Intelligent data processing and management system based on downhole drilling

Abstract

The present application belongs to the field of mine drilling, and particularly relates to an intelligent data processing and management system based on underground drilling. The system comprises an underground data sensing device, a physical simulation digital twin, a cloud virtual evolution platform, a dynamic reinforcement learning decision engine, a drilling parameter adaptive control terminal and a central comprehensive management database. The sensing device collects real-time operation data; the digital twin reconstructs the characteristics of the drilling machine in a virtual space and realizes real-time mapping; the evolution platform provides massive samples through parallel simulation; the decision engine deduces strategies with the optimal mechanical specific work as the target; and the control terminal realizes closed-loop dynamic adjustment of parameters. Through integration and reconstruction of multi-source heterogeneous data, the present application realizes advanced trial and error in the drilling process, can adaptively adjust the penetration pressure and rotational speed according to geological fluctuations, reduces the dependence on artificial experience, and improves the construction efficiency, equipment life and safety of mine drilling operations.

Description

Technical Field

[0001] This invention belongs to the field of mine drilling information technology, specifically involving an intelligent data processing and management system based on downhole drilling. Background Technology

[0002] With the deepening of intelligent mining construction, digital management of underground drilling operations has become a core element in ensuring construction safety and improving production efficiency. Traditional mining processes heavily rely on the precision of drilling operations. By establishing an integrated data processing and management system, key parameters such as pressure, rotational speed, and flushing volume during drilling can be monitored in real time. In complex and variable geological environments, the effective integration and management of drilling data is of profound significance for optimizing construction plans and reducing safety risks.

[0003] Intelligent decision-making and parameter optimization based on drilling data are key directions in the evolution of current downhole drilling technology. This field focuses on building parameter models that can guide actual operations through in-depth analysis of historical drilling data and real-time geological feedback. An ideal intelligent management system needs to scientifically configure process parameters such as feed pressure, rotational torque, and flushing flow rate for specific formation conditions, reducing over-reliance on operators' field experience and improving the overall standardization of construction.

[0004] Existing downhole drilling systems tend to rely on static data presentation and passive early warning, lacking deep-level intelligent decision-making capabilities. Traditional parameter configuration models, due to a lack of effective simulation methods, exhibit significant nonlinear deviations in drilling efficiency when facing complex geological fluctuations. Furthermore, existing management systems suffer from logical lag when processing multi-source heterogeneous data, failing to optimize parameter architecture through virtual space verification before physical operations, and struggling to accurately capture the optimal specific energy output under different geological conditions. In addition, because the system lacks an adaptive evolutionary decision-making engine, non-professional operators often struggle to make scientific decisions under complex conditions, limiting overall drilling efficiency and construction quality. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent data processing and management system for downhole drilling, which can solve the problems of experience dependence and low construction efficiency in the above-mentioned background technology.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The intelligent data processing and management system for downhole drilling includes downhole data sensing devices, physical simulation digital twins, cloud-based virtual evolution platforms, dynamic reinforcement learning decision engines, drilling parameter adaptive control terminals, and a central integrated management database. The central integrated management database is connected to the downhole data sensing device, the physical simulation digital twin, the cloud virtual evolution platform, the dynamic reinforcement learning decision engine, and the drilling parameter adaptive control terminal, respectively, and is used to store and provide each device with historical drilling cases, geological exploration data, real-time operation trajectory and evolution model data. The downhole data sensing device is used to capture multi-dimensional raw operating data during downhole drilling rig operations in real time, and transmits the multi-dimensional raw operating data to the central integrated management database after digital processing, and synchronously transmits the real-time operating data to the physical simulation digital twin. The physical simulation digital twin is logically synchronized with the physical entity of the downhole drilling rig. It is used to receive real-time operating data transmitted by the downhole data sensing device, reconstruct the dynamic characteristics of the drilling rig and the physical interaction characteristics of the drill bit and the rock formation contact surface in the virtual digital space, realize the real-time mapping of the physical drilling process, and transmit the reconstructed virtual simulation data to the cloud virtual evolution platform. The cloud-based virtual evolution platform is connected to the physical simulation digital twin and is used to receive virtual simulation data, construct parallel simulation scenarios in the virtual environment, execute simulated drilling tests at a preset frequency, generate simulated sample data for parameter optimization, and transmit it to the dynamic reinforcement learning decision engine. The dynamic reinforcement learning decision engine is integrated into the cloud virtual evolution platform and is equipped with a logical computing model. It is used to receive simulated sample data and perform iterative training. With mechanical work ratio as the objective function, it outputs a set of drilling strategy parameters adapted to the current geological conditions and transmits the set of drilling strategy parameters to the drilling parameter adaptive control terminal. At the same time, the evolution model generated by the training is synchronously stored in the central integrated management database. The drilling parameter adaptive control terminal is connected to the dynamic reinforcement learning decision engine and the actuator of the downhole drilling rig. It is used to receive and parse the drilling strategy parameter set, issue control commands to the actuator of the downhole drilling rig according to the real-time feedback of geological fluctuations, realize closed-loop dynamic adjustment of drilling parameters, and simultaneously upload the real-time operation data during the adjustment process to the central integrated management database.

[0007] Preferably, the downhole data sensing device includes a pressure sensing component, a torque monitoring component, a displacement analysis component, a flow metering component, and a digital processing unit; The pressure sensing component includes strain gauge pressure transmitters installed at the oil inlet and return port of the feed cylinder, used to detect pressure fluctuations in the feed cylinder during different working cycles. By calculating the difference between the oil inlet pressure signal and the oil return pressure signal, and combining it with the effective pressure area of ​​the cylinder piston, the pressure signal is converted into a corresponding feed force logic value. The feed force logic value is a component of the multidimensional raw operating data. The torque monitoring component is integrated into the drive circuit of the drill rig power head. It is used to capture the real-time load during the rotation of the power head by monitoring the current change of the drive motor or the inlet and outlet pressure difference of the hydraulic motor, and to determine the cutting resistance of the drill bit in the current rock formation. The cutting resistance and real-time load data are components of the multi-dimensional raw operating data. The displacement analysis component uses a draw-wire displacement encoder or laser rangefinder with explosion-proof function to measure the cumulative depth of the borehole, the instantaneous drilling speed, and the displacement trajectory of the feed machine body. The cumulative depth, instantaneous drilling speed, and displacement trajectory data are components of the multidimensional raw operating data. The flow metering component is installed on the flushing fluid circulation pipeline to monitor the flushing fluid's inlet pressure, injection flow rate, and return flow status, and to determine whether the slag discharge efficiency in the orifice is within a preset range. The flushing fluid's pressure, flow rate, and return flow status data are components of the multidimensional raw operating data. The digital processing unit is connected to the pressure sensing component, torque monitoring component, displacement analysis component, and flow metering component, respectively. It is used to perform analog-to-digital conversion on the acquired raw analog voltage signal, apply Kalman filtering algorithm to filter out periodic noise generated by the hydraulic pump station pulsation, extract drilling load characteristics and add timestamps, and then send them to the central integrated management database and physical simulation digital twin through the communication link.

[0008] Preferably, the physical simulation digital twin has a geological environment reconstruction function and a drill fatigue state monitoring sub-item, and adopts a mechanical model based on finite element analysis and discrete element coupling; The geological environment reconstruction function is used to dynamically update the formation attribute parameters in the virtual space based on the ratio of historical drilling data to torque and pressure during the current drilling process, using a stochastic modeling algorithm based on implicit sequential Gaussian simulation. The formation attribute parameters include rock compressive strength, rock fracturing and borehole wall stability index, to ensure the consistency of the digital twin and the real downhole environment in terms of physical and mechanical properties, so as to complete the reconstruction of the drilling rig dynamic characteristics and the physical interaction characteristics of the drill bit and rock formation. The drill string fatigue condition monitoring sub-item is used to calculate the remaining life percentage of the drill pipe and drill bit in real time in virtual space based on the cumulative number of drill string rotations, average compressive strength and vibration spectrum characteristics, and to issue maintenance suggestions before the remaining life percentage reaches a predetermined threshold. The mechanical model based on the coupling of finite element analysis and discrete element method is used to simulate the micromechanical behavior of diamond composite plates or alloy cylindrical teeth in drill bits when breaking rocks. The axial feed force, tangential torque, lateral vibration and springback stiffness parameters are introduced in the simulation process to achieve real-time mapping of the physical drilling process.

[0009] Preferably, the cloud-based virtual evolution platform is equipped with a high-concurrency computing matrix, which can support the execution of tens of thousands of virtual drilling cycles within a predetermined time. By changing the combination ratio of feed pressure and rotation speed, the extreme point of the drilling efficiency curve is detected, and the energy consumption index under each set of parameter combinations is recorded. The cloud-based virtual evolution platform is used to introduce random noise disturbance factors during simulation experiments to simulate geological changes and mechanical vibrations in the downhole environment, thereby enhancing the robustness of the decision-making logic under extreme working conditions. The cloud-based virtual evolution platform also has a multi-machine collaborative evolution function. When multiple drilling rigs are operating simultaneously in the same mining area, the cloud-based virtual evolution platform is used to spatially correlate the real-time geological feedback of multiple drilling rigs, and to simulate the spatial extension trend of the strata using physical simulation digital twins, thereby generating advanced evolution strategies for adjacent drilling rigs.

[0010] Preferably, the logical computation model of the dynamic reinforcement learning decision engine is a model based on proximal policy optimization, adopting a deep Actor-Critic architecture, wherein the Actor network is used to output the probability distribution of drilling parameters, and the Critic network is used to evaluate the expected specific work benefit under the current parameter configuration. The dynamic reinforcement learning decision engine adopts a multi-dimensional reward function logic during training. The reward function logic sets the increase in drilling speed, the reduction in drill bit wear, the reduction in energy consumption, and the avoidance of safety hazards in the hole as positive incentives, and sets parameter mutations, equipment overload, and vibration exceeding limits as negative penalty terms. The process parameters are obtained by maximizing the expected return value. The reward function logic also introduces a mechanical specific work term and a drill string vibration energy distribution index. The mechanical specific work is defined as the ratio of input energy to broken volume. The drill string vibration energy distribution index is extracted by analyzing the power spectral density of torque and pressure signals. When the vibration component is within the resonant frequency range of the drill string, the reward function logic outputs a negative penalty. The decision suggestions generated by the dynamic reinforcement learning decision engine have self-evolutionary characteristics and are used to automatically correct the neuron weights within the model based on the errors fed back from actual construction.

[0011] Preferably, the drilling parameter adaptive control terminal adopts a multi-level control strategy when performing closed-loop adjustment: the first level is a global trend adjustment based on cloud-based suggestions, used to set the feed pressure and speed reference; The second level is local error compensation based on real-time feedback from downhole sensors, used to cope with geological disturbances on the order of seconds. The drilling parameter adaptive control terminal integrates a PLC as the underlying execution module. The PLC is configured with smoothing filtering logic to convert the adaptive adjustment instructions into a step-like gradual sequence. The drilling parameter adaptive control terminal also includes an operation interface, which provides parameter architecture guidance to operators and transforms decision logic into preset construction instruction suggestions. At the same time, it displays the geological stratification, real-time trajectory, and predicted trajectory reconstructed by the physical simulation digital twin.

[0012] Preferably, the central integrated management database adopts a hybrid storage mode that combines time-series database and relational database, wherein: the time-series database is used to store high-frequency waveform data during the drilling process to support spectrum analysis and fault diagnosis; The relational database is used to store geological logic, process specifications, and evolution model parameters, and supports cross-hole comparison analysis; The central integrated management database has a built-in specific work evaluation logic, which is used to evaluate drilling efficiency by calculating the energy consumed per unit volume of rock breaking, i.e., the mechanical specific work value. When the real-time monitored mechanical specific work exceeds a preset threshold, the dynamic reinforcement learning decision engine is triggered to enter a high-frequency evolution mode. The central integrated management database is also equipped with a data integrity verification unit, which uses the hash chain feature of blockchain technology to sign and encrypt the stored construction data.

[0013] Preferably, the system further includes a multi-source heterogeneous data integration module, which is equipped with a protocol adaptive parsing unit and internally stores a mining sensor and controller protocol library, used to identify the communication protocol of the access device and uniformly map it into a standardized drilling physical quantity vector. The multi-source heterogeneous data integration module also includes a signal quality assessment unit, which is used to monitor the integrity of sensor data and use a long short-term memory neural network model to reconstruct and predict missing data based on historical sequences.

[0014] Preferably, the system further includes a safety risk warning component, which is used to predict the risk of stuck drill, buried drill or blowout by analyzing the stress distribution in the physical simulation digital twin. When the predicted risk value exceeds the safety limit, the drilling parameter adaptive control terminal forcibly executes emergency decompression or drilling stop logic; The safety risk early warning component is also used to link with the gas concentration sensor. When an abnormal increase in flushing fluid backflow pressure is detected and accompanied by a gas concentration exceeding the safety threshold, an emergency response mechanism is triggered, causing the cloud-based virtual evolution platform to enter the disaster prevention evolution mode. The dynamic reinforcement learning decision engine then calculates the parameter combination that can maintain borehole stability and prevent gas outbursts.

[0015] Preferably, the system adopts a distributed architecture design, including downhole edge computing nodes and cloud server clusters; The downhole edge computing node is used to undertake data acquisition and basic control functions, and performs lightweight evolution tasks through the built-in edge evolution module when the communication link bandwidth is lower than a preset threshold. The edge evolution module uses a search algorithm based on a proxy model to predict the trend of mechanical specific work change. The cloud server group is used to perform physical simulation reconstruction and reinforcement learning evolution tasks; The downhole edge computing node and the cloud server group interact with each other through an asynchronous synchronization mechanism. High-frequency real-time data is stored locally in a loop, while low-frequency trend data after feature extraction is uploaded to the cloud server group when the communication link is idle.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves high-precision mapping and advanced trial and error in the downhole drilling process in virtual space by constructing a physical simulation digital twin and a cloud-based virtual evolution platform. This changes the traditional mode of parameter adjustment that relies on human experience, and improves the scientific nature and predictability of construction parameter configuration.

[0017] 2. This invention introduces a dynamic evolution engine based on reinforcement learning, which makes the system no longer a simple static data storage and display tool, but a decision-making brain with self-learning and self-evolution capabilities; By conducting extremely high-frequency simulation tests in a virtual environment, the optimal specific power point under different complex geological conditions can be accurately captured, enabling non-professionals to achieve expert-level drilling efficiency and reducing reliance on highly skilled operators.

[0018] 3. This invention realizes adaptive dynamic adjustment and closed-loop control of drilling parameters. By feeding back the optimal strategy derived from the cloud to the physical actuator in real time, it can effectively cope with the nonlinear fluctuations of downhole geological conditions, avoid low drilling efficiency and equipment wear caused by parameter lag or improper configuration, and ensure the standardization and efficiency of the overall construction.

[0019] 4. This invention solves the problems of data silos and logical lag in traditional management systems by deeply integrating and logically reconstructing multi-source heterogeneous data, enhances the system's ability to identify and warn of complex geological risks, and avoids safety risks in actual operations by verifying the parameter architecture in virtual space in advance, thereby improving the overall safety and intelligent management level of mine drilling operations.

[0020] This invention uses mechanical specific work as the core evaluation index and deeply integrates reinforcement learning algorithms with physical mechanics simulation to construct an interdisciplinary intelligent decision-making model. This model not only optimizes energy consumption and extends the service life of drilling tools, but also provides complete technical architecture support for the digital transformation of downhole drilling technology, and has high industrial application value and economic benefits. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram illustrating the core principle framework of the collaborative operation of the dynamic reinforcement learning decision engine and cloud-based virtual evolution in this invention. Figure 3 This is a logical flowchart of the physical simulation digital twin used in this invention to reconstruct the geological environment and simulate physical interactions. Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between cloud-based decision-making commands and downhole adaptive control terminals in this invention; Figure 5 This is a schematic diagram comparing the technical effects and principles of triggering high-frequency evolution modes based on mechanical specific work evaluation indicators in this invention. Detailed Implementation

[0022] Example 1: Please refer to the appendix Figure 1 Only attached Figure 5 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0023] The intelligent data processing and management system for downhole drilling includes downhole data sensing devices, physical simulation digital twins, cloud-based virtual evolution platforms, dynamic reinforcement learning decision engines, drilling parameter adaptive control terminals, and a central integrated management database. The downhole data sensing device is used to capture multi-dimensional raw operating data of the downhole drilling rig in real time during operation. This raw operating data includes the feed system status, rotary drive parameters, and flushing fluid circulation data. The multi-dimensional raw operating data is digitized and then transmitted to a central integrated management database. The downhole data sensing device includes a pressure sensing component, a torque monitoring component, a displacement analysis component, and a flow metering component. The pressure sensing component includes high-precision pressure transmitters installed at the inlet and outlet of the feed cylinder. These transmitters are configured to detect pressure fluctuations in the feed cylinder during different operating cycles. By calculating the inlet and outlet pressure difference and combining it with the effective pressure-bearing area of ​​the cylinder piston, the pressure signal is converted into a corresponding feed force logic value.

[0024] The torque monitoring component is integrated into the drive circuit of the drill rig's power head. By monitoring the current fluctuations of the drive motor or the inlet and outlet pressure difference of the hydraulic motor, it captures the real-time load during the rotation of the power head and determines the cutting resistance of the drill bit in the current rock formation. The displacement analysis component uses an explosion-proof draw-wire displacement encoder or laser rangefinder to accurately measure the cumulative drilling depth, instantaneous drilling speed, and displacement trajectory of the feed body. The flow metering component is installed on the flushing fluid circulation pipeline and uses electromagnetic induction or ultrasonic measurement technology to monitor the flushing fluid's inlet pressure, injection flow rate, and return status, ensuring that the in-hole slag removal efficiency is within a specific range and monitoring in real time whether leakage occurs due to rock formation fissures.

[0025] The physical simulation digital twin, built within the system, is logically completely synchronized with the physical entity of the downhole drilling rig. By receiving real-time operating conditions from the downhole data sensing device, it reconstructs the dynamic characteristics, kinematic state, and physical interaction features of the drill bit and rock formation contact surface within a virtual digital space, achieving real-time mapping and high-fidelity simulation of the physical drilling process. The physical simulation digital twin also possesses geological environment reconstruction capabilities. Based on historical borehole data and the torque-pressure ratio during the current drilling process, it dynamically updates formation property parameters in the virtual space, including rock compressive strength, rock fracturing, and borehole wall stability indicators, ensuring consistency between the digital twin and the real downhole environment in terms of physical and mechanical properties.

[0026] The physical simulation digital twin also includes a drill bit fatigue condition monitoring sub-item, which calculates the remaining life percentage of the drill pipe and drill bit in real time in virtual space based on the cumulative number of rotations, average compressive strength, and vibration spectrum characteristics, and issues maintenance suggestions through the management system before reaching a predetermined threshold. During its construction, the physical simulation digital twin employs a mechanical model based on a coupling of finite element analysis and discrete element method, capable of simulating the microscopic mechanical behavior of each diamond composite sheet or alloy cylindrical tooth when breaking rock.

[0027] The cloud-based virtual evolution platform, connected to the physical simulation digital twin, is used to construct large-scale parallel simulation scenarios in a virtual environment. Utilizing the decoupling between virtual and physical spaces, it executes simulated drilling tests at a preset frequency within a short period. By simulating drilling responses under different formation hardness, abrasiveness, and fracture development levels, it provides massive amounts of simulated sample data for parameter optimization. The cloud-based virtual evolution platform is equipped with a high-concurrency computing matrix, capable of supporting tens of thousands of virtual drilling cycles within a predetermined time. By continuously varying the combination ratio of feed pressure and rotational speed, it detects the extreme points of the drilling efficiency curve and records the energy consumption indicators for each parameter combination. During simulation tests, the cloud-based virtual evolution platform introduces random noise disturbance factors to simulate unpredictable geological changes and mechanical vibrations in the real downhole environment, enhancing the robustness and reliability of the decision-making logic under extreme conditions.

[0028] The dynamic reinforcement learning decision engine, integrated into a cloud-based virtual evolution platform, is equipped with a logic computation model based on proximal policy optimization. Through iterative training on simulated samples generated by the cloud-based virtual evolution platform, it aims to find the optimal mechanical specific work under specific geological conditions as the objective function. It automatically deduces and outputs the optimal set of drilling strategy parameters, including optimized feed pressure, rotational speed, and flushing volume. During training, the dynamic reinforcement learning decision engine employs a multi-dimensional reward function logic. This logic sets positive incentives for increased drilling speed, reduced drill string wear, reduced energy consumption, and avoidance of in-hole safety hazards, while setting negative incentives for parameter mutations, equipment overload, and excessive vibration. It obtains globally optimal process parameters by maximizing the expected return value.

[0029] The decision recommendations generated by the dynamic reinforcement learning decision engine have self-evolutionary characteristics. This means the system can automatically correct the neuron weights within the model based on errors reported during actual construction, ensuring that the decision recommendations become increasingly accurate as drilling depth increases and known geological data becomes more comprehensive. When outputting the optimal specific power parameter, the engine automatically compares it with historical similar geological cases. If the deviation between the currently evolved parameter and the historical optimal parameter exceeds a predetermined range, the system will initiate a secondary verification procedure, using a physical simulation digital twin for further verification to ensure the logical rationality of the instructions issued to the execution mechanism.

[0030] The drilling parameter adaptive control terminal is connected to the dynamic reinforcement learning decision engine and the execution mechanism of the downhole drilling rig. It is used to receive and parse the optimal drilling strategy parameter set, and issue control commands to the execution mechanism according to the current real-time geological fluctuations, so as to realize the closed-loop dynamic adjustment of drilling parameters and compensate for the small deviations between physical operation and virtual simulation.

[0031] The adaptive drilling parameter control terminal includes an interactive interface configured to provide intuitive parameter architecture guidance to non-professional operators. This interface transforms complex decision-making logic into preset construction instruction suggestions, allowing operators to achieve expert-level drilling efficiency simply by following the navigation parameters generated by the system. The control terminal also features remote synchronization capabilities, enabling the transmission of real-time downhole images, parameter curves, and system-generated decision reports to the surface monitoring center via an explosion-proof communication link, facilitating collaborative management between the surface and underground.

[0032] The central integrated management database stores historical drilling cases, geological exploration data, real-time operation trajectories, and evolutionary models generated by a dynamic reinforcement learning decision engine. It also provides standardized data access interfaces and logical support for various components within the system. The central integrated management database includes a built-in specific energy evaluation logic, which calculates the energy consumed per unit volume of rock breaking, i.e., the mechanical specific energy value, as a core indicator for evaluating drilling efficiency. When the real-time monitored mechanical specific work exceeds a preset threshold, the dynamic reinforcement learning decision engine is triggered to enter a high-frequency evolution mode to find the best parameter combination that is suitable for the current hard rock or complex tectonic zone.

[0033] Furthermore, the intelligent data processing and management system based on downhole drilling also includes a multi-source heterogeneous data integration module. This module is used to convert and normalize the formats of sensor signals from different manufacturers and using different protocols, solving the logical lag problem caused by inconsistent data interfaces of downhole equipment, and ensuring that the data acquired by the physical simulation digital twin has a high degree of real-time performance and integrity. The system also includes a safety risk early warning component, which predicts potential risks of stuck drill, buried drill bit, or blowout by analyzing the stress distribution in the physical simulation digital twin. When the predicted risk value exceeds a certain safety limit, the drilling parameter adaptive control terminal will forcibly execute emergency decompression or drilling stop logic, taking precedence over human operation commands.

[0034] In the specific system deployment architecture, the intelligent data processing and management system based on downhole drilling adopts a distributed architecture design. Data acquisition and basic control functions are undertaken by downhole edge computing nodes, while complex physical simulation reconstruction and reinforcement learning evolution tasks are completed collaboratively by cloud server groups to reduce the pressure of downhole narrowband communication and ensure that the system response speed is within the preset time window.

[0035] The pressure sensing component in the downhole data sensing device employs a redundant strain gauge pressure transmitter with a sampling frequency configured to be no less than 1000 times per second to capture instantaneous pressure spikes when encountering hard interlayers. The digital processing unit performs analog-to-digital conversion on the acquired raw analog voltage signal and applies a Kalman filter algorithm to filter out periodic noise generated by hydraulic pump station pulsations, extracting the true drilling load characteristics. The pre-processed data is timestamped with high precision and transmitted to the central integrated management database via industrial Ethernet or an intrinsically safe wireless access point.

[0036] The physical simulation digital twin utilizes a stochastic modeling algorithm based on implicit sequential Gaussian simulation when constructing the geological environment reconstruction function. This algorithm is configured to use coal seam occurrence data from historical neighboring boreholes as a prior probability distribution, combined with the torque-to-speed ratio (i.e., real-time cutting performance index) fed back by the current drill bit, to dynamically correct the rock physical and mechanical properties in the three-dimensional mesh model. When simulating the interaction between the drill bit and rock strata, the physical simulation digital twin considers not only axial feed force and tangential torque, but also introduces lateral vibration and rebound stiffness parameters to simulate the drill skipping phenomenon that may occur when drilling in fractured zones. This high-fidelity physical mapping enables the system to predict the stress trends of the drill bit within the next few meters of drilling depth in a virtual environment.

[0037] The cloud-based virtual evolution platform integrates a high-concurrency simulation container cluster. When significant changes occur in geological conditions (such as an abnormal increase in mechanical specific work), the platform immediately launches tens of thousands of independent virtual simulation instances. Each instance is assigned a different combination of drilling parameters. For example, the first group of instances focuses on a high-speed, low-pressure process combination, the second group focuses on a low-speed, high-pressure process combination, and the third group performs sensitivity analysis under different flushing ratios. Utilizing GPU-accelerated computing logic, the platform simulates a drilling process that would take hours in the physical world within seconds, generating an efficiency distribution map covering the entire parameter space.

[0038] The proximal policy optimization logic of the dynamic reinforcement learning decision engine adopts a deep Actor-Critic architecture. The Actor network is configured to output the probability distribution of drilling parameters, while the Critic network is responsible for evaluating the expected specific work gain under the current parameter configuration. During the evolution process, the decision engine introduces a shearing mechanism to ensure that the magnitude of each policy update remains within a reasonable trust range, avoiding destructive abrupt changes in parameter configuration due to minor distortions in the virtual and physical environments. The mechanical specific work term in the reward function is defined as the ratio of input energy to fracture volume, and the objective function is to minimize this ratio while ensuring that the drilling speed remains within a preset range.

[0039] The drilling parameter adaptive control terminal employs a multi-level control strategy when performing closed-loop adjustment. The first level is a global trend adjustment based on cloud-based suggestions, used to set basic feed pressure and rotational speed references; The second level is local error compensation based on real-time feedback from downhole sensors, used to cope with geological disturbances on the order of seconds. If the torque acquired in real time instantaneously exceeds the yield strength limit of the drill string, the hardware safety logic of the control terminal will bypass the software control layer and directly drive the pressure relief valve to open, achieving millisecond-level overload protection.

[0040] The central integrated management database adopts a hybrid storage model combining time-series databases and relational databases. High-frequency waveform data generated during drilling is stored in the time-series database for spectrum analysis and fault diagnosis. The geological logic, process specifications, and model parameters of the evolutionary output are stored in a relational database, supporting multi-dimensional cross-hole comparison analysis and association rule mining.

[0041] Example 2: Based on the intelligent data processing and management system for downhole drilling described in Example 1, this example provides a variant of the distributed architecture for extreme narrowband communication environments. In some deep mines or situations where communication infrastructure is limited, large-scale data exchange between the mine and the cloud may experience delays or interruptions.

[0042] In this embodiment, the intelligent data processing and management system based on downhole drilling further includes an edge evolution module deployed within an explosion-proof cabinet downhole. The edge evolution module is a lightweight mirror of the cloud-based virtual evolution platform, containing pre-trained convolutional neural network logic capable of performing rapid evolution within a specific parameter range. When the system detects that the bandwidth of the uphole / downhole communication link is lower than a preset minimum communication threshold, the central integrated management database automatically transfers the computational weights to the edge evolution module.

[0043] The edge evolution module is configured to perform small-scale parallel simulation tests locally using real-time data acquired downhole. Due to limited computing resources, the edge evolution module employs a surrogate model-based search algorithm instead of large-scale Monte Carlo simulations. This surrogate model predicts the trend of mechanical specific work change under different parameter combinations by fitting historical evolution trajectories. Meanwhile, a dynamic reinforcement learning decision engine performs fine-tuning training on the edge side to maintain the stability of the drilling process until the communication link is restored.

[0044] In this embodiment, the downhole data sensing device includes a signal quality assessment unit. This unit monitors the integrity of sensor data. If some pressure signals are lost due to electromagnetic interference, the signal quality assessment unit uses a long short-term memory neural network model to reconstruct and predict the missing data based on historical sequences, ensuring that the data input to the physical simulation digital twin is uninterrupted.

[0045] In this embodiment, the drilling parameter adaptive control terminal is configured with an offline expert system mode. In the event of a complete loss of cloud-based commands, the terminal maintains stable operation of the drilling rig in low-power mode based on temporary strategies output by the local edge evolution module and a preset safety process package. Simultaneously, the control terminal transmits critical early warning information to nearby mobile terminals via low-frequency carrier communication or a mining wireless communication link, ensuring that operators are aware of key operating conditions.

[0046] Furthermore, when the physical simulation digital twin is executed at the edge, the microscopic mechanical interaction calculations are simplified, and a large-scale dynamic model based on transfer functions is adopted instead to reduce the CPU clock usage. Although the simulation accuracy decreases slightly, it has advantages in ensuring real-time decision-making.

[0047] The central integrated management database employs a distributed architecture with hierarchical data storage and an asynchronous synchronization mechanism. High-frequency real-time data is stored locally only, while low-frequency trend data, after feature extraction, is asynchronously uploaded to the cloud database when the data link is idle, to refine the global evolution model. This architectural design enhances the system's survivability and decision-making continuity in complex mining environments.

[0048] Example 3: Based on the intelligent data processing and management system for downhole drilling described in Example 1, this example describes in detail the internal logic of the system when processing multi-source heterogeneous data integration and actively avoiding geological risks.

[0049] The multi-source heterogeneous data integration module is configured with protocol adaptive parsing capabilities. Internally, it stores mainstream mining sensor and controller protocol libraries, covering Modbus, CANopen, Profibus, EtherCAT, and proprietary serial port protocols from various manufacturers. When a new sensing device is connected, the integration module automatically detects the handshake signal, identifies its baud rate, parity bit, and data frame structure, and uniformly maps them to standardized drilling physical quantity vectors (such as rotational speed, torque, and displacement). This normalization process eliminates logical inconsistencies caused by differences in equipment brands, providing a standardized input stream for the physical simulation digital twin.

[0050] The physical simulation digital twin integrates a stress-strain field simulation unit when performing geological risk prediction. This unit is configured to calculate the shear stress distribution around the borehole wall based on the current borehole geometry, confining pressure environment of the rock strata, and the pulsating response of the drill bit. When the simulation results show that the effective stress in a specific area exceeds the brittle failure limit of the rock, the system determines that there is a risk of borehole wall instability or collapse.

[0051] The dynamic reinforcement learning decision engine addresses the aforementioned risks by dynamically adding risk weight factors to the reward function. When the risk value output by the safety risk warning component increases, the reward function significantly increases the penalty for fluctuations in feed pressure, causing the parameter set generated by the decision engine to shift towards a stable drilling mode characterized by high rotation speed, constant feed pressure, and large flushing volume. Simultaneously, the decision engine automatically retrieves successful disaster avoidance cases under similar geological conditions from the database, injecting them as experience trajectories into the current training loop to accelerate the generation of optimal risk avoidance parameters.

[0052] Upon receiving a high-risk warning and adjustment strategy, the drilling parameter adaptive control terminal will automatically initiate a preventative control sequence. This sequence includes: first, slightly reducing the feed pressure to release pressure buildup in the drill bit; Secondly, gradually increase the speed of the power head to enhance the centrifugal slag removal effect; Finally, monitor whether the return flow has returned to normal. If the control logic fails to eliminate the risk characteristics within the predetermined time window, the terminal will trigger the highest priority safety instruction, guiding the actuator into an emergency shutdown and maintaining a stress state.

[0053] Furthermore, in this embodiment, the cloud-based virtual evolution platform is configured to have multi-machine collaborative evolution capabilities. When multiple drilling rigs are operating simultaneously in the same mining area, the evolution platform spatially correlates the real-time geological feedback from all drilling rigs. If a drilling rig detects hardening of the strata, the platform will pre-simulate the spatial extension trend of the hard rock layer using a physical simulation digital twin and generate evolution strategies in advance for neighboring drilling rigs.

[0054] In this embodiment, the central integrated management database incorporates knowledge graph management logic. It establishes a multi-dimensional correlation between drilling parameters, geological attributes, drill bit wear, and final construction efficiency, constructing a knowledge chain of geology-process-effect. When operators query the current working conditions through the user interface, the system not only provides recommended parameters but also uses graph reasoning to display historical cases and simulation data supporting the decision-making process, enhancing its interpretability.

[0055] Example 4: Based on the above examples, this example focuses on describing the adaptive evolution details of the intelligent data processing and management system for downhole drilling under different geological scenarios.

[0056] When encountering high-hardness sandstone formations, the downhole data sensing device detects high-frequency, small-amplitude vibrations in the torque signal, with slow increases in feed displacement. At this point, the mechanical specific work calculated by the central integrated management database rapidly increases and exceeds the preset warning threshold. A trigger signal is then sent to the cloud-based virtual evolution platform, initiating a high-frequency evolution mode for efficient hard rock fracturing scenarios.

[0057] During the virtual evolution process, the dynamic reinforcement learning decision engine discovered that simply increasing the feed pressure would lead to increased drill bit wear, while decreasing the rotational speed would result in a decrease in slag removal efficiency. Through tens of thousands of simulations, the engine found a nonlinear equilibrium point: using pulse-type feed pressure adjustment logic, combined with specific flushing fluid pressure fluctuations, could generate an impact fracturing effect. This strategy parameter set was verified by a physical simulation digital twin before being sent to the drilling parameter adaptive control terminal.

[0058] After parsing the strategy, the adaptive control terminal controls the actuator to simulate an effect similar to impact drilling, effectively increasing the drilling speed in hard rock. Simultaneously, the physical simulation digital twin continuously monitors the thermodynamic state of the drill bit to prevent damage to the diamond composite sheet caused by localized high temperatures due to high-frequency impacts.

[0059] When the drill bit enters a fractured zone or a formation containing tectonic fractures, the flow metering component detects a sudden decrease in the flushing fluid return flow rate, and the displacement analysis component shows an abnormal decrease in drilling resistance. The safety risk warning component immediately identifies potential risks of stuck drill bit or burial.

[0060] The cloud-based virtual evolution platform rapidly adjusts simulation parameters, introducing numerous geological random factors to simulate the lateral pressure exerted on the drill pipe by falling rock fragments. The optimized parameter set output by the dynamic reinforcement learning decision engine switches to a safety-first mode: reducing the feed force to minimize drill bit disturbance to the rock formation, while maximizing flushing volume to fill fractures and remove fallen rock fragments.

[0061] The user interface issues a yellow warning to the operator and simultaneously displays suggested parameter adjustment curves. After confirmation by the operator through the interface, the control terminal executes closed-loop adaptive adjustment. During the subsequent drilling process, the system uses a physical simulation digital twin to invert the geometry of the fracture in real time and stores it in the central integrated management database, providing detailed data support for subsequent grouting reinforcement or tunnel layout.

[0062] After completing a drilling operation, the system automatically generates a construction quality evaluation report. This report is based on the mechanical specific work curve, parameter fluctuation rate, design trajectory deviation, and energy consumption statistics throughout the entire process. The dynamic reinforcement learning decision engine uses the successful trajectory of this operation as a positive sample, automatically updating its internal neural network weights to achieve continuous self-evolution of the system.

[0063] Example 5: Based on the system described in Example 1, this example further illustrates its in-depth optimization in terms of hardware deployment and software algorithm collaboration.

[0064] All components in the downhole data sensing device have undergone electromagnetic compatibility (EMC) hardening design, and its core circuit board is encapsulated in an explosion-proof cast aluminum housing. The sensor signal transmission cables use double-shielded twisted wire, and transient voltage suppressors are configured at each acquisition node to prevent power surges generated when high-power motors start up in the downhole from interfering with data accuracy.

[0065] The physical simulation digital twin employs a multi-level scheduling logic for computing resource allocation. For keyframe calculations involving drill string dynamics, it runs on a local edge gateway equipped with a high-performance graphics card to ensure sub-second real-time performance. For simulations of the long-term evolution of complex geological stress fields, the data packets are sliced ​​and uploaded to a cloud server cluster for parallel processing. This dynamic and static simulation strategy ensures that the system can both respond quickly to sudden on-site situations and perform in-depth long-term trend predictions.

[0066] The cloud-based virtual evolution platform employs a microservice architecture for its computational matrix. Each simulation task is encapsulated in an independent container, and computational resources are dynamically scaled based on the number of drilling rigs currently in operation. When multiple drilling rigs are operating in the same work area, the platform reduces the computational overhead of redundant modeling by sharing a geological environment model.

[0067] The reward function logic of the dynamic reinforcement learning decision engine has been further refined. In addition to mechanical work ratio, a drill string vibration energy distribution index has been introduced. The system extracts vibration components in specific frequency bands by analyzing the power spectral density of torque and pressure signals. If the vibration component is near the resonant frequency of the drill string, the reward function will impose a large negative penalty. In this way, the parameter set evolved by the decision engine can naturally avoid the resonance range of the equipment, extending the fatigue life of the mechanical structure.

[0068] The drilling parameter adaptive control terminal integrates an industrial-grade programmable logic controller (PLC) as the underlying execution module. The PLC communicates with the host computer management system via encrypted communication using the OPCUA protocol. When the issued adaptive adjustment command involves significant parameter changes, the smoothing filtering logic inside the PLC will convert it into a step-like gradual sequence to avoid generating huge hydraulic slamming forces on the hydraulic system due to sudden changes in pressure or speed.

[0069] The central integrated management database is also equipped with a data integrity verification unit. Utilizing the hash chain characteristics of blockchain technology, each segment of stored construction data is signed and encrypted. This ensures that the construction process data is tamper-proof, providing legally valid electronic evidence for retrospective analysis of potential construction accidents or determination of quality liability in the future.

[0070] Furthermore, the intelligent data processing and management system based on downhole drilling also has self-diagnosis and fault tolerance functions. If a pressure sensor fails, the system can use a virtual sensing algorithm to deduce the current feed pressure estimate based on torque, rotational speed, and historical correlation models, maintaining degraded operation of the system and ensuring that drilling operations are not completely interrupted due to the failure of a single component.

[0071] Example 6: This example describes the working mode of the system of the present invention in a specific application scenario of large-diameter directional drilling. In this scenario, due to the large diameter and depth of the borehole, the requirements for drilling stability are extremely high.

[0072] The downhole data sensing device integrates a measurement-while-drilling (MWD) unit at the rear end of the drill bit. This unit uses electromagnetic waves or mud pulse technology to transmit the drill bit's attitude angle and azimuth angle data in three-dimensional space to the surface in real time. A physical simulation digital twin integrates this attitude data into the simulation logic to construct a drill pipe flexibility mechanical model that includes the gravity-induced sagging effect.

[0073] Because directional drilling often requires switching between sliding drilling and combined drilling, the cloud-based virtual evolution platform is specifically designed with a dual-mode evolution matrix. In sliding drilling mode, the evolution objective focuses on precise control of the hole orientation and minimization of friction. In the combined drilling mode, the target switches to the highest mechanical specific power and slag removal efficiency.

[0074] The dynamic reinforcement learning decision engine generates a set of feature parameters for different borehole inclination angles by learning from a large number of directional drilling cases. For example, when the borehole is inclined upwards, the engine will automatically suggest increasing the flushing fluid flow rate to counteract the accumulation effect of rock powder caused by gravity.

[0075] The interactive interface of the drilling parameter adaptive control terminal provides a three-dimensional transparent borehole view. In this view, the geological stratification reconstructed by the physical simulation digital twin, the real-time trajectory, and the future trajectory predicted by the decision engine are highlighted in different colors. Non-professional operators only need to adjust the control lever according to the optimal strategy arrows prompted on the screen, and the terminal will automatically compensate for minor deviations, achieving a drilling experience similar to that of an aircraft autopilot.

[0076] In this embodiment, the central integrated management database also stores a large amount of mechanical property data for directional drilling assembly (BHA). When different specifications of drill bits or screw drilling tools are changed during operation, the system can automatically load the corresponding physical model without the need for complex re-initialization configuration.

[0077] Example 7: Based on the system described above, this example describes the safety emergency response mechanism of the present invention in response to geological anomalies such as water inrush or gas outburst in mines.

[0078] The flow metering component is logically linked to the gas concentration sensor installed at the borehole opening. When the intelligent data processing and management system based on downhole drilling detects an abnormal increase in flushing fluid backflow pressure accompanied by a gas concentration exceeding the safety threshold, the safety risk warning component instantly triggers the highest level alarm.

[0079] At this point, the cloud-based virtual evolution platform immediately ceases all efficiency optimization tasks and enters disaster prevention evolution mode. In this mode, the physical simulation digital twin is configured to simulate downhole fluid dynamics processes and calculate the minimum well control pressure required to maintain wellbore equilibrium.

[0080] The dynamic reinforcement learning decision engine rapidly calculates parameter combinations that maintain borehole stability and prevent further gas outbursts, such as specific rotational speeds, feed rates, and recommended values ​​for increasing mud density. Upon receiving instructions, the drilling parameter adaptive control terminal displays a red forced command on the operating interface and automatically adjusts the actuators according to preset safety procedures in the event of a delayed operator response, preventing the disaster from escalating.

[0081] Meanwhile, the system's remote synchronization function pushes all abnormal data, video streams, and emergency response logic from the site to the mine dispatch center in real time. Experts at the dispatch center can then use the historical evolution model of the central integrated management database to conduct remote secondary intervention, ensuring efficient disaster prevention through coordinated efforts between the surface and mine.

[0082] Through the detailed descriptions of the above embodiments, this invention constructs a closed-loop intelligent system integrating perception, simulation, evolution, decision-making, control, and management. Its core lies in utilizing the high-fidelity physical environment provided by digital twins to support reinforcement learning algorithms in advancing beyond physical space and time, realizing the transformation of downhole drilling operations from being driven by human experience to being driven by model data. This system not only improves operational efficiency under specific working conditions but also, through a continuous self-learning process, constructs a digital expert brain capable of coping with various complex and even unknown geological environments.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it; Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features therein; These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions in the embodiments of this invention. All contents not detailed in this invention are implemented using techniques known in the art or conventional techniques.

Claims

1. A smart data processing and management system for downhole drilling, characterized in that, It includes downhole data sensing devices, physical simulation digital twins, cloud-based virtual evolution platforms, dynamic reinforcement learning decision engines, drilling parameter adaptive control terminals, and a central integrated management database; The central integrated management database is connected to the downhole data sensing device, the physical simulation digital twin, the cloud virtual evolution platform, the dynamic reinforcement learning decision engine, and the drilling parameter adaptive control terminal, respectively, and is used to store and provide each device with historical drilling cases, geological exploration data, real-time operation trajectory and evolution model data. The downhole data sensing device is used to capture multi-dimensional raw operating data during downhole drilling rig operations in real time, and transmits the multi-dimensional raw operating data to the central integrated management database after digital processing, and synchronously transmits the real-time operating data to the physical simulation digital twin. The physical simulation digital twin is logically synchronized with the physical entity of the downhole drilling rig. It is used to receive real-time operating data transmitted by the downhole data sensing device, reconstruct the dynamic characteristics of the drilling rig and the physical interaction characteristics of the drill bit and the rock formation contact surface in the virtual digital space, realize the real-time mapping of the physical drilling process, and transmit the reconstructed virtual simulation data to the cloud virtual evolution platform. The cloud-based virtual evolution platform is connected to the physical simulation digital twin and is used to receive virtual simulation data, construct parallel simulation scenarios in the virtual environment, execute simulated drilling tests at a preset frequency, generate simulated sample data for parameter optimization, and transmit it to the dynamic reinforcement learning decision engine. The dynamic reinforcement learning decision engine is integrated into the cloud virtual evolution platform and is equipped with a logical computing model. It is used to receive simulated sample data and perform iterative training. With mechanical work ratio as the objective function, it outputs a set of drilling strategy parameters adapted to the current geological conditions and transmits the set of drilling strategy parameters to the drilling parameter adaptive control terminal. At the same time, the evolution model generated by the training is synchronously stored in the central integrated management database. The drilling parameter adaptive control terminal is connected to the dynamic reinforcement learning decision engine and the actuator of the downhole drilling rig. It is used to receive and parse the drilling strategy parameter set, issue control commands to the actuator of the downhole drilling rig according to the real-time feedback of geological fluctuations, realize closed-loop dynamic adjustment of drilling parameters, and simultaneously upload the real-time operation data during the adjustment process to the central integrated management database.

2. The intelligent data processing and management system based on downhole drilling as described in claim 1, characterized in that, The downhole data sensing device includes a pressure sensing component, a torque monitoring component, a displacement analysis component, a flow metering component, and a digital processing unit; The pressure sensing component includes strain gauge pressure transmitters installed at the oil inlet and return port of the feed cylinder, used to detect pressure fluctuations in the feed cylinder during different working cycles. By calculating the difference between the oil inlet pressure signal and the oil return pressure signal, and combining it with the effective pressure area of ​​the cylinder piston, the pressure signal is converted into a corresponding feed force logic value. The feed force logic value is a component of the multidimensional raw operating data. The torque monitoring component is integrated into the drive circuit of the drill rig power head. It is used to capture the real-time load during the rotation of the power head by monitoring the current change of the drive motor or the inlet and outlet pressure difference of the hydraulic motor, and to determine the cutting resistance of the drill bit in the current rock formation. The cutting resistance and real-time load data are components of the multi-dimensional raw operating data. The displacement analysis component uses a draw-wire displacement encoder or laser rangefinder with explosion-proof function to measure the cumulative depth of the borehole, the instantaneous drilling speed, and the displacement trajectory of the feed machine body. The cumulative depth, instantaneous drilling speed, and displacement trajectory data are components of the multidimensional raw operating data. The flow metering component is installed on the flushing fluid circulation pipeline to monitor the flushing fluid's inlet pressure, injection flow rate, and return flow status, and to determine whether the slag discharge efficiency in the orifice is within a preset range. The flushing fluid's pressure, flow rate, and return flow status data are components of the multidimensional raw operating data. The digital processing unit is connected to the pressure sensing component, torque monitoring component, displacement analysis component, and flow metering component, respectively. It is used to perform analog-to-digital conversion on the acquired raw analog voltage signal, apply Kalman filtering algorithm to filter out periodic noise generated by the hydraulic pump station pulsation, extract drilling load characteristics and add timestamps, and then send them to the central integrated management database and physical simulation digital twin through the communication link.

3. The intelligent data processing and management system based on downhole drilling as described in claim 1, characterized in that, The physical simulation digital twin has the functions of geological environment reconstruction and drill fatigue state monitoring, and adopts a mechanical model based on finite element analysis and discrete element coupling. The geological environment reconstruction function is used to dynamically update the formation attribute parameters in the virtual space based on the ratio of historical drilling data to torque and pressure during the current drilling process, using a stochastic modeling algorithm based on implicit sequential Gaussian simulation. The formation attribute parameters include rock compressive strength, rock fracturing and borehole wall stability index, to ensure the consistency of the digital twin and the real downhole environment in terms of physical and mechanical properties, so as to complete the reconstruction of the drilling rig dynamic characteristics and the physical interaction characteristics of the drill bit and rock formation. The drill string fatigue condition monitoring sub-item is used to calculate the remaining life percentage of the drill pipe and drill bit in real time in virtual space based on the cumulative number of drill string rotations, average compressive strength and vibration spectrum characteristics, and to issue maintenance suggestions before the remaining life percentage reaches a predetermined threshold. The mechanical model based on the coupling of finite element analysis and discrete element method is used to simulate the micromechanical behavior of diamond composite plates or alloy cylindrical teeth in drill bits when breaking rocks. The axial feed force, tangential torque, lateral vibration and springback stiffness parameters are introduced in the simulation process to achieve real-time mapping of the physical drilling process.

4. The intelligent data processing and management system based on downhole drilling according to claim 1, characterized in that, The cloud-based virtual evolution platform is equipped with a high-concurrency computing matrix, which can support tens of thousands of virtual drilling cycles within a predetermined time. By changing the combination ratio of feed pressure and rotation speed, it detects the extreme points of the drilling efficiency curve and records the energy consumption index under each set of parameters. The cloud-based virtual evolution platform is used to introduce random noise disturbance factors during simulation experiments to simulate geological changes and mechanical vibrations in the downhole environment, thereby enhancing the robustness of the decision-making logic under extreme working conditions. The cloud-based virtual evolution platform also has a multi-machine collaborative evolution function. When multiple drilling rigs are operating simultaneously in the same mining area, the cloud-based virtual evolution platform is used to spatially correlate the real-time geological feedback of multiple drilling rigs, and to simulate the spatial extension trend of the strata using physical simulation digital twins, thereby generating advanced evolution strategies for adjacent drilling rigs.

5. The intelligent data processing and management system based on downhole drilling according to claim 1, characterized in that, The logical computation model of the dynamic reinforcement learning decision engine is a model based on proximal policy optimization, which adopts a deep Actor-Critic architecture. The Actor network is used to output the probability distribution of drilling parameters, and the Critic network is used to evaluate the expected specific work benefit under the current parameter configuration. The dynamic reinforcement learning decision engine adopts a multi-dimensional reward function logic during training. The reward function logic sets the increase in drilling speed, the reduction in drill bit wear, the reduction in energy consumption, and the avoidance of safety hazards in the hole as positive incentives, and sets parameter mutations, equipment overload, and vibration exceeding limits as negative penalty terms. The process parameters are obtained by maximizing the expected return value. The reward function logic also introduces a mechanical specific work term and a drill string vibration energy distribution index. The mechanical specific work is defined as the ratio of input energy to broken volume. The drill string vibration energy distribution index is extracted by analyzing the power spectral density of torque and pressure signals. When the vibration component is within the resonant frequency range of the drill string, the reward function logic outputs a negative penalty. The decision suggestions generated by the dynamic reinforcement learning decision engine have self-evolutionary characteristics and are used to automatically correct the neuron weights within the model based on the errors fed back from actual construction.

6. The intelligent data processing and management system based on downhole drilling according to claim 1, characterized in that, The drilling parameter adaptive control terminal adopts a multi-level control strategy when performing closed-loop adjustment: the first level is a global trend adjustment based on cloud-based suggestions, used to set the feed pressure and speed reference; The second level is local error compensation based on real-time feedback from downhole sensors, used to cope with geological disturbances on the order of seconds. The drilling parameter adaptive control terminal integrates a PLC as the underlying execution module. The PLC is configured with smoothing filtering logic to convert the adaptive adjustment instructions into a step-like gradual sequence. The drilling parameter adaptive control terminal also includes an operation interface, which provides parameter architecture guidance to operators and transforms decision logic into preset construction instruction suggestions. At the same time, it displays the geological stratification, real-time trajectory, and predicted trajectory reconstructed by the physical simulation digital twin.

7. The intelligent data processing and management system based on downhole drilling according to claim 1, characterized in that, The central integrated management database adopts a hybrid storage mode that combines time-series database and relational database. The time-series database is used to store high-frequency waveform data during the drilling process to support spectrum analysis and fault diagnosis. The relational database is used to store geological logic, process specifications, and evolution model parameters, and supports cross-hole comparison analysis; The central integrated management database has a built-in specific work evaluation logic, which is used to evaluate drilling efficiency by calculating the energy consumed per unit volume of rock breaking, i.e., the mechanical specific work value. When the real-time monitored mechanical specific work exceeds a preset threshold, the dynamic reinforcement learning decision engine is triggered to enter a high-frequency evolution mode. The central integrated management database is also equipped with a data integrity verification unit, which uses the hash chain feature of blockchain technology to sign and encrypt the stored construction data.

8. The intelligent data processing and management system based on downhole drilling according to claim 1, characterized in that, The system also includes a multi-source heterogeneous data integration module, which is equipped with a protocol adaptive parsing unit. The module stores a mining sensor and controller protocol library, which is used to identify the communication protocols of the access devices and map them uniformly into standardized drilling physical quantity vectors. The multi-source heterogeneous data integration module also includes a signal quality assessment unit, which is used to monitor the integrity of sensor data and use a long short-term memory neural network model to reconstruct and predict missing data based on historical sequences.

9. The intelligent data processing and management system based on downhole drilling according to claim 1, characterized in that, The system also includes a safety risk early warning component, which is used to predict the risk of stuck drill, buried drill or blowout by analyzing the stress distribution in the physical simulation digital twin. When the predicted risk value exceeds the safety limit, the drilling parameter adaptive control terminal forcibly executes emergency decompression or drilling stop logic; The safety risk early warning component is also used to link with the gas concentration sensor. When an abnormal increase in flushing fluid backflow pressure is detected and accompanied by a gas concentration exceeding the safety threshold, an emergency response mechanism is triggered, causing the cloud-based virtual evolution platform to enter the disaster prevention evolution mode. The dynamic reinforcement learning decision engine then calculates the parameter combination that can maintain borehole stability and prevent gas outbursts.

10. The intelligent data processing and management system based on downhole drilling according to claim 1, characterized in that, The system adopts a distributed architecture design, including downhole edge computing nodes and cloud server clusters; The downhole edge computing node is used to undertake data acquisition and basic control functions, and performs lightweight evolution tasks through the built-in edge evolution module when the communication link bandwidth is lower than a preset threshold. The edge evolution module uses a search algorithm based on a proxy model to predict the trend of mechanical specific work change. The cloud server group is used to perform physical simulation reconstruction and reinforcement learning evolution tasks; The downhole edge computing node and the cloud server group interact with each other through an asynchronous synchronization mechanism. High-frequency real-time data is stored locally in a loop, while low-frequency trend data after feature extraction is uploaded to the cloud server group when the communication link is idle.

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