A small-diameter deep well mixed reeling system dynamics precise regulation method and system

Abstract

This invention belongs to the technical field of precise dynamic control methods, and particularly relates to a precise dynamic control method and system for a small-diameter deep well hybrid-to-winding system. It constructs a multi-field coupled dynamic framework integrating gas-solid, contact, and structural interactions to accurately characterize the multi-body strong coupling dynamic characteristics of the small-diameter deep well hybrid-to-winding system. A time-varying parameter adaptive modeling system is established, dynamically updating the characteristic parameters of the winding system in real time based on the system's operating state to adapt to the system's nonlinear time-varying characteristics. Multi-condition tests are conducted on a test bench to complete model calibration and parameter optimization, reducing the prediction error of the dynamic model. Efficient solution algorithms and engineering integration tools are developed to achieve real-time solution of the dynamic model and bidirectional interaction with field data, enabling online adaptive correction of model parameters. Field industrial tests are conducted to verify the model accuracy, and an iterative optimization and self-calibration mechanism is established based on long-term field operating data to adapt to changes in system characteristics caused by equipment wear.

Description

Technical Field

[0001] This invention belongs to the technical field of dynamic precision control methods, and particularly relates to a dynamic precision control method and system for a small-diameter deep well hybrid-to-winding system. Background Technology

[0002] Small-diameter deep-well hybrid hoisting systems are core hoisting equipment in deep-well mining. Constrained by the shaft space with a net diameter ≤6m, complex gas-solid-contact-structure multi-body strong coupling relationships are formed between the hoisting system, sheave, wire rope, cage, and shaft wall during the mixed hoisting process of skip and cage. At high speeds, the system also exhibits unique dynamic characteristics such as a shaft "wind tunnel effect," high-frequency contact collisions between the wire rope and cage through small gaps, and cross-vibration of the dual hoisting systems. Traditional modeling methods, often using lumped mass models or simplified finite element models, cannot fully capture these multi-field coupling effects, resulting in significant deviations in the characterization of the system's dynamic characteristics. Dynamic response prediction errors can reach 15%-25%, making it difficult to support the precise control requirements of the system.

[0003] The core characteristic parameters of the small-diameter deep-well hybrid-to-winding system exhibit significant time-varying and nonlinear features. The effective length of the wire rope changes in real time with the hoisting height, directly causing dynamic changes in the system's stiffness, damping, and mass distribution. Furthermore, the hoisting load fluctuates randomly due to uneven rock load and changes in transported objects. The friction coefficient exhibits nonlinear changes due to factors such as wear of the sheave pad, oil contamination of the wire rope, and environmental temperature and humidity. Traditional fixed-parameter models are no longer suitable for the characteristics of this system. Simultaneously, existing technologies suffer from low model solving efficiency, insufficient engineering integration, and a lack of model self-calibration mechanisms for long-term equipment wear. This makes system control prone to tension imbalances and increased vibration, not only reducing the efficiency of mine hoisting operations but also posing safety hazards such as wire rope breakage and hoisting container collisions, severely restricting the safe, efficient, and precise operation of the small-diameter deep-well hybrid-to-winding system. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for precise dynamic control of a hybrid-to-winding system in a small-diameter deep well, the method comprising:

[0005] Step S110: Construct a multi-field coupled dynamic framework that integrates gas-solid, contact, and structural interactions to accurately characterize the multi-body strong coupling dynamic characteristics of the hybrid modified winch system in small-diameter deep wells;

[0006] Step S120: Establish a time-varying parameter adaptive modeling system, and dynamically update the characteristic parameters of the modified system in real time based on the system operating status to adapt to the nonlinear time-varying characteristics of the system;

[0007] Step S130: Conduct multi-condition tests on the test bench to complete model calibration and parameter optimization, thereby reducing the prediction error of the dynamic model;

[0008] Step S140: Develop efficient solution algorithms and engineering integration tools to achieve real-time solution of dynamic models and bidirectional interaction with field data, and complete online adaptive correction of model parameters;

[0009] Step S150: Conduct on-site industrial tests to verify the accuracy of the model, and establish a model iterative optimization and self-calibration mechanism based on long-term on-site operating data to adapt to changes in system characteristics caused by equipment wear.

[0010] Preferably, in step S110, constructing a multi-field coupled dynamics framework specifically includes fluid-structure interaction modeling, contact collision modeling, and cross-vibration coupling modeling of a dual-lift system. Through multi-dimensional modeling, the integration of multi-field coupling effects of gas-solid, contact, and structure is achieved.

[0011] Preferably, the fluid-structure interaction modeling adopts a two-way fluid-structure interaction technology to integrate computational fluid dynamics and multibody dynamics, realizing two-way feedback between aerodynamic loads and mechanical motion of the modified winch system; the contact collision modeling adopts nonlinear contact theory combined with penalty function method, and incorporates wellbore deformation model based on ground pressure monitoring data; the cross-vibration coupling modeling of the dual lifting system adopts a lumped mass and elastic beam hybrid modeling method, establishes dynamic correlation equations and characterizes the vibration energy transfer path through coupling matrix.

[0012] Preferably, in S120, the core characteristic parameters of the modified stranding system include the geometric and mechanical parameters of the wire rope, the lifting load parameters, and the friction coefficient parameters. The time-varying parameter adaptive modeling system establishes dynamic characterization models for each core characteristic parameter by collecting system operation data in real time.

[0013] Preferably, for the geometric and mechanical parameters of the wire rope, the effective length of the wire rope is calculated in real time based on the lifting height, and the time-varying functions of the wire rope stiffness and damping coefficient are established by the piecewise stiffness matrix method. At the same time, the finite element model order reduction technique is used to optimize the model solution efficiency. For the lifting load parameters and friction coefficient parameters, the random load changes are characterized by fusing sensor data through a filtering algorithm, and the dynamic prediction function of the friction coefficient is trained by a neural network algorithm.

[0014] Preferably, in step S130, a test bench matching the actual working conditions of a small-diameter deep well is built, a high-precision sensor array is configured to collect dynamic response data of the modified winch system under multiple working conditions and establish a test database, and a numerical fitting algorithm is used to calibrate and iteratively correct the key parameters of the dynamic model.

[0015] Preferably, in step S140, the solution efficiency of the dynamic model is optimized based on a multi-timescale hybrid solution strategy, and parallel computing technology is introduced to improve the real-time update rate of the model; a multi-field coupled dynamic simulation module is built based on an industrial simulation platform, an integrated modeling and simulation platform is developed and a real-time data interaction interface is embedded to realize bidirectional transmission of field operation data and model parameters.

[0016] Preferably, the multi-timescale hybrid solution strategy is as follows: an explicit solution algorithm is used for the rapidly changing dynamic process of the modified system, and an implicit solution algorithm is used for the slowly changing structural and parameter processes; the dynamic model parameters are adaptively corrected online according to a preset period through the real-time data interaction interface.

[0017] Preferably, in step S150, a full-condition industrial test is carried out at a typical small-diameter deep well engineering site to verify the characterization accuracy of the dynamic model under actual working conditions; model optimization is completed for working conditions with large model prediction deviations in the field test, and the dynamic characterization model of core characteristic parameters is continuously trained based on long-term field operation data. The developed model self-calibration module can automatically identify system characteristic drift caused by equipment wear and complete adaptive adjustment of model parameters.

[0018] Furthermore, embodiments of the present invention also provide a dynamic precision control system for a small-diameter deep well hybrid-to-winding system, characterized in that it includes:

[0019] A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described method for precise dynamic control of a small-diameter deep well hybrid-to-winding system by executing the machine-executable instructions.

[0020] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-described method for precise dynamic control of a small-diameter deep well hybrid-to-winding system.

[0021] Based on the above, by constructing a multi-field coupled dynamic framework integrating gas-solid, contact, and structural interactions, the precise characterization of the multi-body strong coupling dynamic characteristics of a small-diameter deep well hybrid-to-winding system was achieved. This breakthrough overcomes the technical bottleneck of traditional models failing to fully capture coupling effects such as wellbore aerodynamic interference, contact collisions, and cross vibrations of the dual-lifting system. Simultaneously, the established time-varying parameter adaptive modeling system can update core characteristic parameters in real time according to the actual operating state of the system, effectively adapting to the nonlinear time-varying characteristics of the system. This solves the adaptability defects of traditional fixed-parameter models and provides a reliable model foundation for the precise control of system dynamics.

[0022] This invention improves the solution efficiency and real-time update capability of the dynamic model by developing a highly efficient hybrid solution algorithm and introducing parallel computing technology. Combined with the bidirectional data interaction function of the integrated modeling and simulation platform, it achieves online adaptive correction of model parameters, meeting the real-time requirements of on-site dynamic control. Furthermore, relying on on-site industrial test verification and a long-term operational data-driven iterative optimization mechanism, coupled with a model self-calibration module, it can automatically identify system characteristic drift caused by equipment wear and dynamically adjust model parameters. This significantly improves the accuracy and stability of system control, effectively suppresses problems such as tension imbalance and increased vibration, reduces various safety hazards, and significantly improves the safe operation level of the small-diameter deep well hybrid-to-winding conversion system and the overall efficiency of mine hoisting operations. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the execution flow of the method for precise dynamic control of a small-diameter deep well hybrid-to-winding system provided in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of exemplary hardware and software components of the dynamic precision control system for a small-diameter deep well hybrid-to-winding system provided in an embodiment of the present invention. Detailed Implementation

[0025] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a method for precise dynamic control of a hybrid-to-winding system in a small-diameter deep well, according to an embodiment of the present invention. The following is a detailed description of this method for precise dynamic control of a hybrid-to-winding system in a small-diameter deep well.

[0026] Includes the following steps:

[0027] Step 1: Construct a multi-field coupled dynamic framework, integrating gas-solid-contact-structure interactions;

[0028] 1.1 Two-way fluid-structure interaction (FSI) technology is adopted to integrate computational fluid dynamics (CFD) and multibody dynamics (MBD): Based on the RNG k-ε turbulence model, a simulation model of the aerodynamic environment inside a small-diameter well is established. The mesh generation adopts adaptive refinement (minimum mesh size ≤ 5mm) to simulate the "wind tunnel effect" at wind speeds ≥ 20m / s, calculate the aerodynamic load and air damping coefficient on the lifting container surface, and output the aerodynamic parameter field that varies with height and velocity; using ANSYS System... The Coupling interface transmits CFD calculation results to the multibody dynamics model in real time, enabling bidirectional feedback between aerodynamic loads and mechanical motion. 1.2 Establishing a refined contact and collision model: Using nonlinear Hertzian contact theory combined with the penalty function method, the contact stiffness and damping coefficient between the wire rope and the guideway are defined, and the generalized coordinate method is used for contact detection. A well wall elliptical deformation model based on ground pressure monitoring data is introduced, and the well wall deformation is transformed into the dynamic offset of the guideway reference surface, which is then incorporated into the contact and collision model. 1.3 Constructing a cross-vibration coupling model for the dual lifting system: Using a lumped mass-elastic beam hybrid modeling method, the wire rope is discretized into multiple elastic beam elements (each segment ≤ 5m in length), considering the coupling of bending, torsion, and longitudinal vibrations. The sheave is simplified into a flexible body with rotational inertia and damping. The dynamic correlation equations of the two lifting systems are established, and start-stop synchronization error parameters are introduced. The vibration energy transfer path is characterized by the coupling matrix.

[0029] Step 2: Develop time-varying parameter adaptive modeling technology to dynamically update system characteristic parameters.

[0030] 2.1 Real-time modeling of time-varying parameters of wire rope: The effective length of the wire rope is calculated in real time based on the lifting height (accuracy ≤ 0.1m). The time-varying functions of stiffness and damping coefficient are established by the piecewise stiffness matrix method. The first 20 dominant modes are retained by the finite element model reduction technique (modal synthesis method), reducing the model degrees of freedom by more than 60%, and the time for a single update is ≤ 10ms. 2.2 Dynamic characterization of random load and nonlinear friction: Based on the extended Kalman filter (EKF) algorithm, the load fluctuation model is established by fusing data from tension sensor (accuracy ≤ 1%) and velocity sensor. The dynamic prediction function μ(Δv,w,T,H) of the friction coefficient μ is trained by a BP neural network, and the prediction error is controlled within ±5%.

[0031] Step 3: Model test calibration and parameter optimization

[0032] 3.1 Construct a full-size test bench: Construct a small-diameter deep well test system simulating a diameter ≤6m and a depth ≥800m, configure physical models of skips and cages with a scale ratio of 1:5, and install a high-precision sensor array; conduct multi-condition tests, collect data such as tension fluctuations and container swing amplitude, and establish a test database; 3.2 Model parameter calibration: Use the least squares method to calibrate key parameters, reduce the dynamic response prediction error to ≤8%, and the synchronization error prediction deviation to ≤0.05m / s.

[0033] Step 4: Develop real-time solution algorithms and engineering integration tools

[0034] 4.1 Optimize solution efficiency: Adopt an explicit-implicit hybrid solution strategy and introduce GPU parallel computing technology to make the model update frequency ≥50Hz in real time; 4.2 Develop an integrated platform: Based on MATLAB / Simulink or ANSYS Workbench, build a multi-field coupled dynamics simulation module, embed a real-time data interface, and realize online adaptive correction of model parameters (correction period ≤100ms).

[0035] Step 5: On-site verification and iterative optimization

[0036] 5.1 Industrial Test: Select typical small-diameter deep wells with a net diameter ≤6m and a depth ≥1000m, connect the model with the on-site hoisting system, and verify the model accuracy under different working conditions; 5.2 Establish a maintenance and update mechanism: Based on ≥6 months of long-term on-site operation data, continuously train the prediction model, develop a model self-calibration module, and deal with changes in system characteristics caused by equipment wear.

[0037] Example 1: An Example of Accurate Aerodynamic Disturbance Modeling Based on Bidirectional Coupling of RNG k-ε and MBD

[0038] This embodiment addresses the modeling problem of the "wind tunnel effect" during high-speed operation of a small-diameter deep well hoisting container. The specific implementation steps are as follows:

[0039] The test parameters were determined as follows: the net diameter of the small-diameter deep well was 4.5m, the depth was 800m, the hoisting container was a skip (load capacity 20t), the hoisting speed was 12m / s, and the maximum wind speed inside the well was 22m / s.

[0040] Aerodynamic environment simulation model construction: The RNG k-ε turbulence model was adopted with a minimum mesh size of 3mm to simulate the airflow field inside the wellbore, and the pressure difference distribution (maximum pressure difference 180Pa), shear stress (average shear stress 0.8Pa) and air damping coefficient (varying with velocity from 0.05 to 0.12N·s / m) on the surface of the skip were calculated.

[0041] Two-way fluid-structure interaction is achieved by transmitting the aerodynamic loads calculated by CFD to the ADAMS multibody dynamics model in real time through the ANSYS System Coupling interface, and feeding back the skip attitude change (maximum tilt angle 0.8°) to the CFD model to update the airflow field distribution.

[0042] Results verification: The experimental results showed that the aerodynamic drag error of the skip was ≤6% and the predicted deviation of the container swing amplitude was ≤5mm. Compared with the traditional model (aerodynamic drag error ≥15%), the characterization accuracy was significantly improved.

[0043] Example 2: Modeling of contact collision between wire rope and guideway with elliptical deformation of well wall

[0044] This embodiment addresses the problem of amplified contact collision force caused by elliptical deformation of the well wall. The specific implementation steps are as follows:

[0045] Wellbore deformation data acquisition: A small-diameter deep well (net diameter 5m) with a depth of 1000m was selected. The ellipticity of the wellbore was monitored by distributed strain sensors. The ellipticity distribution function ε(z)=0.02+0.00001z (z is the depth coordinate) was fitted, and the maximum ellipticity was 3.5%.

[0046] Contact collision model construction: Nonlinear Hertzian contact theory is adopted, with contact stiffness k = 2.5 × 10⁻⁶. 6 N / m (dynamically adjusted with contact load, adjustment range ±15%), damping coefficient c is based on the material damping characteristics of No. 45 steel tank guide and steel wire rope, and is fitted to obtain c(v)=50+10v (v is the contact velocity); contact detection adopts the generalized coordinate method, sampling frequency 120Hz;

[0047] Well wall deformation integration: The ellipticity distribution function is transformed into a dynamic offset Δy(z,t)=ε(z)×D / 2 (D is the well diameter) of the guideway reference plane, and the position of the guideway in the contact collision model is updated in real time;

[0048] Results verification: The peak value of the simulated contact collision force deviated from the experimental value by ≤7%. Compared with the model that did not consider the well wall deformation (deviation ≥20%), the amplification effect of well wall deformation on the contact force was accurately quantified (amplification factor 1.8-2.3).

[0049] Example 3: Example of cross-vibration coupling modeling for a dual-lift system

[0050] This embodiment addresses the cross-vibration coupling problem during mixed hoisting of skips and cages. The specific implementation steps are as follows:

[0051] The parameters of the dual hoisting system are determined as follows: the net diameter of the small-diameter deep well is 5.5m and the depth is 900m. One system is a skip hoisting system (42mm wire rope diameter, hoisting speed 10m / s), and the other system is a cage hoisting system (38mm wire rope diameter, hoisting speed 8m / s).

[0052] Hybrid modeling implementation: The lumped mass-elastic beam hybrid modeling method is adopted, and the steel wire ropes of both systems are discretized into elastic beam elements with a length of 4m each, considering the coupling of bending, torsion and longitudinal vibration; the sheave is simplified as a flexible body with a moment of inertia of 0.8kg·m² and a damping coefficient of 1.2N·m·s / rad;

[0053] Establishment of coupling equations: Establishment of dynamic correlation equations , Where K_c is the coupling stiffness matrix (calibrated based on wellbore constraints and airflow interference), and start-stop synchronization error parameters Δv=0.5m / s and Δa=0.2m / s² are introduced;

[0054] Results verification: The simulated cross vibration amplitude (maximum longitudinal vibration amplitude of skip 8mm, maximum longitudinal vibration amplitude of cage 6mm) deviated from the experimental value by ≤6%, and the synchronous error prediction deviation was ≤0.04m / s, accurately characterizing the vibration energy transfer path (energy transfer efficiency from skip system to cage system 15%-20%).

[0055] Example 4: Adaptive Modeling Example of Time-Varying Parameters of Wire Rope

[0056] This embodiment addresses the problem of system characteristic changes caused by time-varying wire rope length. The specific implementation steps are as follows:

[0057] The wire rope parameters are determined as follows: the wire rope material is 6×19S+FC, the linear density is ρ=5.2kg / m, the initial length is L0=900m, and the initial stiffness is k0=1.8×10⁻⁶. 8 N / m;

[0058] Time-varying function establishment: Based on the real-time calculation of the effective length L(t) (accuracy 0.08m) of the lifting height, the fitting coefficient a=1.2 was calibrated using experimental data, and the time-varying stiffness function k(L,t)=1.8×10 was established. 8 ×(900 / L) 1·2 The mass time-varying function m(L,t) = 5.2 × L; the damping coefficient was obtained by fitting the energy decay test data, c(L,t) = 2000 + 5L;

[0059] Model order reduction optimization: Using the modal synthesis method, the first 20 dominant modes (covering more than 95% of vibration energy) are retained, and the model degrees of freedom are reduced from 1200 to 450, with a single update taking 8ms;

[0060] Results verification: The predicted values ​​of stiffness and damping at different lifting heights (200m, 500m, 800m) deviated from the experimental values ​​by ≤5%, and the model update frequency was 60Hz, which met the real-time calculation requirements.

[0061] Example 5: Dynamic Characterization of Random Load and Nonlinear Friction

[0062] This embodiment addresses the problem of random fluctuations in lifting load and nonlinearity of friction coefficient. The specific implementation steps are as follows:

[0063] Sensor configuration: Install a tension sensor with an accuracy of 0.8% (sampling frequency 150Hz) and a speed sensor with an accuracy of 0.1m / s. Install a displacement sensor (to monitor the wear of the liner, accuracy 0.01mm) and an infrared spectral sensor (to monitor the oil content of the wire rope) at the sheave.

[0064] Random load estimation: Based on the Extended Kalman Filter (EKF) algorithm, the state equation is x_k=Ax_{k-1}+Bu_{k-1}+w_{k-1}, and the observation equation is z_k=Hx_{k-1}+v_k, where x=[F,ΔF] T Using a state vector (F is the average load, ΔF is the fluctuating load), load changes are estimated in real time by fusing sensor data to establish a load fluctuation model. normal distribution);

[0065] Friction coefficient prediction: Friction coefficient data were collected under different relative velocities Δv (0-15m / s), wear amount w (0-5mm), temperature T (5-40℃), and humidity H (30%-90%). A BP neural network (4 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer) was constructed and trained to obtain the prediction function μ(Δv,w,T,H).

[0066] Results verification: Load estimation error ≤3%, friction coefficient prediction error ≤4%, compared with the traditional fixed friction coefficient model (error ≥15%), significantly improved the accuracy of nonlinear characteristic characterization.

[0067] Example 6: Model Experiment Calibration and Parameter Optimization Example

[0068] This embodiment addresses the issue of model parameter calibration and accuracy improvement, and the specific implementation steps are as follows:

[0069] Test bench setup: Construct a small-diameter deep well test system simulating a diameter of 5m and a depth of 850m. The scale ratio of the skip to the cage physical model is 1:5. Install sensor arrays: wire rope tension sensor (sampling frequency 120Hz), container attitude IMU sensor (acceleration accuracy 0.008m / s²), cage guide contact force sensor (range 0-50kN), and wind speed sensor (measurement range 0-30m / s).

[0070] Multi-condition test: Conduct multi-condition combination tests with hoisting speeds of 5m / s, 10m / s, and 15m / s, loads of 50%, 100%, and 150% of the rated load, and wellbore ellipticity of 0%, 2%, and 5%, collect 120 sets of dynamic response data, and establish a test database.

[0071] Parameter calibration: The least squares method was used, with experimental data as the benchmark, to iteratively correct the contact stiffness (initial value 2.0 × 10⁻⁶). 6 N / m corrected to 2.3 × 10 6 Key parameters include N / m), aerodynamic damping (initial value 0.08 N·s / m corrected to 0.095 N·s / m), and wire rope elastic modulus (initial value 206 GPa corrected to 210 GPa);

[0072] Results verification: After calibration, the dynamic response prediction error (tension, vibration amplitude) of the model was reduced to 7%, and the prediction deviation of the start-stop synchronization error of the dual system was ≤0.03m / s, which meets the requirements of accurate modeling.

[0073] Example 7: Real-time Solving Algorithm and Integration Platform Development Example

[0074] This embodiment addresses the issues of model solution efficiency and engineering integration, and the specific implementation steps are as follows:

[0075] Solution algorithm optimization: An explicit-implicit hybrid solution strategy is adopted. For rapidly changing processes such as contact collisions and aerodynamic loads, an explicit algorithm (time step 8e-5s) is used, while for slowly changing processes such as structural deformation and time-varying parameters, an implicit algorithm (time step 5e-3s) is used. GPU parallel computing technology (NVIDIA A100 graphics card) is introduced to split the multi-unit coupled calculation task into 20 parallel threads.

[0076] Integrated platform development: Based on MATLAB / Simulink, a multi-field coupled dynamics simulation module was built, integrating sub-models such as aerodynamic calculation, contact collision, time-varying parameter adaptation, and dual-system coupling, and a visual modeling interface was developed (supporting parameter input, model building, and real-time display of simulation results).

[0077] Real-time data interface embedding: The OPCUA communication protocol is adopted to develop a real-time data interface, which supports bidirectional data interaction with the field monitoring system and sets the online adaptive correction period of model parameters to 80ms.

[0078] Results verification: The model updates in real time at a frequency of 65Hz, the time for a single simulation is ≤15ms, the data interaction delay of the integrated platform is ≤20ms, and it can be directly connected to the field control system to meet the needs of engineering applications.

[0079] Example 8: Field Validation and Long-Term Iterative Optimization Example

[0080] This embodiment addresses the issues of model application in the field and long-term optimization. The specific implementation steps are as follows:

[0081] Field test selection: A small-diameter deep well with a net diameter of 5.8m and a depth of 1050m was selected (hybrid-to-winch system: skip load 25t, cage load 10t), and the developed dynamic model was connected with the field hoisting system;

[0082] Field verification: Real-time data on lifting height, speed, tension, container posture, and contact force under various working conditions such as full load / no load, acceleration / uniform speed / deceleration, and normal / well wall deformation (3% ellipticity) are collected and compared with model prediction results;

[0083] Model optimization: For scenarios with large deviations, such as sudden load changes (a 30% increase in gangue) and emergency braking (deceleration of 2.5 m / s²), the dynamic adjustment strategy for contact stiffness (adding a load change coefficient correction term) and the calculation method for transient response of aerodynamic loads (using dynamic mesh update technology) were optimized, further reducing the prediction error to 6%.

[0084] Long-term iteration: Collect 8 months of long-term field operation data, continuously train the friction coefficient prediction model (update 300 sets of training samples), and the well wall deformation fitting function (correct ellipticity distribution coefficient). Develop a model self-calibration module to identify characteristic drift caused by wire rope fatigue (stiffness decrease of 5%) and cannula wear (contact stiffness decrease of 8%) through monitoring data, and automatically adjust model parameters to ensure that the prediction error remains stable at ≤7% during long-term operation.

[0085] The above eight embodiments elaborate on the specific implementation of the present invention from different dimensions such as multi-field coupling, contact collision, cross vibration, time-varying parameters, nonlinear characteristics, parameter calibration, real-time solution, and field application. Each embodiment can be implemented independently or in combination, and all can achieve accurate modeling of the strong multi-body coupling and nonlinear time-varying characteristics of the hybrid-modified small-diameter well system.

[0086] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

[0087] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for precise dynamic control of a hybrid-winding system in a small-diameter deep well, characterized in that: Includes the following steps: Step S110: Construct a multi-field coupled dynamic framework that integrates gas-solid, contact, and structural interactions to accurately characterize the multi-body strong coupling dynamic characteristics of the hybrid modified winch system in small-diameter deep wells; Step S120: Establish a time-varying parameter adaptive modeling system, and dynamically update the characteristic parameters of the modified system in real time based on the system operating status to adapt to the nonlinear time-varying characteristics of the system; Step S130: Conduct multi-condition tests on the test bench to complete model calibration and parameter optimization, thereby reducing the prediction error of the dynamic model; Step S140: Develop efficient solution algorithms and engineering integration tools to achieve real-time solution of dynamic models and bidirectional interaction with field data, and complete online adaptive correction of model parameters; Step S150: Conduct on-site industrial tests to verify the accuracy of the model, and establish a model iterative optimization and self-calibration mechanism based on long-term on-site operating data to adapt to changes in system characteristics caused by equipment wear.

2. The method for precise dynamic control of a small-diameter deep well hybrid-to-winding system according to claim 1, characterized in that: In step S110, the construction of the multi-field coupled dynamics framework specifically includes fluid-structure interaction modeling, contact collision modeling, and cross-vibration coupling modeling of the dual-lift system. Through multi-dimensional modeling, the integration of multi-field coupling effects of gas-solid, contact, and structure is achieved.

3. The method and system for precise dynamic control of a small-diameter deep well hybrid-to-winding system according to claim 1, characterized in that: The fluid-structure interaction modeling adopts a two-way fluid-structure interaction technique to integrate computational fluid dynamics and multibody dynamics, realizing two-way feedback between aerodynamic loads and mechanical motion of the modified winch system; the contact collision modeling adopts nonlinear contact theory combined with penalty function method, and incorporates wellbore deformation model based on ground pressure monitoring data; the cross-vibration coupling modeling of the dual lifting system adopts a lumped mass and elastic beam hybrid modeling method, establishes dynamic correlation equations and characterizes the vibration energy transfer path through coupling matrix.

4. The method and system for precise dynamic control of a small-diameter deep well hybrid-to-winding system according to claim 2, characterized in that: In S120, the core characteristic parameters of the modified stranding system include the geometric and mechanical parameters of the wire rope, the lifting load parameters, and the friction coefficient parameters. The time-varying parameter adaptive modeling system establishes dynamic characterization models for each core characteristic parameter by collecting system operation data in real time.

5. The method and system for precise dynamic control of a small-diameter deep well hybrid-to-winding system according to claim 4, characterized in that: For the geometric and mechanical parameters of the wire rope, the effective length of the wire rope is calculated in real time based on the lifting height, and the time-varying functions of the wire rope stiffness and damping coefficient are established by the piecewise stiffness matrix method. At the same time, the finite element model order reduction technique is used to optimize the model solution efficiency. For the lifting load parameters and friction coefficient parameters, the random load changes are characterized by fusing sensor data through a filtering algorithm, and the dynamic prediction function of the friction coefficient is trained by a neural network algorithm.

6. The method and system for precise dynamic control of a small-diameter deep well hybrid-to-winding system according to claim 1, characterized in that: In step S130, a test bench matching the actual working conditions of a small-diameter deep well is built, a high-precision sensor array is configured to collect dynamic response data of the modified winch system under multiple working conditions and establish a test database, and a numerical fitting algorithm is used to calibrate and iteratively correct the key parameters of the dynamic model.

7. The method and system for precise dynamic control of a small-diameter deep well hybrid-to-winding system according to claim 1, characterized in that: In S140, the solution efficiency of the dynamic model is optimized based on a multi-timescale hybrid solution strategy, and parallel computing technology is introduced to improve the real-time update rate of the model. A multi-field coupled dynamics simulation module was built based on an industrial simulation platform. An integrated modeling and simulation platform was developed and embedded with a real-time data interaction interface to realize bidirectional transmission of field operation data and model parameters.

8. The method and system for precise dynamic control of a small-diameter deep well hybrid-to-winding system according to claim 7, characterized in that: The multi-timescale hybrid solution strategy is as follows: an explicit solution algorithm is used for the rapidly changing dynamic process of the modified system, and an implicit solution algorithm is used for the slowly changing structural and parameter processes; the dynamic model parameters are adaptively corrected online according to a preset period through the real-time data interaction interface.

9. The method and system for precise dynamic control of a small-diameter deep well hybrid-to-winding system according to claim 1, characterized in that: In S150, a full-condition industrial test was carried out on a typical small-diameter deep well engineering site to verify the characterization accuracy of the dynamic model under actual working conditions. For working conditions with large model prediction deviations in the field test, the model was optimized, and the dynamic characterization model of core characteristic parameters was continuously trained based on long-term field operation data. The developed model self-calibration module can automatically identify system characteristic drift caused by equipment wear and complete adaptive adjustment of model parameters.

10. A dynamic precision control system for a small-diameter deep well hybrid-to-winding system, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the method for precise dynamic control of a hybrid-tunneling system for small-diameter deep wells according to any one of claims 1 to 9 by executing the machine-executable instructions.

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