Physical simulation method and system for practical operation assessment of miner-bolter

Abstract

The invention relates to the technical field of motion control of mining machinery, in particular to a physical simulation method and system for practical operation assessment of a bolter miner, and the method comprises the steps: obtaining working condition reference data, and constructing the working condition reference data into a working condition vector; and calculating the dynamic friction coefficient reflecting the real-time friction resistance of the contact interface between the drill bit and the rock debris by analyzing the physical characteristics, humidity distribution and pore structure of the rock debris in the working condition vector. According to the method, a fixed friction coefficient used in traditional simulation is abandoned, a dynamic friction model integrating rock debris physical characteristics, environment states and pore structures is established, and the friction coefficient dynamically adjusted along with changes of working conditions is calculated in real time; particularly, a humidity phase change function is introduced to accurately describe the nonlinear sudden drop characteristic of the friction force when the rock debris is transited from a dry state to a muddy state. The core problem of friction model distortion in virtual simulation is fundamentally solved, and the stress condition of equipment in a simulation environment is consistent with the real underground height.

Description

Technical Field

[0001] This invention relates to the field of motion control technology for mining machinery, and in particular to a physical simulation method and system for practical assessment of tunneling and anchoring machines. Background Technology

[0002] As coal, metal, and non-metal mining develops towards deeper, larger-scale, and more intelligent operations, roadheader-and-anchor (TBA) machines, as key high-end equipment integrating tunneling and support functions, directly impact mine production efficiency, operational safety, and cost control through safe, efficient, and precise operation. Therefore, the scientific and rigorous assessment and certification of TBA operators' skills has become a core aspect of modern mine human resource management. While the current practical assessment method requires trainees to operate real TBA machines, it suffers from fundamental shortcomings in four key dimensions: safety, cost, standardization, and scenario coverage. It not only involves high risks and significant economic costs but also suffers from a lack of fairness due to subjective evaluation, and it cannot simulate extreme working conditions, making it difficult to comprehensively assess the operator's overall capabilities.

[0003] To overcome the shortcomings of practical assessment, pure virtual simulation systems based on computer graphics and virtual reality technologies have emerged. Trainees use peripherals such as controllers and steering wheels to perform simulated operations in a virtual environment. However, in the special scenario of deep-dipping coal seam roadway excavation, when the roadway slope exceeds a certain critical value, the downward trend of the roadheader due to its own weight will cause the cutting head positioning coordinates to deviate from the actual excavation position, and the support device cannot effectively fit with the roadway sidewall. This is because the equipment generates a downward force under the action of gravity when the roadway is inclined. When the downward force is greater than the friction force, it will continue to slide. Coupled with the untimely response of the hydraulic system, this further exacerbates the control difficulty. Ultimately, this leads to roadway deviation, increased risk of support failure, and increased construction costs. Existing virtual simulation technology simplifies the dynamic coupling mechanism of gravity and friction, using a fixed friction coefficient to replace the dynamic friction coefficient that varies with the characteristics of rock cuttings in actual working conditions. This results in a serious mismatch between control commands and equipment actions, ultimately causing key operations such as slip control and support positioning to deviate from actual working conditions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a physical simulation method and system for practical assessment of roadheader and anchor machine operations, solving the technical problem of inaccurate assessment caused by slippage distortion in virtual simulation of roadheader and anchor machine operations in steeply inclined roadways.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a physical simulation method for practical assessment of a roadheader, the method comprising the following steps: S1. Acquire baseline operating condition data through sensors and construct it into an operating condition vector; S2. By analyzing the physical properties, humidity distribution and pore structure of the rock cuttings in the working condition vector, calculate the dynamic friction coefficient that reflects the real-time frictional resistance of the interface between the drill bit and the rock cuttings. S3. Calculate the resultant force acceleration based on the dynamic friction coefficient to analyze the motion trend of the equipment, and predict the theoretical slippage of the equipment within a given time based on the resultant force acceleration. S4. Calculate the compensation force required to resist the downward slip based on the theoretical slip amount, and convert the compensation force into a pressure compensation command for the hydraulic system; S5. Monitor the actual sliding distance of the monitoring equipment, calculate the sliding error and error change rate between the actual sliding distance and the theoretical sliding distance, identify the error type based on the sliding error and error change rate, and adjust the control parameters accordingly to generate an optimized pressure command.

[0006] Furthermore, the operating condition baseline data includes roadway dip angle, equipment lateral velocity, equipment longitudinal acceleration, rock cuttings coverage, rock cuttings humidity, rock cuttings temperature, and porosity voxels.

[0007] Furthermore, the calculation of the dynamic friction coefficient, which reflects the real-time frictional resistance at the interface between the drill bit and the cuttings, includes: S201. Based on the bottom plate image in the working condition vector, identify the rock cutting contour and calculate the grain size of each rock cutting, i.e.: In the formula, denoted as the particle size of a single rock cutting; where k is the index number of the rock cutting particle; Let be the outline area of ​​the k-th rock fragment in the base image; S202. Calculate the average grain size based on the grain size of individual rock cutting particles to reflect the overall size level of the rock cuttings, i.e.: In the formula, The average particle size of the rock cuttings is denoted as ; N is the total number of rock cuttings counted. S203. Calculate the standard deviation of particle size, which characterizes the uniformity of particle size dispersion, based on the average particle size of rock cuttings. In the formula, This represents the particle size distribution of rock cuttings. S204. Based on the particle size calculation of individual rock cuttings, the proportional composition of key size ranges of rock cuttings is revealed, namely: In the formula, denoted as the percentage of the classification; where j represents the particle size classification of the rock cuttings. The number of rock fragments of the jth order; S205. Based on the moisture distribution in the contact area between the equipment track plate and the rock cuttings layer, calculate the effective humidity characterizing the actual friction interface state, i.e.: In the formula, For effective contact with humidity; Let be the humidity at location (x, y); The contact pressure distribution at position (x, y) represents the contact area between the equipment track plate and the rock debris layer. This refers to the contact area between the equipment track plates and the rock debris layer. This refers to the effective contact area between the equipment track plates and the rock cuttings layer. S206. Calculate the equivalent pore radius for quantifying the average pore size based on pore voxels, i.e.: In the formula, The equivalent pore radius; This represents the total pore volume; Number of pores; S207. Calculate the connectivity reflecting the fluid's passage capacity based on porosity tortuosity, i.e.: In the formula, Pore ​​connectivity; Pore ​​tortuosity; For feature scale; S208. Calculate the dynamic capillary coefficient, which characterizes the water transport capacity of pores, based on the equivalent pore radius and pore connectivity. In the formula, The dynamic capillary coefficient; The basic capillary coefficient; Used as a reference pore radius; Connectivity sensitivity factor; The porosity coupling coefficient; This represents the change in porosity.

[0008] Furthermore, the calculation of the dynamic friction coefficient, which reflects the real-time frictional resistance at the interface between the drill bit and the cuttings, also includes: S209. The critical humidity for determining the triboelectric threshold is calculated based on the dynamic capillary coefficient and interfacial energy, i.e.: In the formula, The dynamic critical humidity; The characteristic particle diameter; It is a mineral adsorption correction factor; The adsorption energy per unit; The porosity and interfacial tension gradient are given by the following equation: For solid-liquid interfacial tension, Porosity of the rock debris layer; S210. Based on the moisture content of rock cuttings and the dynamic critical moisture content, a phase transition function is calculated to describe the degree of solid-liquid transition, namely: In the formula, It is a phase transition state function; This is the threshold for the initial frictional decay. The threshold for complete lubrication; This refers to the slope coefficient of the transition zone. This is the frictional decay rate factor; It is a lubrication hysteresis inhibitor; S211. Calculate the wet lubrication coefficient based on the mineral water absorption coefficient and phase transition state to quantify the drag reduction effect of moisture, i.e.: In the formula, This is the wet lubrication coefficient; The coefficient of performance is the dry lubrication factor. The water absorption coefficient of the mineral is: In the formula, The extreme dry frictional heat conversion rate; Humidity thermal inhibition coefficient; S212. Calculate the final coefficient used to compensate for the effect of temperature gradient based on the wet lubrication coefficient, namely: In the formula, This is the final lubrication coefficient after temperature and humidity coupling correction; For reference humidity; This represents the real-time temperature of the rock cuttings surface. For reference temperature; This is the coupling coefficient between temperature and humidity.

[0009] Furthermore, the formula for calculating the dynamic friction coefficient is as follows: In the formula, The coefficient of friction is the dynamic friction coefficient. is the basic friction coefficient; 'a' is the velocity factor; Let be the temperature decay function, i.e.: In the formula, This is the temperature effect amplification factor.

[0010] Furthermore, the formula for calculating the resultant force acceleration is: In the formula, g is the net acceleration; g is the acceleration due to gravity. The value of the sine function of the tunnel inclination angle; The value of the cosine function of the tunnel inclination angle; The formula for calculating the theoretical slip is: In the formula, This represents the theoretical slip.

[0011] Furthermore, the calculation of the compensating force required to resist slippage based on the theoretical slip amount includes: S401, based on theoretical slippage, dynamic friction coefficient, and roadway dip angle, calculates the dynamic requirements for real-time counteracting of sliding forces, namely: In the formula, The compensating force is m; the mass of the equipment is m. The differential gain coefficient; This represents the rate of change of the theoretical slip. S402. Calculate the required increase in working pressure for the hydraulic system based on the compensating force, i.e.: In the formula, This represents the total pressure increment required for the hydraulic system. The effective working area of ​​the hydraulic cylinder piston is L; the stroke length of the hydraulic cylinder is L. The lever amplification ratio of the mechanical mechanism; The efficiency coefficient of the hydraulic system; S403, based on the tortuous shape of the roadway and the layout of the hydraulic cylinders, distributes the total pressure increment to intelligently adjust the load of each hydraulic cylinder to ensure equipment stability, namely: In the formula, The target vector is to be executed; where Let be the compensation pressure value of the i-th hydraulic cylinder; n be the total number of hydraulic cylinders; v be the moving speed of the equipment; and R be the radius of curvature of the roadway centerline. Let M be the pseudo-inverse of the cylinder layout matrix, where M is the cylinder layout matrix, i.e.: In the formula, Let be the installation angle between the i-th hydraulic cylinder and the tunnel axis; Let be the component of the thrust of the i-th cylinder in the direction of the tunnel advance; Let be the normal component of the thrust of the i-th cylinder in the tunnel. S404. Dynamic pre-compensation is performed on the delay of the hydraulic system based on the execution target vector to eliminate control lag and achieve time synchronization between command and execution, i.e.: In the formula, This is the final pre-compensation control quantity; It is the inverse Laplace transform operator; This is the proportional gain coefficient; The time decay constant; This is the transfer function of the hydraulic system, describing the system's dynamic response characteristics.

[0012] Furthermore, the step of identifying the error type based on the slip error and the rate of change of error includes: S501, the data acquisition device during the specified time period The actual average slip distance within, i.e.: In the formula, This represents the actual average sliding distance; The instantaneous movement speed of the track is measured in real time by a laser displacement sensor; S502. Calculate the slip error based on the actual average slip distance and the theoretical slip amount, which is used to quantify the difference between the prediction and the actual slip. In the formula, This refers to the slip error; S503. Calculate the rate of change of error, which characterizes the speed of error evolution, based on slip error, i.e.: In the formula, This is the rate of change of error, used to identify the system response state; S504. Compare the slippage error and the rate of change of error with a preset judgment threshold to identify the root cause of the error, i.e.: In the formula, This is the error tolerance threshold; is the critical value of the rate of change; q is the abnormal fluctuation coefficient.

[0013] Furthermore, the corresponding modification of control parameters to generate the optimized pressure command also includes: S505. When the error is due to a misalignment of the friction model, the friction coefficient is corrected in reverse based on the measured motion state, i.e.: In the formula, The updated dynamic friction coefficient; Adaptive learning rate; This is the correction amount for the coefficient of friction, i.e.: in, These are actual measurements from the accelerometer. S506. When the error is hydraulic response hysteresis, dynamically adjust the proportional gain coefficient and establish an attenuation optimization mechanism, namely: In the formula, The optimized control gain coefficient; It is the attenuation factor; For learning factors; This is the error direction function; S507. When the error is external interference, the spectrum filtering algorithm is activated for physical isolation, that is: In the formula, This is the compensated pressure vector after filtering out interference; Here is the filter matrix; This is the inverse Fourier transform operator; For Fourier transform operators; Let u be the system transfer function, where u is the imaginary unit and w is the angular frequency. S508, merge all correction parameters and regenerate the compensation pressure command. This completes the iterative optimization of closed-loop control, namely: In the formula, The optimized pressure command for the final output; This is a compensation pressure command recalculated based on the new parameters.

[0014] By employing the above technical solution, the present invention provides a physical simulation method and system for practical assessment of roadheader and anchor machine operation, which has at least the following beneficial effects: This invention abandons the fixed friction coefficient used in traditional simulations and establishes a dynamic friction model that integrates the physical properties of rock cuttings (particle size distribution, gradation), environmental conditions (humidity, temperature), and pore structure (water storage capacity, connectivity). This model uses image recognition, sensor arrays, and 3D scanning data to calculate a friction coefficient that dynamically adjusts with changing operating conditions in real time. In particular, it introduces a humidity phase transition function to accurately describe the nonlinear drop in friction force as rock cuttings transition from a dry to a muddy state. This fundamentally solves the core problem of friction model distortion in virtual simulations, ensuring that the stress conditions on equipment in the simulated environment are highly consistent with those in actual downhole conditions. This lays a reliable physical foundation for accurate slip prediction and control, greatly enhancing the realism of the simulation.

[0015] Furthermore, this solution changes the control strategy from passively correcting displacement to actively offsetting it after displacement is predicted. Based on real-time calculated dynamic friction coefficients and roadway inclination angles, it uses Newton's laws of motion to calculate the theoretical future slippage and direction of the equipment in advance. This allows for the pre-calculation of the required compensation force before actual slippage occurs. This achieves proactive prevention of equipment slippage, rather than reactive remediation, significantly improving the timeliness and stability of control and effectively simulating the critical performance indicator of anti-slippage operation.

[0016] Furthermore, at the execution level, the system not only calculates the total compensation force but also innovatively incorporates the tunnel curvature and equipment speed into its considerations. Through a cylinder layout matrix, the total pressure is intelligently distributed to each hydraulic cylinder, ensuring equipment stability in curves. Simultaneously, a built-in dynamic pre-compensation algorithm based on the system transfer function actively counteracts the inherent response delay of the hydraulic system, ensuring real-time synchronization between control commands and equipment actions. This solves the problems of multi-actuator collaborative control and system lag, guaranteeing precise execution of control commands. This not only improves the accuracy of equipment attitude control but also makes the simulator's operational response highly consistent with the real equipment, enhancing the realism of the operating experience.

[0017] In particular, the system possesses self-diagnosis and optimization capabilities. It continuously compares the predicted slip with the actual slip, generates error signals, and intelligently diagnoses the root causes of errors (such as friction model inaccuracies, hydraulic delays, or external disturbances). Based on different diagnostic results, the system adaptively adjusts the corresponding model parameters (such as updating the friction coefficient) or control parameters (such as adjusting the control gain) to achieve continuous online optimization. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of the physical simulation method of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0020] Example 1 This embodiment proposes a physical simulation method for practical assessment of roadheader and anchor machine operation. It achieves proactive prevention of equipment slippage, rather than reactive remediation, significantly improving the timeliness and stability of control and effectively simulating the key assessment point of anti-slippage operation. Simultaneously, it incorporates the roadway curvature and equipment speed, intelligently distributing the total pressure to each hydraulic cylinder through a cylinder layout matrix to ensure equipment stability in curves. Figure 1 As shown, the method includes the following steps: S1. Baseline data on working conditions, such as dip angle, acceleration, and cuttings coverage, are acquired through sensors to provide accurate foundational data for subsequent compensation. The baseline data includes the roadway dip angle. Horizontal speed of equipment longitudinal acceleration of equipment Rock cuttings coverage C, rock cuttings humidity W, rock cuttings temperature T, pore volumetric properties Texture characteristics of the tunnel floor. Among them: Lane slope angle The slope of the roadway floor is obtained in real time by installing a high-precision tilt sensor at the center of the equipment chassis. This is used to accurately calculate the gravity sliding component of the equipment and determine the benchmark magnitude of the sliding force.

[0021] equipment lateral speed The velocity is obtained by measuring the speed of the track relative to the roadway using a laser Doppler velocimeter, and the velocity attenuation term of the friction coefficient is dynamically corrected to reflect the influence of motion inertia.

[0022] longitudinal acceleration of equipment It is obtained by detecting instantaneous changes in the motion state of the equipment through MEMS accelerometers, and identifies sudden slip events that trigger emergency compensation mechanisms, such as track slippage.

[0023] The rock cuttings coverage rate C, calculated by analyzing the base plate image using a machine vision system, quantifies the attenuation effect of rock cuttings on the friction coefficient and determines the magnitude of the friction reduction; that is: In the formula, The total area occupied by the rock debris identified in the image represents the physical extent of the rock debris cover on the substrate. is the reference total area of ​​the tunnel floor in the image, used to provide a reference system for coverage calculation; C is the rock cuttings coverage, a dimensionless ratio that quantifies the degree to which rock cuttings obscure the track contact surface.

[0024] The moisture content W of the rock debris was obtained by measuring an array of infrared moisture sensors.

[0025] The temperature T of the rock fragments was obtained by measuring with an infrared thermal imager.

[0026] Pore ​​voxels Generated through X-ray CT scan, its value range is Boolean: i.e. When this occurs, the spatial location (x, y, z) is a pore containing air / moisture; that is... When the time is right, the spatial location (x, y, z) contains rock fragments.

[0027] The texture features of the tunnel floor were obtained by acquiring rock surface roughness point cloud data through structured light 3D scanning. This data was used to correct the basic friction coefficient and distinguish the friction characteristics of different rock types such as sandstone and shale.

[0028] The collected baseline operating data are aligned according to the collection timestamp t and merged to construct an operating condition vector. As input for subsequent calculations, it enables a precise mapping from the physical environment to the mathematical model.

[0029] S2. First, image recognition technology is used to quantitatively analyze the size distribution characteristics of rock cutting particles and extract key size features. Next, a humidity sensor array is used to monitor the moisture distribution in the contact area between the equipment track plates and the rock cutting layer in real time, calculating the effective humidity index. A 3D scanning technology is used to construct a pore structure model of the rock cutting layer, and critical humidity parameters are calculated in real time based on pore characteristics, constructing a humidity phase transition state judgment function. Then, the basic friction coefficient is corrected for wettability by incorporating the water absorption characteristics of minerals, while comprehensively compensating for the influence of temperature changes on humidity effects. Finally, multiple factors such as movement speed, rock cutting coverage, and temperature changes are integrated to generate a dynamic friction coefficient that comprehensively reflects the actual working conditions, thereby overcoming the problem of distorted prediction of changes in the physical properties of rock cuttings in traditional models. Specifically: S201. Based on the bottom plate image in the working condition vector, identify the rock cutting contour and calculate the grain size of each rock cutting, i.e.: In the formula, The particle size of a single rock cutting is used to form a statistical basic unit; where k is the index number of the rock cutting particle, used to identify different rock cutting particles. Let be the outline area of ​​the k-th rock fragment in the base plate image.

[0030] S202. Calculate the average grain size based on the grain size of individual rock cutting particles to reflect the overall size level of the rock cuttings, i.e.: In the formula, is the average particle size of the rock cuttings, representing the overall size level of the rock cuttings; N is the total number of rock cutting particles counted.

[0031] S203. Calculate the standard deviation of particle size, which characterizes the uniformity of particle size dispersion, based on the average particle size of rock cuttings. In the formula, This represents the particle size dispersion of rock cuttings, used to quantify the uniformity of the size distribution of rock cutting particles.

[0032] S204. Based on the particle size calculation of individual rock cuttings, the proportional composition of key size ranges of rock cuttings is revealed, namely: In the formula, The percentage of graded rock cuttings represents the proportion of the j-th grade, reflecting the gradation characteristics of the rock cuttings. Here, j represents the particle size classification of the rock cuttings, used to identify the size range of the rock cuttings. For example, when j=1, it represents rock cuttings with a diameter less than 5mm that dominate lubrication characteristics; when j=2, it represents rock cuttings with a diameter between 5-20mm that are the main bearing layer; when j=3, it represents rock cuttings with a diameter greater than 20mm that affect mechanical interlocking. denoted as the number of rock fragments of the jth order.

[0033] S205. Based on the moisture distribution in the contact area between the equipment track plate and the rock cuttings layer, calculate the effective humidity characterizing the actual friction interface state, i.e.: In the formula, To effectively contact humidity, characterize the humidity of the actual friction interface; Let be the humidity at location (x, y); The contact pressure distribution at position (x, y) in the contact area between the equipment track plate and the rock debris layer is obtained by calibration using a track pressure sensor and is used to weight the importance of humidity. The contact area between the equipment track plate and the rock cuttings layer is used to define the integration range; This provides a normalized baseline for the effective contact area between the equipment track plates and the rock debris layer, eliminating the influence of contact area variations in the calculation of effective contact humidity.

[0034] S206. Calculate the equivalent pore radius for quantifying the average pore size based on pore voxels, i.e.: In the formula, The equivalent pore radius; The total pore volume is used to quantify the water-storable space. The number of pores reflects the degree of pore fragmentation.

[0035] S207. Calculate the connectivity reflecting the fluid's passage capacity based on porosity tortuosity, i.e.: In the formula, Pore ​​connectivity reflects the accessibility of a pore network. Pore ​​tortuosity, which characterizes the degree of tortuosity of the fluid path, is a real number greater than 1, obtained by CT three-dimensional path scanning of pore voxels; The characteristic scale characterizes the spatial ratio between the pore network and the particle size of rock cuttings, and its value is the average particle size of the rock cuttings.

[0036] S208. Calculate the dynamic capillary coefficient, which characterizes the water transport capacity of pores, based on the equivalent pore radius and pore connectivity. In the formula, It is the dynamic capillary coefficient, used to quantify the water transport capacity of pores; The basic capillary coefficient reflects the intrinsic properties of the rock debris layer and is obtained through core laboratory calibration. This serves as a reference pore radius, providing a standardized baseline value. This is a connectivity sensitivity factor used to control the influence weight of connectivity, and it is determined through geotechnical experiments based on seepage experiments. This is the porosity coupling coefficient, used to adjust the sensitivity to porosity changes; The change in porosity reflects the dynamic changes in the pore structure, that is: In the formula, Initial porosity at the initial moment of the CT scan; The porosity is the real-time porosity at time t in a CT scan.

[0037] S209. The critical humidity for determining the triboelectric threshold is calculated based on the dynamic capillary coefficient and interfacial energy, i.e.: In the formula, The dynamic critical humidity is the humidity threshold that triggers a sudden change in friction. When the humidity of the rock cuttings exceeds the dynamic critical humidity, fluid lubrication is triggered, and the coefficient of friction drops sharply. The characteristic particle diameter is obtained by analyzing the particle size of rock cutting samples using a laser particle size analyzer, and the cumulative distribution D50 value is taken, which is the diameter of 50% of the particles. This is a mineral adsorption correction factor used to correct the influence of different mineral surface properties on water molecule adsorption; it was obtained based on experimental calibration. The adsorption energy per unit reflects the energy intensity of water molecules bound to the mineral surface, and is determined by the BET nitrogen adsorption experiment. The porosity-interfacial tension gradient characterizes the rate of change in pore water absorption capacity as interfacial tension decreases. It is obtained through controlled wettability experiments, i.e., adjusting the solid-liquid interfacial tension. Simultaneously measure the porosity of the rock debris layer. Rate of change.

[0038] S210. Based on the moisture content of rock cuttings and the dynamic critical moisture content, a phase transition function is calculated to describe the degree of solid-liquid transition, namely: In the formula, is the phase transition state function, which describes the solid-liquid transition process, and its value ranges from [0, 1]. and All of these are phase transition interval thresholds, used to delineate different states of matter, and determined based on rheological experiments; among which... This is the threshold for the initial decrease in frictional decay; friction begins to decrease when the moisture content of the rock cuttings reaches this value. This is the threshold for complete lubrication; when the moisture content of the rock cuttings reaches this level, friction becomes zero. The slope coefficient for the transition zone is used to control the linear descent rate and is calibrated through cuttings shearing experiments. The frictional decay rate factor controls the rate of change from... arrive The steepness of the descent curve was determined by fitting friction experiments under different humidity gradients. It is a lubrication hysteresis suppression factor used to suppress the risk of friction rebound in high humidity areas, and was determined through cyclic loading and unloading experiments.

[0039] S211. Calculate the wet lubrication coefficient based on the mineral water absorption coefficient and phase transition state to quantify the drag reduction effect of moisture, i.e.: In the formula, is the wet lubrication coefficient, used to quantify the friction-reducing effect of moisture, and its value ranges from [0, 1]. The dry lubrication coefficient reflects the basic friction characteristics and is obtained based on rock chip friction experiments. The water absorption coefficient of a mineral characterizes the maximum drag reduction capacity of a rock, namely: In the formula, The limiting dry frictional heat conversion rate is used to define the upper limit of heat conversion; This is the humidity-induced thermal inhibition coefficient, used to control the rate at which moisture reduces thermal efficiency.

[0040] S212. Calculate the final coefficient used to compensate for the effect of temperature gradient based on the wet lubrication coefficient, namely: In the formula, The final lubrication coefficient, corrected by temperature and humidity coupling, quantifies the competition between the thermal expansion effect and the moisture lubrication effect on the rock cuttings surface, and directly controls the power output of the drilling rig. The reference humidity is a humidity correction benchmark point calibrated through experiments. The real-time temperature of the rock cuttings surface represents the degree of frictional heat accumulation, i.e., the friction coefficient increases nonlinearly as the temperature rises. This is a reference temperature used to provide a correction benchmark and eliminate the influence of ambient temperature differences; This is the coupling coefficient between temperature and humidity, used to quantify the competing effects of thermal expansion and moisture lubrication.

[0041] S213. The dynamic friction coefficient, which reflects the actual friction under working conditions, is calculated by combining basic friction, motion speed, and final lubrication coefficient. In the formula, The dynamic friction coefficient reflects the real-time frictional resistance at the interface between the drill bit and the cuttings. is the basic friction coefficient; 'a' is the velocity factor, reflecting the influence of velocity on friction, calibrated based on traction experiments. This is a temperature decay function, reflecting the temperature softening effect, i.e.: In the formula, This is the temperature effect amplification factor, used to control the function decay rate.

[0042] S3. Analyze the tunnel inclination angle and dynamic friction coefficient. First, comprehensively assess the current downward force on the equipment. Then, based on the system's set time window, extrapolate the equipment's movement trend to accurately predict the equipment's future instantaneous position change trend, thus locking in the possible direction and distance of equipment displacement in advance. This establishes a quantitative prediction benchmark before actual displacement occurs, providing accurate reverse compensation basis for the subsequent hydraulic system, ensuring that the equipment maintains its predetermined position under gravity, achieving proactive displacement control. Specifically: S301. Calculate the resultant acceleration of the equipment in an inclined roadway based on the roadway inclination angle and dynamic friction coefficient, i.e.: In the formula, ρ is the resultant force acceleration, used to quantitatively analyze the motion trend of the equipment; when the resultant force acceleration is greater than 0, it indicates that the equipment is sliding downwards; when the resultant force acceleration is less than 0, it indicates that the equipment is sliding upwards; when the resultant force acceleration is 0, it indicates that the equipment remains stationary; g is the acceleration due to gravity. This is the sine function value of the roadway inclination angle, used to calculate the component of gravity along the roadway inclination direction; This is the cosine function value of the tunnel dip angle, used to calculate the component of gravity perpendicular to the rock surface.

[0043] S302. The theoretical slip amount used to quantify and predict equipment position offset is calculated based on the resultant force acceleration, namely: In the formula, The theoretical slip represents the amount of displacement that the equipment will undergo within a given time t; This is the integral constant of the uniformly accelerated motion formula, used to ensure the physical accuracy of displacement calculation.

[0044] S4. Based on the theoretical slippage, equipment mass, and roadway inclination, calculate the compensation force required to resist slippage, while incorporating speed sensitivity adjustments to anticipate sudden changes. This compensation force is then converted into specific pressure increment commands for the hydraulic cylinders, and intelligent pressure distribution is implemented in a multi-cylinder layout, considering the roadway's curvature, to ensure equipment stability. Subsequently, a dynamic pre-compensation mechanism is used to eliminate the inherent response delay of the hydraulic system. Specifically: S401, based on theoretical slippage, dynamic friction coefficient, and roadway dip angle, calculates the dynamic requirements for real-time counteracting of sliding forces, namely: In the formula, The compensating force includes static equilibrium and dynamic suppression components; m is the mass of the equipment. The differential gain coefficient is used to amplify the effect of slip velocity changes and improve the system response speed. It is obtained based on the test platform simulation of slip abrupt change conditions. It represents the rate of change of the theoretical slip, used to quantify slip acceleration and provide early warning of sudden instability trends.

[0045] S402. Calculate the required increase in working pressure for the hydraulic system based on the compensating force, which is used to convert the mechanical requirements into executable hydraulic control commands, i.e.: In the formula, This represents the total pressure increment required for the hydraulic system. The effective working area of ​​the cylinder piston is L; the stroke length of the cylinder is used to convert linear displacement into hydraulic compression space. This is the lever amplification ratio of the mechanical mechanism, used as a conversion coefficient to adjust the final output torque; It is the efficiency coefficient of a hydraulic system, used to compensate for energy losses such as pipeline friction and internal leakage.

[0046] S403, based on the tortuous shape of the roadway and the layout of the hydraulic cylinders, distributes the total pressure increment to intelligently adjust the load of each hydraulic cylinder to ensure equipment stability, namely: In the formula, The target vector is defined as follows: it contains the pressure increment that each cylinder ultimately needs to execute; where... Let be the compensation pressure value of the i-th hydraulic cylinder; n be the total number of hydraulic cylinders; v be the moving speed of the equipment, used to quantify the intensity of the centrifugal effect; and R be the radius of curvature of the roadway centerline, reflecting the degree of roadway curvature and determining the additional compensation requirement. The pseudo-inverse of the cylinder layout matrix is ​​used to solve the overdetermined equations, decomposing the overall requirements into individual cylinders; where M is the cylinder layout matrix, i.e.: In the formula, Let be the installation angle between the i-th hydraulic cylinder and the tunnel axis; Let be the component of the thrust of the i-th cylinder in the direction of the tunnel advance; Let be the normal component of the thrust of the i-th cylinder in the tunnel.

[0047] S404. Dynamic pre-compensation is performed on the delay of the hydraulic system based on the execution target vector to eliminate control lag and achieve time synchronization between command and execution, i.e.: In the formula, The final pre-compensation control quantity, namely the pre-compensation control current of the servo valve, eliminates the inherent response delay of the hydraulic system through dynamic pre-compensation, so that the actual pressure output accurately tracks the target command. This is the inverse Laplace transform operator, used to convert a frequency domain model into a time domain control signal; This is the proportional gain coefficient, used to adjust the initial response intensity and suppress transient overshoot; It is a time decay constant used to control the duration of the exponential term and determine the duration of dynamic correction; The transfer function of a hydraulic system describes the dynamic response characteristics of the system, namely: Where s is a complex frequency variable; The system's natural frequency determines the fundamental speed of the hydraulic pressure response: The damping ratio is used to control the oscillation characteristics of the system.

[0048] S5. Real-time monitoring of the actual sliding displacement data of the equipment after executing the pre-compensation pressure command, and comparison with the predicted value to calculate the sliding error and its rate of change, used to diagnose the root cause of system errors. Furthermore, misoperation is classified into three operating conditions: when the error value is large but changes slowly, it is determined to be a friction model misalignment; when the error and the changing trend are in the same direction, it is attributed to hydraulic system response lag; when the error rate abnormally increases suddenly, it is identified as an external interference factor. Specifically: S501, the data acquisition device during the specified time period The actual average slip distance within is used to quantitatively characterize the actual displacement deviation of the equipment after compensation execution, i.e.: In the formula, This represents the actual average sliding distance; The instantaneous movement speed of the track is measured in real time by a laser displacement sensor.

[0049] S502. Calculate the slip error based on the actual average slip distance and the theoretical slip amount, which is used to quantify the difference between the prediction and the actual slip. In the formula, This represents the slippage error.

[0050] S503. Calculate the rate of change of error, which characterizes the speed of error evolution, based on slip error, i.e.: In the formula, This is the error change rate, used to identify the system response state.

[0051] S504. Compare the slippage error and the rate of change of error with preset judgment thresholds to identify the root cause of the error and accurately classify the problem type so as to take targeted measures, namely: In the formula, The error tolerance threshold determines whether correction needs to be initiated; q is the critical value of the rate of change, used to distinguish between gradual and abrupt changes; q is the abnormal fluctuation coefficient, used to determine the sensitivity to external disturbances.

[0052] S505. When the error is due to a misalignment of the friction model, the friction coefficient is corrected in reverse based on the measured motion state, i.e.: In the formula, This is the updated dynamic friction coefficient, used to address model distortion caused by changes in rock cuttings properties; This is the adaptive learning rate, used to control the magnitude of the correction. This is the correction amount for the coefficient of friction, i.e.: in, These are actual measurements from the accelerometer, reflecting the true motion state.

[0053] S506. When the error is hydraulic response hysteresis, dynamically adjust the proportional gain coefficient and establish an attenuation optimization mechanism, namely: In the formula, The optimized control gain coefficient is used to eliminate phase lag caused by hydraulic delay; This is the attenuation factor, used to control the degree of gain attenuation with error; This is a learning factor used to adjust the weight of the effect of the rate of change; This is the error direction function, with a value of ±1.

[0054] S507. When the error is external interference, the spectrum filtering algorithm is activated for physical isolation, that is: In the formula, This is the compensation pressure vector after filtering out interference, used to eliminate the coupling effect of external disturbances such as rock mass vibration on the control system; This is a filter matrix used to selectively attenuate the characteristic frequency bands of rock mass vibration; It is the inverse Fourier transform operator, used to convert frequency domain signals back to the time domain; It is a Fourier transform operator used to convert time-domain signals into frequency-domain representations; Let be the system transfer function, which characterizes the frequency response characteristics of the hydraulic system; where u is the imaginary unit and w is the angular frequency, which characterizes the periodicity of the signal or disturbance.

[0055] S508, merge all correction parameters and regenerate the compensation pressure command. This completes the iterative optimization of closed-loop control, namely: In the formula, The optimized pressure command for the final output; This is a compensation pressure command recalculated based on the new parameters.

[0056] Example 2 Based on Example 1, this invention proposes a physical simulation system for practical assessment of a roadheader, including a data acquisition module, a dynamic friction calculation module, a slip prediction module, a hydraulic compensation control module, and an adaptive correction module. Among them: The data acquisition module collects baseline operating data. It utilizes various high-precision sensors (tilt sensors, laser velocimeters, accelerometers, vision systems, humidity / temperature sensors, CT scans, etc.) to collect real-time data on the tunnel environment and equipment status, including tilt angle, velocity, acceleration, cuttings coverage, humidity, temperature, and pore structure. All data is aligned by timestamp and integrated into a comprehensive operating condition vector, providing a unified and accurate data foundation for subsequent calculations. Compared to the limitations of traditional simulations that rely solely on angle and velocity, this module introduces key environmental variables such as the physical properties of cuttings (grain size, humidity, porosity), transforming the complex physical environment into a structured mathematical vector, achieving a precise and reliable mapping from the real world to the digital model.

[0057] The dynamic friction calculation module is used to calculate the dynamic friction coefficient based on benchmark data. By receiving the operating condition vector from the data acquisition module, it first analyzes the gradation (particle size distribution) of rock cuttings from the image, calculates the water storage and transport capacity (critical humidity) of the pore structure from the CT data, and combines it with real-time measured humidity and temperature data to dynamically calculate the dynamic friction coefficient between the rock cuttings and the track under the current condition. This coefficient comprehensively reflects the combined effects of multiple complex factors such as rock cutting particle size, humidity phase change (from dry to muddy), and temperature effects. It completely overcomes the key defect of friction model distortion in existing virtual simulations. By introducing a dynamic coupling mechanism of rock cutting physical properties (especially the phase change effect of humidity), the calculated friction coefficient is highly consistent with the actual operating conditions, providing crucial and accurate physical parameters for subsequent slip prediction.

[0058] The slip prediction module is used to predict the theoretical slippage of the equipment. By receiving the roadway inclination angle and dynamic friction coefficient, it calculates the resultant force on the equipment on the inclined plane using Newton's laws of motion, thus obtaining the resultant force acceleration. Based on this acceleration, the module can predict the theoretical slippage and direction of the equipment within a short future time window. This shifts the control strategy from passive post-event correction to proactive pre-event prevention. Accurate slippage prediction provides valuable lead time for the hydraulic system, enabling it to apply control forces in advance to counteract slippage, thereby actively suppressing slippage and significantly improving the timeliness and stability of control.

[0059] The hydraulic compensation control module generates pressure compensation commands for the hydraulic system. Based on the theoretical slippage provided by the prediction module, it calculates the compensation force required to counteract the slippage on the equipment and converts this force into specific pressure commands for each cylinder in the hydraulic system. Taking into account the curvature of the tunnel and the layout of each cylinder, the total pressure is rationally distributed to each cylinder to ensure equipment stability. Simultaneously, the module integrates a dynamic pre-compensation algorithm to compensate for the inherent response delay of the hydraulic system, ensuring that commands are executed accurately and promptly. This achieves efficient and precise conversion from theoretical calculations to physical actions. Intelligent pressure distribution ensures the stability of the equipment's posture in complex tunnels, while the dynamic pre-compensation mechanism solves the hydraulic system lag problem, enabling real-time synchronization between control commands and equipment actions, greatly improving control accuracy.

[0060] The adaptive correction module monitors the actual slippage, identifies errors, and corrects control parameters to achieve closed-loop control. It continuously monitors the actual slippage of the equipment after the hydraulic system executes compensation commands and compares it in real time with the predicted theoretical slippage to generate slippage error. Subsequently, the module intelligently diagnoses the root cause of the error: whether it's due to changes in rock cuttings characteristics causing friction model inaccuracies, hydraulic response lag, or a sudden external disturbance. Based on the different diagnostic results, the module adaptively adjusts the corresponding control parameters and generates optimized pressure commands, initiating a new control cycle.

[0061] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Since the above embodiments are substantially similar to the method embodiments, their descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0063] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A physical simulation method for practical assessment of a roadheader and anchor operator, characterized in that, The method includes the following steps: S1. Acquire baseline operating condition data through sensors and construct it into an operating condition vector; S2. By analyzing the physical properties, humidity distribution and pore structure of the rock cuttings in the working condition vector, calculate the dynamic friction coefficient that reflects the real-time frictional resistance of the interface between the drill bit and the rock cuttings. S3. Calculate the resultant force acceleration based on the dynamic friction coefficient to analyze the motion trend of the equipment, and predict the theoretical slippage of the equipment within a given time based on the resultant force acceleration. S4. Calculate the compensation force required to resist the downward slip based on the theoretical slip amount, and convert the compensation force into a pressure compensation command for the hydraulic system; S5. Monitor the actual sliding distance of the monitoring equipment, calculate the sliding error and error change rate between the actual sliding distance and the theoretical sliding distance, identify the error type based on the sliding error and error change rate, and adjust the control parameters accordingly to generate an optimized pressure command.

2. The physical simulation method according to claim 1, characterized in that, The operating condition baseline data includes roadway dip angle, equipment lateral velocity, equipment longitudinal acceleration, rock cuttings coverage, rock cuttings humidity, rock cuttings temperature, and porosity voxels.

3. The physical simulation method according to claim 2, characterized in that, The calculation of the dynamic friction coefficient, which reflects the real-time frictional resistance at the interface between the drill bit and the cuttings, includes: S201. Based on the bottom plate image in the working condition vector, identify the rock cutting contour and calculate the grain size of each rock cutting, i.e.: In the formula, denoted as the particle size of a single rock cutting; where k is the index number of the rock cutting particle; Let be the outline area of ​​the k-th rock fragment in the base image; S202. Calculate the average grain size based on the grain size of individual rock cutting particles to reflect the overall size level of the rock cuttings, i.e.: In the formula, The average particle size of the rock cuttings is denoted as ; N is the total number of rock cuttings counted. S203. Calculate the standard deviation of particle size, which characterizes the uniformity of particle size dispersion, based on the average particle size of rock cuttings. In the formula, This represents the particle size distribution of rock cuttings. S204. Based on the particle size calculation of individual rock cuttings, the proportional composition of key size ranges of rock cuttings is revealed, namely: In the formula, denoted as the percentage of the classification; where j represents the particle size classification of the rock cuttings. The number of rock fragments of the jth order; S205. Based on the moisture distribution in the contact area between the equipment track plate and the rock cuttings layer, calculate the effective humidity characterizing the actual friction interface state, i.e.: In the formula, For effective contact with humidity; Let be the humidity at location (x, y); The contact pressure distribution at position (x, y) represents the contact area between the equipment track plate and the rock debris layer. This refers to the contact area between the equipment track plates and the rock debris layer. This refers to the effective contact area between the equipment track plates and the rock cuttings layer. S206. Calculate the equivalent pore radius for quantifying the average pore size based on pore voxels, i.e.: In the formula, The equivalent pore radius; This represents the total pore volume; Number of pores; S207. Calculate the connectivity reflecting the fluid's passage capacity based on porosity tortuosity, i.e.: In the formula, Pore ​​connectivity; Pore ​​tortuosity; For feature scale; S208. Calculate the dynamic capillary coefficient, which characterizes the water transport capacity of pores, based on the equivalent pore radius and pore connectivity. In the formula, The dynamic capillary coefficient; The basic capillary coefficient; Used as a reference pore radius; Connectivity sensitivity factor; The porosity coupling coefficient; This represents the change in porosity.

4. The physical simulation method according to claim 3, characterized in that, The calculation of the dynamic friction coefficient, which reflects the real-time frictional resistance at the interface between the drill bit and the cuttings, also includes: S209. The critical humidity for determining the triboelectric threshold is calculated based on the dynamic capillary coefficient and interfacial energy, i.e.: In the formula, The dynamic critical humidity; The characteristic particle diameter; It is a mineral adsorption correction factor; The adsorption energy per unit; The porosity and interfacial tension gradient are given by the following equation: For solid-liquid interfacial tension, Porosity of the rock debris layer; S210. Based on the moisture content of rock cuttings and the dynamic critical moisture content, a phase transition function is calculated to describe the degree of solid-liquid transition, namely: In the formula, It is a phase transition state function; This is the threshold for the initial frictional decay. The threshold for complete lubrication; This refers to the slope coefficient of the transition region. This is the frictional decay rate factor; It is a lubrication hysteresis inhibitor; S211. Calculate the wet lubrication coefficient based on the mineral water absorption coefficient and phase transition state to quantify the drag reduction effect of moisture, i.e.: In the formula, This is the wet lubrication coefficient; The coefficient of performance is the dry lubrication factor. The water absorption coefficient of the mineral is: In the formula, The extreme dry frictional heat conversion rate; Humidity thermal inhibition coefficient; S212. Calculate the final coefficient used to compensate for the effect of temperature gradient based on the wet lubrication coefficient, namely: In the formula, This is the final lubrication coefficient after temperature and humidity coupling correction; For reference humidity; This represents the real-time temperature of the rock cuttings surface. For reference temperature; This is the coupling coefficient between temperature and humidity.

5. The physical simulation method according to claim 4, characterized in that, The formula for calculating the dynamic friction coefficient is as follows: In the formula, The coefficient of friction is the dynamic friction coefficient. is the basic friction coefficient; 'a' is the velocity factor; Let be the temperature decay function, i.e.: In the formula, This is the temperature effect amplification factor.

6. The physical simulation method according to claim 1, characterized in that, The formula for calculating the resultant acceleration is: In the formula, g is the net acceleration; g is the acceleration due to gravity. The value of the sine function of the tunnel inclination angle; The value of the cosine function of the tunnel inclination angle; The formula for calculating the theoretical slip is: In the formula, This represents the theoretical slip.

7. The physical simulation method according to claim 6, characterized in that, The calculation of the compensating force required to resist slippage based on the theoretical slip amount includes: S401, based on theoretical slippage, dynamic friction coefficient, and roadway dip angle, calculates the dynamic requirements for real-time counteracting of sliding forces, namely: In the formula, The compensating force is m; the mass of the equipment is m. The differential gain coefficient; This represents the rate of change of the theoretical slip. S402. Calculate the required increase in working pressure for the hydraulic system based on the compensating force, i.e.: In the formula, This represents the total pressure increment required for the hydraulic system. The effective working area of ​​the hydraulic cylinder piston is L; the stroke length of the hydraulic cylinder is L. The lever amplification ratio of the mechanical mechanism; The efficiency coefficient of the hydraulic system; S403, based on the tortuous shape of the roadway and the layout of the hydraulic cylinders, distributes the total pressure increment to intelligently adjust the load of each hydraulic cylinder to ensure equipment stability, namely: In the formula, The target vector is to be executed; where Let be the compensation pressure value of the i-th hydraulic cylinder; n be the total number of hydraulic cylinders; v be the moving speed of the equipment; and R be the radius of curvature of the roadway centerline. Let M be the pseudo-inverse of the cylinder layout matrix, where M is the cylinder layout matrix, i.e.: In the formula, Let be the installation angle between the i-th hydraulic cylinder and the tunnel axis; Let be the component of the thrust of the i-th cylinder in the direction of the tunnel advance; Let be the normal component of the thrust of the i-th cylinder in the tunnel. S404. Dynamic pre-compensation is performed on the delay of the hydraulic system based on the execution target vector to eliminate control lag and achieve time synchronization between command and execution, i.e.: In the formula, This is the final pre-compensation control quantity; It is the inverse Laplace transform operator; This is the proportional gain coefficient; The time decay constant; This is the transfer function of the hydraulic system, describing the system's dynamic response characteristics.

8. The physical simulation method according to claim 7, characterized in that, The step of identifying the error type based on the slip error and the rate of change of error includes: S501, the data acquisition device during the specified time period The actual average slip distance within, i.e.: In the formula, This represents the actual average sliding distance; The instantaneous movement speed of the track is measured in real time by a laser displacement sensor; S502. Calculate the slip error based on the actual average slip distance and the theoretical slip amount, which is used to quantify the difference between the prediction and the actual slip. In the formula, This refers to the slip error; S503. Calculate the rate of change of error, which characterizes the speed of error evolution, based on slip error, i.e.: In the formula, This is the rate of change of error, used to identify the system response state; S504. Compare the slippage error and the rate of change of error with a preset judgment threshold to identify the root cause of the error, i.e.: In the formula, This is the error tolerance threshold; is the critical value of the rate of change; q is the abnormal fluctuation coefficient.

9. The physical simulation method according to claim 1, characterized in that, The corresponding modified control parameters for generating the optimized pressure command also include: S505. When the error is due to a misalignment of the friction model, the friction coefficient is corrected in reverse based on the measured motion state, i.e.: In the formula, The updated dynamic friction coefficient; Adaptive learning rate; This is the correction amount for the coefficient of friction, i.e.: in, These are actual measurements from the accelerometer. S506. When the error is hydraulic response hysteresis, dynamically adjust the proportional gain coefficient and establish an attenuation optimization mechanism, namely: In the formula, The optimized control gain coefficient; It is the attenuation factor; For learning factors; This is the error direction function; S507. When the error is external interference, the spectrum filtering algorithm is activated for physical isolation, that is: In the formula, This is the compensated pressure vector after filtering out interference; Here is the filter matrix; This is the inverse Fourier transform operator; For Fourier transform operators; Let u be the system transfer function, where u is the imaginary unit and w is the angular frequency. S508, merge all correction parameters and regenerate the compensation pressure command. This completes the iterative optimization of closed-loop control, namely: In the formula, The optimized pressure command for the final output; This is a compensation pressure command recalculated based on the new parameters.

10. A system for implementing the physical simulation method according to any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect baseline operating condition data; The dynamic friction calculation module is used to calculate the dynamic friction coefficient based on the operating condition reference data; The slip prediction module is used to predict the theoretical slip amount of the equipment; The hydraulic compensation control module is used to generate pressure compensation commands for the hydraulic system. The adaptive correction module is used to monitor the actual slip, identify errors, and correct control parameters to achieve closed-loop control.

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