A real-time monitoring system for open-pit mine slope stability based on digital twinning

Abstract

The application discloses a kind of based on digital twinning open-pit mine slope stability real-time monitoring system, belong to mine slope monitoring technical field, including data acquisition module, for collecting real-time data from mine site, data processing and analysis module, for receiving data from data acquisition module, and to the received data preliminary cleaning and formatting processing, digital twinning model construction module, for constructing a high-precision digital twinning model according to the data processing and analysis module processed data, model dynamic updating module, for dynamically updating digital twinning model according to the latest acquisition results of data acquisition module, the invention solves the problem that digital twinning model in the prior art cannot accurately reflect the complex situation of actual slope, and cannot be updated in time according to new data, thereby leading to system analysis and actual situation disconnection or deviation.

Description

Technical Field

[0001] This invention belongs to the field of mine slope monitoring technology, and particularly relates to a real-time monitoring system for the stability of open-pit mine slopes based on digital twins. Background Technology

[0002] The open-pit mine slope stability monitoring system is a comprehensive system used to monitor the condition of open-pit mine slopes in real time and accurately, preventing safety accidents caused by slope instability. This system utilizes various advanced sensor technologies, such as displacement sensors, tilt sensors, and stress sensors, to collect key parameters in real time, including changes in slope displacement, tilt angle, and internal stress distribution. Simultaneously, a data transmission module enables the rapid and stable transmission of massive amounts of collected data to a data processing center. At the data processing center, professional data analysis algorithms and models are used to conduct in-depth analysis and processing of the data, accurately assessing the slope's stability. If any abnormalities are detected in the monitoring data, the system immediately activates an early warning mechanism, promptly notifying relevant personnel through SMS, audio-visual communication, and other means, prompting them to take swift and effective measures to ensure mine operation safety, personnel safety, and the stability of the surrounding environment.

[0003] The existing real-time monitoring systems for slope stability in open-pit mines based on digital twins generally suffer from the following problems:

[0004] 1. Oversimplification of digital twin models: In order to reduce the difficulty of modeling and the amount of computation, existing digital twin models have oversimplified key factors such as the geological structure and mechanical properties of slopes, which cannot accurately reflect the complex situation of actual slopes, resulting in a large deviation between the stability analysis based on the model and the actual situation.

[0005] 2. Untimely updates of digital twin models: Open-pit mining activities are ongoing, and slope morphology and geological conditions are constantly changing. However, existing digital twin models cannot be updated in a timely manner based on new data, resulting in a disconnect between the model and the actual slope condition, and failing to provide accurate data for real-time monitoring. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a real-time monitoring system for the stability of open-pit mine slopes based on digital twins. It has the advantages of accurately reflecting the slope conditions and updating the digital twin model in real time. This solves the problem that in the prior art, the digital twin model cannot accurately reflect the complex situation of the actual slope and cannot be updated in a timely manner according to new data, which leads to the system analysis being out of sync with the actual situation or having deviations.

[0007] The present invention is implemented as follows: a real-time monitoring system for slope stability in open-pit mines based on digital twins, including a data acquisition module for collecting real-time data from the mine site. The real-time data includes, but is not limited to, geological structure, soil moisture, temperature, rainfall, seismic activity and blasting vibration. The real-time data is transmitted in real time through a sensor network.

[0008] The data processing and analysis module is used to receive data from the data acquisition module and perform preliminary cleaning and formatting on the received data. At the same time, the data processing and analysis module will also use advanced data mining and machine learning algorithms to conduct in-depth analysis of the data to identify potential slope stability risks.

[0009] The digital twin model building module is used to build a high-precision digital twin model based on the data processed by the data processing and analysis module. The digital twin model building module can use complex algorithms to simulate the geological structure and mechanical properties of the slope so that the model can more realistically reflect the complex situation of the actual slope.

[0010] The model dynamic update module is used to dynamically update the digital twin model based on the latest acquisition results of the data acquisition module. It can adjust the model parameters in real time to reflect the latest changes in slope morphology and geological conditions.

[0011] The stability assessment and prediction module is used to perform real-time assessment of slope stability and prediction of future trends using a digital twin model that is updated in real time. It can provide instant stability analysis reports and predict possible slope instability events.

[0012] The early warning and decision support module is used to generate early warning signals based on the analysis results of the stability assessment and prediction module, and to provide decision support to mine managers. It can propose corresponding preventive measures and emergency response plans according to different risk levels.

[0013] As a preferred embodiment of the present invention, the data acquisition module includes a sensor network unit for monitoring and collecting data on geological structure, soil moisture, temperature, rainfall, seismic activity and blasting vibration. It contains several sensors designed for specific physical quantities to ensure that the data is accurate and reliable.

[0014] A data transmission unit is used to transmit the data collected by the sensor network unit in real time;

[0015] A data storage unit is used to store the data collected by the sensor network unit for a long time. It includes local storage devices and cloud storage solutions to ensure data storage security.

[0016] The user interface unit provides an interactive interface for mine workers, through which they can access, monitor and manage the system's real-time and historical data, and automatically generate reports.

[0017] As a preferred embodiment of the present invention, the data processing and analysis module includes a data receiving unit, which is used to receive raw data from the data acquisition module and ensure that the data can be correctly identified by the system for subsequent processing;

[0018] The data cleaning unit is used to perform preliminary cleaning on the received data, including but not limited to removing irrelevant data, correcting errors, and filling in missing values.

[0019] The data formatting unit is used to convert the cleaned data into a unified format for subsequent processing and analysis. Formatting includes, but is not limited to, data type conversion and standardized data representation.

[0020] The data mining unit is used to conduct in-depth analysis of formatted data using advanced data mining techniques to discover patterns, correlations, and trends in the data, so as to extract valuable information from large amounts of data.

[0021] The machine learning algorithm unit is used to perform advanced analysis of data using machine learning algorithms to identify potential slope stability risks.

[0022] The risk identification unit is used to identify and mark data points and patterns indicating slope stability problems based on the analysis results of the data mining unit and the machine learning algorithm unit, so as to take preventive measures in a timely manner.

[0023] As a preferred embodiment of the present invention, the digital twin model building module includes a data integration unit for collecting and integrating slope data from the data processing and analysis module for subsequent model building;

[0024] The geological structure modeling unit is used to construct a geological structure model of the slope based on integrated data. It can use geological exploration and monitoring data to identify and simulate the characteristics of different soil and rock layers, faults and cracks in the slope.

[0025] The mechanical property simulation unit is used to simulate the mechanical behavior of slopes using physical and engineering principles. It can predict the slope response under gravity, water pressure and seismic force conditions based on the mechanical parameters of the materials and the stress relationship of the slope.

[0026] The algorithm application unit is used to apply complex algorithms to optimize and refine the digital twin model. These algorithms include, but are not limited to, machine learning, artificial intelligence, and finite element analysis, in order to improve the model's accuracy and predictive ability.

[0027] The verification and calibration unit is used to verify and calibrate the constructed digital twin model to ensure that the model's output matches the actual slope monitoring data.

[0028] The visualization and interaction unit is used to present the digital twin model to the user in an intuitive way. The presentation methods include, but are not limited to, 3D visualization and dynamic simulation. The visualization and interaction unit can also allow users to interact with the model in order to support decision-making and risk assessment.

[0029] As a preferred embodiment of the present invention, the model dynamic update module includes a data acquisition interface unit for interacting with the data acquisition module, receiving real-time data from sensors and other data sources, and ensuring accurate data transmission.

[0030] The real-time data processing unit is used to quickly analyze and process the data after the data acquisition interface unit receives the data, detect data anomalies, and directly use the processed data to update the digital twin model.

[0031] The model parameter adjustment unit is used to dynamically adjust the parameters of the digital twin model based on the processed real-time data. It uses algorithms to analyze the impact of data changes on the model and automatically adjusts the model parameters to match the latest slope morphology and geological conditions.

[0032] The digital twin model update unit is used to apply the adjusted parameters to the digital twin model, enabling real-time updates of the model.

[0033] As a preferred embodiment of the present invention, the stability assessment and prediction module includes a real-time assessment unit, which is used to perform real-time analysis on the updated digital twin model through algorithms and computational models to assess the current stability status of the slope. The assessment includes, but is not limited to, safety factor assessment, stress distribution assessment, and displacement trend assessment.

[0034] The predictive analysis unit is used to predict potential stability problems and instability events of the slope in the future based on the evaluation results of the real-time evaluation unit using an algorithm.

[0035] The report generation unit is used to integrate the evaluation and predictive analysis results of the real-time evaluation unit and the predictive analysis unit into an easy-to-understand report and provide it to staff. The report includes, but is not limited to, charts, key indicators, risk levels, and recommended measures.

[0036] As a preferred embodiment of the present invention, the early warning and decision support module includes an early warning signal generation unit, which is used to receive the analysis results of the stability assessment and prediction module and generate an early warning signal based on the received data. It can judge the current stability status of the mine according to a preset threshold and algorithm, and trigger the corresponding early warning signal after detecting potential risks.

[0037] The risk level classification unit is used to categorize early warning signals according to their severity, forming different risk levels to help mine managers understand the severity of the problem;

[0038] The preventive action suggestion unit is used to automatically generate preventive action suggestions based on the risk level. The suggestions include, but are not limited to, technical improvements, operational process adjustments, and equipment maintenance upgrades to ensure mine stability.

[0039] The emergency response plan unit is used to automatically activate the corresponding emergency response plan when the risk level is high. The plan includes, but is not limited to, emergency evacuation routes, rescue coordination and accident handling procedures, in order to minimize casualties and property losses.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] The data acquisition module ensures the real-time and comprehensiveness of the data, providing a reliable foundation for subsequent analysis. The data processing and analysis module, through advanced algorithms, can accurately identify slope stability risks, thereby enabling proactive measures to prevent potential disasters. The combination of the digital twin model construction module and the model dynamic update module allows the slope model to continuously reflect the real situation, providing an accurate simulation environment for stability assessment. The stability assessment and prediction module provides real-time stability analysis reports and future trend predictions, helping mine managers make timely decisions. The early warning and decision support module generates early warning signals and provides decision support, helping managers formulate effective preventive measures and emergency response plans, thereby reducing or avoiding casualties and property losses caused by slope instability. Attached Figure Description

[0042] Figure 1 This is a principle block diagram provided in the embodiments of the present invention;

[0043] Figure 2 This is a schematic block diagram of the data acquisition module provided in an embodiment of the present invention;

[0044] Figure 3 This is a principle block diagram of the data processing and analysis module provided in an embodiment of the present invention;

[0045] Figure 4 This is a principle block diagram of the digital twin model construction module provided in an embodiment of the present invention;

[0046] Figure 5 This is a principle block diagram of the model dynamic update module provided in an embodiment of the present invention;

[0047] Figure 6 This is a principle block diagram of the stability assessment and prediction module provided in an embodiment of the present invention;

[0048] Figure 7 This is a principle block diagram of the early warning and decision support module provided in an embodiment of the present invention. Detailed Implementation

[0049] To further understand the invention's content, features, and effects, the following embodiments are provided, and detailed descriptions are given in conjunction with the accompanying drawings.

[0050] The structure of the present invention will now be described in detail with reference to the accompanying drawings.

[0051] refer to Figures 1 to 7 The present invention provides a real-time monitoring system for slope stability in open-pit mines based on digital twins, including a data acquisition module for collecting real-time data from the mine site. The real-time data includes, but is not limited to, geological structure, soil moisture, temperature, rainfall, seismic activity and blasting vibration. The real-time data is transmitted in real time through a sensor network.

[0052] The data processing and analysis module is used to receive data from the data acquisition module and perform preliminary cleaning and formatting on the received data. At the same time, the data processing and analysis module will also use advanced data mining and machine learning algorithms to conduct in-depth analysis of the data to identify potential slope stability risks.

[0053] The digital twin model building module is used to build a high-precision digital twin model based on the data processed by the data processing and analysis module. The digital twin model building module can use complex algorithms to simulate the geological structure and mechanical properties of the slope so that the model can more realistically reflect the complex situation of the actual slope.

[0054] The model dynamic update module is used to dynamically update the digital twin model based on the latest acquisition results of the data acquisition module. It can adjust the model parameters in real time to reflect the latest changes in slope morphology and geological conditions.

[0055] The stability assessment and prediction module is used to perform real-time assessment of slope stability and prediction of future trends using a digital twin model that is updated in real time. It can provide instant stability analysis reports and predict possible slope instability events.

[0056] The early warning and decision support module is used to generate early warning signals based on the analysis results of the stability assessment and prediction module, and to provide decision support to mine managers. It can propose corresponding preventive measures and emergency response plans according to different risk levels.

[0057] During system operation, the data acquisition module collects real-time data from the mine site, including geological structure, soil moisture, temperature, rainfall, seismic activity, and blasting vibrations. This data is transmitted in real-time to the data processing and analysis module via a sensor network. This module is responsible for cleaning and formatting the data, and using data mining and machine learning algorithms to conduct in-depth analysis to identify potential slope stability risks. The digital twin model construction module builds a high-precision digital twin model based on the processed data, using complex algorithms to simulate the geological structure and mechanical properties of the slope, ensuring that the model accurately reflects the actual slope conditions. The model dynamic update module adjusts the parameters of the digital twin model in real-time based on the latest data acquisition results to reflect the latest changes in slope morphology and geological conditions. The stability assessment and prediction module uses the updated model to conduct real-time assessments of slope stability and predict future trends, providing immediate stability analysis reports and predicting possible slope instability events. The early warning and decision support module generates early warning signals based on the assessment and prediction results and provides decision support to mine management personnel, proposing preventive measures and emergency response plans.

[0058] The data acquisition module ensures the real-time and comprehensiveness of the data, providing a reliable foundation for subsequent analysis. The data processing and analysis module, through advanced algorithms, can accurately identify slope stability risks, thereby enabling proactive measures to prevent potential disasters. The combination of the digital twin model construction module and the model dynamic update module allows the slope model to continuously reflect the real situation, providing an accurate simulation environment for stability assessment. The stability assessment and prediction module provides real-time stability analysis reports and future trend predictions, helping mine managers make timely decisions. The early warning and decision support module generates early warning signals and provides decision support, helping managers formulate effective preventive measures and emergency response plans, thereby reducing or avoiding casualties and property losses caused by slope instability.

[0059] Furthermore, the data acquisition module includes a sensor network unit for monitoring and collecting data on geological structure, soil moisture, temperature, rainfall, seismic activity, and blasting vibration. It contains several sensors designed for specific physical quantities to ensure that the data is accurate and reliable.

[0060] A data transmission unit is used to transmit the data collected by the sensor network unit in real time;

[0061] A data storage unit is used to store the data collected by the sensor network unit for a long time. It includes local storage devices and cloud storage solutions to ensure data storage security.

[0062] The user interface unit provides an interactive interface for mine workers, through which they can access, monitor and manage the system's real-time and historical data, and automatically generate reports.

[0063] The sensor network unit consists of multiple specially designed sensors that can monitor and collect data on specific physical quantities such as geological structure, soil moisture, temperature, rainfall, seismic activity, and blasting vibrations. These sensors ensure the accuracy and reliability of the data. The data transmission unit is responsible for transmitting the data collected by the sensor network unit in real time. The data storage unit consists of local storage devices and cloud storage solutions for long-term data preservation and to ensure data security. The user interface unit provides an interactive interface for mine workers. Through this interface, workers can access, monitor, and manage the system's real-time and historical data, and reports can be automatically generated.

[0064] By using specially designed sensors, this system provides accurate and reliable monitoring data, helping mine workers better understand geological and environmental conditions and make more informed decisions. Real-time data transmission ensures timely information, enabling workers to respond quickly to any potential problems. The dual-layer storage strategy (local and cloud storage) of the data storage unit not only guarantees the long-term safe preservation of data but also provides data redundancy to prevent data loss due to single points of failure. The user interface unit's interactive interface makes data access and management intuitive and efficient. Workers can easily monitor real-time and historical data and analyze trends and formulate plans through automatically generated reports, thereby improving the safety and efficiency of mine operations.

[0065] Furthermore, the data processing and analysis module includes a data receiving unit, used to receive raw data from the data acquisition module and ensure that the data can be correctly identified by the system for subsequent processing;

[0066] The data cleaning unit is used to perform preliminary cleaning on the received data, including but not limited to removing irrelevant data, correcting errors, and filling in missing values.

[0067] The data formatting unit is used to convert the cleaned data into a unified format for subsequent processing and analysis. Formatting includes, but is not limited to, data type conversion and standardized data representation.

[0068] The data mining unit is used to conduct in-depth analysis of formatted data using advanced data mining techniques to discover patterns, correlations, and trends in the data, so as to extract valuable information from large amounts of data.

[0069] The machine learning algorithm unit is used to perform advanced analysis of data using machine learning algorithms to identify potential slope stability risks.

[0070] The risk identification unit is used to identify and mark data points and patterns indicating slope stability problems based on the analysis results of the data mining unit and the machine learning algorithm unit, so as to take preventive measures in a timely manner.

[0071] During system operation, the data receiving unit receives raw data from the data acquisition module and ensures that this data can be correctly identified by the system, laying the foundation for subsequent processing. Then, the data cleaning unit performs preliminary processing on this data, including removing irrelevant data, correcting errors, and filling in missing values ​​to improve data quality. Next, the data formatting unit converts the cleaned data into a unified format, such as data type conversion and standardized data representation, ensuring data consistency and facilitating subsequent analysis. The data mining unit uses advanced data mining techniques to conduct in-depth analysis of the formatted data to discover patterns, correlations, and trends, thereby extracting valuable information. The machine learning algorithm unit uses machine learning algorithms to perform advanced analysis of the data to identify potential slope stability risks. Finally, based on the analysis results of the data mining and machine learning algorithm units, the risk identification unit identifies and marks data points and patterns indicating slope stability problems so that preventative measures can be taken in a timely manner.

[0072] Through this system setup, the system can effectively process and analyze large amounts of raw data, thereby improving the accuracy and efficiency of data processing. The data cleaning unit ensures the accuracy and reliability of the data, providing a high-quality data foundation for subsequent analysis. The unified format processing of the data formatting unit makes the data easier to store, manage, and analyze, improving the standardization of data processing. The advanced analytical capabilities of the data mining unit and machine learning algorithm unit help to discover potential risks and trends from complex datasets, providing a scientific basis for decision-making. The real-time monitoring and labeling of the risk identification unit enables staff to promptly identify slope stability problems, thereby taking preventive measures, reducing potential disaster risks, and ensuring slope safety.

[0073] Furthermore, the digital twin model building module includes a data integration unit for collecting and integrating slope data from the data processing and analysis module for subsequent model building;

[0074] The geological structure modeling unit is used to construct a geological structure model of the slope based on integrated data. It can use geological exploration and monitoring data to identify and simulate the characteristics of different soil and rock layers, faults and cracks in the slope.

[0075] The mechanical property simulation unit is used to simulate the mechanical behavior of slopes using physical and engineering principles. It can predict the slope response under gravity, water pressure and seismic force conditions based on the mechanical parameters of the materials and the stress relationship of the slope.

[0076] The algorithm application unit is used to apply complex algorithms to optimize and refine the digital twin model. These algorithms include, but are not limited to, machine learning, artificial intelligence, and finite element analysis, in order to improve the model's accuracy and predictive ability.

[0077] The verification and calibration unit is used to verify and calibrate the constructed digital twin model to ensure that the model's output matches the actual slope monitoring data.

[0078] The visualization and interaction unit is used to present the digital twin model to the user in an intuitive way. The presentation methods include, but are not limited to, 3D visualization and dynamic simulation. The visualization and interaction unit can also allow users to interact with the model in order to support decision-making and risk assessment.

[0079] During system operation, the data integration unit collects and integrates slope data from the data processing and analysis module, providing basic information for subsequent model construction. Then, the geological structure modeling unit uses this data to construct a geological structure model of the slope, identifying and simulating the characteristics of soil and rock layers, faults, and cracks in the slope. The mechanical property simulation unit, based on physical and engineering principles, combines material mechanical parameters and slope stress relationships to predict the slope's response under different external forces. The algorithm application unit further applies complex algorithms such as machine learning, artificial intelligence, and finite element analysis to optimize and refine the digital twin model, improving its accuracy and predictive ability. The verification and calibration unit ensures that the model output matches the actual monitoring data. The visualization and interaction unit presents the model to the user intuitively in a 3D visualization and dynamic simulation manner, allowing users to interact with the model and supporting decision-making and risk assessment.

[0080] By integrating and analyzing slope data, the system can construct accurate geological structure models, thereby better understanding the intrinsic structure and potential risks of slopes. The predictive function of the mechanical property simulation unit helps assess the stability of slopes under various natural forces, providing a scientific basis for preventing potential slope disasters. The algorithm application unit significantly improves the predictive accuracy and reliability of the model by applying advanced algorithms, enabling decision-makers to more accurately assess risks and formulate countermeasures. The verification and calibration unit ensures the accuracy and reliability of the model, while the visualization and interaction unit greatly enhances decision-makers' understanding of slope conditions through intuitive displays and user interaction functions, improving decision-making efficiency and risk management levels.

[0081] Furthermore, the model dynamic update module includes a data acquisition interface unit for interacting with the data acquisition module, receiving real-time data from sensors and other data sources, and ensuring accurate data transmission.

[0082] The real-time data processing unit is used to quickly analyze and process the data after the data acquisition interface unit receives the data, detect data anomalies, and directly use the processed data to update the digital twin model.

[0083] The model parameter adjustment unit is used to dynamically adjust the parameters of the digital twin model based on the processed real-time data. It uses algorithms to analyze the impact of data changes on the model and automatically adjusts the model parameters to match the latest slope morphology and geological conditions.

[0084] The digital twin model update unit is used to apply the adjusted parameters to the digital twin model, enabling real-time updates of the model.

[0085] During system operation, the data acquisition interface unit first interacts with the data acquisition module to receive real-time data from sensors and other data sources, ensuring the accurate transmission of this data. After receiving the data from the data acquisition interface unit, the real-time data processing unit quickly analyzes and processes the data, detecting any anomalies. The processed data is then used to update the digital twin model. The model parameter adjustment unit dynamically adjusts the parameters of the digital twin model based on the processed real-time data. It uses algorithms to analyze the impact of data changes on the model and automatically adjusts the model parameters to match the latest slope morphology and geological conditions. Finally, the digital twin model update unit applies the adjusted parameters to the digital twin model, achieving real-time model updates.

[0086] By setting up a data acquisition interface unit, accurate data transmission can be ensured. The real-time data processing unit can quickly analyze and process data, and promptly detect and respond to data anomalies, thereby ensuring the quality of input data for the digital twin model. The model parameter adjustment unit can dynamically adjust model parameters according to real-time data, ensuring that the model can reflect the latest slope morphology and geological conditions, thus improving the accuracy and reliability of the model. The digital twin model update unit updates model parameters in real time, enabling the digital twin model to continuously reflect changes in the actual environment, providing decision-makers with a dynamic and accurate virtual environment, thereby helping to conduct slope monitoring, risk assessment, and management decisions more effectively.

[0087] Furthermore, the stability assessment and prediction module includes a real-time assessment unit, which is used to perform real-time analysis on the updated digital twin model through algorithms and computational models to assess the current stability status of the slope. The assessment includes, but is not limited to, safety factor assessment, stress distribution assessment, and displacement trend assessment.

[0088] The predictive analysis unit is used to predict potential stability problems and instability events of the slope in the future based on the evaluation results of the real-time evaluation unit using an algorithm.

[0089] The report generation unit is used to integrate the evaluation and predictive analysis results of the real-time evaluation unit and the predictive analysis unit into an easy-to-understand report and provide it to staff. The report includes, but is not limited to, charts, key indicators, risk levels, and recommended measures.

[0090] During system operation, the real-time assessment unit analyzes the digital twin model in real time using algorithms and computational models to assess the current stability of the slope. This includes safety factor assessment, stress distribution assessment, and displacement trend assessment, ensuring a comprehensive understanding of the slope's current state. The predictive analysis unit utilizes the assessment results from the real-time assessment unit and employs advanced algorithms to predict potential stability problems and instability events that may occur in the future, thereby providing early warnings of potential risks. The report generation unit integrates the results of the real-time assessment and predictive analysis into an easy-to-understand report, which includes charts, key indicators, risk levels, and recommended measures, providing intuitive decision support for staff.

[0091] By setting up real-time assessment units, slope stability can be continuously monitored, and abnormal changes in safety factors, stress distribution, and displacement trends can be detected in a timely manner. This enables immediate assessment and early warning of slope stability. The application of predictive analysis units further enhances the foresight of slope management. By predicting potential future problems, measures can be taken in advance to avoid or mitigate potential instability events. The report generation unit transforms complex data analysis results into intuitive reports, enabling staff to quickly understand the slope's condition and risk level, and make effective response decisions based on the recommended measures, thereby improving the efficiency and effectiveness of slope safety management.

[0092] Furthermore, the early warning and decision support module includes an early warning signal generation unit, which receives the analysis results of the stability assessment and prediction module and generates an early warning signal based on the received data. It can determine the current stability status of the mine according to a preset threshold and algorithm, and trigger a corresponding early warning signal after detecting potential risks.

[0093] The risk level classification unit is used to categorize early warning signals according to their severity, forming different risk levels to help mine managers understand the severity of the problem;

[0094] The preventive action suggestion unit is used to automatically generate preventive action suggestions based on the risk level. The suggestions include, but are not limited to, technical improvements, operational process adjustments, and equipment maintenance upgrades to ensure mine stability.

[0095] The emergency response plan unit is used to automatically activate the corresponding emergency response plan when the risk level is high. The plan includes, but is not limited to, emergency evacuation routes, rescue coordination and accident handling procedures, in order to minimize casualties and property losses.

[0096] During system operation, the early warning signal generation unit receives the analysis results from the stability assessment and prediction module. It uses preset thresholds and algorithms to determine the stability of the mine. Once a potential risk is detected, the early warning signal generation unit generates a corresponding early warning signal. The risk level classification unit then categorizes these early warning signals according to their severity, forming different risk levels. This helps mine managers quickly understand the severity of the problem. The preventive measure suggestion unit automatically proposes corresponding preventive measures based on these risk levels. These measures may include technical improvements, operational process adjustments, and equipment maintenance upgrades to ensure the stable operation of the mine. Finally, the emergency response plan unit automatically activates the corresponding emergency response plan when the risk level is high. These plans may include emergency evacuation routes, rescue coordination, and accident handling procedures to ensure rapid and effective action in emergency situations.

[0097] By setting up an early warning signal generation unit, potential risks in the mine can be detected in a timely manner. The risk level classification unit categorizes risks, enabling mine managers to quickly identify the severity of problems and take corresponding measures. The customized suggestions provided by the preventive measures suggestion unit help the mine take proactive measures to prevent potential accidents, thereby improving the overall safety of the mine. The emergency response plan unit ensures that pre-set emergency plans can be executed quickly in high-risk situations. This helps to minimize casualties and property losses, ensuring that the mine can respond in an orderly manner when facing emergencies, protecting personnel safety and reducing economic losses.

[0098] Working principle of the invention:

[0099] During system operation, the data acquisition module collects real-time data from the mine site, including geological structure, soil moisture, temperature, rainfall, seismic activity, and blasting vibrations. This data is transmitted in real-time to the data processing and analysis module via a sensor network. This module is responsible for cleaning and formatting the data, and using data mining and machine learning algorithms to conduct in-depth analysis to identify potential slope stability risks. The digital twin model construction module builds a high-precision digital twin model based on the processed data, using complex algorithms to simulate the geological structure and mechanical properties of the slope, ensuring that the model accurately reflects the actual slope conditions. The model dynamic update module adjusts the parameters of the digital twin model in real-time based on the latest data acquisition results to reflect the latest changes in slope morphology and geological conditions. The stability assessment and prediction module uses the updated model to conduct real-time assessments of slope stability and predict future trends, providing immediate stability analysis reports and predicting possible slope instability events. The early warning and decision support module generates early warning signals based on the assessment and prediction results and provides decision support to mine management personnel, proposing preventive measures and emergency response plans.

[0100] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring system for slope stability in open-pit mines based on digital twins, characterized in that: It includes a data acquisition module for collecting real-time data from the mine site. The real-time data includes, but is not limited to, geological structure, soil moisture, temperature, rainfall, seismic activity and blasting vibration. The real-time data is transmitted in real time through a sensor network. The data processing and analysis module is used to receive data from the data acquisition module and perform preliminary cleaning and formatting on the received data. At the same time, the data processing and analysis module will also use advanced data mining and machine learning algorithms to conduct in-depth analysis of the data to identify potential slope stability risks. The digital twin model building module is used to build a high-precision digital twin model based on the data processed by the data processing and analysis module. The digital twin model building module can use complex algorithms to simulate the geological structure and mechanical properties of the slope so that the model can more realistically reflect the complex situation of the actual slope. The model dynamic update module is used to dynamically update the digital twin model based on the latest acquisition results of the data acquisition module. It can adjust the model parameters in real time to reflect the latest changes in slope morphology and geological conditions. The stability assessment and prediction module is used to perform real-time assessment of slope stability and prediction of future trends using a digital twin model that is updated in real time. It can provide instant stability analysis reports and predict possible slope instability events. The early warning and decision support module is used to generate early warning signals based on the analysis results of the stability assessment and prediction module, and to provide decision support to mine managers. It can propose corresponding preventive measures and emergency response plans according to different risk levels.

2. The real-time monitoring system for slope stability in open-pit mines based on digital twins as described in claim 1, characterized in that: The data acquisition module includes a sensor network unit for monitoring and collecting data on geological structure, soil moisture, temperature, rainfall, seismic activity, and blasting vibration. It contains several sensors designed for specific physical quantities to ensure that the data is accurate and reliable. A data transmission unit is used to transmit the data collected by the sensor network unit in real time; A data storage unit is used to store the data collected by the sensor network unit for a long time. It includes local storage devices and cloud storage solutions to ensure data storage security. The user interface unit provides an interactive interface for mine workers, through which they can access, monitor and manage the system's real-time and historical data, and automatically generate reports.

3. The real-time monitoring system for slope stability in open-pit mines based on digital twins as described in claim 1, characterized in that: The data processing and analysis module includes a data receiving unit, which receives raw data from the data acquisition module and ensures that the data can be correctly identified by the system for subsequent processing. The data cleaning unit is used to perform preliminary cleaning on the received data, including but not limited to removing irrelevant data, correcting errors, and filling in missing values. The data formatting unit is used to convert the cleaned data into a unified format for subsequent processing and analysis. Formatting includes, but is not limited to, data type conversion and standardized data representation. The data mining unit is used to conduct in-depth analysis of formatted data using advanced data mining techniques to discover patterns, correlations, and trends in the data, so as to extract valuable information from large amounts of data. The machine learning algorithm unit is used to perform advanced analysis of data using machine learning algorithms to identify potential slope stability risks. The risk identification unit is used to identify and mark data points and patterns indicating slope stability problems based on the analysis results of the data mining unit and the machine learning algorithm unit, so as to take preventive measures in a timely manner.

4. The real-time monitoring system for slope stability in open-pit mines based on digital twins as described in claim 1, characterized in that: The digital twin model building module includes a data integration unit, which is used to collect and integrate slope data from the data processing and analysis module for subsequent model building; The geological structure modeling unit is used to construct a geological structure model of the slope based on integrated data. It can use geological exploration and monitoring data to identify and simulate the characteristics of different soil and rock layers, faults and cracks in the slope. The mechanical property simulation unit is used to simulate the mechanical behavior of slopes using physical and engineering principles. It can predict the slope response under gravity, water pressure and seismic force conditions based on the mechanical parameters of the materials and the stress relationship of the slope. The algorithm application unit is used to apply complex algorithms to optimize and refine the digital twin model. These algorithms include, but are not limited to, machine learning, artificial intelligence, and finite element analysis, in order to improve the model's accuracy and predictive ability. The verification and calibration unit is used to verify and calibrate the constructed digital twin model to ensure that the model's output matches the actual slope monitoring data. The visualization and interaction unit is used to present the digital twin model to the user in an intuitive way. The presentation methods include, but are not limited to, 3D visualization and dynamic simulation. The visualization and interaction unit can also allow users to interact with the model in order to support decision-making and risk assessment.

5. The real-time monitoring system for slope stability in open-pit mines based on digital twins as described in claim 1, characterized in that: The model dynamic update module includes a data acquisition interface unit, which interacts with the data acquisition module to receive real-time data from sensors and other data sources and ensure accurate data transmission. The real-time data processing unit is used to quickly analyze and process the data after the data acquisition interface unit receives the data, detect data anomalies, and directly use the processed data to update the digital twin model. The model parameter adjustment unit is used to dynamically adjust the parameters of the digital twin model based on the processed real-time data. It uses algorithms to analyze the impact of data changes on the model and automatically adjusts the model parameters to match the latest slope morphology and geological conditions. The digital twin model update unit is used to apply the adjusted parameters to the digital twin model, enabling real-time updates of the model.

6. The real-time monitoring system for slope stability in open-pit mines based on digital twins as described in claim 1, characterized in that: The stability assessment and prediction module includes a real-time assessment unit, which is used to perform real-time analysis on the updated digital twin model through algorithms and computational models to assess the current stability status of the slope. The assessment includes, but is not limited to, safety factor assessment, stress distribution assessment, and displacement trend assessment. The predictive analysis unit is used to predict potential stability problems and instability events of the slope in the future based on the evaluation results of the real-time evaluation unit using an algorithm. The report generation unit is used to integrate the evaluation and predictive analysis results of the real-time evaluation unit and the predictive analysis unit into an easy-to-understand report and provide it to staff. The report includes, but is not limited to, charts, key indicators, risk levels, and recommended measures.

7. The real-time monitoring system for slope stability in open-pit mines based on digital twins as described in claim 1, characterized in that: The early warning and decision support module includes an early warning signal generation unit, which receives the analysis results from the stability assessment and prediction module and generates an early warning signal based on the received data. It can determine the current stability status of the mine according to a preset threshold and algorithm, and trigger a corresponding early warning signal after detecting potential risks. The risk level classification unit is used to categorize early warning signals according to their severity, forming different risk levels to help mine managers understand the severity of the problem; The preventive action suggestion unit is used to automatically generate preventive action suggestions based on the risk level. The suggestions include, but are not limited to, technical improvements, operational process adjustments, and equipment maintenance upgrades to ensure mine stability. The emergency response plan unit is used to automatically activate the corresponding emergency response plan when the risk level is high. The plan includes, but is not limited to, emergency evacuation routes, rescue coordination and accident handling procedures, in order to minimize casualties and property losses.

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