scale coal gangue slope repair project commissioned a national-level geological survey institution to conduct a third-party assessment. The assessment agency used methods such as borehole sampling, physical testing, and numerical simulation to comprehensively evaluate the restoration effect. The assessment results showed that the slope stability coefficient reached 1.35, exceeding the design requirement of 1.25. The compressive strength and durability indicators of the restoration materials both met relevant standards. Environmental assessment showed that the vegetation coverage rate reached 85%, and the soil and water loss control effect was significant. Third-party certification provided authoritative assurance for the project quality and enhanced public trust. Step S100 involves establishing a standardized management system for the restoration project to promote the improvement and development of industry technical standards. In one possible implementation, the standardized management system includes three levels: technical standards, management standards, and work standards.Technical standards specify the technical requirements, design methods, construction techniques, and quality control standards for restoration projects. Management standards specify the project organizational structure, division of responsibilities, work processes, and performance evaluation system. Work standards specify specific operating procedures, safety regulations, and emergency response requirements. The establishment of a standardized management system helps improve the standardization of restoration projects and promotes the inheritance and dissemination of technical experience. The system also includes a complete mechanism for standard formulation, implementation, supervision, and improvement, ensuring the scientific validity and practicality of the standards. For example, a certain province established a standardized management system for coal gangue slope restoration, formulating local technical standards and management specifications. The technical standards specify the principles for selecting restoration schemes under different geological conditions, the range of key parameter values, and quality acceptance standards. The management standards establish a management process covering the entire project lifecycle, including preliminary research, scheme design, construction implementation, acceptance evaluation, and post-construction maintenance. The work standards detail the operating procedures and safety requirements for each trade. The implementation of the standardized management system has increased the success rate of coal gangue slope restoration projects in this province to over 95%, reduced restoration costs by 20%, and provided a demonstration and reference for similar projects nationwide. Step S101 involves constructing an innovative R&D platform for repair technologies to promote their continuous development and breakthroughs. Specifically, this platform integrates resources from universities, research institutes, engineering companies, and government departments to form a collaborative innovation system integrating production, education, research, and application. The platform focuses on research in new materials, new processes, new equipment, and new technologies. New materials development includes high-performance repair materials, environmentally friendly cementitious materials, and intelligent responsive materials. New processes include precision construction technologies, automated construction processes, and green construction methods. New equipment development includes intelligent monitoring equipment, automated construction equipment, and mobile repair equipment. New technology applications include the application of artificial intelligence, the Internet of Things, and big data technologies in repair engineering. The platform also establishes a technology transfer mechanism to promote the industrial application of research results. In one embodiment, the platform developed a nano-modified sprayed concrete material, significantly improving the strength and durability of repair materials. The platform also developed an automated slope monitoring system based on unmanned aerial vehicles (UAVs), achieving large-scale, high-precision real-time monitoring. The application of intelligent construction robots improves construction efficiency and safety. These technological innovations provide more advanced and reliable technical means for coal gangue slope restoration. Step S102 involves establishing an international cooperation and exchange mechanism for restoration projects, learning from and drawing on advanced foreign technologies and management experience. It should be noted that the international cooperation and exchange mechanism includes various forms such as technical exchanges, personnel training, project cooperation, and standards alignment. Technical exchanges are conducted through international conferences, academic forums, and technical seminars to share the latest technological achievements and development trends. Personnel training includes sending technical personnel abroad to learn advanced technologies and inviting foreign experts to China for guidance and training. Project cooperation includes jointly carrying out demonstration projects, jointly developing new technologies, and establishing long-term cooperative relationships.Standard alignment includes learning from advanced international standards, participating in the development of international standards, and promoting the internationalization of Chinese standards. International cooperation and exchange help enhance the international competitiveness of China's coal gangue slope restoration technology and promote technological innovation and industrial development. For example, a restoration company established a long-term cooperative relationship with a German geotechnical engineering company, introducing advanced slope stability analysis software and construction equipment. The cooperative project applied German prestressed anchor cable technology in a large-scale coal gangue slope restoration project, significantly improving the stability and safety of the slope. Technical personnel mastered internationally advanced design concepts and construction techniques through training, accumulating valuable experience for subsequent projects. International cooperation also promotes the alignment of Chinese restoration technology standards with international standards, enhancing the competitiveness of Chinese enterprises in the international market. Step S103: Establish a social benefit assessment system for restoration projects to comprehensively evaluate the overall value of restoration projects. In one possible implementation, the social benefit assessment system evaluates the overall value of restoration projects from four dimensions: economic benefits, environmental benefits, social benefits, and ecological benefits. Economic benefits include direct and indirect economic benefits. Direct benefits include increased land value, resource recycling, and job creation. Indirect benefits include industrial development and regional development promotion. Environmental benefits include pollution control effectiveness, ecological environment improvement, and reduced environmental risks. Social benefits include improved quality of life for residents, enhanced public safety, and increased social stability. Ecological benefits include biodiversity conservation, restoration of ecosystem services, and contributions to sustainable development. The evaluation system establishes a quantitative and qualitative evaluation index system, employing the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method for comprehensive evaluation. Specifically, the social benefit assessment of a coal gangue slope restoration project shows that the project directly created 200 jobs and boosted related industries by 50 million yuan. In terms of environmental benefits, the project eliminated the risk of slope landslides, protecting the lives and property of 1,000 downstream households. In terms of ecological benefits, the restored slope achieved a vegetation coverage rate of 90%, forming a good ecological landscape. The comprehensive evaluation results show that the project's social benefit return on investment reached 3.2, demonstrating significant social value. Step S104 involves constructing a sustainable development evaluation framework for the restoration project to ensure its long-term sustainability. In one embodiment, the sustainable development assessment framework is constructed based on three dimensions: economic sustainability, environmental sustainability, and social sustainability. Economic sustainability assesses the sustainability of the economic benefits of remediation projects, their cost-effectiveness, and resource utilization efficiency. Environmental sustainability assesses the long-term impacts of remediation projects on the ecological environment, resource consumption, and waste generation. Social sustainability assesses the contributions of remediation projects to social development, the level of public participation, and social acceptance. The assessment framework establishes a dynamic evaluation mechanism to regularly assess the sustainable development level of projects and promptly identify and address problems in sustainable development. The framework also includes a complete process encompassing sustainable development goal setting, indicator monitoring, evaluation analysis, and improvement measures.For example, a coal gangue slope restoration project established a sustainable development evaluation framework, setting 20 evaluation indicators. Regarding economic sustainability, the project established a long-term maintenance fund to ensure the sustainability of the restoration effects. Regarding environmental sustainability, the project adopted environmentally friendly materials and green construction techniques to minimize environmental impact. Regarding social sustainability, the project established a public participation mechanism, regularly soliciting opinions and suggestions from surrounding residents. The evaluation results showed that the project's sustainable development level reached an excellent level, providing a model for similar projects. Step S105 involves establishing a mechanism for promoting and applying restoration technologies to facilitate the popularization and application of advanced technologies. Specifically, the promotion and application mechanism includes various methods such as technology demonstration, training and promotion, policy support, and market promotion. Technology demonstration involves constructing demonstration projects to showcase the application effects and advantages of advanced technologies, providing models for technology promotion. Training and promotion involves holding technical training courses, compiling technical manuals, and establishing training bases to improve the professional level of technical personnel. Policy support includes formulating technology promotion policies, providing financial support, and establishing incentive mechanisms. Market promotion involves establishing a technical service system, cultivating the technical service market, and improving technical standards to promote the market application of technologies. The promotion and application mechanism also established a technology promotion effect evaluation system, regularly assessing the promotion effect and continuously improving the promotion strategy. In one possible implementation, a province established a coal gangue slope restoration technology promotion center, responsible for the promotion and application of related technologies throughout the province. The center built five technology demonstration bases to showcase the application effects of restoration technologies under different geological conditions. The center holds 10 technical training courses annually, training 500 technical personnel. The provincial government issued technology promotion support policies, providing financial subsidies to projects adopting advanced technologies. The center also established a technical service network to provide technical consultation and guidance services for restoration projects throughout the province. After five years of promotion and application, the level of coal gangue slope restoration technology in the province has significantly improved, with the restoration success rate increasing to over 98%. Step S106 involves constructing a smart management platform for restoration projects to achieve digital and intelligent management of restoration projects. It should be noted that the smart management platform integrates advanced technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence to achieve digital management of the entire life cycle of restoration projects. The platform includes four subsystems: a project management system, a monitoring and early warning system, a decision support system, and a service guarantee system. The project management system implements functions such as project information management, schedule control, quality management, and cost control. The monitoring and early warning system provides functions such as real-time monitoring, data analysis, risk warning, and emergency response. The decision support system provides functions such as data analysis, scheme optimization, effect prediction, and decision recommendations. The service support system provides functions such as technical support, training services, maintenance support, and user services. The platform adopts a cloud computing architecture, supporting concurrent access and data sharing for multiple users and projects. For example, a smart management platform for coal gangue slope restoration has been built in a certain region, integrating monitoring data from 50 restoration projects.The platform enables unified management of project information, real-time collection and analysis of monitoring data, and automatic risk warning and emergency response. The decision support system, based on historical data and artificial intelligence algorithms, provides solution suggestions and effect predictions for new projects. The application of the platform significantly improves project management efficiency, reduces management costs, and enhances the scientific nature and accuracy of decision-making. Step S107: Establish a quality traceability system for the repair project to ensure full controllability and traceability of repair quality. In one embodiment, the quality traceability system establishes a full-process quality record and traceability mechanism from raw material procurement to project acceptance. The system includes four links: material quality traceability, construction process traceability, quality testing traceability, and acceptance evaluation traceability. Material quality traceability records the source, specifications, performance indicators, and quality certification documents of raw materials. Construction process traceability records construction techniques, operating parameters, quality control measures, and on-site inspection records. Quality testing traceability records testing items, testing methods, testing results, and testing reports. Acceptance evaluation traceability records acceptance standards, acceptance procedures, acceptance results, and rectification measures. The quality traceability system uses technologies such as QR codes and RFID to achieve rapid query and traceability of quality information. The system also establishes a quality responsibility system, clarifying the quality responsibilities and accountability mechanisms for each link. Specifically, a coal gangue slope restoration project established a comprehensive quality traceability system. The project created quality files for each batch of raw materials, recording the manufacturer, production date, quality indicators, and inspection reports. During construction, a construction log was maintained, detailing daily construction activities, quality control measures, and inspection results. For quality testing, the project commissioned a third-party testing agency, and all test reports were included in the traceability system. Upon project acceptance, an acceptance file was established, recording the acceptance process and results. The establishment of the quality traceability system provides strong assurance for project quality, enabling rapid identification of the cause and responsible party in case of quality issues. Step S108 involves constructing an emergency management system for the restoration project to improve the ability and level of response to emergencies. In one possible implementation, the emergency management system includes four components: emergency plan development, emergency resource preparation, emergency response mechanism, and emergency recovery procedures. Emergency plan development involves developing detailed emergency response plans for various possible emergencies, including slope instability, extreme weather, equipment failure, and personnel casualties. Emergency resource preparation includes emergency material reserves, emergency equipment configuration, emergency team building, and emergency funding guarantees. The emergency response mechanism has established rapid response procedures, including incident reporting, emergency activation, on-site handling, and information dissemination. The emergency recovery procedures include loss assessment, recovery plan development, recovery implementation, and effectiveness evaluation. The emergency management system also includes an emergency drill mechanism, regularly organizing emergency drills to improve emergency response capabilities. For example, a large-scale coal gangue slope restoration project has established a comprehensive emergency management system. The project has developed 15 specific emergency plans covering various possible emergencies.In terms of emergency resources, the project has stockpiled 100 tons of emergency rescue materials, equipped itself with 10 sets of emergency rescue equipment, and established a professional emergency response team of 30 people. The emergency response mechanism has established a 24-hour duty system to ensure timely response to emergencies. The project organizes an emergency drill every quarter to improve its proficiency in emergency response. The establishment of the emergency management system has significantly improved the project's ability to respond to emergencies, ensuring the smooth progress of the project construction.

[0013] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for ecological restoration of coal gangue slopes along mountain highways, characterized in that, include: Real-time monitoring data is acquired from coal gangue slopes through a sensor network, wherein the real-time monitoring data includes weathering degree and compaction density index, to obtain an initial feature set; The raw monitoring dataset is obtained by collecting raw data on the weathering degree and compaction density of coal gangue slopes through a sensor network; Outlier removal is performed on the original monitoring dataset. If a data point deviates from the mean by more than a preset standard deviation, the outlier is removed to obtain a cleaned monitoring dataset. The weathering degree and compaction density time series were extracted from the post-cleaning monitoring dataset, and multi-scale decomposition was performed using wavelet transform algorithm to obtain the multi-scale decomposition coefficient sequence. The energy distribution characteristics are calculated for the multi-scale decomposition coefficient sequence. If the energy concentration is lower than a preset threshold, it is determined that the weathering is accelerated, and a weathering state characteristic sequence is obtained. A compaction density correlation model is constructed based on the weathering state characteristic sequence, and a support vector machine algorithm is used to predict the density change trend to obtain a density prediction sequence. By jointly analyzing the density prediction sequence and the weathering state characteristic sequence, the comprehensive slope stability index is calculated to obtain the stability assessment result. The stability assessment results are used to divide risk level ranges. If the index is lower than the preset safety range, an alarm message is generated to obtain real-time risk monitoring results.

2. The method for ecological restoration of coal gangue slopes along mountain highways according to claim 1, characterized in that, The initial feature set includes: The initial feature set is clustered using the K-means algorithm, and the cluster centers are determined through iterative optimization to obtain the slope grouping results. Based on the slope grouping results, the characteristic distribution of each group of samples is calculated, and the classification boundary is determined by analysis of variance to obtain the classification boundary parameters. For the classification boundary parameters, a preset threshold range is used for filtering. If the boundary parameters exceed the threshold range, the cluster centers are adjusted and the slope grouping results are obtained again. By using the adjusted slope grouping results, the sample allocation ratio of each group is extracted, the concentration of the characteristic distribution within the group is calculated, and the grouping characteristic sequence is obtained. Based on the grouping feature sequence, a classification standard model is constructed, and the decision tree algorithm is used to predict the stability trend of the slope grouping to obtain the stability prediction sequence; For the stability prediction sequence, the matching degree of each group of classification criteria is calculated. If the matching degree is lower than a preset threshold, it is marked as a high-risk group, and the risk assessment result is obtained. Based on the risk assessment results, a threshold range adjustment scheme for slope grouping is generated, and the final classification standard threshold is determined.

3. The method for ecological restoration of coal gangue slopes along mountain highways according to claim 2, characterized in that, The slope grouping results include: The feedback data generated during the repair process is obtained, and the data preprocessing module is used to standardize the format and fill in missing values ​​to obtain a standardized feedback dataset. Based on the timestamp information and repair identifier in the standardized feedback dataset, a data grouping algorithm is used to automatically classify the feedback data according to repair batches and types to determine the current repair data grouping structure; Preliminary grouping benchmark parameters are extracted from the preset database. The current repair data grouping structure is compared with the preliminary grouping benchmark parameters item by item by the parameter matching module to obtain the grouping difference matrix. For the magnitude of numerical change in the group difference matrix, a threshold comparison method is used to calculate the deviation coefficient of each group item. If the deviation coefficient exceeds the preset threshold range, it is marked as an abnormal deviation item. The effect evaluation module performs cluster analysis on the marked abnormal deviation items, and classifies the abnormal items into the corresponding effect categories according to the similarity of the deviation patterns, thus obtaining the effect deviation classification results. Based on the deviation intensity values ​​in the effect deviation classification results, a weighted calculation method is used to comprehensively score each category of deviation to determine the overall repair effect deviation level.

4. The method for ecological restoration of coal gangue slopes along mountain highways according to claim 3, characterized in that, The effect deviation classification results include: Obtain historical classification results data of the current classification system, and calculate the effect deviation value between the actual classification result and the standard classification result through a deviation detection algorithm; If the effect deviation value exceeds the preset threshold range, the threshold update process is triggered, and the historical classification data is standardized using the data preprocessing module to obtain a standardized training dataset. Based on the standardized training dataset, a regression model is constructed and trained. The regression coefficients and intercept parameters are determined by the least squares method to obtain the trained regression model. The trained regression model is used to adjust the parameters of the current classification standard threshold, and a new classification standard threshold value is obtained through regression prediction calculation.

5. The ecological restoration method for coal gangue slopes along mountain highways according to claim 4, characterized in that, The new classification standard threshold values ​​include: Obtain the adjusted classification system identifier and classification hierarchy structure, and use identifier parsing to obtain the hierarchical relationship and classification node information of the classification system; Based on the classification node information, database query conditions are constructed, and a keyword matching algorithm is used to retrieve the corresponding template records in the repair scheme template library to obtain a candidate template set. The candidate template set is filtered by template matching rules. If the category label of a template completely matches the category system node, the template is marked as a valid template.

6. The method for ecological restoration of coal gangue slopes along mountain highways according to claim 5, characterized in that, The candidate template set includes: Obtain the version data of the optimization scheme to be tested, parse the configuration parameters and algorithm logic in the scheme, and obtain a standardized scheme description file; A simulation environment model is constructed based on the characteristics of the business environment, the boundary conditions and constraint rules of the virtual running scenario are set, and the range of running parameters for simulation testing is determined. The random forest algorithm is used to train historical operating data to establish a performance prediction model and obtain performance benchmark values ​​under different operating conditions; The optimization scheme configuration is loaded into the simulation environment to simulate the execution of business processes and record the response time and resource consumption data during the process.

7. The ecological restoration method for coal gangue slopes along mountain highways according to claim 6, characterized in that, The performance benchmark values ​​include: Obtain slope restoration prediction effect index data, classify and evaluate the prediction effect index using the random forest algorithm, and obtain the effect evaluation results; If the effect evaluation result exceeds the preset matching optimization standard threshold, the corresponding optimization solution version is extracted from the solution library to obtain the solution version configuration file; Based on the deployment parameters in the configuration file of the proposed solution version, initiate the system deployment process for the slope restoration implementation phase and determine the deployment status information; The deployment status information-driven parameter configuration module is used to process slope geological data and restoration requirement data through a support vector machine algorithm to generate the final set of execution parameters.

8. The method for ecological restoration of coal gangue slopes along mountain highways according to claim 7, characterized in that, The final set of execution parameters includes: Obtain parameter values ​​from the execution parameter configuration table and capture and monitor data streams in real time through the data acquisition module; If the data integrity check in the monitoring data stream passes, the monitoring data is sorted by time series to obtain an ordered monitoring dataset. Based on the ordered monitoring dataset, a baseline value for the predictive indicator is constructed, and a linear regression algorithm is used to calculate the trend line of the predictive indicator to obtain the trajectory of the predictive indicator change. If the slope of the predicted indicator's change trajectory exceeds a preset threshold range, a deviation analysis program is triggered to determine the deviation analysis result. By associating and matching the deviation analysis results with historical execution parameters, a parameter deviation mapping table is established.

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