Three-dimensional lithium battery mine visualization system based on digital twinning

Abstract

The invention provides a three-dimensional lithium battery mine visualization system based on digital twinning, and the system comprises a physical sensing layer which is disposed at a lithium battery mine site, and comprises a plurality of sensor networks used for collecting geologic bodies, production equipment, environment parameters, personnel positions, and vehicle states; and the data interaction layer comprises a data integration platform and an edge computing node, and is used for carrying out cleaning, fusion, protocol conversion and edge side preliminary analysis on the multi-source heterogeneous data uploaded by the physical sensing layer, and uploading the multi-source heterogeneous data to a cloud. By integrating the slope monitoring sensor and the professional analysis model, real-time evaluation and advanced early warning of the slope stability and the tailing pond safety are achieved, major accidents such as collapse and dam break are effectively prevented, meanwhile, the harmful element diffusion model is utilized, and the safety of the tailing pond is improved. Migration paths of fluoride dust, radioactive substances and potential leakage pollutants in the atmosphere and the water system are dynamically simulated, accurate decision support is provided for environmental risk emergency response, and the influence on surrounding ecology is reduced to the maximum extent.

Description

Technical Field

[0001] This invention relates to the field of energy development technology, and in particular to a three-dimensional lithium battery mine visualization system based on digital twins. Background Technology

[0002] With the acceleration of the global energy transition, the demand for lithium resources from the new energy vehicle and energy storage industries has exploded, driving the continuous expansion of lithium battery mining scale. However, lithium battery mines are usually located in mountainous areas with complex geological conditions, and mining activities face multiple severe challenges such as safety, environmental protection, and efficiency. The traditional mine management model mainly relies on two-dimensional drawings, periodic reports, and decentralized monitoring systems, which have obvious drawbacks. Information is not presented intuitively, and management cannot quickly grasp the overall production situation. Data from various systems are isolated, forming information silos. Decisions rely on lagging and one-sided data. Especially considering the special characteristics of lithium mining, such as the environmental diffusion risks of radioactive elements and harmful substances associated with the ore body, the stability and leakage risks of dams caused by tailings slurry with special composition, and the collaborative optimization of the process chain from mining to beneficiation, traditional systems lack effective specialized analysis models and visualization methods, making it difficult to achieve forward-looking early warning and refined management.

[0003] Therefore, in order to address the above problems, a three-dimensional lithium battery mine visualization system based on digital twins is now being developed. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a three-dimensional lithium battery mine visualization system based on digital twins.

[0005] The technical solution of this invention is: a three-dimensional lithium battery mine visualization system based on digital twins, comprising: a physical perception layer deployed at the lithium battery mine site, including a network of multiple sensors for collecting data on geological bodies, production equipment, environmental parameters, personnel locations, and vehicle status; a data interaction layer, including a data integration platform and edge computing nodes, used to clean, fuse, convert protocols, and perform preliminary edge-side analysis on the multi-source heterogeneous data uploaded from the physical perception layer, and then upload it to the cloud; a twin model layer running on a cloud server, comprising: a three-dimensional geological model unit, constructed based on geological exploration data, finely depicting the three-dimensional spatial distribution of ore bodies, rock strata, faults, and groundwater, and including attributes such as lithium grade and associated element content; a facility and equipment model unit, constructing high-precision three-dimensional models of mining equipment, transport vehicles, ore dressing facilities, tailings ponds, etc., and associating their static attributes with dynamic operating parameters; and a behavior rule model library, storing mining process logic and equipment... The system includes: linkage rules, safety procedures, and physical law models; a data analysis and simulation engine with built-in professional analysis algorithms specific to lithium battery mines, including a dynamic resource reserve estimation model based on geostatistics, a slope stability analysis model, a tailings dam seepage-stability coupling analysis model, and a associated harmful element diffusion simulation model; a business application layer providing a human-computer interaction interface, including: a 3D visualization cockpit for the entire mining area, using a 3D scene as the entry point to integrate and display real-time production data, equipment status, and safety early warning information; a mining process twin module, realizing full-process 3D dynamic simulation and real-time data-driven operation from drilling, blasting, loading, transportation to crushing; a safety and environmental protection early warning module, based on the data analysis and simulation engine, to monitor and provide early warnings for slope displacement, tailings dam safety, harmful gas concentration, and associated radioactive element radiation levels in real time; and an intelligent decision support module, providing virtual simulation functions for production plan simulation, mining scheme optimization, and resource allocation suggestions.

[0006] As a preferred embodiment of the present invention, the sensor network in the physical sensing layer specifically includes: a geological monitoring sensor array, including borehole inclinometers, strain gauges, and groundwater level monitors buried inside the slope, for real-time sensing of geological deformation and hydrological changes; equipment status sensors, including GPS / BeiDou positioning modules, fuel consumption sensors, load sensors, vibration sensors, and running time recorders integrated into equipment such as excavators and mining trucks; environmental monitoring sensors, deployed in the mining area and downwind of air quality monitoring stations, gamma-ray radiation dose rate monitors, and weather stations; personnel and vehicle positioning tags, equipping personnel and vehicles with positioning tags to achieve real-time location tracking and electronic fence management with centimeter-level accuracy; and a video surveillance system, deploying high-definition PTZ cameras in key areas, supporting AI image recognition functions, for automatically identifying unsafe behaviors, vehicle speeding, illegal intrusion, etc.

[0007] As a preferred embodiment of the present invention, the construction process of the three-dimensional geological model unit includes: comprehensively utilizing geological borehole data, geophysical data, and laser scanning point cloud data, and using the Kriging interpolation method to establish a block model of the ore body and surrounding rock; in the block model, assigning lithium grade, associated harmful element content, and rock mechanical parameter attributes to each unit block; the model supports dynamic updates, and when new exploration data is added or new geological information is revealed during mining, the model is corrected in real time through data assimilation technology to ensure the consistency between the digital twin and the physical entity.

[0008] As a preferred embodiment of the present invention, the specific workflow of the tailings dam seepage-stability coupled analysis model in the data analysis and simulation engine is as follows: Real-time data is received from pore water pressure gauges, flow meters, and surface displacement monitoring points embedded inside the tailings dam body; the real-time data is used as boundary conditions to drive a numerical simulation of the seepage field based on the finite element method, calculating the change in the phreatic line within the dam body; the calculated pore water pressure distribution results are input into the slope stability calculation model in real time to dynamically calculate the safety factor of the dam body; when the safety factor is lower than a preset threshold, or when the phreatic line is detected to rise too quickly, the engine immediately triggers an early warning, highlights the risk area in the three-dimensional scene of the business application layer, and provides suggestions for drainage and pressure reduction.

[0009] As a preferred embodiment of the present invention, the mining process twin module has the following functions: virtual-real linkage, in the three-dimensional scene, the position, posture, and movement of the equipment model are kept synchronized with the real equipment in the physical world through positioning data and equipment operation data; process chain traceability, clicking on any ore block in the three-dimensional scene can trace its complete logistics trajectory from blasting the raw ore, through loading, transportation, crushing to entering the beneficiation plant, and display information such as the grade and weight of the batch of ore; comparison between plan and reality, the daily production plan is loaded into the three-dimensional model and visually overlaid and compared with the real-time transmitted actual mining progress, and different colors are used to mark the planned, advanced, and lagging areas to assist in production scheduling.

[0010] As a preferred embodiment of the present invention, the safety and environmental protection early warning module further includes a sub-module for simulating the diffusion of associated harmful elements. Its working method is as follows: establishing an atmospheric diffusion model and a hydrogeological model of the mining area; combining real-time meteorological data and dust monitoring data to simulate the diffusion range and concentration distribution of pollutants such as fluorine-containing dust generated during mining and transportation, and dynamically visualizing them on a three-dimensional map in the form of isosurfaces; combining groundwater hydrological data to simulate the migration path and rate of pollutants in groundwater under the scenario of tailings dam leakage, providing a decision-making basis for environmental risk emergency response.

[0011] As a preferred embodiment of the present invention, the mining scheme optimization function in the intelligent decision support module is implemented in the following way: multiple alternative mining scheme models are established in the twin model layer, each scheme including different mining sequences, step parameters, and equipment configurations; the data analysis and simulation engine is invoked to virtually run each scheme, simulating its key performance indicators such as economic indicators, resource recovery rate, stripping ratio, and energy consumption throughout the entire mining cycle; the simulation results of each scheme are compared and analyzed against multiple objectives, and displayed in the business application layer in the form of charts to assist managers in selecting the optimal scheme.

[0012] As a preferred embodiment of the present invention, the business application layer is also connected to a mobile terminal, which provides the following functions through an APP or WeChat mini-program: real-time alarm push, receiving safety, environmental protection, and equipment warning information generated by the system, and supporting online receipt and processing feedback; lightweight 3D scene browsing, supporting loading and interaction of lightweight 3D mine scenes on mobile devices, enabling real-time visualization query of key information; and inspection task management, allowing inspection personnel to receive inspection tasks through mobile devices, plan paths in the 3D model, and upload inspection results on-site, realizing digital closed-loop management of the inspection process.

[0013] By adopting the above technical solution, the present invention has the following advantages:

[0014] 1. This invention integrates slope monitoring sensors and professional analysis models to achieve real-time assessment and early warning of slope stability and tailings dam safety, effectively preventing major accidents such as collapses and dam failures. At the same time, it uses a hazardous element diffusion model to dynamically simulate the migration paths of fluoride dust, radioactive materials, and potential leaked pollutants in the atmosphere and water system, providing accurate decision support for environmental risk emergency response, minimizing the impact on the surrounding ecology, and ensuring the safety of the mining area and downstream environment.

[0015] 2. Through the integration of virtual and real processes and data-driven operation, this invention allows managers to intuitively grasp the real-time dynamics of drilling, blasting, loading, transportation, and crushing in a 3D visualization dashboard. It enables precise identification of bottlenecks, timely adjustment of scheduling instructions by comparing planned and actual data, reduction of equipment downtime, optimization of production rhythm, improvement of resource recovery rate and equipment utilization, and ultimately cost reduction and efficiency improvement.

[0016] 3. This invention breaks down information silos by integrating and unifying scattered multi-source data, providing a single, reliable data source for collaborative management. The mobile application extends the management radius, enabling real-time alarm information push and digital closed-loop management of inspection tasks, thereby improving collaborative efficiency. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0018] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0019] A 3D lithium-ion battery mine visualization system based on digital twins aims to address the unique challenges faced by lithium-ion battery mines in terms of safety, efficiency, environmental protection, and comprehensive resource utilization. These challenges include monitoring associated radioactive elements and harmful elements (such as fluorine), addressing the specific safety risks of tailings ponds, and optimizing the mining and beneficiation process chain. The system consists of four core layers: a physical sensing layer, a data interaction layer, a twin model layer, and a business application layer. These layers work together to form a complete digital twin closed loop. The physical sensing layer is densely deployed at the mine site, forming a comprehensive sensor network, specifically including sensors for real-time sensing of geological deformation and hydrological changes. Geological monitoring sensor arrays, such as borehole inclinometers, strain gauges, and groundwater level monitors buried inside slopes; equipment status sensors integrated into key equipment such as excavators and mining trucks, such as GPS / BeiDou positioning modules, fuel consumption sensors, load sensors, vibration sensors, and running time recorders, used to collect equipment operating parameters; environmental monitoring sensors deployed in mining areas and downwind, such as air quality monitoring stations (specifically monitoring PM2.5, PM10, and fluoride concentrations), gamma-ray radiation dose rate monitors, and weather stations (collecting wind speed, wind direction, and rainfall); and personnel and vehicle tracking using UWB or RFID technology. The system utilizes geotagging to achieve real-time location tracking and electronic fence management with centimeter-level accuracy, and deploys high-definition video surveillance systems in key areas that support AI image recognition. These systems automatically identify unsafe behaviors such as not wearing helmets, speeding, and illegal intrusion. The data interaction layer includes a data integration platform and edge computing nodes, responsible for cleaning, fusing, protocol conversion, and preliminary analysis of the multi-source, heterogeneous, and massive real-time data uploaded from the physical perception layer. This provides high-quality, standardized data streams for upper-layer applications. The twin model layer runs on a cloud server and consists of multiple deeply integrated units: a three-dimensional geological model. The unit is constructed by comprehensively utilizing geological borehole, geophysical, and laser scanning point cloud data, and employing geostatistical methods such as Kriging interpolation to establish a block model of the ore body and surrounding rock. Each unit block is assigned attributes such as lithium grade, content of associated harmful elements, and rock mechanical parameters. The model supports dynamic updates through data assimilation technology to ensure consistency with the physical geological conditions. The facility and equipment model unit constructs high-precision three-dimensional models of facilities and equipment throughout the entire process of mining, transportation, mineral processing, and tailings disposal, and links them in real time with the static attributes (such as model and specifications) and dynamic operating parameters (such as location, status, and load) of the equipment.The behavioral rule model library stores the technological logic, equipment linkage rules, safety operating procedures, and related physical and chemical laws of mining, as well as a data analysis and simulation engine. It includes built-in professional analysis algorithms specific to lithium battery mines, such as a dynamic resource reserve estimation model based on geostatistics, a slope stability analysis model, and especially a tailings dam seepage-stability coupling analysis model (its workflow involves real-time reception of data from pore water pressure gauges, flow meters, and displacement monitoring points inside the dam, driving a finite element-based numerical simulation of the seepage field to calculate the change in the phreatic line, and inputting the results into the stability model in real-time to dynamically calculate the safety factor and achieve early warning), and a associated harmful element diffusion simulation model (its working method involves establishing an atmospheric diffusion and hydrogeological model of the mining area, combining real-time meteorological and monitoring data, and dynamically simulating the migration of pollutants such as fluorine-containing dust in the atmosphere or groundwater). The system's business application layer, which provides decision-making support for environmental risk emergency response, transforms the data and model capabilities of the lower layer into specific business functions. These functions primarily include: a 3D visualization dashboard for the entire mining area, serving as a unified entry point to integrate and display all real-time production, equipment, and safety / environmental data; a mining process twin module with advanced functions such as virtual-real linkage (synchronization of equipment models and physical entities), process chain traceability (tracing the entire process of a single ore block from blasting to beneficiation), and comparison of plans and actual results (visual overlay comparison of plans and actual performance); a safety and environmental early warning module, based on the lower-level simulation engine, providing real-time monitoring and intelligent early warning of slope displacement, tailings dam safety, harmful gas concentrations, and radiation levels; and an intelligent decision support module, providing advanced analysis functions such as mining scheme optimization (by establishing multiple virtual simulations and comparing key performance indicators), production plan simulation, and resource allocation suggestions. Furthermore, the system's business application layer extends to mobile terminals, providing real-time alarm push notifications, lightweight 3D scene browsing, and digital inspection task management functions integrating task assignment, path planning, and result uploading through a dedicated APP or WeChat mini-program.

[0020] Specifically, such as Figure 1As shown, the system includes: a physical sensing layer deployed at the lithium-ion battery mine site, comprising a network of various sensors used to collect data on geological bodies, production equipment, environmental parameters, personnel locations, and vehicle status; a data interaction layer, including a data integration platform and edge computing nodes, used to clean, fuse, convert protocols, and perform preliminary edge-side analysis on the multi-source heterogeneous data uploaded from the physical sensing layer, before uploading it to the cloud; and a twin model layer running on a cloud server, which includes: a 3D geological model unit, constructed based on geological exploration data, meticulously depicting the 3D spatial distribution of ore bodies, strata, faults, and groundwater, along with attributes such as lithium grade and associated element content; a facility and equipment model unit, constructing high-precision 3D models of mining equipment, transport vehicles, ore dressing facilities, tailings ponds, etc., and associating their static attributes with dynamic operating parameters; and a behavioral rule model library, storing mining process logic, equipment linkage rules, safety procedures, and physical law models. The system includes: a data analysis and simulation engine with built-in professional analysis algorithms specific to lithium battery mines, including a dynamic resource reserve estimation model based on geostatistics, a slope stability analysis model, a tailings dam seepage-stability coupling analysis model, and a associated harmful element diffusion simulation model; a business application layer providing a human-computer interaction interface, including: a 3D visualization cockpit for the entire mining area, using a 3D scene as the entry point to integrate and display real-time production data, equipment status, and safety early warning information; a mining process twin module, realizing full-process 3D dynamic simulation and real-time data-driven operation from drilling, blasting, loading, transportation to crushing; a safety and environmental protection early warning module, based on the data analysis and simulation engine, to monitor and provide early warnings for slope displacement, tailings dam safety, harmful gas concentration, and associated radioactive element radiation levels in real time; and an intelligent decision support module, providing virtual simulation functions for production plan simulation, mining scheme optimization, and resource allocation suggestions.

[0021] The sensor network in the physical sensing layer specifically includes: a geological monitoring sensor array, including borehole inclinometers, strain gauges, and groundwater level monitors buried inside the slope, used to sense geological deformation and hydrological changes in real time; equipment status sensors, including GPS and Beidou positioning modules, fuel consumption sensors, load sensors, vibration sensors, and running time recorders integrated into equipment such as excavators and mining trucks; environmental monitoring sensors, deployed in the mining area and downwind of air quality monitoring stations, gamma-ray radiation dose rate monitors, and weather stations; personnel and vehicle positioning tags, equipping personnel and vehicles with positioning tags to achieve real-time location tracking and electronic fence management with centimeter-level accuracy; and a video surveillance system, deploying high-definition PTZ cameras in key areas, supporting AI image recognition functions, used to automatically identify unsafe behaviors, vehicle speeding, illegal intrusion, etc.

[0022] The construction process of the three-dimensional geological model unit includes: comprehensively utilizing geological borehole data, geophysical data, and laser scanning point cloud data, and using the Kriging interpolation method to establish a block model of the ore body and surrounding rock; in the block model, each unit block is assigned lithium grade, associated harmful element content, and rock mechanical parameter attributes; the model supports dynamic updates, and when new exploration data is added or new geological information is revealed during mining, the model is corrected in real time through data assimilation technology to ensure the consistency between the digital twin and the physical entity.

[0023] The specific workflow of the tailings dam seepage-stability coupled analysis model in the data analysis and simulation engine is as follows: Real-time data is received from pore water pressure gauges, flow meters, and surface displacement monitoring points embedded inside the tailings dam body; real-time data is used as boundary conditions to drive a numerical simulation of the seepage field based on the finite element method, calculating the change in the phreatic line within the dam body; the calculated pore water pressure distribution results are input into the slope stability calculation model in real time to dynamically calculate the dam's safety factor; when the safety factor is lower than a preset threshold, or when the phreatic line is detected to rise too quickly, the engine immediately triggers an early warning, highlights the risk area in the 3D scene of the business application layer, and provides suggestions for drainage and pressure reduction.

[0024] The mining process twin module has the following functions: virtual-real linkage: in the 3D scene, the position, posture, and movement of the equipment model are synchronized with the real equipment in the physical world through positioning data and equipment operation data; process chain traceability: clicking on any ore block in the 3D scene allows tracing its complete logistics trajectory from blasting the raw ore, through loading, transportation, crushing, and finally entering the beneficiation plant, and displays information such as the grade and weight of the batch of ore; comparison between plan and reality: the daily production plan is loaded into the 3D model and visually overlaid and compared with the real-time transmitted actual mining progress, using different colors to mark the planned, advanced, and lagging areas to assist in production scheduling.

[0025] The safety and environmental protection early warning module also includes a sub-module for simulating the diffusion of associated harmful elements. Its working method is as follows: establish an atmospheric diffusion model and a hydrogeological model of the mining area; combine real-time meteorological data and dust monitoring data to simulate the diffusion range and concentration distribution of pollutants such as fluorine-containing dust generated during mining and transportation, and dynamically visualize it in the form of isosurfaces on a three-dimensional map; combine groundwater hydrological data to simulate the migration path and rate of pollutants in groundwater under the scenario of tailings dam leakage, providing a basis for decision-making in environmental risk emergency response.

[0026] The mining scheme optimization function in the intelligent decision support module is implemented in the following way: multiple alternative mining scheme models are established in the twin model layer, each scheme including different mining sequences, step parameters, and equipment configurations; the data analysis and simulation engine is called to virtually run each scheme, simulating its key performance indicators such as economic indicators, resource recovery rate, stripping ratio, and energy consumption throughout the entire mining cycle; the simulation results of each scheme are compared and analyzed against multiple objectives, and displayed in the business application layer in the form of charts to assist managers in selecting the optimal scheme.

[0027] The business application layer also connects to mobile terminals, which provide the following functions through APP or WeChat mini-program: real-time alarm push, receiving safety, environmental protection and equipment warning information generated by the system, and supporting online receipt and processing feedback; lightweight 3D scene browsing, supporting loading and interaction of lightweight 3D mine scenes on mobile devices, realizing the visualization query of key information anytime and anywhere; inspection task management, inspection personnel can receive inspection tasks through mobile devices, plan paths in 3D models, and upload inspection results on site, realizing digital closed-loop management of the inspection process.

[0028] It should be noted that the system continuously captures geological deformation, equipment operating parameters (location, attitude, workload, health status), environmental indicators (concentration of harmful gases, radiation level, wind speed, wind direction, and rainfall), real-time location and trajectory of personnel and vehicles, and video image information through a widely deployed physical sensing layer sensor network in the mining site (including borehole inclinometers, strain gauges, and groundwater level monitors inside the slope; GPS / BeiDou positioning modules, load sensors, fuel consumption sensors, and vibration sensors integrated on mining trucks; air quality monitoring stations (especially for fluoride, PM2.5, and PM10), gamma-ray radiation dose rate monitors, and weather stations in the mining environment; UWB / RFID high-precision positioning tags worn by personnel and vehicles; and high-definition video surveillance with AI recognition capabilities in key areas). These multi-source, heterogeneous, and massive real-time data streams are aggregated and initially processed through a data interaction layer. This layer, composed of edge computing nodes and a data integration platform, cleans, denoises, standardizes formats, and converts protocols for the raw data. It also performs preliminary data fusion and lightweight analysis (such as anomaly filtering and simple threshold alarms) at the network edge, thereby reducing the transmission and computing pressure on the cloud and providing high-quality, structured data services for upper-layer applications. The processed data is then transmitted in real-time to the twin model layer in the cloud via the industrial internet or dedicated network. This layer is the brain and core of the entire system. The twin model layer first contains a high-precision three-dimensional geological model. This model is based on geological borehole data, geophysical data, and laser scanning point cloud data, constructed using geostatistical methods such as Kriging interpolation to model the ore body and surrounding rock blocks. Each unit block not only has a geometric shape but is also endowed with physical and mechanical properties such as lithium grade, associated harmful elements (such as uranium, thorium, and fluorine) content, rock density, and compressive strength. Through data assimilation technology, newly added geological exploration data can be integrated into the model. New geological information revealed during the mining process is integrated in real time to dynamically correct the model, ensuring that the digital twin remains highly consistent with the physical mine geological conditions. At the same time, the twin model layer also includes facility and equipment model units, which establish high-precision three-dimensional models of all key facilities such as mining equipment, transport vehicles, crushing stations, ore dressing plants, and tailings dams. These models are linked to real-time data uploaded from the data interaction layer, enabling the virtual model to accurately reflect the location, status (operation, shutdown, maintenance), actions (loading, lifting, driving), and process parameters (such as crusher inlet particle size and hydrocyclone pressure) of the physical entities.Furthermore, the behavioral rule model library embedded in the twin model layer defines the technological logic of mining (such as the temporal and spatial constraints of blasting-loading-transportation), equipment linkage rules (such as the start-stop interlock between the crushing station and the belt conveyor), safety procedures (such as the safe distance between personnel and equipment), and basic physical and chemical laws (such as rock crushing mechanics and reagent reaction kinetics). Most importantly, the twin model layer's data analysis and simulation engine incorporates several specialized analysis algorithms tailored to the specific needs of lithium battery mines, such as dynamic resource reserves based on geostatistics and machine learning algorithms. The estimation model can update the ore body reserves and grade distribution in real time as mining progresses; the slope stability analysis model dynamically assesses the slope safety status by combining real-time monitored displacement, groundwater level data, and geomechanical parameters; and, in particular, the tailings dam seepage-stability coupled analysis model works by receiving data in real time from pore water pressure gauges, seepage flow meters, and surface displacement monitoring points embedded inside the tailings dam body. This field data is used as boundary conditions to drive a finite element method-based numerical simulation of the seepage field, calculating the real-time position and changing trend of the phreatic line within the dam body. The calculated pore water pressure distribution results are input in real time into slope stability calculation models such as the limit equilibrium method or the finite element strength reduction method. The overall safety factor of the dam body under the current working conditions is dynamically calculated. Once the safety factor falls below a preset threshold, or the system detects an abnormal phreatic line uplift rate, the analysis engine immediately triggers an early warning and pushes the warning information, risk locations (such as highlighting potential slip surfaces in the 3D model), and treatment suggestions (such as strengthening drainage) to the upper-level application. Another important model is the associated harmful element diffusion simulation model, which establishes atmospheric diffusion in the mining area. The model, along with hydrogeological models and real-time meteorological data (wind direction, wind speed, atmospheric stability) and dust monitoring data, dynamically simulates the diffusion range, sedimentation path, and near-surface concentration distribution of specific pollutants such as fluoride-containing dust generated during mining, transportation, and spoil disposal. This data is visualized on a 3D map using isosurfaces of different colors. Furthermore, the model can simulate the migration paths, rates, and concentration changes of pollutants in groundwater aquifers under simulated tailings dam leakage emergency scenarios, providing a scientific basis for environmental risk prediction, early warning, and emergency response plan development. The business application layer serves as the interface between the system and users (such as mine managers, engineers, and inspection personnel), presenting the complex model calculations and data analysis results from the lower layers in an intuitive and easy-to-understand manner.The core interface is a 3D visualization cockpit covering the entire mining area. It seamlessly integrates and overlays all the aforementioned information—real-time equipment location, production statistics, environmental monitoring values, and early warning alarms—onto a high-fidelity 3D mining scene, providing a unified situational awareness view. The mining process twin module achieves true virtual-real linkage within this 3D environment; that is, the movement of equipment models in the virtual world and the actions of real equipment in the physical world are synchronized through positioning data and control signals. Users can intuitively observe the dynamics of the entire production process. This module also supports process chain traceability; users can click on any block in the 3D scene to... The system allows for transparent tracking of the entire logistics and information flow of ore, from its location at the blasting site, to which electric shovel it was loaded onto, which mining truck transported it, when it entered the crushing station, and finally to which silo in the concentrator. It also displays key attributes such as the tonnage and grade of the ore batch. Furthermore, the module can pre-load daily production plans (such as planned blasting piles and planned ore quantities) into the 3D model and visually overlay and compare them with real-time data on actual mining progress (such as shoveled areas and transported quantities). Green, blue, and red are used to indicate areas progressing according to plan, ahead of schedule, and behind schedule, respectively, greatly facilitating dynamic adjustments by production schedulers. The safety and environmental protection early warning module focuses on risk management. It continuously receives calculation results from the data analysis and simulation engine (such as slope safety factor change curves and pollutant diffusion simulation results). Once any parameter exceeds a preset threshold, the system immediately issues multi-level alarms (prompt, warning, severe) in the 3D scene through highlighting, flashing, and pop-up windows, and automatically pushes predefined emergency plans or lists of disposal measures to relevant personnel. The intelligent decision support module goes a step further, utilizing the repeatable simulation characteristics of digital twins to allow users to pre-assess the consequences of different decisions in a virtual space.

[0029] For example, in the mining scheme optimization function, users can create multiple alternative mining scheme models (such as different advance directions, mining sequences, and equipment configuration schemes) in the twin model layer. Then, they can call the data analysis and simulation engine to virtually run and simulate each scheme for the entire mining cycle (such as the next week, month, or year). The simulation calculates the performance of each scheme on multiple key performance indicators such as economic benefits (net present value, cost), resource recovery rate (ore dilution loss rate), production efficiency (stripping ratio, platform efficiency), energy consumption, and environmental impact. Finally, the simulation results of each scheme are presented in the form of comparative charts to assist managers in making data-driven optimal decisions. The system's reach extends to the field via mobile terminals (mobile apps, WeChat mini-programs), enabling ubiquitous access. On-site personnel can receive alarm information pushed by the system in real time via mobile devices and sign off on processing online. They can also browse 3D scenes that have been lightweighted but still retain key information, and perform digital inspection tasks—receiving inspection work orders, viewing optimized paths in 3D models, collecting equipment status or environmental data on-site (taking photos, recording videos, and entering readings) and transmitting the data back instantly, forming a closed-loop management system for inspection work.

[0030] In summary, this system is a continuously operating dynamic closed loop of perception, transmission, modeling, analysis, decision-making, and feedback. The real-time status of the physical mine is continuously captured and mapped into a virtual twin. The twin uses built-in professional models and algorithms to conduct in-depth analysis of the current situation and simulate and predict future trends. It then provides the analysis results and decision recommendations to users in an intuitive way. Furthermore, it can directly act on physical equipment through the control system (such as automatically adjusting equipment parameters and triggering alarm shutdowns). This enables precise insight, intelligent early warning, and scientific optimization of the entire lifecycle activities of the lithium battery mine, ultimately achieving the goal of safe, efficient, green, and intelligent mine operation.

[0031] The above embodiments are provided for those skilled in the art to implement or use the present invention. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the spirit of the present invention. Therefore, the scope of protection of the present invention is not limited to the above embodiments, but should be the maximum scope that conforms to the innovative features mentioned in the claims.

Claims

1. A three-dimensional lithium battery mine visualization system based on digital twins, characterized in that, This includes: a physical sensing layer, deployed at lithium battery mine sites, comprising a network of various sensors used to collect data on geological formations, production equipment, environmental parameters, personnel locations, and vehicle status; The data interaction layer, including the data integration platform and edge computing nodes, is used to clean, fuse, convert protocols, and perform preliminary edge analysis on the multi-source heterogeneous data uploaded from the physical sensing layer, and then upload it to the cloud. The twin model layer, running on a cloud server, includes: a 3D geological model unit, constructed based on geological exploration data, meticulously depicting the 3D spatial distribution of ore bodies, strata, faults, and groundwater, along with attributes such as lithium grade and associated element content; a facility and equipment model unit, constructing high-precision 3D models of mining equipment, transport vehicles, beneficiation facilities, tailings ponds, etc., and associating them with their static attributes and dynamic operating parameters; a behavioral rule model library, storing mining process logic, equipment linkage rules, safety procedures, and physical law models; and a data analysis and simulation engine, incorporating specialized analysis algorithms specific to lithium battery mines, including a dynamic resource reserve estimation model based on geostatistics, a slope stability analysis model, and a tailings model. The system includes a tailings dam seepage-stability coupling analysis model and a associated harmful element diffusion simulation model. The business application layer provides a human-machine interface, including: a 3D visualization cockpit for the entire mining area, using a 3D scene as the entry point to integrate and display real-time production data, equipment status, and safety warning information; a mining process twin module, enabling full-process 3D dynamic simulation and real-time data-driven operation from drilling, blasting, loading, transportation to crushing; a safety and environmental protection early warning module, based on data analysis and simulation engines, to monitor and provide early warnings of slope displacement, tailings dam safety, harmful gas concentrations, and associated radioactive element radiation levels in real time; and an intelligent decision support module, providing virtual simulation functions for production plan simulation, mining scheme optimization, and resource allocation suggestions.

2. A three-dimensional lithium battery mine visualization system based on digital twins as described in claim 1, characterized in that, The sensor network in the physical sensing layer specifically includes: a geological monitoring sensor array, including borehole inclinometers, strain gauges, and groundwater level monitors buried inside the slope, used to sense geological deformation and hydrological changes in real time; equipment status sensors, including GPS and Beidou positioning modules, fuel consumption sensors, load sensors, vibration sensors, and running time recorders integrated into equipment such as excavators and mining trucks; environmental monitoring sensors, deployed in the mining area and downwind of air quality monitoring stations, gamma-ray radiation dose rate monitors, and weather stations; personnel and vehicle positioning tags, equipping personnel and vehicles with positioning tags to achieve real-time location tracking and electronic fence management with centimeter-level accuracy; and a video surveillance system, deploying high-definition PTZ cameras in key areas, supporting AI image recognition functions, used to automatically identify unsafe behaviors, vehicle speeding, illegal intrusion, etc.

3. A three-dimensional lithium battery mine visualization system based on digital twins as described in claim 1, characterized in that, The construction process of the three-dimensional geological model unit includes: comprehensively utilizing geological borehole data, geophysical data, and laser scanning point cloud data, and using the Kriging interpolation method to establish a block model of the ore body and surrounding rock; in the block model, each unit block is assigned lithium grade, associated harmful element content, and rock mechanical parameter attributes; the model supports dynamic updates, and when new exploration data is added or new geological information is revealed during mining, the model is corrected in real time through data assimilation technology to ensure the consistency between the digital twin and the physical entity.

4. A three-dimensional lithium battery mine visualization system based on digital twins as described in claim 1, characterized in that, The specific workflow of the tailings dam seepage-stability coupled analysis model in the data analysis and simulation engine is as follows: Real-time data is received from pore water pressure gauges, flow meters, and surface displacement monitoring points embedded inside the tailings dam body; real-time data is used as boundary conditions to drive a numerical simulation of the seepage field based on the finite element method, calculating the change in the phreatic line within the dam body; the calculated pore water pressure distribution results are input into the slope stability calculation model in real time to dynamically calculate the dam body's safety factor; when the safety factor is lower than a preset threshold, or when the phreatic line is detected to rise too quickly, the engine immediately triggers an early warning, highlights the risk area in the 3D scene of the business application layer, and provides suggestions for drainage and pressure reduction.

5. A three-dimensional lithium battery mine visualization system based on digital twins as described in claim 1, characterized in that, The mining process twin module has the following functions: virtual-real linkage, in the three-dimensional scene, the position, posture and movement of the equipment model are kept synchronized with the real equipment in the physical world through positioning data and equipment operation data; process chain traceability, by clicking on any ore block in the three-dimensional scene, its complete logistics trajectory from blasting raw ore, through loading, transportation, crushing and finally entering the ore dressing plant can be traced, and information such as the grade and weight of the batch of ore can be displayed. The plan is compared with the actual situation. The daily production plan is loaded into the 3D model and visualized and compared with the actual mining progress transmitted in real time. Different colors are used to mark the planned, advanced and lagging areas to assist in production scheduling.

6. A three-dimensional lithium battery mine visualization system based on digital twins as described in claim 1, characterized in that, The safety and environmental protection early warning module also includes a sub-module for simulating the diffusion of associated harmful elements. Its working method is as follows: establish an atmospheric diffusion model and a hydrogeological model of the mining area; combine real-time meteorological data and dust monitoring data to simulate the diffusion range and concentration distribution of pollutants such as fluorine-containing dust generated during mining and transportation, and dynamically visualize it in the form of isosurfaces on a three-dimensional map; combine groundwater hydrological data to simulate the migration path and rate of pollutants in groundwater under the scenario of tailings dam leakage, providing a decision-making basis for environmental risk emergency response.

7. A three-dimensional lithium battery mine visualization system based on digital twin as described in claim 1, characterized in that, The mining scheme optimization function in the intelligent decision support module is implemented in the following way: multiple alternative mining scheme models are established in the twin model layer, each scheme including different mining sequences, step parameters, and equipment configurations; the data analysis and simulation engine is called to virtually run each scheme, simulating its key performance indicators such as economic indicators, resource recovery rate, stripping ratio, and energy consumption throughout the entire mining cycle; the simulation results of each scheme are compared and analyzed against multiple objectives, and displayed in the business application layer in the form of charts to assist managers in selecting the optimal scheme.

8. A three-dimensional lithium battery mine visualization system based on digital twin as described in claim 1, characterized in that, The business application layer also connects to mobile terminals, which provide the following functions through APP or WeChat mini-program: real-time alarm push, receiving safety, environmental protection and equipment warning information generated by the system, and supporting online receipt and processing feedback; lightweight 3D scene browsing, supporting loading and interaction of lightweight 3D mine scenes on mobile devices, realizing the visualization query of key information anytime and anywhere; inspection task management, inspection personnel can receive inspection tasks through mobile devices, plan paths in 3D models, and upload inspection results on-site, realizing digital closed-loop management of the inspection process.

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