A walking positioning method of a bucket wheel machine based on asymmetric wheel pressure

Abstract

The application discloses a walking positioning method for a bucket wheel machine based on asymmetric wheel pressure, and specifically comprises the following steps: acquiring position, spatial attitude, instantaneous load bias characteristics and motor state data in real time; calculating overturning torque based on the spatial attitude and load bias data, and calculating the instantaneous dynamic normal pressure of each wheel by combining with the drive wheel group coordinates; generating a dynamic slip threshold and a confidence decay factor by using the normal pressure, and feeding forward adjusting the trust weight of incremental position data in fusion positioning; and simultaneously, obtaining the load bias friction work by integrating the normal pressure and stroke in a pure window, and adaptively correcting the wheel diameter wear correction coefficient prediction model. The application deeply couples rigid body dynamics and control algorithm, solves the problems of transient anti-slip misjudgment and long-term wear loss under complex load bias of the bucket wheel machine, and significantly improves the walking positioning accuracy and robustness in the whole life cycle.

Description

Technical Field

[0001] This invention relates to the field of automated control technology for bucket wheel excavators in thermal power plants, and in particular to a method for positioning and traveling bucket wheel excavators based on asymmetric wheel pressure. Background Technology

[0002] Bucket wheel stacker-reclaimers (abbreviated as bucket wheel reclaimers) are large-scale core equipment used in coal yards of thermal power plants for continuous stacking and reclaiming operations. With the development of industrial intelligence and unmanned operation technologies, the fully automated stacking and reclaiming operation of bucket wheel reclaimers places extremely high demands on the positioning accuracy and robustness of the trolley travel.

[0003] According to industry standards related to the automatic control system of bucket wheel excavators, the positioning and correction device typically consists of positioning devices for the trolley position, rotation angle, and pitch angle, as well as a correction device. Currently, the positioning technologies used in bucket wheel excavators mainly include the following: The first method is based on satellite positioning technology (such as BeiDou precise positioning). This method utilizes real-time kinematic (RTK) positioning technology, placing two positioning receivers at the cantilever and slewing center of the bucket wheel excavator to obtain the precise absolute satellite positions of the cantilever and slewing center, and then calculating the cantilever slewing angle, pitch angle, and trolley position. Theoretically, this method has extremely high positioning accuracy (horizontal error approximately 2cm, vertical error approximately 5cm). However, due to the harsh environment of open-air coal yards, its reliability is easily affected by extreme weather, large metal structures blocking the view, and strong electromagnetic radiation from power plants, often resulting in signal loss and positioning failure. Since this technology is not suitable for enclosed coal yards, sensor technology is mainly used for bucket wheel excavator positioning after the coal yard has been enclosed and modified.

[0004] The second method is wireless positioning technology based on UWB (Ultra-Wideband) or WIFI. This method calculates the location by deploying several base stations around the coal yard and using signal strength or time-of-flight. However, in the dusty and metallic reflective environment of a coal yard, such technology suffers from severe signal attenuation and multipath effects, resulting in low positioning accuracy and poor reliability, making it difficult to meet the precise operation requirements of fully automated unattended operations.

[0005] The third type is an electromechanical fusion redundancy scheme combining absolute position sensors (such as Gray busbars) and incremental position sensors (such as high-precision encoders), suitable for both open-pit and enclosed coal yards. This is currently the most widely used combination to balance accuracy and stability. However, as a special type of rail equipment with ultra-long cantilever and heavy-load off-center loading characteristics, the complex spatial attitude and variable stress conditions of bucket wheel excavators mean that conventional positioning and correction technologies still suffer from the following two major technical shortcomings: (1) Frequent transient anti-slip misjudgments: Traditional anti-slip logic relies solely on sudden changes in encoder speed or a decrease in drive motor current to determine whether the wheels are slipping. However, when the bucket wheel excavator's boom is fully loaded and rotates at a large angle, the machine's center of gravity shifts drastically, resulting in minimal positive pressure on the wheels on the lightly loaded side. If the trolley accelerates at this time, slight slippage or current fluctuations are very likely to occur. Existing technology cannot distinguish whether this is due to light-load acceleration caused by uneven loading or actual slippage caused by slippery tracks, leading to frequent system misjudgments and triggering of emergency stops, which seriously affects the efficiency of automatic operation.

[0006] (2) Long-term wear correction inaccuracy: Relevant industry standards clearly state that a correction device should be equipped to eliminate the cumulative positioning error. The mechanical wear rate of the wheel is strongly correlated with the actual wheel pressure (normal normal pressure) it bears. In actual production, bucket wheel excavators often operate on one side for a long time, resulting in extremely uneven nonlinear wear on both wheels. Traditional correction methods simply calibrate the wheel diameter (i.e., update the encoder proportional coefficient) by comparing the mileage and the number of pulses. This global averaging algorithm completely masks the physical nature of the asymmetric wear of the wheel, causing the incremental coding position data correction coefficient of the encoder to drift for a long time, and it is impossible to truly eliminate the cumulative error. When absolute position sensors such as the Gray busbar fail temporarily or have blind spots, the coordinates calculated by the encoder alone will produce huge deviations. Summary of the Invention

[0007] To overcome the shortcomings of existing technologies, this invention provides a bucket wheel excavator walking and positioning method based on asymmetric wheel pressure. The aim is to introduce a rigid multibody dynamics and electromechanical coupling model of the bucket wheel excavator, utilize the real-time calculated instantaneous dynamic normal pressure, feedforward to intercept transient slippage errors, and a posteriori to correct long-term wear errors, thereby achieving high-precision adaptive positioning of the bucket wheel excavator.

[0008] The present invention addresses the aforementioned technical problems primarily through the following technical solution: a method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure, comprising the following steps: S1: Acquire multi-source synchronous data: acquire the absolute position data and incremental position data of the trolley's travel positioning in real time, and simultaneously acquire the spatial attitude data of the bucket wheel excavator during operation, the instantaneous off-center load characteristic data used to characterize the instantaneous off-center load of the bucket wheel excavator's cantilever, and the state perception data of the trolley's drive motor. S2: Calculate the instantaneous dynamic normal pressure: Establish a plane rectangular coordinate system with the slewing center of the bucket wheel excavator as the origin. Based on the spatial attitude data, the preset cantilever structure self-weight parameters, and the instantaneous off-center load characteristic data, determine the current joint center of mass position and the corresponding joint gravity load, and then calculate the longitudinal overturning moment and the lateral overturning moment generated relative to the slewing center of the bucket wheel excavator. Combine the position coordinates of each drive wheel group in the plane rectangular coordinate system, convert the longitudinal overturning moment and the lateral overturning moment into the off-center load wheel pressure change of the corresponding drive wheel group, and algebraically superimpose it with the static load distribution pressure of the whole machine to obtain the instantaneous dynamic normal pressure of each drive wheel group. S3: Feedforward anti-slip fusion positioning based on dynamic threshold: Calculate the dynamic slip threshold of the corresponding drive wheel group based on the instantaneous dynamic normal pressure of each drive wheel group; verify the slip condition by combining incremental position data, absolute position data and state perception data; generate a confidence decay factor based on the extreme value change of instantaneous dynamic normal pressure; adjust the trust weight of incremental position data in adaptive fusion positioning calculation with feedforward; and output the real-time fusion position coordinates of the bucket wheel excavator. S4: Long-term wear self-learning based on the integral of off-center load work: When the positioning system meets the preset clean window opening conditions, the effective stroke of the vehicle is integrated using the instantaneous dynamic normal pressure within the window period to calculate the off-center load friction work of the target wheel; the transient correction coefficient is calculated based on the displacement increment of the absolute position data and the cumulative change of the incremental position data within the window period; the global wheel diameter wear correction coefficient prediction model is adaptively corrected using the off-center load friction work as the update weight and the transient correction coefficient; when the positioning system leaves the clean window, the wheel diameter wear correction coefficient prediction model outputs the incremental encoded position data correction coefficient of the calibration incremental position data.

[0009] Preferably, in step S1, the absolute position data is obtained through a Gray busbar positioning system arranged along the trolley's travel track; the incremental position data is obtained through a high-precision absolute encoder on the trolley's wheels; the spatial attitude data includes the rotation angle obtained through the cantilever slewing positioning device and the pitch angle obtained through the cantilever pitch positioning device; the instantaneous off-center load characteristic data is the instantaneous load mass, which is calculated based on the instantaneous cross-sectional volume data of the coal flow measured in real time by a laser scanning device installed above the cantilever belt and a preset empirical value of coal density; the state perception data includes the real-time operating current of the trolley drive motor.

[0010] At the physical level, the laser scanning device can only acquire the instantaneous cross-sectional volume of the coal flow on the cantilever conveyor belt. In order to convert it into the instantaneous load mass required for mechanical calculation, this invention introduces a preset empirical value of coal density into the control system. The dynamic mass is calculated in real time by multiplying the volume and density, which fills the data gap between the visual sensor and the mechanical model and ensures the scientificity and accuracy of the subsequent torque calculation.

[0011] Preferably, in step S2, the established Cartesian coordinate system has the X-axis parallel to the direction of the trolley's travel track and the Y-axis perpendicular to the direction of the trolley's travel track. The track gauge of the main train is 2L. y The wheelbase of the large vehicle is 2L. x The position coordinates (x, y) of the four drive wheel sets of the bucket wheel excavator in the Cartesian coordinate system. i ,y i (i=1,2,3,4) are defined as: the left front drive wheel set (L x ,L y ), right front drive wheel set (L x ,-L y ), left rear drive wheel set (-L x ,L y ), right rear drive wheel set (-L x ,-L y The instantaneous dynamic positive pressure N of each drive wheel assembly i The calculation formula is: N i =G total / 4+M y ·x i / (4×L x 2 )+M x ·y i / (4×L y 2 ); Among them, G total M represents the total weight of the bucket wheel excavator, including the instantaneous eccentric load. y M is the longitudinal overturning moment about the Y-axis. x x is the lateral overturning moment about the X-axis; i and y i These are the horizontal and vertical coordinates of the i-th drive wheel group in the coordinate system, respectively, and they inherently possess algebraic positive and negative properties.

[0012] The bucket wheel excavator's gantry and trolley chassis are welded from high-strength steel, resulting in extremely high structural rigidity. In the engineering algorithm model, they are approximated as absolutely rigid bodies. The total weight of the machine and the cantilever eccentric load are ultimately transmitted to the four drive wheel sets through the four gantry legs, forming a four-point supported spatial static model.

[0013] Since four-point support is a statically indeterminate structure in theoretical mechanics, in order to achieve real-time microsecond-level calculation at the controller level, this method introduces the assumption of rigid plane settlement: that is, assuming that under the condition of uniform foundation stiffness, the change in normal pressure at each support point (wheel) caused by the overturning moment is proportional to its vertical distance to the corresponding geometric axis of symmetry (neutral axis).

[0014] Based on the stress calculation principle of eccentrically compressed components in mechanics of materials, the instantaneous dynamic normal force N of each driving wheel is... i It is composed of three linearly superimposed parts: (1) Uniformly distributed static load component: composed of combined gravity load G total The generated material is evenly distributed among four wheel groups, i.e., N. base =G total / 4.

[0015] (2) Longitudinal eccentric load component: composed of the longitudinal overturning moment M about the Y-axis y Generate. Based on the rigid plane assumption, the additional force ΔN of each wheel set is generated. y It is proportional to its X-coordinate, i.e., ΔN y =k·x i .

[0016] From the torque balance equation, we can obtain: Σ(ΔN) y ·x i )=M y ; Substitute the coordinates of the four wheel sets: k·(L) x 2 +L x 2 +(-L x ) 2 +(-L x ) 2 )=M y ; The proportionality constant is obtained as: k=M y / (4×L x 2 ); Therefore, the change in wheel pressure generated by the longitudinal overturning moment on the i-th wheel set is: ΔN yi =M y ·x i / (4×L x 2 ).

[0017] (3) Lateral eccentric load component: Similarly, the lateral overturning moment M about the X-axis x The change in wheel pressure generated on the i-th wheel set is: ΔN xi =M x ·y i / (4×L y 2 ).

[0018] In actual operation, the longitudinal overturning moment M experienced by the slewing center of the bucket wheel excavator y and lateral overturning moment M xIt is composed of the superposition of gravitational vectors from multiple dynamic components. The system performs real-time calculations by establishing the following multibody dynamic component model: 1. Cantilever system (fixed load and variable amplitude compensation) The torque contribution of the cantilever system mainly depends on the mass G of the cantilever body. boom And the distance of its center of mass projected onto the horizontal plane.

[0019] The system calculates the horizontal projection radius R of the cantilever's center of mass based on the real-time acquired rotation angle α and pitch angle β. boom (β). As the pitch angle β increases, the horizontal projected radius of the cantilever's center of mass decreases accordingly.

[0020] The torque component generated by this part is: M y_boom =G boom ·R boom (β)·cosα; M x_boom =G boom ·R boom (β)·sinα; 2. Cantilever load (dynamic coal quantity and operating mode) The system adjusts the centroid distribution of the coal flow on the cantilever based on whether the current mode is stacking or reclaiming: Stacking mode: Coal enters from the tail car and is conveyed from the center of the main trolley to the cantilever head. At this time, the coal flow mass G... coal On a cantilever, the center of mass is offset towards the tail and middle, and the equivalent radius of the center of mass R coal Smaller.

[0021] Material handling mode: Coal is excavated by the bucket wheel at the head of the cantilever and transported to the center of the trolley via the cantilever. At this time, the center of mass of the coal flow is biased towards the head of the cantilever, and the equivalent radius of the center of mass R coal Relatively large.

[0022] The system uses the instantaneous coal quantity acquired by the laser scanner, combined with the centroid empirical correction coefficient corresponding to the current operating mode, to calculate the dynamic torque generated by the coal flow: M y_coal =G coal ·R coal (β,mode)·cosα; M x_coal =G coal ·R coal (β,mode)·sinα; 3. Tail-end system (variable height operation and load feedback) The tail car (including the receiving platform and the unloading hopper) is usually located behind the main car and is connected to the main machine via a connecting bridge or belt axle. The torque generated depends not only on the mass but also on the force distribution at the connection point.

[0023] The tail car of a bucket wheel excavator operates at different heights during material stacking and retrieving. During stacking, the tail car is raised (above the center point of rotation of the cantilever belt), and coal falls from a height into the cantilever belt via the hopper; during retrieving, the tail car is lowered. The change in tail car height alters the elevation angle of the connecting arm, thereby changing the vertical and horizontal tensile components of the tail car acting on the rear connection point of the main trolley.

[0024] The horizontal distance from the tail car connection point to the center of rotation is L. tail The system determines the height of the rear vehicle based on its current height H. tail And the instantaneous coal storage G on the conveyor belt of the last car section tail_coal Calculate the counteracting torque M generated by the tail vehicle system on the center of rotation. tail Since the tail car is usually located behind the main car, the longitudinal moment M it generates is... y_tail Typically associated with the M generated by cantilever y_boom With opposite signs, the calculation formula is as follows: M y_tail =F v ·L tail ±F h ·ΔH; M x_tail =F v ·y tail ±F side ·ΔH; F v It is the vertical component of the force transmitted from the connecting bridge to the main unit, which is affected by the tail car's counterweight and belt tension; F h The horizontal pushing and pulling force is caused by the height difference; L tail It is the longitudinal lever arm from the tail car connection point to the center of rotation; ΔH is the horizontal component force F. h The vertical arm acting on the main engine is the vertical height of the hinge point center of the tail car connecting axle on the main engine side relative to the horizontal plane of the rotation center. Since the mounting position of this hinge support on the main vehicle's steel structure is fixed, ΔH is a preset structural design constant. tail It is the lateral lever arm of the tail section connecting axle at the center of the hinged support on the main engine side, which is ideally 0; F side It is the lateral horizontal force exerted by the tailgate connecting axle on the main unit's hinge point, which can be measured directly by an added pin sensor, or via F h The calculation method is as follows.

[0025] Vertical component F at the connection point v With horizontal push and pull force F h It can be obtained in one of the following two ways: Method 1: Real-time software solution based on multibody statics model.

[0026] The system acquires the current operating mode and the real-time height H of the tail car luffing mechanism. tail Calculate the equivalent tilt angle γ of the connecting bridge: γ = arctan((H) tail -H pivot ) / L bridge H pivot L is the fixed height of the connection point on the host side. bridge This represents the length of the connecting bridge.

[0027] The system presets the structural self-weight G of the tail vehicle connecting axle. bridge and the location of its local centroid; and obtain the real-time instantaneous coal quantity G on the bridge belt by means of a belt scale or vision scanner installed at the tail section. tail_coal The tail-end connecting bridge is considered as a simply supported beam model, with one end hinged to the main vehicle (stress point) and the other end supported on the tail-end chassis (fulcrum). Based on the currently calculated connecting bridge inclination angle γ, the system establishes a moment balance equation with the tail-end chassis fulcrum as the moment center. It then calculates the support reaction force at the main vehicle's hinge point in real time and orthogonally decomposes it along the vertical and horizontal directions of the absolute coordinate system, thus obtaining the vertical component F in real time. v With horizontal force F h In addition, the horizontal force F h It is also necessary to vector superimpose the longitudinal tension component of the belt transmitted by the current tail belt tensioning device.

[0028] Method 2: Direct measurement based on hardware sensors.

[0029] At the mechanical articulation support of the connecting axle between the main trolley and the tail trolley, a three-dimensional shaft pin-type load cell (pin sensor) is replaced or added in situ. This sensor can measure the real mechanical stress at the articulation point in three orthogonal directions in space in real time. After filtering and analog-to-digital conversion, the instantaneous vertical force data F is directly output to the PLC system. v Compared with horizontal force data F h .

[0030] 4. Summary of Resultant Moments Ultimately, the total overturning moment acting on the center of rotation is the vector sum of the above components: M y =M y_boom +M y_coal +M y_tail +M y_wind ; M x =M x_boom +M x_coal +M x_tail +M x_wind ; Among them, M y_wind and M x_windThese are the correction components of the wind load data mentioned above in the x and y directions.

[0031] By algebraically superimposing the above three components, the core dynamic calculation formula for wheel pressure of this invention can be obtained.

[0032] Traditional control systems, when calculating the forces acting on the four wheels, typically need to exhaustively consider the cantilever's conditions in different quadrants and angles, employing numerous conditional statements. This approach is prone to errors and suffers from low computational efficiency. The derivation formula provided in this solution cleverly utilizes the inherently positive and negative signs of the geometric coordinates (x, y, y) of the four drive wheel sets in a Cartesian coordinate system. i ,y i When the combined center of mass of the bucket wheel excavator moves forward (M) y When (positive), the front wheel coordinate x i If positive, the calculated change in wheel pressure due to uneven loading will automatically be positive (increased pressure); rear wheel coordinate x i If the value is negative, the calculated change will automatically be negative (pressure decreases). This physical model not only perfectly reproduces the actual force characteristics of the bucket wheel excavator, but also greatly simplifies the floating-point operation overhead of the underlying PLC or DSP, ensuring the real-time feedforward of the anti-slip warning.

[0033] Preferably, in step S2, when calculating the longitudinal overturning moment and the lateral overturning moment, the current environmental wind load data measured in real time by the anemometer is also acquired simultaneously, and the wind pressure overturning moment component is calculated based on the current windward projection area of ​​the bucket wheel boom. The wind pressure overturning moment components are then vector-superimposed to correct the longitudinal overturning moment and the lateral overturning moment.

[0034] Due to their large size, bucket wheel excavators experience significant changes in their windward projected area during outdoor operations as the boom rotates. The overturning moment generated by wind loads in strong winds cannot be ignored. This step incorporates environmental meteorological parameters into the dynamic model, further improving the accuracy of wheel pressure calculations under harsh conditions. For indoor, enclosed operations, wind load data is not required, meaning no corrections are needed for longitudinal and lateral overturning moments.

[0035] Preferably, in step S3, the dynamic slippage threshold includes the dynamic slippage current threshold I. slip_i ; Based on Coulomb's law of friction, calculate the physical limit F of the instantaneous adhesion force of the i-th drive wheel set. μi =μ·N i Where μ is the preset wheel-rail friction coefficient, N i This represents the instantaneous dynamic positive force of the drive wheel assembly; By combining the electromechanical conversion coefficient, reduction ratio, and transmission efficiency of the trolley drive motor, the instantaneous adhesion physical limit is inversely mapped to the corresponding dynamic slippage current threshold I of the strain frequency motor. slip_i .

[0036] By reverse mapping, the macroscopic mechanical grip force is converted into a current threshold that the underlying frequency converter can directly recognize, enabling the system to respond to slippage trends in the microsecond level.

[0037] As a preferred embodiment, in step S3, the specific judgment logic for verifying the slippage condition by combining incremental position data, absolute position data, and state perception data is as follows: when the following three conditions are met simultaneously, it is determined that a real slippage has occurred: (1) The absolute value of the difference between the wheel linear velocity calculated based on incremental position data and the actual speed of the vehicle calculated based on absolute position data is greater than the preset speed change threshold. (2) The coordinate change rate of the absolute position data is lower than the preset extremely low speed judgment threshold; (3) The real-time operating current of the vehicle drive motor in the state-aware data exceeds the dynamic slippage current threshold I under the current posture. slip_i .

[0038] Only when all three conditions are met simultaneously can it be confirmed as true slippage, completely eliminating misjudgments caused by light-load acceleration or simple electromagnetic interference from the encoder.

[0039] Preferably, step S3, the step of adjusting the trust weights using a feedforward method, specifically includes: Calculation of confidence decay factor K based on instantaneous dynamic positive pressure conf ; The confidence attenuation factor K conf As a multiplier, it dynamically amplifies the diagonal elements of the observation noise covariance matrix associated with the corresponding drive wheel set in the Kalman filter or extended Kalman filter; when the instantaneous dynamic positive pressure of a certain drive wheel set drops to the preset alarm range, through the amplification effect of the multiplier, it actively reduces the weight of the incremental position data on that side during state updates and relatively increases the dependence weight on the absolute position data.

[0040] Existing technologies typically cut off sensor signals only after slippage occurs. This invention, by adjusting the covariance matrix of the Kalman filter, allows the system to smoothly fade out the data weights of the wheel encoder in advance when the model predicts that a wheel is about to be suspended in the air or when the force is extremely small, thus achieving an improvement from post-event remediation to pre-event prevention.

[0041] Preferably, in step S4, the clean window opening condition requires the system to simultaneously meet the following state parameters, and the duration of these conditions must be greater than a preset time threshold T. w Or the continuous driving distance is greater than the preset distance threshold S w : (1) No slippage condition: Absolute velocity V derived from absolute position data grayConversion speed V based on incremental location data encoder The absolute value of the deviation between |V gray -V encoder | Less than the anti-slip safety threshold ε; (2) Signal health conditions: Absolute position data reading is continuous and there are no abnormal step jumps; (3) Effective condition for eccentric loading: the instantaneous dynamic normal force N corresponding to the target wheel. i Greater than the preset effective wear threshold N min .

[0042] These stringent conditions ensure the absolute purity of the data source used for machine learning. In practical engineering, when a bucket wheel excavator performs long-distance inter-section transfers in a strip coal yard (the cantilever is usually in a centered or small-angle state and travels at a constant speed), this pure window will inevitably and stably be triggered, thus making this method highly feasible.

[0043] Preferably, in step S4, the amount of work done by the off-center friction is W. i The formula for calculating integrals is: ; Where [t1,t2] is the time window period that satisfies the conditions for opening a clean window, and N i (t) represents the instantaneous dynamic normal force exerted on the target wheel at time t, V gray (t) represents the true absolute speed of the vehicle at time t, derived from the absolute position data. At the end of the window period [t1, t2], the transient correction coefficient K is calculated in reverse. new The formula is: K new =ΔS gray / ΔP encoder ; Where, ΔS gray ΔP represents the true absolute displacement increment within the window period. encoder The number of raw pulses accumulated by the incremental sensor corresponding to the target wheel.

[0044] This step precisely quantifies the fatigue accumulated by the wheel under pressure, fundamentally resolving the underlying logical fallacy caused by the traditional method of substituting total mileage for effective wear.

[0045] Preferably, in step S4, the wheel diameter wear correction coefficient prediction model is based on the total work done over the life cycle ΣW. i The nonlinear functional relationship between the independent variable and the incremental encoded position data correction coefficient as the dependent variable; the specific calculation steps of the adaptive wheel diameter wear correction coefficient prediction model include: The work done by eccentric friction within a single window period, Wi The total work done over the lifetime of the corresponding target wheel, ΣW i middle; Calculate the adaptive learning rate α, where the value of α is related to the amount of work W done in a single off-center load friction. i There is a positive correlation mapping relationship; Using the recurrence formula K update =(1-α)·K old +α·K new Update the incremental coding position data correction coefficients, where K old K is the correction factor for the historical incremental encoded location data prior to this clean window period. update Correction coefficients for the updated incrementally encoded position data; The updated incremental coding position data correction coefficient K update and the corresponding total work done over its lifecycle ΣW i Substitute the values ​​to correct the nonlinear function relationship, and use the incremental coded position data correction coefficient output by the corrected wheel diameter wear correction coefficient prediction model as the incremental coded position data correction when the incremental position data is converted into displacement data after leaving the pure window period.

[0046] After obtaining the corrected wheel diameter wear correction coefficient prediction model, this scheme further establishes an incremental coded position data correction model based on this model, which is used to convert the underlying raw pulse signal into high-precision displacement data with physical wear compensation. The specific expression of the correction model is as follows: P(t0+t)=P(t0+K update ·ΔP encoder ; Where P(t0+t) is the real-time fused position coordinate of the trolley at time t0+t; P(t0) is the position coordinate at the initial time t0, which in practical applications is preferably the reference position confirmed by the last calibration before the absolute position data (such as Gray bus data) fails; ΔP encoder This refers to the number of raw pulses accumulated by the incremental position sensor (such as a high-precision encoder) from time t0 to time t0+t.

[0047] The introduction of this corrected model has significant engineering redundancy implications: when absolute position data such as the Gray busbar experiences signal interruption, physical failure, or is in an orbital positioning blind zone, the positioning system will automatically switch to dead reckoning mode based on this corrected model. Because K update The model has already absorbed the slight changes in wheel diameter caused by asymmetric wear through the previous clean window self-learning process. Therefore, even in the case of a missing absolute reference frame, the K output by this model is still valid. updateIt can still dynamically compensate for the cumulative error caused by mechanical wear, so that the incremental coded position data always maintains extremely high physical positioning accuracy, ensuring the continuity and safety of unmanned operation of bucket wheel excavators.

[0048] The purpose of setting the adaptive learning rate α is that the greater the amount of work done under the current off-center load (i.e., the more obvious the wear characteristics), the more the system trusts the transient coefficient K calculated in this instance. new However, the smooth recursion prevents coefficient jumps caused by single extreme data. The establishment of this predictive model gives the bucket wheel excavator the ability to self-evolve; even if the absolute sensor is offline for a long time, the system can intelligently predict future wheel diameter reduction based on past off-center loading history.

[0049] Compared with the prior art, the present invention has the following significant advantages: Extremely high robustness under working conditions: It completely overturns the single anti-slip judgment based on encoder fluctuations, and introduces the real-time spatial attitude and load of the mechanical body into the software control algorithm, realizing a dynamic breathing alarm threshold, and completely eliminating anti-slip misjudgment under extreme off-center load conditions such as full-load rotation of bucket wheel excavator and strong wind.

[0050] Empowering equipment with predictive vitality: By introducing material tribology and mechanical work integral into the software positioning algorithm, not only can the wheel diameter be calibrated online, but also a nonlinear wheel diameter wear correction coefficient prediction curve can be established based on the off-center load history, enabling the closed-loop positioning system to have the ability to automatically correct deviations and self-evolve.

[0051] The algorithm architecture is highly reusable: both transient anti-slip control and long-term wear-resistant self-learning are built on the same core physical engine (i.e., the instantaneous dynamic normal pressure model calculated by S2). The algorithm structure is concise and the logic is tightly interlocked, resulting in extremely low system resource consumption and extremely high operational reliability. Attached Figure Description

[0052] Figure 1 This is a flowchart of a bucket wheel excavator travel positioning method based on asymmetric wheel pressure according to the present invention; Figure 2 This is a schematic diagram of a plane rectangular coordinate system according to the present invention. Detailed Implementation

[0053] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0054] Example: A method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure, mainly relying on the existing PLC control system, host computer system, and added multi-source sensor network of the bucket wheel excavator. This method uses the rigid multibody dynamics model of the bucket wheel excavator as the physics engine and specifically includes the following steps: Step S1: Acquisition and preprocessing of multi-source synchronized data.

[0055] During the operation of the bucket wheel excavator, the system synchronously acquires the following heterogeneous data in real time at a preset sampling period (e.g., 10ms): 1. Position data: The absolute position data of the trolley is obtained through the Gray bus system arranged along the trolley's travel track; the incremental position data (number of pulses) of the trolley's movement is obtained through high-precision absolute encoders installed on the trolley wheels.

[0056] 2. Spatial attitude data: The current cantilever rotation angle α1 and pitch angle β1 of the bucket wheel excavator are obtained by using the cantilever rotation positioning device and the pitch tilt meter, respectively.

[0057] 3. Instantaneous Off-center Load Characteristic Data: A laser scanning device installed above the cantilever conveyor belt scans the instantaneous cross-sectional volume of the coal flow in real time. The system has a built-in preset empirical value ρ for coal density; by integrating the product of volume and density, the instantaneous load mass G on the cantilever is calculated in real time. coal .

[0058] 4. Status-aware data: Read the real-time operating current I of each drive motor of the trolley from the underlying frequency converter. actual .

[0059] 5. Environmental meteorological data: The current ambient wind speed v and absolute wind direction angle θ are obtained through an anemometer installed on the top of the gate. wind .

[0060] Step S2: Mechanical calculation of instantaneous dynamic normal force.

[0061] This step converts the three-dimensional spatial attitude and off-center load into one-dimensional force data at the bottom of the wheel.

[0062] (1) Definition of coordinate system and constants Establish a Cartesian coordinate system with the slewing center of the bucket wheel excavator as the origin (X-axis parallel to the track, Y-axis perpendicular to the track). Assume the wheelbase of the trolley is 2L. x The track gauge is 2L. y The coordinates of the four drive wheel sets inherently possess a positive or negative sign attribute: left front (L x ,L y ), right anterior (L) x ,-L y ), left rear (-L x ,L y ), right rear (-L) x ,-L y For details, please refer to [link / reference]. Figure 2 .

[0063] The pre-set structural self-weight parameter of the cantilever and upper slewing structure is G. empty The position vector of its corresponding static centroid in the local coordinate system of the structure is r.empty .

[0064] (2) Calculation of combined center of mass and moment Based on the current rotation angle α1 and pitch angle β1, the coordinate rotation matrix is ​​used to adjust r. empty and off-center load mass G coal The corresponding dynamic centroid r coal Mapping uniformly to the aforementioned Cartesian coordinate system, calculate the current joint centroid coordinates (X). C ,Y C ), and the combined gravity load G total =G empty +G coal .

[0065] This generates an initial overturning moment caused by gravity: Longitudinal overturning moment: M y =G total ·X C ; Lateral overturning moment: M x =G total ·Y C ; (3) Vector superposition of wind pressure overturning moment components Convert wind speed v to instantaneous wind pressure P wind The projected area S of the windward side of the bucket wheel excavator. projected It is a function of the rotation angle α1: S projected (α1)=S boom ·|sin(α1-θ wind )|+S base ; Where S boom S is the side area of ​​the cantilever. base For fixed structural areas such as door seats.

[0066] Let the equivalent windward height be H. wind The wind pressure moment components are calculated and superimposed for correction to obtain the final comprehensive overturning moment: M x_final =M x +P wind ·S projected (α1)·H wind ·cos(α1-θ wind ); M y_final =M y +P wind ·S projected (α1)·H wind ·sin(α1-θ wind ); (4) Instantaneous dynamic positive pressure Ni Output Based on the rigid body plane settlement assumption and the moment balance equation, the instantaneous dynamic normal force N of the i-th drive wheel set is... i The solution formula is as follows: N i =G total / 4+M y_final ·x i / (4×L x 2 )+M x_final ·y i / (4×L y 2 ); In this formula, since the coordinates (x) i ,y i With its own algebraic positive and negative properties, the system can adaptively calculate the changes in wheel force caused by the shift in the center of gravity without performing complex quadrant logic judgments.

[0067] Step S3: Feedforward anti-slip fusion positioning based on dynamic threshold.

[0068] This step converts the mechanical variables calculated in step S2 into feedforward anti-slip commands for the control system.

[0069] (1) Dynamic slippage current threshold I slip_i Reverse mapping Based on Coulomb's law of friction, the physical adhesion limit of the i-th wheel assembly is F. μi =μ·N i (μ is the wheel-rail friction coefficient).

[0070] Combining wheel radius r and reducer transmission ratio i gear Mechanical efficiency η and torque current constant K of the drive motor t The slippage critical current of the motor corresponding to that wheel is calculated in reverse: I slip_i =μ·N i ·r / (K t ·i gear ·η); Slippage conditions are confirmed only when: ① the difference between the encoder-calculated speed and the Gray bus speed exceeds the limit; ② the Gray bus indicates that the trolley has basically not moved; ③ the actual motor current I... actual >I slip_i Only when these three conditions are met will the skid emergency stop alarm be triggered, completely eliminating false alarms.

[0071] (2) Feedforward adjustment of the covariance matrix of the Kalman filter This embodiment uses an extended Kalman filter (EKF) to fuse two location data streams.

[0072] Set the lower limit of safe wheel pressure N safe and critical pulley pressure N min Establish the confidence decay factor K. conf Mapping function: When N i ≥N safe At that time, K conf =1; When N min ≤N i <N safe At that time, K conf For linear decay: K conf =(N i -N min ) / (N safe -N min ); When N i <N min At that time, K conf =0.01.

[0073] In the observation update step of EKF, the diagonal elements R of the observation noise covariance matrix corresponding to the incremental sensor (encoder) are... base Modified to dynamic value R i ':R i '=R base / K conf .

[0074] Through this mechanism, when the bucket wheel excavator is extremely unbalanced, causing one side of the wheel to be about to slip off the ground (K) conf (approaching 0), its observation noise R i The Kalman gain is amplified dramatically, and the system automatically reduces the trust weight of the encoder (before actual slippage occurs), smoothly transitioning to a positioning state based primarily on absolute position data.

[0075] Step S4: Long-term wear self-learning based on the work integral of off-center load.

[0076] This step endows the positioning system with intelligent self-correction capabilities within a closed loop.

[0077] (1) Work done by friction between the pure window and the off-center load When the system detects no slippage (minimal speed difference), a healthy Gray bus signal, and sufficient force on the target wheel (N) i >N min When ), open the pure time window [t1,t2).

[0078] Within this window, the amount of work W done by the eccentric friction on the wheel is... i Perform definite integrals: ; At the same time, the transient correction coefficient K within this pure travel period is calculated: new =ΔS gray / ΔP encoder ; (2) Calculation of adaptive learning rate α Set the ideal effective work value W for a single operation ref With the maximum allowed update step size α max (e.g., 0.15). The adaptive learning rate is calculated as follows: α=min(W i / W ref ,α max ); Update the current incremental coding position data correction coefficient using a recursive formula: K update =(1-α)·K old +α·K new This mechanism ensures that the heavier the pressure on the wheel and the more obvious the wear characteristics, the greater the weight of the calculated coefficient for global correction.

[0079] (3) Correction of nonlinear prediction model The system's background storage contains the total work done over its lifecycle, ΣW. i The relationship between the independent variable and the incremental coding location data correction coefficient K is a nonlinear function, where K is the dependent variable. This embodiment preferably employs a quadratic polynomial model: K=a·(ΣW i ) 2 +b·(ΣW i )+c; At the end of each window, the newly acquired data pairs (ΣW) will be transferred. i ,K update The empirical coefficients a, b, and c of the polynomial are used as training samples. The empirical coefficients a, b, and c of the polynomial are updated online using the recursive least squares (RLS) method.

[0080] When the bucket wheel excavator leaves the clean window or the Gray busbar fails completely, the system directly reads the cumulative work ΣW of the target wheel. i Substituting the above nonlinear function, the most accurate incremental coded position data correction coefficient, including asymmetric wear, is automatically predicted at this moment, which is used by the encoder for dead reckoning, thereby achieving high-precision positioning throughout the entire life cycle.

[0081] That is, the entire process includes two operating conditions: 1. Pure Period (Observation and Energy Storage): The large vehicle meets the pure conditions → K is generated. new →The recursive formula outputs the smoothed K. update →K updateSubstitute the feature parameters of the prediction model correction function (the coefficients a, b, c change, and the prediction curve is reshaped).

[0082] 2. Impure Period (Consumption and Release): Absolute position fails → Recursive formula suspended → Directly read the current cumulative work done ΣW i →The prediction model outputs the most accurate correction coefficients based on the reshaped curve →ensuring that the encoder still has centimeter-level accuracy when calculating the position independently.

[0083] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

[0084] Although this paper makes extensive use of terms such as incremental position data, spatial attitude, and overturning moment, the possibility of using other terms is not excluded. These terms are used merely for the convenience of describing and explaining the essence of the invention; interpreting them as any additional limitation would contradict the spirit of the invention.

Claims

1. A method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure, characterized in that, Includes the following steps: S1: Real-time acquisition of absolute and incremental position data of the trolley's travel positioning, and simultaneous acquisition of the bucket wheel excavator's spatial attitude data, instantaneous off-center load characteristic data, and the trolley drive motor's state perception data; S2: Establish a plane rectangular coordinate system with the slewing center of the bucket wheel excavator as the origin. Based on the spatial attitude data, the preset cantilever structure self-weight parameters and instantaneous off-center load characteristic data, determine the current joint center of mass position and the corresponding joint gravity load, and then calculate the longitudinal overturning moment and the lateral overturning moment generated relative to the slewing center of the bucket wheel excavator. By combining the position coordinates of each drive wheel group in the Cartesian coordinate system, the longitudinal overturning moment and the lateral overturning moment are converted into the eccentric wheel pressure change of the corresponding drive wheel group, and then algebraically superimposed with the static load distribution pressure of the whole machine to obtain the instantaneous dynamic positive pressure of each drive wheel group. S3: Calculate the dynamic slip threshold of the corresponding drive wheel group based on the instantaneous dynamic normal pressure of each drive wheel group; verify the slip condition by combining incremental position data, absolute position data and state perception data, and generate a confidence decay factor based on the extreme value change of instantaneous dynamic normal pressure. Adjust the trust weight of incremental position data in adaptive fusion positioning calculation with feedforward, and output the real-time fusion position coordinates of the bucket wheel excavator. S4: When the positioning system meets the preset clean window opening conditions, the effective stroke of the vehicle is integrated using the instantaneous dynamic positive pressure during the window period to calculate the work done by the eccentric friction of the target wheel. The transient correction coefficient is calculated by back-calculating the displacement increment of the absolute position data and the cumulative change of the incremental position data within the window period. Using the work done by off-center friction as the update weight, the prediction model of wheel diameter wear correction coefficient is adaptively corrected by transient correction coefficient. When the positioning system leaves the clean window, the wheel diameter wear correction coefficient prediction model outputs the incremental encoded position data correction coefficient of the calibration incremental position data.

2. The method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure according to claim 1, characterized in that, In step S1, the absolute position data is obtained through a Gray busbar positioning system arranged along the trolley's travel track; the incremental position data is obtained through a high-precision absolute encoder on the trolley's wheels; the spatial attitude data includes the rotation angle obtained through the cantilever slewing positioning device and the pitch angle obtained through the cantilever pitch positioning device; the instantaneous off-center load characteristic data is the instantaneous load mass, which is calculated based on the instantaneous cross-sectional volume data of the coal flow measured in real time by a laser scanning device installed above the cantilever belt and a preset empirical value of coal density; the state perception data includes the real-time operating current of the trolley drive motor.

3. The bucket wheel excavator travel positioning method based on asymmetric wheel pressure according to claim 1, characterized in that, In step S2, the established Cartesian coordinate system has the X-axis parallel to the direction of the trolley's travel track and the Y-axis perpendicular to the direction of the trolley's travel track. The track gauge of the main train is 2L. y The wheelbase of the large vehicle is 2L. x The position coordinates (x, y) of the four drive wheel sets of the bucket wheel excavator in the Cartesian coordinate system. i ,y i The following are defined as: Left front drive wheel set (L) x ,L y ), right front drive wheel set (L x ,-L y ), left rear drive wheel set (-L x ,L y ), right rear drive wheel set (-L x ,-L y ); The instantaneous dynamic positive pressure N of each drive wheel set i The calculation formula is: N i =G total / 4+M y ·x i / (4×L x 2 )+M x ·y i / (4×L y 2 ); Among them, G total M represents the total weight of the bucket wheel excavator, including the instantaneous eccentric load. y M is the longitudinal overturning moment about the Y-axis. x x is the lateral overturning moment about the X-axis; i and y i These are the horizontal and vertical coordinates of the i-th drive wheel group in the coordinate system, respectively, and they inherently possess algebraic positive and negative properties.

4. A method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure according to claim 1 or 3, characterized in that, In step S2, while calculating the longitudinal overturning moment and the lateral overturning moment, the current environmental wind load data measured in real time by the anemometer is also acquired simultaneously, and the wind pressure overturning moment component is calculated based on the current windward projection area of ​​the bucket wheel boom. The wind pressure overturning moment components are then vector-superimposed to correct the longitudinal overturning moment and the lateral overturning moment.

5. The method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure according to claim 1, characterized in that, In step S3, the dynamic slippage threshold includes the dynamic slippage current threshold I. slip_i ; Based on Coulomb's law of friction, calculate the physical limit F of the instantaneous adhesion force of the i-th drive wheel set. μi =μ·N i Where μ is the preset wheel-rail friction coefficient, N i This represents the instantaneous dynamic positive force of the drive wheel assembly; By combining the electromechanical conversion coefficient, reduction ratio, and transmission efficiency of the trolley drive motor, the instantaneous adhesion physical limit is inversely mapped to the corresponding dynamic slippage current threshold I of the strain frequency motor. slip_i .

6. The method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure according to claim 5, characterized in that, In step S3, the specific judgment logic for verifying the slippage condition by combining incremental position data, absolute position data, and state perception data is as follows: when the following three conditions are met simultaneously, a real slippage is determined to have occurred: (1) The absolute value of the difference between the wheel linear velocity calculated based on incremental position data and the actual speed of the vehicle calculated based on absolute position data is greater than the preset speed change threshold. (2) The coordinate change rate of the absolute position data is lower than the preset extremely low speed judgment threshold; (3) The real-time operating current of the vehicle drive motor in the state-aware data exceeds the dynamic slippage current threshold I under the current posture. slip_i .

7. The method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure according to claim 1, characterized in that, In step S3, the step of adjusting the trust weights using a feedforward method specifically includes: Calculation of confidence decay factor K based on instantaneous dynamic positive pressure conf ; The confidence attenuation factor K conf As a multiplier, it dynamically amplifies the diagonal elements of the observation noise covariance matrix associated with the corresponding drive wheel set in the Kalman filter or extended Kalman filter; when the instantaneous dynamic positive pressure of a certain drive wheel set drops to the preset alarm range, through the amplification effect of the multiplier, it actively reduces the weight of the incremental position data on that side during state updates and relatively increases the dependence weight on the absolute position data.

8. The method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure according to claim 1, characterized in that, In step S4, the condition for opening the clean window requires the system to simultaneously meet the following state parameters, and the duration must be greater than a preset time threshold T. w Or the continuous driving distance is greater than the preset distance threshold S w : (1) No slippage condition: Absolute velocity V derived from absolute position data gray Conversion speed V based on incremental location data encoder The absolute value of the deviation between |V gray -V encoder | Less than the anti-slip safety threshold ε; (2) Signal health conditions: Absolute position data reading is continuous and there are no abnormal step jumps; (3) Effective condition for eccentric loading: the instantaneous dynamic normal force N corresponding to the target wheel. i Greater than the preset effective wear threshold N min .

9. A method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure according to claim 8, characterized in that, In step S4, the amount of work done by the off-center friction is W i The formula for calculating integrals is: ； Where [t1,t2] is the time window period that satisfies the conditions for opening a clean window, and N i (t) represents the instantaneous dynamic normal force exerted on the target wheel at time t, V gray (t) represents the true absolute speed of the vehicle at time t, derived from the absolute position data. At the end of the window period [t1, t2], the transient correction coefficient K is calculated in reverse. new The formula is: K new =ΔS gray / ΔP encoder ; Where, ΔS gray ΔP represents the true absolute displacement increment within the window period. encoder The number of raw pulses accumulated by the incremental sensor corresponding to the target wheel.

10. The method for positioning and traveling of a bucket wheel excavator based on asymmetric wheel pressure according to claim 9, characterized in that, In step S4, the wheel diameter wear correction coefficient prediction model is based on the total work done over the life cycle ΣW. i The relationship between the independent variable and the incremental coding location data correction coefficient as the dependent variable is a nonlinear function. The specific calculation steps of the adaptive wheel diameter wear correction coefficient prediction model include: The work done by eccentric friction within a single window period, W i The total work done over the lifetime of the corresponding target wheel, ΣW i middle; Calculate the adaptive learning rate α, where the value of α is related to the amount of work W done in a single off-center load friction. i There is a positive correlation mapping relationship; Using the recurrence formula K update =(1-α)·K old +α·K new Update the incremental coding position data correction coefficients, where K old K is the correction factor for the historical incremental encoded location data prior to this clean window period. update Correction coefficients for the updated incrementally encoded position data; The updated incremental coding position data correction coefficient K update and the corresponding total work done over its lifecycle ΣW i Substitute the values ​​to correct the nonlinear function relationship, and use the incremental coded position data correction coefficient output by the corrected wheel diameter wear correction coefficient prediction model as the incremental coded position data correction coefficient for the subsequent conversion of incremental position data to displacement data when leaving the pure window period.

Images