Intelligent comprehensive management and control system and method based on full-scene fusion digital architecture

Abstract

The invention discloses an intelligent comprehensive management and control system and method based on a full-scene fusion digital architecture, and relates to the technical field of intelligent management and control of mine data. The system comprises a twinborn body construction module, a range adjustment module, a diagnosis decision module and a model optimization module, wherein the twinborn body construction module constructs a digital twinborn body of mine equipment; the range adjustment module is used for obtaining a theoretical expected range of equipment operation parameters based on a physical model of the mine equipment established based on a first principle, and dynamically adjusting the theoretical expected range of the equipment operation parameters according to associated equipment data of the mine equipment; the diagnosis decision module is used for outputting a diagnosis result and a decision suggestion according to a comparison result of the equipment operation data and the corrected theoretical expected range and a data change trend of the associated equipment; and the model optimization module is used for verifying the diagnosis accuracy and optimizing the physical model based on the response data of the equipment and the environment after the diagnosis result and the decision suggestion are executed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine data intelligent management and control, and particularly relates to an intelligent comprehensive management and control system and method based on a full-scene fusion digital architecture. BACKGROUND

[0002] The full-scene fusion digital architecture is the next-generation information paradigm driving the intelligent upgrading of industries. Its core lies in taking data as the engine, deeply fusing Internet of Things, big data, artificial intelligence, and digital twinning and other frontier technologies, aiming to break through the information barriers caused by traditional silo systems, and realize seamless connection and business collaboration of cross-domain data. This architecture is committed to building a digital twin that maps and controls the physical world "full airspace, full process, and full elements", providing a foundation for global optimization and intelligent decision-making. Under this background, the mine intelligent comprehensive management and control platform is the specific practice and core carrier of the architecture in the energy mining field. It responds to the urgent needs of mine enterprises for safety production, centralized scheduling, and intelligent decision-making, and is the key infrastructure for promoting the transformation of mines from traditional production mode to intensive, information-based, and intelligent mode.

[0003] The existing mine comprehensive management and control system mainly uses Internet of Things technology to access and integrate monitoring and control data of each production link such as coal mining, tunneling, electromechanical, transportation, and ventilation, forming a unified mine data warehouse. Through analysis, aggregation, and governance of these massive, multi-source, and heterogeneous data, high-quality data fuel is provided for upper-layer applications; a unified comprehensive information portal is constructed to centrally monitor and control scattered information such as safety production monitoring, industrial video, personnel positioning, equipment running status, hydrology power supply, etc.; based on the aggregated data, the system uses big data analysis and AI algorithm models for safety warning, fault diagnosis, risk assessment, and auxiliary decision-making. Through data interaction and intelligent linkage logic, centralized control and collaborative scheduling of main production systems and equipment are realized.

[0004] For example, the patent application CN119539706B discloses a mine production process management and control system, which includes: a data acquisition module for acquiring real-time data of each production process in the mine field and outputting initial data; a preprocessing module for preprocessing the initial data to obtain analysis data; a model training module for training the selected analysis model from the model database in combination with the preset data resources, outputting the selected model, and outputting the selected model meeting the preset performance conditions as an analysis model; and a data analysis module for analyzing the analysis data based on the analysis model, outputting a process analysis result, and updating and optimizing the parameters of the analysis model based on the preset data resources and cloud data.

[0005] For example, patent application CN118134070A discloses a mine main coal flow transportation system control sub-platform, which includes: based on multi-source data information such as main transportation equipment, safe area access control, equipment fault early warning, personnel and environmental safety management, and operation management, it highly integrates various system information of the main coal flow transportation system control sub-platform through AI and neural network deep learning technologies, and provides users with an advanced and intuitive visualization management platform using GIS, AR, AI, CV and BI technologies.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In existing technologies, due to the differences in geological conditions and equipment operating conditions among various working faces in mines, and the fact that unified control system data is based solely on sensor data, which only statistically correlates data and lacks in-depth analysis and modeling of physical causality, the decisions generated by the system are disconnected from the actual situation on site. This makes it impossible to achieve high-fidelity simulation and precise control, resulting in poor intelligent integrated control of mines. Summary of the Invention

[0007] This application provides an intelligent integrated management and control system and method based on a full-scenario fusion digital architecture. This addresses the problem in existing technologies where, due to differences in geological conditions and equipment operating conditions across various working faces in a mine, the unified management and control system data is based solely on sensor data, focusing only on statistical correlation without in-depth analysis and modeling of physical causality. This leads to a disconnect between the system's decisions and the actual situation on-site, hindering high-fidelity simulation and precise control, resulting in poor intelligent integrated management and control in mines. The proposed system achieves dynamic simulation and precise control of mine equipment based on a deep fusion of physical mechanisms and data-driven approaches, fundamentally improving the consistency between system decisions and actual on-site conditions.

[0008] This application provides an intelligent integrated management and control system based on a full-scenario fusion digital architecture, comprising: a twin construction module, a range adjustment module, a diagnostic decision module, and a model optimization module. The twin construction module constructs a digital twin of the mining equipment. This digital twin is formed by associating and fusing data from internal subsystems, associated equipment, equipment operation data, and spatial location data of the mining equipment, and is used to dynamically map the full lifecycle state of the physical equipment. The range adjustment module establishes a physical model of the mining equipment based on first principles. It takes the digital twin of the mining equipment, geological condition data, and mining equipment operation commands as inputs to obtain the theoretical expected range of the equipment's operating parameters. It then dynamically adjusts the theoretical expected range of the equipment's operating parameters based on the associated equipment data to obtain a corrected theoretical expected range. The diagnostic decision module compares the equipment's operating data with the corrected theoretical expected range in real time and outputs diagnostic results and decision suggestions based on the comparison results and the data change trends of associated equipment. The model optimization module continuously monitors the response data of the equipment and the environment after implementing the diagnostic results and decision suggestions, and optimizes the physical model based on the response data to verify the accuracy of the diagnosis.

[0009] This application provides an intelligent integrated management and control method based on a full-scenario fusion digital architecture, comprising: constructing a digital twin of the mine equipment, the digital twin being formed by associating and fusing data from internal subsystems of the mine equipment, associated equipment data, equipment operation data, and spatial location data, used to dynamically map the full lifecycle state of the physical equipment; establishing a physical model of the mine equipment based on first principles, using the digital twin of the mine equipment, geological condition data, and mine equipment operation instructions as inputs to obtain the theoretical expected range of equipment operation parameters, dynamically adjusting the theoretical expected range of equipment operation parameters according to the associated equipment data of the mine equipment to obtain a corrected theoretical expected range; comparing the equipment operation data with the corrected theoretical expected range in real time, and outputting diagnostic results and decision suggestions based on the comparison results and the data change trends of associated equipment; continuously monitoring the response data of the equipment and the environment after executing the diagnostic results and decision suggestions, and optimizing the physical model based on the response data to verify the accuracy of the diagnosis.

[0010] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By constructing a digital twin that integrates multi-source data, dynamic mapping of the equipment's full lifecycle status is achieved. A physical model is established based on first principles, and the influence of related equipment is integrated to generate a theoretically expected range for dynamic correction. Then, by comparing real-time data with the expected range and combining it with trend analysis of related equipment, accurate diagnosis and decision output are achieved. Furthermore, the physical model is continuously verified and optimized using response data. This enables accurate simulation of the operating status of mine equipment, early diagnosis and root cause analysis of faults, and a comprehensive improvement in the system's adaptive optimization capabilities.

[0011] 2. By mapping geological condition data and operation instructions to precise input parameters of the physical model based on the engineering geological database and weighting factors, and by using real-time equipment operation data to perform probabilistic modeling and sequential updates of parameter uncertainty, a dynamic theoretical expected range based on quantile intervals is generated. This enables the quantitative assessment of equipment operating status to move from deterministic thresholds to probabilistic confidence intervals, significantly improving the adaptability to operating conditions and the reliability of early warning.

[0012] 3. By generating dynamic coupling coefficients through quantifying the degree of coupling influence of related devices on the operating status of target devices, and by adopting differentiated strategies based on system configuration requirements to correct the theoretical expectation range, a dynamic balance is achieved between ensuring computational efficiency and model accuracy. This enables a leap from isolated judgment of a single machine to collaborative perception of the system, significantly improving the systematicness and accuracy of equipment status assessment under complex working conditions.

[0013] 4. By comparing the equipment operating parameters with the corrected theoretical expected range in real time, and simultaneously analyzing the temporal correlation of the changing trends of related equipment data, the root cause of equipment parameter over-limit can be accurately located and targeted decision-making suggestions can be generated. This achieves a leap from single parameter over-limit alarm to system-level root cause diagnosis, significantly improving the accuracy of fault diagnosis and the effectiveness of decision support.

[0014] 5. By constructing a digital twin that integrates multi-source data, dynamic perception of the equipment's status throughout its entire lifecycle is achieved. A physical model is established based on first principles, and related equipment data is integrated to generate a theoretically expected range for dynamic correction. Accurate diagnosis and decision output are achieved through real-time data comparison and trend analysis. The physical model is continuously verified and optimized using response data, thus realizing accurate simulation of the operating status of mine equipment, early diagnosis and root cause analysis of faults, and a comprehensive improvement in the system's adaptive optimization capabilities. Attached Figure Description

[0015] Figure 1 A schematic diagram of the structure of an intelligent integrated management and control system based on a full-scenario fusion digital architecture provided in an embodiment of this application; Figure 2 A flowchart illustrating the calculation of the theoretical expected range of mine equipment for an intelligent integrated management and control system based on a full-scenario fusion digital architecture, provided in an embodiment of this application. Figure 3 A flowchart of an intelligent integrated management and control method based on a full-scenario fusion digital architecture provided in this application embodiment. Detailed Implementation

[0016] This application provides an intelligent integrated management and control system and method based on a full-scenario fusion digital architecture, which solves the problems in the prior art. The overall idea is as follows: First, a digital twin of the mine equipment is constructed. By integrating data from internal subsystems, related equipment, operational data, and spatial location data, a dynamic mapping of the physical equipment's entire lifecycle status is achieved. Second, a physical model of the mine equipment is established based on first principles. Using the digital twin, geological condition data, and equipment operation commands as inputs, the theoretical expected range of equipment operating parameters is calculated through probability sampling, and this range is dynamically corrected based on related equipment data. Then, the real-time operating data of the equipment is compared with the corrected theoretical expected range, and trend consistency analysis is performed in conjunction with the changing trends of related equipment data. Based on this, diagnostic results and decision recommendations are output. Finally, the response data of the equipment and the environment after the execution of decisions are continuously monitored. The physical model is continuously optimized through sensitivity analysis and parameter inversion, forming a closed-loop self-learning intelligent control mechanism.

[0017] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0018] like Figure 1 The diagram shown is a structural schematic of an intelligent integrated management and control system based on a full-scenario fusion digital architecture provided in this application embodiment. The intelligent integrated management and control system based on a full-scenario fusion digital architecture provided in this application embodiment includes: a twin construction module, a range adjustment module, a diagnostic decision module, and a model optimization module. The twin construction module is used to construct a digital twin of the mine equipment. The digital twin is formed by associating and fusing data from internal subsystems of the mine equipment, associated equipment data, equipment operation data, and spatial location data, achieving dynamic and transparent mapping of the physical equipment's entire lifecycle status. The equipment operation data includes real-time operation data and historical operation data. The range adjustment module is used to build a model based on first principles... The physical model of the mine equipment takes the digital twin of the mine equipment, geological condition data, and mine equipment operation instructions as inputs to obtain the theoretical expected range of equipment operating parameters. This physical model is a BIM model. The theoretical expected range of equipment operating parameters is dynamically adjusted according to the associated equipment data to obtain the corrected theoretical expected range. The diagnostic decision module is used to compare the equipment operating data with the corrected theoretical expected range in real time, and output diagnostic results and decision suggestions based on the comparison results and the data change trends of associated equipment. The model optimization module is used to continuously monitor the response data of the equipment and the environment after implementing the diagnostic results and decision suggestions, and optimize the physical model based on the response data to verify the accuracy of the diagnosis.

[0019] In this embodiment, "mining equipment" refers to the collective term for a series of mechanical and electrical devices involved in the core production processes of mining resources, including tunneling, mining, transportation, hoisting, ventilation, drainage, support, and safety assurance. This includes mining equipment for direct extraction (such as tunneling machines and coal mining machines), transportation equipment for material transfer (such as scraper conveyors, belt conveyors, and hoists), ventilation and drainage equipment to ensure a safe working environment (such as main ventilation fans and water pumps), support equipment for roadway support (such as hydraulic supports and anchor drilling rigs), and auxiliary systems such as power supply, compressed air, and safety monitoring. Internal subsystem data of the mining equipment refers to the status, parameters, and alarm information of each independent functional unit constituting the equipment. This type of data describes the internal health status of the equipment and is the core of fault diagnosis and refined management. Related equipment data refers to the status and operating information of other equipment directly connected to and cooperating with the target equipment in the production process. Related equipment includes upstream equipment, downstream equipment, and cooperating equipment. This type of data provides the context of equipment operation, which is crucial for understanding system-level linkages and troubleshooting cascading failures. Equipment operation data is a macro-level collection that records various key performance indicators and operational records generated by the equipment during operation over time, serving as the primary basis for trend analysis, lifespan prediction, and performance evaluation. Spatial location data describes the geometric position, attitude, and trajectory of the equipment and its components in the three-dimensional space of the mine. It is the link connecting the virtual model with the spatial relationship of the physical world, and the foundation for achieving virtual-real synchronization and precise navigation in digital twins. This invention also compares the real-time reported spatial location data with the position calculated by the model, and uses data assimilation techniques such as Kalman filtering to correct the model's state in real time, making predictions more accurate. This invention constructs a digital twin that deeply integrates multi-source data, breaking down information silos between internal subsystems, related devices, and spatial locations. It achieves dynamic and transparent mapping of the physical device's state throughout its entire lifecycle, laying a unified and accurate data foundation for subsequent analysis. By combining first-principles physical models with probabilistic statistical methods, geological conditions and operational instructions are transformed into model parameters and assigned probability distributions. Through extensive sampling and calculation, a dynamic and quantitative theoretical expectation range is output, rather than a fixed threshold, significantly improving the scientific rigor and adaptability of state determination. By introducing data from related devices and using dynamic coupling coefficients to correct this theoretical range in real time, the system can accurately capture complex linkage effects between devices, reducing the false alarm rate caused by interference from related devices.When an anomaly occurs, the diagnostic decision module not only compares thresholds, but more importantly, it simultaneously analyzes the data change trends of related devices. Through time-series correlation analysis, it accurately locates the root cause of the fault, whether it originates from upstream supply, downstream load, or its own operation, thereby outputting clear and targeted decision suggestions, achieving a leap from alarm to diagnosis. This invention also constitutes the system's self-evolution engine. It verifies the accuracy of diagnosis by continuously monitoring the actual response data after decision execution, and automatically calibrates and optimizes the key parameters of the physical model using inversion calculation and sensitivity analysis. This enables the entire system to maintain high precision and high reliability as equipment ages and geological conditions change, ultimately forming a virtuous cycle of becoming smarter with use.

[0020] like Figure 2 The diagram shown is a flowchart for calculating the theoretical expected range of mine equipment in an intelligent integrated management and control system based on a full-scenario fusion digital architecture, provided in this application embodiment. Specifically, the steps for establishing a physical model of the mine equipment based on first principles, using the digital twin of the mine equipment, geological condition data, and mine equipment operation commands as inputs, to obtain the theoretical expected range of equipment operating parameters include: first principles including the basic principles of Newtonian mechanics, rock mechanics, thermodynamics, and electromagnetism; establishing a physical model of the energy conversion and mechanical transmission process of the mine equipment based on first principles, with the digital twin of the mine equipment, operation commands, and geological condition data as inputs and equipment operating parameters as outputs; mapping the digital twin of the mine equipment to the inherent parameters and initial state variables of the physical model, wherein the inherent parameters of the equipment are used to define the physical structure and properties of the model. The initial state variables, including mass, moment of inertia, structural stiffness, damping coefficient, motor winding resistance and inductance, are used to define the starting conditions for model calculations, including the initial position, initial velocity, and initial temperature of the equipment. Real-time acquired geological condition data is mapped to the rock mechanics parameters of the physical model, and current operating commands are mapped to the driving boundary conditions of the physical model. Based on the equipment operation data in the digital twin of the mine equipment, prior probability distributions are assigned to the rock mechanics parameters and driving boundary conditions to characterize their uncertainty. The Monte Carlo method is used to sample the prior probability distribution a preset number of times, and the parameter sets obtained from each sampling are substituted into the physical model to perform forward calculations, obtaining the output probability distribution of the equipment operation parameters. The corresponding quantile intervals are extracted from the output probability distribution of the equipment operation parameters as the dynamic theoretical expected range of the equipment operation parameters.

[0021] In this embodiment, the present invention establishes a first-principles model based on Newtonian mechanics, rock mechanics, thermodynamics, and electromagnetism, mapping the digital twin to the inherent parameters and initial state of the equipment. This ensures that the model kernel strictly follows physical laws and can fundamentally simulate the energy conversion and mechanical transmission processes of the equipment. Instead of treating geological conditions and operating commands as deterministic values, it treats them as random variables with prior probability distributions, reflecting the uncertainty of geological structures and the volatility of operation in the downhole environment. Through large-scale sampling and parallel forward computation using the Monte Carlo method, all these input uncertainties are propagated. The final output is no longer a single theoretical value, but a complete probability distribution of the equipment's operating parameters. This distribution is ultimately transformed into a dynamic theoretical expectation range, enabling the system to enhance its sensitivity to early anomalies and gradual faults, and providing statistically significant and accurate judgment criteria far exceeding traditional experience thresholds for subsequent diagnostic decisions.

[0022] Furthermore, the steps of mapping the real-time acquired current geological condition data to the rock mechanics parameters of the physical model, and mapping the current operation commands to the driving boundary conditions of the physical model, include: obtaining mechanical weight factor parameters and initial driving weight factor parameters from a preset engineering geological database. The mechanical weight factor parameters include lithological benchmark coefficient weight factor, rock mass integrity coefficient weight factor, and environmental condition coefficient weight factor. The initial driving weight factor parameters include operation benchmark coefficient weight factor, equipment performance coefficient weight factor, and environmental impact coefficient weight factor. The engineering geological database is a structured, dynamically updated core component of an expert system, encapsulating in-depth knowledge about the interaction between geological conditions and equipment behavior in specific engineering fields (such as tunnel excavation and mining). Its data sources are diverse, mainly including statistical regularities obtained from mining and regression analysis of a large amount of historical engineering project data; mechanical models verified through numerical simulation and physical experiments; and the digital accumulation of domain expert experience. Based on the mechanical weight factor parameters, the lithological benchmark coefficient, rock mass integrity coefficient, and environmental condition coefficient are processed respectively to obtain the initial mechanical conversion factor. The initial mechanical conversion factor is determined comprehensively based on the lithological benchmark coefficient, rock mass integrity coefficient, and environmental condition coefficient. The determination of lithological benchmark coefficients is achieved by comparing the lithological types identified through field exploration with a standard database containing preset benchmark values ​​for various rock types. These benchmark values ​​characterize the basic mechanical properties of the lithology itself; for example, the benchmark value for hard granite might be 1.2, while for soft shale it might be 0.7. The determination of rock mass integrity coefficients is a standardized process that systematically transforms qualitative geological descriptions into quantitative values: First, based on field-measured structural surface data (such as joint density, rock quality index RQD, structural surface occurrence and roughness, etc.), internationally accepted rock mass quality classification indices (such as RMR, Q, or GSI) are calculated; then, through preset, engineering-verified mathematical relationships (such as piecewise functions or lookup tables), the above comprehensive indices are mapped to a set coefficient range. Taking the RMR index as an example, its mapping relationship follows the principle of "the higher the score, the larger the coefficient." For instance, an RMR greater than 80 points corresponds to excellent rock mass, with an integrity coefficient ranging from 0.9 to 1.0; an RMR between 40 and 60 points corresponds to moderate rock mass, with a coefficient ranging from 0.4 to 0.7; and an RMR below 20 points corresponds to extremely poor rock mass, with a coefficient potentially below 0.2. The determination of the environmental condition coefficient aims to accurately couple the combined effects of two key environmental factors: groundwater and geostress. A step-by-step, dynamically corrected strategy is adopted: First, geostress measurement data (such as the maximum and minimum principal stresses obtained through hydraulic fracturing or stress relief methods) are normalized and compared with the uniaxial compressive strength of the rock mass to set the initial value.For example, in low-stress environments, the initial coefficient can be set to 1.0, indicating no significant impact; in high-stress hard rock environments, considering the energy accumulation effect, the initial coefficient can be increased to 1.1 to 1.3 to reflect the strengthening effect; while in soft rock, it may be reduced to 0.8 to 0.9 due to plastic deformation. Subsequently, a reduction function based on the effective stress principle is introduced to dynamically correct the above initial coefficients according to groundwater level or pore water pressure monitoring data. This correction is usually achieved through an empirical formula related to the rock mass type, for example: the corrected final environmental condition coefficient = initial coefficient × (1 - α * pore water pressure / minimum principal stress), where α is a material constant characterizing the degree of water-rock interaction, thus quantitatively reflecting the weakening effect of pore water pressure on rock mass strength. The mechanical conversion factor is obtained by multiplying the response correlation coefficient based on equipment operating status measurements and the empirical calibration coefficient based on historical equipment operation data with the initial mechanical conversion factor. The response correlation coefficient based on equipment operating status measurements is obtained by establishing a quantitative relationship model (such as a pre-trained machine learning model or empirical formula) between real-time monitoring parameters of the equipment (e.g., vibration spectrum, driving torque) and the mechanical state of the rock-equipment interaction. This model takes real-time sensor data streams as input and outputs a dynamic correction coefficient to capture subtle mechanical changes not reflected in macroscopic geological data. The empirical calibration coefficient based on historical equipment operation data is obtained through big data analysis and machine learning. The system retrieves records highly similar to the current operating conditions from historical operation data (including similar lithology, similar integrity coefficients, similar equipment operation modes, etc.). Through statistical methods such as regression analysis, the differences between the mechanical parameters predicted by the initial model and the mechanical parameters inverted from actual operation in these historical cases are compared. Based on this difference pattern, a calibration coefficient is calculated to correct any systematic biases in the current prediction, making the final output mechanical conversion factor closer to the actual engineering response. Based on the mechanical transformation factor, the current geological condition data are multiplied to obtain the rock mechanical parameters of the physical model. The initial drive conversion factor is obtained by processing the operating baseline coefficient, equipment performance coefficient, and environmental impact coefficient based on the initial drive weight factor parameters. The operating baseline coefficient is determined by parsing the specific operating commands issued by the equipment control system (e.g., full-speed tunneling, slow-speed support, or positioning adjustment) and mapping them to a pre-set baseline value database. This baseline value represents the expected standard load level when executing a specific command; for example, a high-power-demand full-speed tunneling command may correspond to a higher baseline coefficient (e.g., 1.1), while a low-power slow-speed support command may correspond to a lower baseline coefficient (e.g., 0.8). The determination of the equipment performance coefficient is a quantitative assessment process based on the current health status of the equipment. This assessment integrates historical maintenance records, periodic performance test data, and life loss models of key components (e.g., motors, hydraulic systems, bearings). Its core lies in quantifying the degree of performance degradation of the equipment relative to its new state through a data-driven method. For example, the coefficient for a newly manufactured or overhauled piece of equipment in good working order can be set to a baseline value of 1.0. For aging equipment with performance degradation or component wear, the coefficient is reduced accordingly based on the calculation results of the loss model, and its value will be less than 1.0. The determination of the environmental impact coefficient aims to dynamically couple the real-time impact of the working face geological conditions and external loads on the equipment drive system. The calculation of this coefficient relies on an integrated data analysis process: First, real-time rock mechanics parameters (such as surrounding rock strength and integrity) mapped using the aforementioned methods are input. These parameters define the geological environment background and potential resistance of the working face. Second, real-time external load data from onboard sensors (such as torque sensors, pressure sensors, and vibration sensors) are integrated. Finally, through a pre-defined mathematical model that considers the physical characteristics of the equipment (e.g., a function that correlates geological resistance with measured loads), these input parameters are transformed into a comprehensive impact coefficient. This coefficient quantitatively reflects the expected resistance or assistance generated by the current complex operating conditions on the operational performance. Its value can be greater than 1.0 (indicating extremely harsh environment with a higher demand for driving force) or less than 1.0 (indicating favorable environment with a driving load below the baseline level). Based on the response correlation coefficient and the empirical calibration coefficient, the initial driving conversion factor is multiplied to obtain the driving conversion factor. The acquisition of the response correlation coefficient based on real-time equipment monitoring relies on real-time feedback data within the equipment control system (such as hydraulic system pressure and motor current). This data is mapped to an instantaneous correction coefficient through a preset dynamic relationship function to capture the dynamic impact of the equipment's own transmission efficiency, hydraulic leakage, or instantaneous overload on the theoretical driving boundary conditions.The acquisition of empirical calibration coefficients based on historical operation data focuses on the mapping relationship between operation commands and equipment responses. The system searches for successful cases in historical operation records that are similar to the current operation mode, equipment status, and working face environment. Through data-driven models (such as time series matching), it analyzes the actual boundary conditions under the historical optimal operation to obtain calibration coefficients, making the current theoretically driven boundary conditions closer to historically verified successful experiences. When no successful cases with sufficiently similar characteristics to the current operation mode, equipment health status, and working face environment can be found in the historical operation data records, the system will activate an adaptive processing mechanism. The core of this mechanism is to adopt a proximity matching and weighted interpolation strategy, that is, to find multiple historical cases that are similar in some key features (such as the main operation type or core lithology), and to evaluate their local similarity with the current working condition through an algorithm. Then, based on these similarity weights, the actual driving boundary conditions of the selected cases are weighted and fused to generate an approximate calibration benchmark. If the data is extremely sparse, a simulation model based on physical mechanisms or an expert rule base is further introduced to assist in the deduction, thereby synthesizing a calibration coefficient with engineering reference value. This ensures that even in the absence of direct historical experience references, the system can still perform reasonable deviation correction and decision support based on the existing knowledge base. The driving boundary conditions of the physical model are obtained by multiplying the current operation command data based on the driving conversion factor. Geological condition data is a comprehensive collection of multi-source information, including lithology identification data, rock mass structural surface data, and groundwater condition data. Lithology identification data encompasses rock types directly identified through geological drilling and core sampling, lithological boundaries and strata distribution information obtained through geophysical exploration such as ground-penetrating radar or seismic wave methods, and lithology distribution data recorded in real-time at the working face using manual mapping or computer vision technology. Rock mass structural surface data includes joint density and spacing to characterize the degree of rock mass fragmentation, rock quality indicators reflecting the integrity of core samples, and structural surface attitude information describing the geometric and mechanical properties of joints and faults. Groundwater condition data includes the inflow of water into the excavated space, rock mass fissure water pressure measured by borehole pressure gauges, and ambient humidity at the working face, which can serve as an auxiliary basis for judgment. These data collectively constitute a comprehensive basis for assessing current geological conditions.

[0023] Specifically, the initial mechanical conversion factor and the initial driving conversion factor are obtained as follows: ; EI; In the formula, and These represent the initial mechanical conversion factor and the initial driving conversion factor, respectively. , and These are the weighting factors for lithological benchmark coefficient, rock mass integrity coefficient, and environmental condition coefficient, respectively. , and These are the lithological baseline coefficient, the rock mass integrity coefficient, and the environmental condition coefficient, respectively. , and These are the operating baseline coefficient weighting factor, the equipment performance coefficient weighting factor, and the environmental impact coefficient weighting factor, respectively. , EI and EI are the operating baseline coefficient, equipment performance coefficient, and environmental impact coefficient, respectively.

[0024] In this embodiment, the present invention uses a structured engineering geological database integrating statistical laws, numerical simulation, and expert experience as its knowledge core, ensuring that the conversion process has a solid engineering science foundation rather than a simple linear conversion. First, through three major coefficients—lithological benchmark, rock mass integrity, and environmental conditions—geological conditions are systematically standardized from qualitative description to quantitative characterization. Among them, the environmental condition coefficient, by introducing the effective stress principle, dynamically couples the influence of in-situ stress and groundwater, achieving precise quantification of the water-rock coupling weakening effect of rock mass strength. At the same time, on the driving side, the operating instructions, equipment performance, and external environment are integrated, so that the driving boundary conditions are no longer isolated control commands, but comprehensive instructions reflecting the real driving force required by the equipment to perform specific operations in a specific geological environment under the current healthy state. The present invention also introduces a correlation coefficient based on real-time equipment response and an empirical calibration coefficient based on historical big data mining. The former uses real-time sensor data such as vibration and torque to capture microscopic and transient mechanical changes not reflected in macroscopic geological reports; the latter seeks successful experiences under similar working conditions from massive historical operating data to correct possible systematic biases in the model. This design enables the final output rock mechanics parameters and driving boundary conditions to not only embed deep domain knowledge, but also possess dynamic adaptive capabilities based on actual equipment response and data-driven approaches. This greatly improves the fidelity and reliability of the physical model input parameters, ensuring the accuracy and engineering guidance value of subsequent Monte Carlo simulations and theoretical expectation range calculations from the source. Ultimately, it allows the digital twin to truly integrate into complex and ever-changing working environments.

[0025] Furthermore, the steps of assigning rock mechanics parameters and driving boundary conditions to a priori probability distributions based on equipment operation data in a digital twin of the mine equipment include: obtaining the prior probability distribution, which is obtained through model inversion and statistical fitting based on historical operation data of the digital twin; dynamically updating the prior probability distribution using a sequential Monte Carlo method, specifically including: sampling from the prior joint probability distribution to generate a set of parameter particles, where the parameter particles represent the combination of rock mechanics parameters and driving boundary conditions; and inputting the set of parameter particles into the physical model of the mine equipment to obtain the corresponding predicted set of equipment operation parameters. The system compares real-time equipment operation data obtained from the digital twin with the predicted set of equipment operation parameters, and redistributes weights to each parameter particle in the parameter particle set based on the comparison results. The particle weights are positively correlated with the similarity between their predicted and observed values. From the redistributed parameter particle set, parameter particles with weights exceeding a preset weight threshold are selected, and the mean of the empirical distribution represented by the selected parameter particles is calculated as the posterior joint probability distribution at the current moment. The posterior joint probability distribution at the current moment is used as the prior probability distribution for dynamic updates at the next moment, realizing the dynamic online update of the prior probability distribution.

[0026] In this embodiment, the present invention samples and generates multiple possible combinations of geological and driving conditions from a prior distribution representing current knowledge, and simulates the corresponding predicted values ​​of equipment operating parameters through a physical model. Subsequently, these predicted values ​​are compared with real observation data transmitted back in real time from the digital twin, and weights are reassigned to each particle according to the similarity. The credibility of various parameter assumptions is evaluated through actual observation evidence. By selecting high-weight particles and calculating their empirical distribution mean, the posterior probability distribution at the current moment is obtained. This posterior distribution, which incorporates the latest observation evidence and provides a more accurate probability estimate of rock mechanics parameters and driving boundary conditions, is used as the prior distribution for the next moment, and this process is repeated. This allows the system to digest and absorb new operating data online and in real time, continuously correcting its understanding of complex time-varying downhole environments (such as rock stress adjustment and gradual changes in equipment performance). As a result, the theoretical expectation range output by the model can closely follow the evolution of real working conditions, significantly improving the timeliness and accuracy of condition warnings and the ability to detect slow-drifting faults, ensuring the long-term robustness and reliability of the entire intelligent control system in uncertain environments.

[0027] Furthermore, the step of extracting the corresponding quantile interval from the output probability distribution of the equipment operating parameters as the dynamic theoretical expected range of the equipment operating parameters includes: obtaining a preset confidence level and converting the confidence level into upper and lower limit probability values ​​for quantile calculation, wherein the confidence level is the expected probability that the true value of the equipment operating parameters falls within the dynamic theoretical expected range; calculating the corresponding lower quantile and upper quantile from the output probability distribution of the equipment operating parameters based on the upper and lower limit probability values; and constructing the dynamic theoretical expected range of the equipment operating parameters from the lower quantile and upper quantile.

[0028] In this embodiment, the invention transforms the probability distribution into a dynamic criterion that can be directly used for engineering decision-making, possessing both scientific rigor and flexible adaptability. By introducing a preset confidence level and calculating the corresponding quantile interval from the output probability distribution accordingly, the generated theoretical expected range is no longer a fixed numerical threshold, but a confidence channel with clear statistical significance. This fundamentally overcomes the shortcomings of traditional fixed threshold methods, such as poor adaptability and susceptibility to false alarms or missed alarms when facing complex and time-varying operating conditions. This range can dynamically change with the uncertainty of the input conditions. When the input is highly deterministic, the range narrows, improving monitoring sensitivity; when the input is highly uncertain, the range is appropriately widened, avoiding unnecessary false alarms. Thus, while ensuring reliability, intelligent optimization of the warning threshold is achieved, making anomaly detection no longer a simple over-limit judgment, but a more refined and reliable decision-making process based on probability statistics.

[0029] Furthermore, the steps for dynamically adjusting the theoretical expected range of equipment operating parameters based on the associated equipment data of the mine equipment to obtain the corrected theoretical expected range include: extracting key influencing parameters from the associated equipment data, including the operating status, load current, speed, and pressure data of the associated equipment; calculating the normalized state offset of each key influencing parameter based on its real-time value and its preset benchmark value; performing a weighted comprehensive calculation on the state offset of each key influencing parameter and its preset weighting factor to obtain the dynamic coupling coefficient, which is a dimensionless correction factor that quantifies the degree of coupling influence of associated equipment on the operating status of the target equipment; and correcting the theoretical expected range based on the dynamic coupling coefficient to obtain the corrected theoretical expected range.

[0030] The corrected theoretical expected range is obtained as follows: When the system is configured to prioritize computational efficiency and real-time performance, the dynamic coupling coefficient is directly multiplied by the initial theoretical expected range calculated by the physical model, thereby scaling the range proportionally to obtain the corrected theoretical expected range; when the system is configured to prioritize model accuracy and physical mechanism integrity, the dynamic coupling coefficient is superimposed on the data of the internal subsystems of the mine equipment in the physical model, and the model calculation is re-executed to obtain the corrected theoretical expected range.

[0031] In this embodiment, the present invention breaks through the limitations of traditional equipment monitoring that focuses solely on individual equipment, achieving a leap from single-machine monitoring to system-level collaborative intelligence. By extracting key influencing parameters (such as operating status and load current) from related equipment and calculating their normalized state offset, it can accurately quantify the real-time coupling influence of other equipment in the entire production chain on the operating status of the target equipment. Through weighted summation using preset weighting factors, these dispersed influences are aggregated into a unified, quantified dynamic coupling coefficient, enabling the system to perceive cross-equipment chain effects such as a reduction in the load on the machine due to a decrease in the speed of the upstream conveyor or a sudden increase in the pressure on the machine due to a jam in the downstream crusher. This invention also provides two configurable correction strategies: When the system is configured to prioritize computational efficiency and real-time performance, a proportional scaling mode is adopted, which directly multiplies the dynamic coupling coefficient by the initial theoretical range. This method has a low computational load and a fast response, making it suitable for real-time control scenarios with rapidly changing operating conditions. When configured to prioritize model accuracy and the integrity of the physical mechanism, the coupling effect is fed back to the physical model, and the correction range is obtained by re-executing the simulation calculation. This strictly follows physical laws and can more accurately reveal the deep mechanism of state changes, making it suitable for deep diagnosis and strategy optimization. This allows the system to intelligently balance computational efficiency and model accuracy to adapt to different scenario requirements. Ultimately, it ensures that the output corrected theoretical expected range not only reflects the operating rules of the equipment itself but also embeds the real-time interactive status of the entire production system, greatly reducing false alarms caused by interference from related equipment and significantly improving the systematicness, accuracy, and engineering practicality of fault warning and diagnosis.

[0032] Furthermore, the steps of comparing the actual measured values ​​of equipment operating parameters with the corresponding corrected theoretical expected range, and outputting diagnostic results and decision suggestions based on the comparison results and the data change trends of related equipment include: if the actual measured values ​​of all equipment operating parameters of the mine equipment are within the corresponding corrected theoretical expected range, the mine equipment is marked as normally operating equipment without additional processing; if the actual measured value of any equipment operating parameter of the mine equipment is not within the corresponding theoretical expected range, the abnormal diagnosis process is triggered: the linkage data change trends of each related equipment within a preset time window at the time point when the actual measured value exceeds the limit are extracted simultaneously, and trend consistency analysis is performed. Specifically, the data change trends of related equipment are compared with the actual measured value change trends of the target equipment in terms of time-series correlation, the related parameters with strong positive or strong negative correlation are identified, and based on the results of the trend consistency analysis, the root cause of the target equipment operating parameter exceeding the limit is obtained, and decision suggestions are generated based on the root cause: if the actual measured value is lower than the minimum value of the corrected theoretical expected range, and the trend analysis shows that it is strongly correlated with a certain upstream supply equipment, the diagnostic result is marked as the first result, and the corresponding first decision is output, such as the hydraulic system pressure of the tunneling machine being lower than the expected range. Trend analysis reveals that the upstream scraper conveyor's operating speed is simultaneously decreasing, indicating a poor coal supply. The diagnosis is insufficient upstream coal supply, and the decision is to check and optimize the scraper conveyor's operation to ensure a continuous and stable coal flow for the mining machine. If the actual measured value is lower than the minimum of the corrected theoretical expected range, and trend analysis shows a strong correlation with a downstream load device, the diagnosis is marked as a second result, and a corresponding second decision is output, such as the main shaft hoist's motor current being lower than expected. Trend analysis also reveals that the downstream bottom coal bunker's full-bundle signal is triggered, causing the feeder to stop and the hoist to be in an unloaded or lightly loaded state. The diagnosis is a lack of downstream load or process interruption, and the decision is to coordinate the production rhythm between the surface and underground to restore the normal coal receiving process of the coal bunker. If the actual measured value is lower than the minimum of the corrected theoretical expected range, and trend analysis shows a strong correlation with a sudden increase in the operating commands of the device itself or related equipment, the diagnosis is marked as a third result, and a corresponding third decision is output, such as the local ventilation fan's airflow being lower than expected. Trend analysis revealed that during the same period, the automated control system reduced the fan speed command based on the gas concentration. The diagnosis result was that the control command actively reduced the output, and the decision was to confirm whether the current ventilation strategy met the requirements of the procedure and to verify the accuracy and safety of the control logic. If the actual measured value was lower than the minimum value of the corrected theoretical expected range, and the trend analysis showed that it was strongly correlated with the data of its own internal subsystems, the diagnosis result was marked as the fourth result, and the corresponding fourth decision was output, such as the output pressure of the emulsion pump station being lower than expected.Trend analysis ruled out external factors, but its own vibration monitoring showed abnormal hydraulic end impact, and oil analysis showed that the emulsion concentration was not up to standard. Therefore, the diagnosis result was that the pump station itself was experiencing performance degradation, and the decision recommendation was to immediately perform predictive maintenance on the pump station, check the hydraulic end components and replace the unqualified emulsion. If the actual measured value was higher than the maximum value of the corrected theoretical expected range, and the trend analysis showed that it was strongly correlated with a certain upstream supply equipment, then the diagnosis result was marked as the fifth result, and the corresponding fifth decision was output. For example, the motor torque and current of the crusher were consistently higher than expected. Trend analysis reveals that the upstream coal mining machine is cutting coal at an excessively high speed, causing a sudden influx of excessively large coal chunks. The diagnosis is that the upstream input material quantity is too high or the particle size is too large. The recommended decision is to coordinate the control of the coal mining machine, adjusting the traction speed and cutting depth to match the output coal quantity and chunk size with the crusher's processing capacity. If the actual measured value is higher than the maximum value of the corrected theoretical expected range, and the trend analysis indicates a strong correlation with a downstream load device, the diagnosis is marked as the sixth result, and the corresponding sixth decision is output. For example, the drive motor current of the working face scraper conveyor is abnormally high. Trend analysis also reveals that congestion at the inlet of the downstream transfer conveyor or crusher is causing a surge in load resistance. The diagnosis is that the downstream equipment is blocked, leading to excessive load. The recommended decision is to immediately shut down the machine and check and clear the blockage at the inlet of the transfer conveyor or crusher. If the actual measured value is higher than the maximum value of the corrected theoretical expected range, and the trend analysis indicates a strong correlation with the operating commands of the machine itself or related equipment, the diagnosis is marked as the seventh result, and the corresponding seventh decision is output. For example, the belt conveyor's operating speed exceeds the set range. Trend analysis revealed that the dispatch center issued an incorrect acceleration command, or the brake failed to engage properly after the anti-skid protection system was erroneously triggered. The diagnosis was an incorrect operating command or an abnormal execution of the safety control system. The decision recommendation was emergency intervention, reviewing the control command, and checking safety circuits such as the brake and anti-skid protection sensors. If the actual measured value was higher than the maximum value of the corrected theoretical expected range, and the trend analysis showed a strong correlation with its own internal subsystem data, the diagnosis result was marked as the eighth result, and the corresponding eighth decision was output. For example, the hydraulic system temperature of the tunneling machine was continuously higher than the safety threshold. If the trend analysis did not find any abnormal external load, but its internal filter differential pressure alarm and oil quality test showed that the oil contamination level exceeded the standard, the diagnosis was a specific hidden fault inside the equipment (such as hydraulic valve sticking, oil circuit blockage, or reduced cooler efficiency). The decision recommendation was to arrange maintenance, replace the hydraulic oil and filter element, and clean the hydraulic system and cooling device.

[0033] In this embodiment, the present invention achieves a leap from single-parameter over-limit alarms to systematic root cause diagnosis by comparing real-time equipment operating data with theoretically expected ranges dynamically corrected based on physical principles and system coupling relationships. When an anomaly is detected, the system does not judge the target equipment in isolation, but simultaneously initiates a time-series correlation analysis of the data change trends of all related equipment within a preset time window. This intelligent analysis based on trend consistency can accurately map superficially similar abnormal symptoms to completely different root causes (such as upstream supply, downstream load, operating instructions, or self-fault), thereby outputting decision suggestions with clear direction. This completely changes the limitation of traditional monitoring systems that can only detect problems but cannot locate the cause, transforming operation and maintenance decisions from experience-based to data- and model-driven precise responses, greatly improving fault handling efficiency, reducing misjudgments, and providing a structured data foundation for the accumulation and optimization of equipment knowledge base through the classification and labeling of different causes. It completely changes the current situation where underground fault handling relies on worker experience, elevating safety management and production decisions to a data-driven, system-linked precision level, which is of decisive significance for ensuring continuous safe production in mines and shortening downtime due to faults.

[0034] Furthermore, the steps for optimizing the physical model based on response data to verify diagnostic accuracy include: after implementing the decision recommendations, continuously monitoring the verification response dataset within a preset time window. The verification response dataset includes actual measured values ​​of the target equipment's operating parameters, related equipment data, and environmental monitoring data; based on the verification response dataset, evaluating the effectiveness of the decision recommendations, with evaluation criteria including: whether the target equipment's operating parameters have returned to the corrected theoretical expected range, whether the equipment's operating status has stabilized, and whether the related equipment data has returned to normal collaborative mode; comparing the effectiveness of the decision recommendations with the previously output diagnostic results; if the effectiveness meets expectations, i.e., the equipment status has returned to normal, generating a diagnostic confirmation signal, and storing the current operating condition data sequence, model input parameters, and the corresponding verification response dataset into the equipment operation database to expand the data foundation for subsequent model calibration; if If the execution effect does not meet expectations, i.e., the equipment condition does not improve or deteriorates further, a diagnostic error signal is generated, triggering the model optimization process. This process includes: selecting all input parameters and boundary conditions from the physical model to form a parameter set to be analyzed; using local or global sensitivity analysis to assess the impact of each parameter in the parameter set on the misdiagnosis quantification index and calculating its corresponding sensitivity index; marking parameters whose sensitivity index exceeds a preset sensitivity index threshold as input influencing parameters; using the validation response dataset as the fitting target, employing optimization algorithms such as gradient descent and sequential quadratic programming to inversely calculate the input influencing parameters, obtaining a set of optimized parameter values ​​that best match the model output with the actual response data; and using the optimized parameter values ​​to update the corresponding empirical calibration coefficients in the physical model, thereby reducing prediction errors under similar operating conditions in the future.

[0035] In this embodiment, after executing the diagnostic decision, the present invention does not terminate the task, but initiates a rigorous effect verification and feedback learning loop: it continuously monitors the verification response dataset (including the comprehensive status of the target device, related devices, and environment) within a preset time window, and objectively evaluates the execution effect of the previous decision recommendations based on clear criteria such as whether the device parameters have returned to the normal range and whether the operation has returned to stability. This mechanism enables the system to quantitatively evaluate its own diagnostic accuracy. If the effect meets expectations, a confirmation signal is generated, and the complete data sequence is stored in the database, continuously enriching the system's historical experience base and providing valuable incremental learning material for future data analysis and model calibration; if the effect does not meet expectations, a sophisticated model optimization process is triggered: this process first uses global or local sensitivity analysis to accurately identify the key parameters that have the greatest impact on this misdiagnosis from numerous model parameters, uses the actual verification response data as the fitting target, and employs optimization algorithms to inversely calculate these key parameters, finding a set of optimized parameter values ​​that best match the model's predicted values ​​with the real data, and uses these optimized values ​​to update the empirical calibration coefficients in the physical model in a targeted manner. This enables the model to learn from its own prediction errors and automatically correct its internal cognitive biases regarding the interaction between equipment behavior and the environment, thereby significantly reducing prediction errors under similar operating conditions in the future and ensuring its long-term adaptability and reliability.

[0036] like Figure 3 The diagram shows a flowchart of an intelligent integrated management and control method based on a full-scenario fusion digital architecture provided in this application embodiment. Specifically, it includes: constructing a digital twin of the mine equipment, which is formed by associating and fusing data from internal subsystems, associated equipment, equipment operation, and spatial location data of the mine equipment, used to dynamically map the full lifecycle state of the physical equipment; establishing a physical model of the mine equipment based on first principles, using the digital twin of the mine equipment, geological condition data, and mine equipment operation instructions as inputs to obtain the theoretical expected range of equipment operation parameters; dynamically adjusting the theoretical expected range of equipment operation parameters according to the associated equipment data to obtain a corrected theoretical expected range; comparing the equipment operation data with the corrected theoretical expected range in real time, and outputting diagnostic results and decision suggestions based on the comparison results and the data change trends of associated equipment; continuously monitoring the response data of the equipment and the environment after executing the diagnostic results and decision suggestions, and optimizing the physical model based on the response data to verify the accuracy of the diagnosis.

[0037] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0038] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0039] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0040] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0041] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0042] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An intelligent integrated management and control system based on a full-scenario fusion digital architecture, characterized in that, It includes a twin construction module, a range adjustment module, a diagnostic decision module, and a model optimization module: The twin construction module is used to construct a digital twin of the mining equipment. The digital twin is formed by associating and fusing data from the internal subsystems of the mining equipment, associated equipment data, equipment operation data, and spatial location data, and is used to dynamically map the full life cycle status of the physical equipment. The range adjustment module is used to establish a physical model of the mine equipment based on first principles, take the digital twin of the mine equipment, geological condition data and mine equipment operation instructions as inputs to obtain the theoretical expected range of the equipment operating parameters, and dynamically adjust the theoretical expected range of the equipment operating parameters according to the associated equipment data of the mine equipment to obtain the corrected theoretical expected range. The diagnostic decision module is used to compare the equipment operating data with the corrected theoretical expected range in real time, and output diagnostic results and decision suggestions based on the comparison results and the data change trends of related equipment. The model optimization module is used to continuously monitor the response data of the device and environment after implementing diagnostic results and decision suggestions, and optimize the physical model based on the response data to verify the accuracy of the diagnosis.

2. The intelligent integrated management and control system based on a full-scenario fusion digital architecture as described in claim 1, characterized in that, The steps of establishing a physical model of the mine equipment based on first principles, using a digital twin of the mine equipment, geological condition data, and mine equipment operation commands as inputs, to obtain the theoretical expected range of equipment operating parameters include: The first principle includes the basic principles of Newtonian mechanics, rock mechanics, thermodynamics, and electromagnetism; A physical model of the mine equipment is established based on first principles. The physical model takes the digital twin of the mine equipment, operating instructions and geological condition data as inputs and the equipment operating parameters as outputs. The digital twin of the mining equipment is mapped to the inherent parameters and initial state variables of the physical model; The real-time acquired current geological condition data is mapped to the rock mechanics parameters of the physical model, and the current operation command is mapped to the driving boundary conditions of the physical model. Based on the equipment operation data in the digital twin, the dynamic theoretical expected range of the equipment operation parameters is calculated after probabilistic distribution modeling of the rock mechanics parameters and driving boundary conditions.

3. The intelligent integrated management and control system based on a full-scenario fusion digital architecture as described in claim 2, characterized in that, The steps of mapping the real-time acquired current geological condition data to the rock mechanics parameters of the physical model, and mapping the current operation command to the driving boundary conditions of the physical model, include: Mechanical weighting factor parameters and initial driving weighting factor parameters are obtained from a pre-set engineering geological database. The mechanical weighting factor parameters include lithological benchmark coefficient weighting factor, rock mass integrity coefficient weighting factor, and environmental condition coefficient weighting factor. The initial driving weighting factor parameters include operational benchmark coefficient weighting factor, equipment performance coefficient weighting factor, and environmental impact coefficient weighting factor. The initial mechanical conversion factor is obtained by processing the lithological benchmark coefficient, rock mass integrity coefficient, and environmental condition coefficient based on the mechanical weighting factor parameters. Based on the response correlation coefficient and the empirical calibration coefficient, the initial mechanical conversion factor is corrected to obtain the mechanical conversion factor; Based on the mechanical conversion factor, the current geological condition data is processed to obtain the rock mechanical parameters of the physical model; The initial driving conversion factor is obtained by processing the operating baseline coefficient, equipment performance coefficient, and environmental impact coefficient based on the initial driving weight factor parameters. Based on the response correlation coefficient and the empirical calibration coefficient, the initial driving conversion factor is corrected to obtain the driving conversion factor; The driving boundary conditions of the physical model are obtained by processing the current operation instruction data based on the driving conversion factor.

4. The intelligent integrated management and control system based on a full-scenario fusion digital architecture as described in claim 2, characterized in that, The step of assigning prior probability distributions to the rock mechanics parameters and driving boundary conditions based on equipment operation data in a digital twin of mining equipment includes: Generate a set of parameter particles by sampling from a prior joint probability distribution; The predicted set of equipment operating parameters is calculated based on the physical model and compared with real-time equipment operating data to reallocate parameter particle weights. Parameter particles are selected based on weights, and the posterior joint probability distribution is updated based on the selected parameter particles for probability distribution modeling at the next time step.

5. The intelligent integrated management and control system based on a full-scenario fusion digital architecture as described in claim 2, characterized in that, The expected range of the dynamic theory is obtained by extracting quantile intervals from the output probability distribution of the equipment operating parameters, wherein the quantile intervals are determined based on a preset confidence level, which is the expected probability that the true value of the equipment operating parameters falls within the expected range of the dynamic theory.

6. The intelligent integrated management and control system based on a full-scenario fusion digital architecture as described in claim 1, characterized in that, The step of dynamically adjusting the theoretical expected range of equipment operating parameters based on the associated equipment data of the mine equipment to obtain the corrected theoretical expected range includes: Extract key influencing parameters from the associated device data; Based on the real-time values ​​of each key influencing parameter and its preset baseline values, the normalized state offset is calculated respectively. The dynamic coupling coefficient is obtained by weighting and combining the state offset of each key influencing parameter with its preset weighting factor. The dynamic coupling coefficient is a dimensionless correction factor that quantifies the degree of coupling influence of the associated device on the operating state of the target device. The theoretical expected range is corrected based on the dynamic coupling coefficient to obtain the corrected theoretical expected range.

7. The intelligent integrated management and control system based on a full-scenario fusion digital architecture as described in claim 6, characterized in that, The revised theoretical expectation range is obtained as follows: When the system is configured to prioritize computational efficiency and real-time performance, the dynamic coupling coefficient is directly applied to the initial theoretical expectation range calculated by the physical model to obtain the corrected theoretical expectation range. When the system is configured to prioritize the accuracy of the model and the integrity of the physical mechanism, the dynamic coupling coefficient is superimposed on the data of the internal subsystems of the mine equipment in the physical model, and the model calculation is re-executed to obtain the corrected theoretical expected range.

8. The intelligent integrated management and control system based on a full-scenario fusion digital architecture as described in claim 1, characterized in that, The steps of comparing the actual measured values ​​of the equipment operating parameters with the corresponding corrected theoretical expected range, and outputting diagnostic results and decision suggestions based on the comparison results and the data change trends of related equipment, include: If the actual measured values ​​of the operating parameters of the mine equipment are all within the corresponding corrected theoretical expected range, the mine equipment will be marked as normal operating equipment and no additional processing will be performed. If the actual measured value of any operating parameter of the mine equipment is outside the corresponding theoretical expected range, the anomaly diagnosis process is triggered: The linkage data change trend of each associated device is extracted synchronously within a preset time window when the actual measured value exceeds the limit. The data change trend of the associated device is compared with the actual measured value change trend of the target device in terms of time series correlation, and decision suggestions are generated based on the time series correlation comparison results.

9. The intelligent integrated management and control system based on a full-scenario fusion digital architecture as described in claim 1, characterized in that, The steps for verifying diagnostic accuracy and optimizing the physical model based on the response data include: After implementing the decision recommendation, the verification response dataset within the preset time window is continuously monitored. The verification response dataset includes the actual measured values ​​of the target device's operating parameters, associated device data, and environmental monitoring data. Based on the verification response dataset, evaluate the effectiveness of the decision recommendations. The effectiveness of the proposed decision-making recommendations is compared with the previously output diagnostic results. If the execution results meet expectations, a diagnostic confirmation signal will be generated. If the execution results do not meet expectations, a diagnostic error signal is generated, and the model optimization process is triggered, which includes: Identify anomalous input data through sensitivity analysis; The abnormal input data is inverted based on the verification response dataset to update the empirical calibration coefficients in the physical model.

10. An intelligent integrated management and control method based on a full-scenario fusion digital architecture, applied to the intelligent integrated management and control system based on a full-scenario fusion digital architecture as described in claim 1, characterized in that, The specific steps include: A digital twin of mining equipment is constructed. The digital twin is formed by associating and fusing data from internal subsystems of the mining equipment, associated equipment data, equipment operation data, and spatial location data, and is used to dynamically map the full life cycle status of the physical equipment. A physical model of the mine equipment is established based on first principles. The digital twin of the mine equipment, geological condition data, and mine equipment operation instructions are used as inputs to obtain the theoretical expected range of the equipment operating parameters. The theoretical expected range of the equipment operating parameters is dynamically adjusted according to the associated equipment data of the mine equipment to obtain the corrected theoretical expected range. The system compares equipment operating data with the corrected theoretical expected range in real time, and outputs diagnostic results and decision recommendations based on the comparison results and the data change trends of related equipment. Continuously monitor the response data of the equipment and environment after implementing diagnostic results and decision recommendations, and optimize the physical model based on the response data to verify the accuracy of the diagnosis.

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