Driving force distribution control method and system applied to monorail hoist operation system

Abstract

The invention relates to the technical field of driving force distribution control, in particular to a driving force distribution control method and system applied to a monorail hoist operation system, and the method comprises the steps: collecting working condition environment parameters and operation state parameters corresponding to each group of driving wheels in the monorail hoist operation system; according to the working condition environment parameters and the operation state parameters, the maximum bearing torque of the driving wheels is calculated, and the driving force target distribution proportion of the multiple sets of driving wheels is determined based on the maximum bearing torque; and rotating speed data of the driving wheel are collected, the real-time rotating speed deviation of the driving wheel is determined, and the driving wheel is dynamically adjusted according to the real-time rotating speed deviation, the maximum bearing torque and the driving force target distribution proportion so as to correct the real-time rotating speed deviation. Therefore, the dynamic change in the operation of the monorail hoist is accurately captured, the driving force distribution imbalance caused by working condition misjudgment is effectively avoided, the adaptability between the driving force distribution strategy and the dynamic working condition is improved, and the operation stability and safety of the system under the complex working condition are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of drive force distribution control technology, and specifically to a drive force distribution control method and system applied to a monorail crane operating system. Background Technology

[0002] In the fields of material transportation such as underground mining and warehousing logistics, monorail crane systems have become important transportation equipment due to their adaptability to complex spaces. The multi-drive unit drive force distribution control technology of this system is the core link that determines the system's operational stability, transportation efficiency, and energy economy, directly affecting the quality of transportation task completion and cost control.

[0003] Currently, the industry's approach to drive force distribution in monorail crane operating systems largely employs control strategies based on fixed load parameters or preset monorail operating conditions, corresponding to flat roads and fixed gradients. Specifically, existing technologies typically distribute drive force proportionally according to the theoretical load percentage of each drive wheel, distribute the drive force evenly, or switch fixed distribution modes based on a single operating condition threshold. While this approach can meet the basic drive force requirements under ideal operating conditions of normal constant speed and stable load, enabling monorail cranes to complete simple material transport tasks, it remains a relatively common technical solution in the industry.

[0004] However, in actual operation of monorail cranes, existing drive force distribution control methods are difficult to adapt to dynamically changing operating conditions. With variations in the actual gradient and load conditions of the monorail, drive force distribution often becomes unbalanced. This imbalance leads to uneven force distribution on the drive wheels, causing slippage or accelerated localized wear on the monorail. This not only shortens the equipment's lifespan but also threatens the system's operational safety. Therefore, existing drive force distribution control methods lack adaptability to dynamic operating conditions, cannot balance the safety and transportation efficiency of monorail crane operation, and are insufficient to meet the transportation needs of complex scenarios. Summary of the Invention

[0005] To address the technical problem that existing drive force distribution control methods are unable to adapt to dynamically changing operating conditions, this invention provides a drive force distribution control method and system for monorail crane operation systems. This improves the adaptability of drive force distribution strategies to dynamic operating conditions, thereby ensuring the safety and transportation efficiency of monorail cranes.

[0006] To achieve the above objectives, in a first aspect, this application proposes a driving force distribution control method for a monorail crane operating system. The driving force distribution control method is applied to a monorail crane operating system, which includes multiple sets of drive wheels. The driving force distribution control method for the monorail crane operating system includes: Collect the environmental parameters and operating status parameters corresponding to each group of drive wheels in the monorail crane operating system; Based on the working environment parameters and operating status parameters, the maximum load torque of the drive wheels is calculated, and the target distribution ratio of the driving force of multiple sets of drive wheels is determined based on the maximum load torque. The drive wheel speed data is collected to determine the real-time speed deviation of the drive wheel. Based on the real-time speed deviation, maximum load torque, and target distribution ratio of driving force, the drive wheel is dynamically adjusted to correct the real-time speed deviation.

[0007] In one embodiment, the operating environment parameters include: wear morphology data and infrared reflection signals; the operating status parameters include: real-time torque value and acceleration value; the monorail crane operating system also includes: a laser profile sensor, an infrared reflection sensor, a torque sensor, and a longitudinal acceleration sensor. The steps for collecting the environmental parameters and operating status parameters corresponding to each set of drive wheels in a monorail crane operating system include: The surface contour of the drive wheel is scanned by a laser contour sensor to generate contour point cloud information including the drive wheel, and wear morphology data is obtained; the wear morphology data is used to represent the degree of wear on the surface of the drive wheel. Infrared reflection signals are generated by transmitting and receiving reflected signals onto the monorail surface of the drive wheel using an infrared reflection sensor; these infrared reflection signals are used to indicate the roughness of the monorail surface and the state of medium adhesion. The output torque of the drive wheel is detected by a torque sensor to obtain the real-time torque value; the real-time torque value is used to represent the magnitude of the driving force currently output by the drive wheel. The longitudinal acceleration of the monorail crane body is detected by a longitudinal acceleration sensor to obtain the acceleration value; the acceleration value is used to represent the actual motion state of the monorail crane body.

[0008] In one embodiment, the monorail crane operating system further includes: a monorail; and the step of calculating the maximum load-bearing torque of the drive wheel based on working environment parameters and operating status parameters includes: Based on wear pattern data, determine the actual contact area ratio between the drive wheel and the monorail; The real-time friction coefficient between the drive wheel and the monorail surface is calculated based on the infrared reflection signal. The clamping force of the drive wheel on the monorail is adaptively adjusted based on the degree of deviation between the real-time torque value and the acceleration value. By combining the actual contact area ratio, real-time friction coefficient, adjusted clamping force, and inherent parameters of the drive wheel, the maximum load-bearing torque of the drive wheel is calculated using a preset torque calculation formula.

[0009] In one embodiment, the step of determining the percentage of the actual contact area between the drive wheel and the monorail based on wear pattern data includes: Using the annular working surface profile of the drive wheel in its new state as the reference profile, the point cloud information of the drive wheel surface profile obtained by the laser profile sensor is compared with the reference profile to calculate the wear amount of the drive wheel. Based on the amount of wear, the drive wheels are classified to determine the wear type of the drive wheels; When the wear type of the drive wheel is a concentrated wear area where the wear amount is higher than the average wear value, the actual contact area ratio is calculated based on the ratio of the actual contact area between the drive wheel and the monorail to the relative rated area.

[0010] In one embodiment, the step of calculating the real-time friction coefficient between the drive wheel and the monorail surface based on the infrared reflection signal includes: Acquire infrared signal intensity and reflectivity distribution data reflected from the surface of a single track; The infrared signal intensity is compared with a preset reference signal to calculate the signal attenuation rate; Based on reflectivity distribution data, identify whether there is an attached medium on the surface of the monorail and the proportion of the covered area. The real-time friction coefficient between the drive wheel and the monorail surface is calculated based on the signal attenuation rate and the proportion of the area covered by the attached medium.

[0011] In one embodiment, the step of adaptively adjusting the clamping force of the drive wheel on the monorail based on the degree of deviation between the real-time torque value and the acceleration value includes: The real-time torque and acceleration values ​​are normalized to their maximum values ​​to calculate the difference between them and obtain the degree of deviation. The degree of deviation reflects the matching state between the driving force output by the drive wheels and the actual motion requirements of the vehicle body, and characterizes the loss in the process of driving force transmission. Generate continuous time series corresponding to the degree of deviation, and determine the trend of deviation change through the continuous time series: If the deviation shows an increasing trend over multiple consecutive time points, the clamping force is gradually increased by using the preset ratio of the basic clamping force set at the factory for the drive wheel as the adjustment step size. If the degree of deviation shows a decreasing trend over multiple consecutive time points, reduce the adjustment step size of the clamping force; If the deviation remains stable within the preset minimum range, stop adjusting the clamping force; If the deviation increases to a preset ratio exceeding the sensor's detection range, the clamping force is increased while the output torque of the drive wheel is simultaneously reduced until the deviation decreases to a preset safe range.

[0012] In one embodiment, the step of dynamically adjusting the drive wheel to correct the real-time speed deviation based on the real-time speed deviation, the maximum load torque, and the target distribution ratio of the driving force includes: If the real-time rotational speed deviation meets the preset deviation threshold for the rotating wheel, then the target adjustment torque is calculated based on the maximum load torque and the target distribution ratio of the driving force. Adjust the torque according to the target, and control the drive wheel to reduce the output torque at a uniform rate within a preset time until the target torque adjustment is completed.

[0013] In one embodiment, the step of dynamically adjusting the drive wheel to correct the real-time speed deviation based on the real-time speed deviation, the maximum load torque, and the target distribution ratio of the driving force includes: If the real-time rotational speed deviation meets the preset deviation threshold for the rotating wheel, then the target adjustment torque is calculated based on the maximum load torque and the target distribution ratio of the driving force. Adjust the torque according to the target, and control the drive wheel to reduce the output torque at a uniform rate within a preset time until the target torque adjustment is completed.

[0014] In one embodiment, the step of calculating the advance adjustment time required for impact buffering based on the current operating speed of the monorail crane and the positional characteristics of the monorail joint includes: Obtain the type parameters of the monorail joint and the real-time operating speed of the monorail crane; Based on the type parameters, the preset impact coefficient table is queried, and combined with the real-time running speed, the theoretical impact duration when passing through the monorail joint is calculated. The theoretical impact duration is superimposed with the response delay time of the drive wheel torque adjustment to obtain the advance adjustment time required for impact buffering; where the advance adjustment time is the time to complete the pre-adjustment of the drive wheel output torque and stabilize the torque state before the monorail crane reaches the monorail joint.

[0015] To achieve the above objectives, secondly, this application also proposes a drive force distribution control system for a monorail crane operating system. The drive force distribution control system is applied to the monorail crane operating system, which includes multiple sets of drive wheels. The drive force distribution control system includes: The dynamic sensing module is used to collect the working environment parameters and operating status parameters corresponding to each set of drive wheels in the monorail crane operating system; The load-bearing calculation module is used to calculate the maximum load-bearing torque of the drive wheels based on the working environment parameters and operating status parameters, and to determine the target distribution ratio of the driving force of multiple sets of drive wheels based on the maximum load-bearing torque; The dynamic adjustment module is used to collect the rotational speed data of the drive wheels, determine the real-time rotational speed deviation of the drive wheels, and dynamically adjust each group of drive wheels according to the maximum load torque and the target distribution ratio of driving force to correct the real-time rotational speed deviation.

[0016] One or more technical solutions proposed in this application have, but are not limited to, the following technical effects: This application collects environmental and operational parameters for each set of drive wheels in a monorail crane operating system. Based on these parameters, it calculates the maximum load-bearing torque of the drive wheels and determines the target distribution ratio of driving force for multiple sets of drive wheels. It also collects drive wheel speed data to determine the real-time speed deviation and dynamically adjusts the drive wheels to correct this deviation based on the real-time speed deviation, maximum load-bearing torque, and target distribution ratio. By using multiple types of sensors to collect environmental parameters such as drive wheel wear patterns and monorail surface conditions, as well as operational parameters such as torque and acceleration, this application accurately captures the dynamic changes during monorail crane operation. Combining these parameters, it calculates the maximum load-bearing torque of the drive wheels and determines the target distribution ratio of driving force, effectively avoiding imbalances in driving force distribution caused by misjudgment of operating conditions. This prevents power lag caused by insufficient torque and avoids slippage risks caused by torque exceeding the load limit, improving the adaptability of the driving force distribution strategy to dynamic operating conditions and significantly enhancing the system's operational stability and safety under complex conditions. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the first embodiment of the drive force distribution control method applied to a monorail crane operating system according to this application; Figure 2 This is a flowchart illustrating the second embodiment of the drive force distribution control method applied to a monorail crane operating system according to this application; Figure 3 This is a flowchart illustrating the third embodiment of the drive force distribution control method applied to a monorail crane operating system in this application; Figure 4 This is a schematic diagram of a drive force distribution control system 40 for use in a monorail crane operating system provided in this application. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0019] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0020] To make the technical solutions and advantages in the embodiments of this application clearer, the existing related technologies are described below.

[0021] In material transportation fields such as underground mining and warehousing logistics, monorail crane systems have become crucial transportation equipment due to their adaptability to complex spaces. The multi-drive unit drive force distribution control technology of monorail crane systems is a core element determining system stability, transportation efficiency, and energy economy, directly impacting the quality and cost control of transportation tasks. Currently, the industry primarily employs control strategies based on fixed load parameters or preset monorail operating conditions, corresponding to level roads and fixed gradients, for drive force distribution in monorail crane systems.

[0022] Specifically, existing technologies typically allocate driving force proportionally based on the theoretical load percentage of each drive wheel, distribute the driving force evenly, or switch to a fixed allocation mode based on a single operating condition threshold. From a technical implementation perspective, existing control systems mostly have preset parameter templates for limited operating conditions such as flat track constant speed and fixed gradient uphill / downhill. During operation, they only call the corresponding template's driving force allocation scheme based on the pre-set load range or single-rail gradient range, without the need for real-time collection of dynamic operating condition data for adjustment. Although this can meet the basic driving force requirements under ideal operating conditions of normal constant speed and stable load, supporting monorail cranes to complete simple material transportation tasks, it is currently a relatively common technical approach in the industry.

[0023] However, in actual operation scenarios of monorail cranes, especially in complex environments such as underground mines, dynamic operating conditions occur frequently. Existing drive force distribution control methods have revealed significant technical limitations and are difficult to adapt to actual needs. The specific technical problems are as follows: (1) It relies on fixed parameters and preset templates and cannot respond to dynamic changes in working conditions, resulting in an imbalance in the distribution of driving force.

[0024] The existing drive force distribution scheme is based on fixed load parameters and preset single-rail working conditions, without taking into account the real-time changes in working conditions during actual operation.

[0025] On the one hand, the gradient of a single track often fluctuates dynamically. Underground single tracks in mines are restricted by terrain and frequently face situations of switching between uphill and downhill slopes and sudden changes in gradient. Existing methods cannot detect gradient changes in real time and still execute the driving force scheme based on the original preset gradient, resulting in insufficient power when going uphill and an imbalance of braking force when going downhill. On the other hand, the load condition is extremely unstable. During the loading and unloading of materials, there are often instantaneous increases and decreases in load and uneven loading of materials. The existing fixed distribution mode cannot adjust the driving force ratio of each drive wheel according to the load changes, which causes some drive wheels to be overloaded due to the load exceeding expectations, while some drive wheels waste power due to insufficient load.

[0026] This disconnect between driving force and actual demand directly leads to problems such as drive wheel slippage or increased local wear on the monorail, which not only shortens the service life of the drive wheels and monorail, but also poses a threat to the operational safety of the monorail crane.

[0027] (2) Lack of perception and compensation for the degradation of critical components.

[0028] The existing design does not consider the degradation of key components in the drive system during long-term operation, which directly affects the actual transmission efficiency and load-bearing capacity of the drive force. During long-term use, the clamping force of the drive wheel clamping mechanism of a monorail crane will gradually decrease due to aging of hydraulic seals and spring fatigue; simultaneously, friction will cause wear on the surface of the drive wheel, reducing the actual contact area with the monorail.

[0029] Both types of attenuation reduce the maximum load-bearing torque of the drive wheel. Attenuation of clamping force reduces the friction between the drive wheel and the monorail, which may cause slippage even if the output torque is normal. Wear on the drive wheel reduces the effective contact area for power transmission, leading to excessive local stress and further aggravation of wear. However, existing technologies do not have a mechanism for detecting and compensating for clamping force and drive wheel wear. They cannot detect these attenuations, nor can they adjust the drive force distribution strategy to adapt to the reduced load-bearing capacity.

[0030] (3) Lack of targeted buffer control affects system stability and component lifespan.

[0031] Monorails used by monorail cranes are often segmented and spliced ​​structures. These monorail joints exhibit differences in physical characteristics such as gap width and step height. When a monorail crane passes over these joints, the discontinuity of the monorail surface causes instantaneous impacts. These impacts not only cause vehicle vibration but also momentarily change the contact state between the drive wheels and the monorail. At the moment of impact, the drive wheels may briefly detach from the monorail surface, interrupting the transmission of driving force. Repeated impacts over time accelerate the wear of drive wheel bearings and monorail joints, and may even lead to component loosening. However, current technology lacks specific control strategies for monorail joints; it neither predicts the location of the joints nor adjusts the driving force to buffer the impact, relying solely on the inherent rigidity of the drive system to withstand the impact.

[0032] Therefore, existing monorail crane drive force distribution control methods, due to their reliance on fixed parameters, lack of compensation for component attenuation, and failure to consider monorail joint impact, cannot adapt to dynamically changing complex working conditions. They are unable to guarantee operational safety, nor can they balance transportation efficiency and component lifespan, making it difficult to meet the high-quality transportation needs of scenarios such as underground mines and complex warehouses. There is an urgent need for an improved control method that can perceive multi-dimensional working conditions in real time and dynamically adjust drive force distribution.

[0033] It's also important to note that the core contradiction in the drive force distribution of monorail cranes lies in the dynamic mismatch between the load-bearing capacity of the drive wheels and the real-time torque demand. On one hand, the load-bearing capacity of the drive wheels is not constant; it changes dynamically with wear and clamping force during operation. On the other hand, the real-time torque demand also fluctuates continuously, requiring consideration of multiple variables such as rail joint impact, multi-wheel synchronization deviation, and system energy consumption constraints. Current technical solutions fail to resolve this contradiction, primarily due to the lack of a comprehensive, interconnected control logic. This directly leads to the isolation of key aspects such as wear condition assessment, dynamic clamping force adjustment, and precise drive torque distribution. These processes cannot coordinate responses based on real-time changes in load-bearing capacity and torque demand, ultimately affecting the efficiency and stability of drive force distribution.

[0034] To address this issue, this application proposes a drive force distribution control method and system for monorail crane operation systems. It abandons fixed parameter templates and utilizes laser profile sensors, infrared reflection sensors, torque sensors, and longitudinal acceleration sensors to collect multi-dimensional data in real time, including drive wheel wear patterns, monorail surface conditions, output torque, and vehicle acceleration. This allows for the inversion of dynamic monorail gradient fluctuations through acceleration changes and the detection of instantaneous load increases / decreases or uneven loads through the deviation between torque and acceleration. Based on this real-time data, the maximum load-bearing torque of each drive wheel group is dynamically calculated, and the drive force is distributed according to actual operating conditions. This enables automatic increase of total drive force when going uphill and adjustment of the torque ratio of individual drive wheels when unevenly loaded, avoiding insufficient power or overload and solving the problem of drive force being out of sync with actual needs.

[0035] Secondly, this application incorporates the component status into the control logic. By comparing the actual profile of the drive wheel with the reference profile using a laser profile sensor, the wear amount and the actual contact area ratio are determined. At the same time, the clamping force attenuation is inverted by the deviation between real-time torque and acceleration. Then, adaptive compensation is initiated to increase the clamping force by a preset step size. The maximum load-bearing torque is corrected by combining the contact area after wear, ensuring that even if the clamping force attenuates or the drive wheel wears, the torque output still matches the actual load-bearing capacity of the component, avoiding the contradiction of normal torque but power transmission failure.

[0036] Finally, this application adds a monorail joint prediction module before the conventional control process. The real-time distance and joint type parameters between the monorail crane and the monorail joint are obtained through the position detection device. Combined with the running speed, the advance adjustment time is calculated. Before reaching the joint, the torque distribution is pre-adjusted to reduce the torque of the drive wheel on the first contact side and increase the torque on the opposite side. In this way, the sudden change in contact pressure and friction fluctuation caused by the impact are buffered, avoiding damage to the motor and gearbox caused by the drive wheel's temporary slippage and instantaneous torque fluctuation, while ensuring material stability.

[0037] Therefore, the driving force distribution control method and system provided in this application for monorail crane operation system, by replacing static preset with dynamic perception, compensating for component attenuation to cover state changes, and predicting and optimizing special scenario control of monorail joints, not only solves the technical problem of poor adaptability between driving force distribution strategies and dynamic working conditions in the prior art, but also achieves synergistic improvement in operation safety, transportation efficiency and component life, and adapts to the high-quality transportation needs in various scenarios such as underground mines and complex warehouses.

[0038] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments.

[0039] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0040] Based on this, this application provides a driving force distribution control method for a monorail crane operating system. This method is applied to a monorail crane operating system, which includes multiple sets of drive wheels. Please refer to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the drive force distribution control method applied to a monorail crane operating system according to this application.

[0041] In this embodiment, the above-mentioned driving force distribution control method applied to the monorail crane operation system includes steps S10 to S30: Step S10: Collect the working environment parameters and operating status parameters corresponding to each group of drive wheels in the monorail crane operating system.

[0042] The monorail crane operation system is a complete set of equipment for transporting materials along a suspended single rail. In this embodiment, the monorail crane operation system may include: an execution component, a detection component, a control component, and a basic component. The execution component may include: multiple sets of drive wheels, a clamping mechanism, and a vehicle body; the detection component may include: various sensors such as laser profile sensors, infrared reflection sensors, torque sensors, and longitudinal acceleration sensors; the control component may include: a PLC controller, a frequency converter, etc.; the control component may include: the suspended monorail for transportation, a power supply / fuel supply system, etc., thereby achieving suspended material transportation based on the coordinated operation of each part of the monorail crane operation system.

[0043] Here, the operating environment parameters represent the influence parameters of the external environment faced by the drive wheels during operation, reflecting the external constraints affecting the power transmission efficiency of the drive wheels, but not directly reflecting the dynamic operating effect of the drive wheels. The operating status parameters, on the other hand, represent the dynamic output and response of the drive wheels in real-time operation, reflecting whether the current performance of the drive wheels matches the system requirements.

[0044] In this embodiment, the working environment parameters may include: wear pattern data and infrared reflection signals; the operating status parameters may include: real-time torque value and acceleration value; the detection components in the monorail crane operating system may include: laser profile sensor, infrared reflection sensor, torque sensor and longitudinal acceleration sensor.

[0045] In a preferred embodiment, the step of collecting the working environment parameters and operating status parameters corresponding to each set of drive wheels in the monorail crane operating system may include: (1) The surface contour of the drive wheel is scanned by a laser contour sensor to generate contour point cloud information including the drive wheel, and wear morphology data is obtained.

[0046] Among them, wear morphology data is used to represent the degree of wear on the surface of the drive wheel.

[0047] Here, the contour point cloud information refers to the digital set of the surface contour of the drive wheel composed of a large number of three-dimensional coordinate points. The laser contour sensor emits a laser beam to the surface of the drive wheel and uses the time difference or phase difference of the reflected signal to accurately capture the spatial position (X, Y, Z coordinates) of each sampling point on the surface of the drive wheel. These dense coordinate points together constitute the three-dimensional point cloud of the drive wheel surface, which is equivalent to completely replicating the surface shape of the drive wheel in the form of digital point cloud. The contour point cloud information can include details such as the curvature of the wheel rim, the surface undulations, and the depressions or protrusions formed by wear.

[0048] Wear morphology data is derived from the analysis and processing of contour point cloud information, and is used to quantify the wear degree and characteristics of the drive wheel surface. The scanned contour point cloud information can be compared with the standard contour point cloud of the drive wheel in a brand-new state as benchmark data. By calculating the coordinate deviation between the two at corresponding positions, the current wear depth, wear area percentage, and wear distribution characteristics of the drive wheel are obtained. This yields wear morphology data, including wear depth, wear area percentage, and wear distribution characteristics. This wear morphology data is used to intuitively reflect the wear state of the drive wheel surface caused by long-term friction.

[0049] It should be noted that wear depth can be the distance difference between the actual contour of a point in the point cloud information and the reference contour, wear area ratio can be the proportion of the wear area in the entire wheel surface, and wear distribution characteristics can be used to indicate whether there is local concentrated wear, such as the wear on one side of the wheel surface being significantly higher than that on the other side.

[0050] The laser profile sensor emits a high-density laser beam onto the surface of the drive wheel. After the laser beam hits the surface, it is reflected. The sensor uses the time difference or phase difference of the reflected signal to generate three-dimensional coordinate data containing countless profile points on the drive wheel surface—that is, profile point cloud information. This point cloud information can completely reconstruct the surface morphology of the drive wheel, such as the thickness variation of the rim, the presence of depressions or protrusions on the surface, and the distribution range of wear areas.

[0051] By analyzing these contour point cloud information, wear morphology data can be further transformed into wear morphology data characterizing the degree of wear. For example, by comparing the actual scanned contour with the standard contour of the drive wheel in its brand-new state, the wear depth at different locations, the overall wear area ratio, or whether the wear is localized concentrated wear can be calculated. The core function of this data is to reflect the actual contact area and contact state between the drive wheel and the monorail. The more severe the wear, the smaller the effective contact area between the drive wheel and the monorail, and the greater the pressure per unit area, which in turn affects the friction transmission efficiency and the maximum torque carried by the drive wheel.

[0052] (2) The infrared reflection signal is generated by transmitting and receiving the reflected signal to the monorail surface of the drive wheel through the infrared reflection sensor.

[0053] Infrared reflection signals are used to indicate the roughness of the monorail surface and the state of medium adhesion.

[0054] Here, the physical state of the monorail surface, including roughness and medium adhesion, is converted into quantifiable signal data through the principle of infrared reflection, resulting in an infrared reflection signal. This infrared reflection signal can be used to represent the infrared signal after reflection from the monorail surface, and can include signal intensity and reflectivity distribution. Correspondingly, it can be used to represent the roughness and medium adhesion state of the monorail surface.

[0055] In practical applications, the infrared reflection sensor will activate its emission module to emit infrared light signals of a specific wavelength in a directional manner toward the monorail surface in contact with the drive wheel. After these infrared light signals come into contact with the monorail surface, they will not be completely absorbed by the monorail surface. Instead, they will be reflected at different intensities depending on the actual condition of the monorail surface, such as the degree of wear, whether there are stains, and the flatness.

[0056] For example, when the surface of the monorail is severely worn, the uneven structure will cause more infrared light to scatter, and the signal reflected back to the sensor will be weakened. Finally, the receiving module of the infrared reflection sensor can capture the infrared light signal reflected from the surface of the monorail and convert the light signal into an electrical signal that can be recognized and processed by the equipment to determine the infrared reflection signal. The specific condition of the monorail surface can be determined by analyzing the strength and variation of the infrared reflection signal.

[0057] (3) The output torque of the drive wheel is detected by the torque sensor to obtain the real-time torque value.

[0058] The real-time torque value is used to represent the magnitude of the driving force currently output by the drive wheels.

[0059] Here, a torque sensor is a device that can sense the magnitude of the torque output by the drive wheel and convert it into a measurable and transmittable signal. The real-time torque value is data collected in real time by the torque sensor and can be used to reflect the magnitude of the driving force output by the drive wheel at the moment. The greater the driving force, the higher the real-time torque value is usually.

[0060] This embodiment establishes a connection between a torque sensor and the drive wheels of the monorail crane, ensuring that the sensor can accurately capture torque changes during drive wheel operation. During drive wheel operation, the torque sensor continuously monitors the real-time torque output by the drive wheels and converts the monitored real-time torque value into an electrical or digital signal. Through signal processing or signal reading, a real-time torque value that directly represents the current output driving force of the drive wheels can be obtained. This real-time torque value can be used to determine whether the driving force matches the operating conditions.

[0061] (4) The longitudinal acceleration of the monorail crane body is detected by a longitudinal acceleration sensor to obtain the acceleration value.

[0062] The acceleration value is used to represent the actual motion state of the monorail crane body.

[0063] Here, a longitudinal acceleration sensor is a device used to detect the acceleration of an object in the longitudinal direction, that is, in the direction of forward or backward movement of a monorail crane. It can convert the physical quantity of acceleration into a readable signal. The acceleration value is the specific data obtained by the sensor. Positive numbers usually represent the vehicle accelerating forward, while negative numbers may represent deceleration or reverse movement. The magnitude of the value directly reflects the strength of the acceleration.

[0064] In this embodiment, a longitudinal acceleration sensor is installed on the body of the monorail crane, ensuring that the sensor's detection direction is consistent with the longitudinal direction (forward / reverse direction) of the monorail crane body to avoid detection deviation. During the operation of the monorail crane, the longitudinal acceleration sensor can continuously sense the velocity changes of the body in the longitudinal direction. When the body accelerates, the sensor can detect positive acceleration; when braking, it can detect negative acceleration; and when traveling at a constant speed, the acceleration value is close to zero. After sensing the acceleration signal, the longitudinal acceleration sensor can convert the acceleration signal into a numerical value to determine the acceleration value. This acceleration value can be used to reflect whether the monorail crane body is currently accelerating, decelerating, or moving at a constant speed, thereby accurately grasping the actual motion state of the monorail crane body.

[0065] Step S20: Calculate the maximum load torque of the drive wheels based on the working environment parameters and operating status parameters, and determine the target distribution ratio of the driving force of multiple sets of drive wheels based on the maximum load torque.

[0066] Among them, the working environment parameters can be key data on the environment and track in which the monorail crane is running, and the operating status parameters can be the real-time operating data of the monorail crane itself.

[0067] In addition, the maximum load-bearing torque can be the upper limit of the maximum torque that the drive wheel can safely withstand under the current working conditions and its own state without slipping or being damaged. The target distribution ratio of driving force can be the proportion of driving force that should be allocated to each group of drive wheels based on the maximum load-bearing torque of each drive wheel.

[0068] It should be noted that, based on two types of real-time data—working environment parameters and operating status parameters—the driving force distribution logic of the monorail crane can achieve dynamic adaptation to application scenarios. Furthermore, the real-time adjustment of the maximum load torque and the target driving force distribution ratio can solve the technical problem of poor adaptability of driving force distribution control to dynamic working conditions in existing technologies.

[0069] Specifically, when the operating environment parameters change, such as when a monorail crane moves from a flat track into a section with joint impact, or switches from a horizontal track to a steep slope, the contact force state between the drive wheels and the track, as well as the external resistance that needs to be overcome, will change accordingly. At this time, the system will recalculate the maximum load torque of each drive wheel based on the new operating data to ensure that the upper limit of the torque can match the safety load requirements of the current scenario. Thus, there will be no insufficient driving force due to insufficient torque reserve, nor will there be an overload risk due to an excessively high upper limit of torque.

[0070] When operating parameters fluctuate, for example, when the drive wheels experience increased wear due to continuous operation, or when the clamping force changes, or when the vehicle body experiences acceleration changes due to load adjustments, the system can combine this real-time status data to readjust the maximum load-bearing torque. At the same time, based on the updated maximum load-bearing torque of each drive wheel, the system redistributes the driving force ratio of each group of drive wheels, allowing drive wheels with stronger load-bearing capacity to bear more driving force, and appropriately reducing the load on drive wheels with weaker load-bearing capacity. This prevents a group of drive wheels from slipping or being damaged due to the allocated torque exceeding its real-time load limit, and also ensures that the overall driving force can efficiently meet the current operating requirements of the vehicle body.

[0071] In this way, through a dynamic adjustment mechanism based on real-time parameters, the maximum load torque and the target distribution ratio of driving force are always kept in sync with the actual application scenario, breaking through the limitation that the driving force distribution under fixed parameters cannot adapt to complex working conditions, and achieving full adaptability to different operating scenarios.

[0072] This application embodiment collects and integrates all operating environment parameters and operational status parameters of the monorail crane during operation, ensuring that all key data affecting the load-bearing capacity of the drive wheels are included in the calculation, thus avoiding subsequent calculation deviations due to missing data. Based on the integrated parameters, the maximum load-bearing torque of each drive wheel can be calculated using a preset algorithm. After determining the maximum load-bearing torque corresponding to each group of drive wheels in the monorail crane operating system, a reasonable allocation rule can be formulated based on the maximum load-bearing torque of each drive wheel to determine the target distribution ratio of driving force among multiple groups of drive wheels in the monorail crane operating system.

[0073] For example, when the track gradient is large, the drive wheels require more torque to move the vehicle body, but their maximum load-bearing torque may decrease due to the force changes caused by the gradient. Similarly, when the drive wheels are severely worn, the friction with the track decreases, and the maximum load-bearing torque also decreases. Therefore, based on the maximum load-bearing torque of each drive wheel, a reasonable allocation rule can be formulated, following the principle of allocating more power to wheels with higher load-bearing capacity and less power to wheels with lower load-bearing capacity. This avoids the drive force allocated to a single drive wheel exceeding its maximum load-bearing torque, while ensuring that the total drive force of all drive wheels meets the current operating conditions, ultimately determining the target drive force allocation ratio for each group of drive wheels.

[0074] Step S30: Collect the rotational speed data of the drive wheel, determine the real-time rotational speed deviation of the drive wheel, and dynamically adjust the drive wheel according to the real-time rotational speed deviation, the maximum load torque, and the target distribution ratio of the driving force to correct the real-time rotational speed deviation.

[0075] Among them, the rotational speed data can be the number of revolutions or angles of the drive wheel per unit time, and can also be used as basic data to reflect the speed of the drive wheel; the real-time rotational speed deviation is the difference between the actual rotational speed of the drive wheel and the preset target rotational speed. A positive difference in the rotational speed deviation indicates that it is rotating too fast, and a negative difference indicates that it is rotating too slow.

[0076] The embodiments of this application collect the current rotational speed data of each drive wheel through sensors to obtain the actual operating conditions. The collected actual rotational speed is compared with the preset target rotational speed to calculate the real-time rotational speed deviation of each drive wheel, so as to determine which drive wheels are rotating too fast and which are rotating too slow. Combined with the calculated real-time rotational speed deviation, as well as the previously determined maximum load torque and target distribution ratio of driving force, the driving force of each drive wheel is dynamically adjusted.

[0077] For example, if the drive wheel rotates too slowly, the driving force can be appropriately increased within the limits of the torque capacity, so that the drive wheel rotates faster, thereby correcting the speed deviation and bringing the speed of all drive wheels closer to the target value.

[0078] In a preferred embodiment, the step of dynamically adjusting the drive wheel to correct the real-time speed deviation based on the real-time speed deviation, the maximum load torque, and the target distribution ratio of the driving force includes: (1) If the real-time rotational speed deviation meets the preset deviation threshold for the rotating wheel, then calculate the target adjustment torque based on the maximum load torque and the target distribution ratio of the driving force; (2) Adjust the torque according to the target and control the drive wheel to reduce the output torque at a uniform rate within a preset time until the target torque adjustment is completed.

[0079] Among them, the preset deviation threshold can be a pre-set critical value for speed deviation. Adjustment needs to be initiated only when the real-time speed deviation of the drive wheel exceeds this value; the target adjustment torque can be the specific value of the torque that the drive wheel needs to increase or decrease according to the rules, which can be the target amount of adjustment; the preset time is the total duration of torque adjustment set in advance. This preset time can be used to ensure that the torque adjustment process is smooth and not abrupt; the uniform rate refers to the torque changing gradually at a fixed speed within the preset time to avoid sudden increases or decreases.

[0080] After determining the real-time speed deviation, maximum load torque, and target drive force distribution ratio, it can be determined whether the real-time speed deviation of each drive wheel exceeds a preset deviation threshold. When the speed deviation of any drive wheel is too large, the subsequent adjustment stage is initiated to avoid over-adjustment of minor deviations. For the drive wheel requiring adjustment, the specific torque value that needs to be adjusted, i.e., the target adjustment torque, is calculated based on its maximum load torque and target drive force distribution ratio. After determining the target adjustment torque, the drive wheel can be controlled to gradually reduce its output torque at a uniform rate within a preset time according to the calculated target adjustment torque.

[0081] It should be noted that the target adjustment torque can be calculated by taking the maximum load-bearing torque as the upper limit constraint, the target distribution ratio of driving force as the allocation basis, and combining the current actual torque value. Specifically, based on the target distribution ratio of driving force, the proportion of the total driving force that the drive wheel should bear is determined, denoted as λ. For example, if the target proportion of a certain wheel in a group of drive wheels is 30%, then λ = 0.3. At the same time, the total driving torque required by the system is determined, denoted as T_total. This total driving torque can be determined by the requirements of the vehicle load, operating conditions, etc. After determining the target distribution ratio corresponding to the drive wheel, the theoretical target torque of the wheel can be calculated proportionally as T_theoretical = λ × T_total. Next, the maximum load-bearing torque of the drive wheel is denoted as T_max. This maximum load-bearing torque can be used as a safety constraint to verify the theoretical target torque. Specifically, if T_theoretical ≤ T_max, the theoretical target torque can be directly used as the benchmark; if T_theoretical > T_max, then T_max should be used as the upper limit benchmark for the wheel to avoid exceeding the load-bearing capacity. Finally, considering the actual output torque of the drive wheel, denoted as Tactual, the target adjustment torque is calculated. When the theoretical target torque is effective, the target adjustment torque = Ttheoretical - Tactual; when constrained by the maximum load torque, the target adjustment torque = Tmax - Tactual. Thus, the target adjustment torque corresponding to the drive wheel is determined based on the target distribution ratio, ensuring that the torque adjustment conforms to the distribution ratio while strictly controlling it within the safe load range of the drive wheel, achieving precise and safe torque correction.

[0082] For example, if the target adjustment torque is to reduce by 50 N / m and the preset time is 10 seconds, the torque is controlled to decrease by 5 N / m per second until the adjustment of 50 N / m is completed. Through this smooth adjustment method, the speed deviation is corrected while avoiding the impact of sudden torque changes on the equipment.

[0083] This application collects environmental and operational parameters for each set of drive wheels in a monorail crane operating system. Based on these parameters, it calculates the maximum load-bearing torque of the drive wheels and determines the target distribution ratio of driving force for multiple sets of drive wheels. It also collects drive wheel speed data to determine the real-time speed deviation and dynamically adjusts the drive wheels to correct this deviation based on the real-time speed deviation, maximum load-bearing torque, and target distribution ratio. By using multiple types of sensors to collect environmental parameters such as drive wheel wear patterns and monorail surface conditions, as well as operational parameters such as torque and acceleration, this application accurately captures the dynamic changes during monorail crane operation. Combining these parameters, it calculates the maximum load-bearing torque of the drive wheels and determines the target distribution ratio of driving force, effectively avoiding imbalances in driving force distribution caused by misjudgment of operating conditions. This prevents power lag caused by insufficient torque and avoids slippage risks caused by torque exceeding the load limit, improving the adaptability of the driving force distribution strategy to dynamic operating conditions and significantly enhancing the system's operational stability and safety under complex conditions.

[0084] Based on the first embodiment of this application, a second embodiment of this application is proposed. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the drive force distribution control method applied to a monorail crane operating system according to this application.

[0085] As a refinement of step S20 in the first embodiment, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment can be referred to the above description, and will not be repeated hereafter. Based on this, the driving force distribution control method applied to the monorail crane operating system of this application further includes steps S21 to S24: Step S21: Based on wear pattern data, determine the actual contact area ratio between the drive wheel and the monorail.

[0086] Among them, wear morphology data can refer to the state information of the drive wheel surface caused by wear obtained through detection, which can include information such as the degree of unevenness after wear, the size and distribution of the wear area, etc.

[0087] This application calculates the proportion of the actual contact area between the drive wheel and the monorail to the original designed total contact area of ​​the drive wheel based on the wear pattern data of the drive wheel. This information is used to determine the state information of the drive wheel surface caused by wear. For example, if the contact area is 100% when the wheel is new, and the effective contact area is only 80% after wear, then the actual contact area percentage is 80%. This proportion reflects the change in contact performance of the drive wheel due to wear.

[0088] In a preferred embodiment, the step of determining the percentage of the actual contact area between the drive wheel and the monorail based on wear morphology data includes: (1) Using the annular working surface profile of the drive wheel in a brand new state as the reference profile, the point cloud information of the drive wheel surface profile obtained by the laser profile sensor is compared with the reference profile to calculate the wear amount of the drive wheel. (2) Classify the drive wheels according to the amount of wear to determine the wear type of the drive wheels; (3) When the wear type of the drive wheel is a wear concentration area where the wear amount is higher than the average wear value, the actual contact area ratio is calculated based on the ratio of the actual contact area between the drive force and the monorail to the relative rated area.

[0089] Here, the reference profile can be the standard profile of the annular working surface of the drive wheel in its brand-new state, serving as the original reference for wear comparison; the laser profile sensor is a device that acquires surface profile point cloud information by emitting a laser to scan the surface of the drive wheel, and this profile point cloud information can be surface morphology data composed of a large number of three-dimensional coordinate points; the wear amount can be the deviation value between the actual surface profile of the drive wheel and the reference profile, and this deviation value can be the distance difference between the point cloud centers corresponding to the actual surface profile of the drive wheel and the reference profile, and this wear amount can be used to reflect the degree of wear of the drive wheel; the wear type can be a classification of the wear state of the drive wheel based on the distribution characteristics of the wear amount; the wear concentration area can be a local area where the wear amount is significantly higher than the average wear level; the relative rated area is the designed contact area between the drive wheel and the monorail in its brand-new state.

[0090] In this embodiment, the annular working surface contour of the new drive wheel is used as a reference. A laser contour sensor is used to scan the surface of the current drive wheel to obtain the point cloud information of its surface contour. Then, these point cloud data are compared with the reference contour one by one. By calculating the contour deviation between the two, the wear amount of the drive wheel is obtained. This wear amount can be used to determine the size and location of the wear.

[0091] After determining the wear amount of the drive wheels, the drive wheels can be classified according to the calculated wear distribution to determine their corresponding wear type. This wear type can include areas where the wear amount is not higher than the average wear amount and areas where there is a concentrated wear amount that is significantly higher than the average wear amount.

[0092] When the wear type is determined to be a concentrated area of ​​severe localized wear, the actual contact area between the drive wheel and the monorail in that area can be calculated. This area is then compared to the relative rated area of ​​the drive wheel; the resulting ratio is the actual contact area percentage. This actual contact area percentage accurately reflects the impact of severe localized wear on the drive wheel's contact performance.

[0093] Step S22: Calculate the real-time friction coefficient between the drive wheel and the monorail surface based on the infrared reflection signal.

[0094] Here, the infrared reflection signal can be used to reflect the state of the monorail surface. The rougher and cleaner the surface, the more stable the intensity and regularity of the infrared reflection signal. If the surface is smooth or has oil stains, the reflection signal will become weaker or show abnormal fluctuations.

[0095] This application embodiment calculates the friction coefficient by utilizing the correlation between infrared reflection signals and friction coefficients. By analyzing data such as the strength and distribution characteristics of infrared reflection signals, the real-time friction coefficient between the drive wheel and the monorail surface is deduced, and the magnitude of the friction force when the two are in contact is reflected by the real-time friction coefficient.

[0096] In a preferred embodiment, the step of calculating the real-time friction coefficient between the drive wheel and the monorail surface based on the infrared reflection signal includes: (1) Obtain infrared signal intensity and reflectivity distribution data reflected from the surface of the monorail; (2) Compare the infrared signal intensity with the preset reference signal and calculate the signal attenuation rate; (3) Based on reflectivity distribution data, identify whether there is an attached medium on the surface of the monorail and the proportion of the covered area; (4) Calculate the real-time friction coefficient between the drive wheel and the monorail surface based on the signal attenuation rate and the proportion of the area covered by the attached medium.

[0097] Here, infrared signal strength can be the strength of the signal received by the infrared reflection sensor and reflected back from the monorail surface; reflectivity distribution data can be the distribution of the ability to reflect infrared signals in different areas of the monorail surface, which can be used to reflect differences in surface condition; the preset reference signal is the standard infrared reflection signal when the monorail surface is clean and without wear, serving as a comparison benchmark; the signal attenuation rate is the attenuation ratio of the actual infrared signal strength compared to the preset reference signal; the adhering medium refers to substances that may exist on the monorail surface, such as oil, dust, and water stains, which affect the coefficient of friction; and the coverage area ratio is the proportion of the area occupied by the adhering medium on the monorail surface.

[0098] The formula for calculating the signal attenuation rate is: (reference signal - actual signal) / reference signal × 100%.

[0099] This application embodiment uses an infrared reflection sensor to collect data including the intensity of the infrared signal reflected from the monorail surface and the reflectivity distribution data at different locations on the monorail surface. After determining the infrared signal intensity and reflectivity distribution data, the collected actual infrared signal intensity can be compared with a preset reference signal to calculate the signal attenuation rate. The more severe the signal attenuation, the more severe the wear on the monorail surface or the more adhering media, and the lower the friction coefficient may be. Analyzing the reflectivity distribution data identifies whether there are adhering media such as oil stains or dust on the monorail surface, and calculates the proportion of these adhering media covering the monorail surface. The higher the coverage proportion, the greater the impact on the friction coefficient. Combining the previously calculated signal attenuation rate and the proportion of adhering media coverage area, and substituting them into a pre-established friction coefficient calculation model, the current real-time friction coefficient between the drive wheel and the monorail surface is finally calculated.

[0100] Here, the friction coefficient calculation model can be based on the baseline friction coefficient under clean, wear-free monorail surface conditions. By quantifying the combined effects of signal attenuation rate and the proportion of the area covered by the adhering medium, a linear correlation derivation of the real-time friction coefficient is achieved, and the real-time friction coefficient is calculated accordingly. Specifically, the baseline friction coefficient under clean, wear-free monorail surface conditions can be calibrated through offline experiments, serving as the basic anchor point for calculation. For the signal attenuation rate, the model will assign corresponding influence weights based on the degree of monorail surface wear or light contamination it reflects. The more severe the signal attenuation, the more obvious the surface wear or the more severe the light contamination, and the greater the weakening of the baseline friction coefficient. Regarding the proportion of the area covered by the adhering medium, the model will assign another set of influence weights based on the degree of heavy contamination it reflects. The higher the coverage proportion, the more prominent the negative impact of pollutants such as oil and dust on the friction coefficient, and the corresponding increase in the attenuation ratio of the baseline friction coefficient.

[0101] In actual calculations, the model first calculates the friction coefficient attenuation caused by wear and light pollution based on the signal attenuation rate and its weight. Then, based on the proportion of the area covered by the attached medium and its weight, it calculates the friction coefficient attenuation caused by heavy pollution. After superimposing these two attenuation amounts, the model deducts them from the reference friction coefficient to finally obtain the real-time friction coefficient that conforms to the current working conditions of the monorail surface.

[0102] Here, the friction coefficient calculation model is: μ_real = μ_0 × (1 − k_1 × η) × (1 − k_2 × β) Where μ_realtime is the real-time friction coefficient, μ0 is the reference friction coefficient, η is the influence coefficient of signal attenuation rate, k1 is the influence coefficient of signal attenuation, β is the coverage area ratio of the monorail surface, and k2 is the influence coefficient of the coverage area ratio.

[0103] Step S23: Based on the degree of deviation between the real-time torque value and the acceleration value, adaptively adjust the clamping force of the drive wheel on the monorail.

[0104] Here, the real-time torque value can be the torque data corresponding to the driving force currently output by the drive wheel, the acceleration value is the actual longitudinal motion acceleration of the monorail crane body, and the deviation can refer to the difference between the actual acceleration and the actual torque and the torque that should theoretically match the current acceleration.

[0105] This embodiment of the application determines the contact state between the drive wheel and the monorail by measuring the deviation between the two values ​​and dynamically adjusts the clamping force. When the deviation between the real-time torque value and the acceleration value exceeds a reasonable range, the system can adaptively increase the clamping force of the drive wheel on the monorail, thereby increasing friction and improving the effective transmission of driving force. If the deviation is too small or reversed, the clamping force may be appropriately reduced to avoid excessive clamping that could cause wear or increased energy consumption. Ultimately, through dynamic adjustment of the clamping force, the torque output is better matched with the vehicle acceleration, ensuring operational efficiency and stability.

[0106] In a preferred embodiment, the step of adaptively adjusting the clamping force of the drive wheel on the monorail based on the degree of deviation between the real-time torque value and the acceleration value includes: (1) Normalize the real-time torque value and acceleration value to the maximum value to calculate the difference between the real-time torque value and acceleration value and obtain the degree of deviation; Among them, the degree of deviation is used to reflect the matching state between the driving force output by the drive wheel and the actual motion requirements of the vehicle body, and to characterize the loss in the process of driving force transmission. (2) Generate a continuous time series corresponding to the degree of deviation, and determine the trend of deviation change through the continuous time series: (3) If the deviation shows an increasing trend over multiple consecutive time points, the clamping force is gradually increased by using the preset ratio of the basic clamping force set at the factory for the drive wheel as the adjustment step size; (4) If the degree of deviation shows a decreasing trend over multiple consecutive time points, reduce the adjustment step size of the clamping force; (5) If the deviation is stable within the preset minimum range, stop adjusting the clamping force; (6) If the deviation increases to a preset ratio exceeding the sensor's detection range, while increasing the clamping force, the output torque of the drive wheel is reduced simultaneously until the deviation decreases to a preset safe range.

[0107] Here, maximum value normalization can be achieved by converting real-time torque and acceleration values ​​into proportions relative to their respective maximum values, eliminating differences in units and magnitudes for direct comparison; the deviation is the difference between the normalized real-time torque and acceleration values, which can reflect the matching degree between the driving force output and the vehicle's motion requirements. A large difference indicates significant losses and potential slippage; the continuous time series can be deviation data arranged in chronological order, which can be used to illustrate the trend of deviation changes; the base clamping force is the standard clamping force set at the factory for the drive wheels; the adjustment step size is the magnitude of each adjustment of the clamping force; and the preset ratio of the sensor detection range is a pre-set critical value that exceeds the effective detection range of the sensor.

[0108] This application embodiment normalizes the real-time torque and acceleration values ​​to their maximum values, calculates the difference between them to obtain the degree of deviation. After the maximum value normalization, the degree of deviation at different time points can be arranged in order to form a continuous time series. This continuous time series can be analyzed to determine whether the deviation is expanding, shrinking or stabilizing.

[0109] Specifically, if the deviation shows an increasing trend over multiple consecutive time points, the clamping force is gradually increased by using a preset ratio of the basic clamping force as the adjustment step size, thereby reducing power loss by increasing friction. If the deviation shows a decreasing trend, the adjustment step size is reduced to avoid excessive increase in clamping force. If the deviation stabilizes within a preset minimum range, it indicates that the driving force and motion requirements are matched, and the adjustment of clamping force can be stopped. If the deviation increases to a preset ratio exceeding the sensor's detection range, the output torque of the drive wheel is reduced simultaneously while increasing the clamping force, thus rapidly reducing the deviation until it returns to a safe range and preventing damage to the equipment due to extreme deviation.

[0110] For example, for a continuous time series {5,6,5,4}, if three consecutive time nodes are used as windows, in the first window {5,6,5}, the change from 5 to 6 is increasing and the change from 6 to 5 is decreasing. Adjacent changes do not show a consistent increasing or decreasing trend, and the fluctuation amplitude exceeds the preset fluctuation threshold. At this time, it is not judged as expanding or shrinking. In the second window {6,5,4}, the change from 6 to 5 and the change from 5 to 4 are both decreasing successively, which meets the judgment condition of continuous decreasing. Therefore, the deviation state corresponding to this window is shrinking.

[0111] Step S24: Combining the actual contact area ratio, real-time friction coefficient, adjusted clamping force, and inherent parameters of the drive wheel, calculate the maximum load torque of the drive wheel using a preset torque calculation formula.

[0112] After determining the actual contact area ratio, real-time friction coefficient, and adjusted clamping force, the inherent parameters of the drive wheel can also be obtained. The actual contact area ratio, real-time friction coefficient, adjusted clamping force, and inherent parameters of the drive wheel are substituted into the preset torque calculation formula. By integrating the influence of these parameters through the formula, the maximum torque value that the drive wheel can safely withstand under the current state is finally obtained.

[0113] As an example, the following detailed implementation provides a second embodiment of the drive force distribution control method for monorail crane operating systems proposed in this application, and is not intended to limit the drive force distribution control method for monorail crane operating systems proposed in this application.

[0114] The monorail crane's drive wheel uses a ring-shaped clamping structure for the monorail. During long-term operation, due to the unevenness of the monorail surface (e.g., localized protrusions or depressions) and uneven stress caused by load fluctuations, the ring-shaped working surface in contact with the monorail is prone to uneven wear, manifesting as some areas experiencing significantly higher wear than others. This wear directly reduces the actual contact area between the drive wheel and the monorail. If torque is distributed according to the drive wheel's rated load-bearing capacity, severely worn areas will experience slippage due to insufficient contact area, resulting in torque exceeding the actual load-bearing capacity. Conversely, lightly worn areas will have their load-bearing potential unused due to insufficient torque distribution, ultimately leading to a decrease in overall power efficiency.

[0115] To accurately monitor the wear condition of the drive wheels, laser profile sensors are deployed on the side of each drive wheel. During daily downtime maintenance, these sensors continuously scan the annular working surface of the drive wheels to acquire wear morphology data for all scanned points on the entire annular surface. Using the working surface profile of the drive wheel in its new condition as a baseline, the difference between the real-time scanned profile and the baseline profile represents the wear amount at each point. The k-means algorithm is used to classify the wear amount of all scanned points (k=2). The category with the highest average wear amount is defined as the wear concentration area. The proportion of scanned points in this area to the total number of scanned points on the annular working surface is calculated (denoted as S), and A=1-S represents the actual contact area ratio. Here, the actual contact area data is updated daily or periodically, not in real-time.

[0116] Infrared reflection sensors are deployed beneath the monorail crane to collect infrared reflection signals from the monorail surface in real time. Since the infrared sensors consistently emit signals at a fixed distance in front of the crane, their timing must be aligned with the crane's speed to ensure that the collected monorail surface data matches the time it takes for the crane to pass.

[0117] Wherein, the corrected signal acquisition time = infrared signal acquisition time + (distance between the infrared signal acquisition position and the real-time position of the crane / real-time speed of the crane).

[0118] When slurry and dust adhere to the surface of the monorail, the reflected signal weakens with the degree of contaminant coverage. By obtaining the ratio of the real-time reflected signal to the reflected signal when the monorail surface is clean, the coverage area of ​​the monorail surface (denoted as E) can be obtained. Combining this with the offline calibrated friction coefficient (y) of the clean monorail surface, multiplying y by E yields the real-time friction coefficient of the monorail surface. Since the infrared reflection sensor is activated in real time, the friction coefficient data is updated continuously.

[0119] After determining the actual contact area ratio, real-time friction coefficient, and adjusted clamping force, the maximum load-bearing torque of the drive wheel can be calculated based on comprehensive multi-parameter calculations. The inherent radius (R) of the drive wheel and the default clamping force (F) are obtained from the equipment's factory-set parameters. Based on the principle that the contact area determines the range of friction and the friction coefficient determines the magnitude of friction per unit area, the product of these two is the maximum static friction force. Multiplying this by the drive wheel radius converts it into the maximum load-bearing torque. However, it should be noted that over time, the aging of the hydraulic seals and spring fatigue in the clamping mechanism will lead to a gradual decrease in clamping force. Even with low wear on the drive wheel and sufficient contact area, insufficient friction will cause a decrease in the maximum load-bearing torque. Simply increasing the torque to solve slippage will exacerbate the slippage, creating a vicious cycle.

[0120] To address this, torque sensors are deployed on each drive wheel axle to collect the real-time output torque value of the drive wheels, and longitudinal acceleration sensors are deployed on the monorail crane body to collect the acceleration changes of the body along the monorail direction. According to mechanical relationships, if the output torque of the drive wheels increases but the body acceleration does not increase synchronously or the increase is lower than theoretically expected, it indicates a loss in the transmission of driving force (clamping force attenuation). The real-time torque and acceleration are normalized to their maximum values ​​to unify their dimensions, and the difference between the two is calculated as the deviation degree (P). A continuous time sequence is generated and transmitted as the core friction feedback signal to the clamping mechanism control unit, adjusting the clamping force according to the trend of the deviation degree.

[0121] Specifically, if the deviation P continues to increase, this can be manifested as an increase in the clamping force in three consecutive time points, with an adjustment step size of 5% of the base clamping force. If P continues to decrease, this can be manifested as a decrease in the adjustment step size in three consecutive time points, with the adjustment step size reduced to 2%. P will eventually stabilize within a very small range, which can be manifested as a standard deviation ≤ 0.02 for three consecutive times, indicating that the friction force matches the output torque of the drive wheel, and the clamping force has been compensated to the required level. If P continues to increase beyond 1 / 3 of the sensor's detection range, the output torque of the drive wheel should be reduced simultaneously while increasing the clamping force until the P value drops back down.

[0122] After adaptive compensation of the clamping force, the current real-time maximum load-bearing torque maxN of each drive wheel can be calculated using the formula maxN=A×F1×y×R, based on the previously obtained actual contact area ratio (A), real-time friction coefficient (y), and real-time clamping force (F1). To address potential speed synchronization deviations that may occur during the operation of multiple drive wheels (typically 2-4 sets arranged symmetrically vertically) on a monorail crane, real-time speed data of all drive wheels is collected, and the real-time speed deviation between the speed of each drive wheel and the average speed is calculated.

[0123] After determining the real-time speed deviation, it can be compared with a preset deviation threshold. Specifically, if the speed deviation of a certain drive wheel continues to increase and the absolute value of the deviation increases three times consecutively, torque reduction correction is performed based on its maximum load torque maxN. Specifically, the deviation change rate of the drive wheel to be corrected is calculated, and the deviation change rates of all drive wheels to be corrected are summed and normalized as the basic correction weight, denoted as z. This basic correction weight is used as the rate of uniform adjustment, and the torque is reduced over a certain period of time based on the maximum torque reduction and the corrected rate. The average torque reduction at each moment is: G = maxN × z.

[0124] This application effectively solves the problem of drive force distribution in monorail cranes through multi-sensor collaborative monitoring and dynamic control. First, by using laser profile sensors and infrared reflection sensors to capture key parameters such as drive wheel wear and monorail friction coefficient in real time, and combining torque and acceleration data, it accurately calculates the maximum load-bearing torque of the drive wheels. This allows for dynamic adjustment of the drive force distribution ratio, preventing drive wheel slippage or idle load-bearing potential, and ensuring torque distribution better matches actual working conditions. Second, addressing the issue of clamping force attenuation, it adaptively adjusts the clamping force through torque and acceleration deviation analysis. This prevents a vicious cycle of slippage, reduces wear and energy consumption caused by excessive clamping, and improves operational stability. Therefore, the overall solution can adapt to complex working conditions such as track slope and surface contamination, optimizing power transmission efficiency without manual intervention, extending equipment life, reducing maintenance costs, and balancing practicality and economy.

[0125] Based on the first embodiment of this application, a third embodiment of this application is proposed. Please refer to [link to third embodiment]. Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the drive force distribution control method applied to a monorail crane operating system according to this application.

[0126] As an extension of step S10 in the first embodiment, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment can be referred to the above description and will not be repeated hereafter. Based on this, the driving force distribution control method of this application is applied to a monorail crane operating system, which also includes a position detection device. The driving force distribution control method applied to the monorail crane operating system further includes steps S101~S103: Step S101: Obtain the distance information between the monorail crane and the monorail joint in front in real time through the position detection device.

[0127] Here, the position detection device can be a device used to sense the relative position of the monorail crane to surrounding objects in real time; the monorail joint is the connection part between monorail tracks, which may have features such as protrusions and gaps, and is a key node affecting the stable operation of the crane.

[0128] This application embodiment uses a position detection device to continuously measure the distance between the current position of the monorail crane and the monorail joint ahead, obtaining dynamically changing distance information. This provides basic data for subsequent prediction of the joint position and advance adjustment of the crane's operating status, ensuring the stability of the crane when passing the joint.

[0129] In a preferred embodiment, the step of calculating the advance adjustment time required for impact buffering based on the current operating speed of the monorail crane and the positional characteristics of the monorail joint includes: (1) Obtain the type parameters of the monorail joint and the real-time operating speed of the monorail crane; (2) Based on the type parameters, query the preset impact coefficient table and calculate the theoretical impact duration when passing through the monorail joint in combination with the real-time running speed; (3) The theoretical impact duration is superimposed with the response delay time of the drive wheel torque adjustment to obtain the advance adjustment time required for impact buffering.

[0130] The advance adjustment time is the time before the monorail crane reaches the monorail joint to pre-adjust the output torque of the drive wheel and stabilize the torque state.

[0131] Here, the type parameter of the monorail joint can refer to the structural characteristics of the joint, such as whether it is a rigid connection, the size of the gap, etc. The type parameter of the monorail joint can directly affect the impact degree; the impact coefficient table is a preset parameter table corresponding to different joint types and impact intensities; the theoretical impact duration can be the duration of impact when the crane passes the joint; the response delay time can be the lag time from the issuance of the command to the actual effect when the drive wheel adjusts the torque; the advance adjustment time can be the advance amount required to ensure that the torque pre-adjustment is completed and stabilized before reaching the joint.

[0132] In this preferred embodiment, by obtaining the type parameters of the monorail joint and the real-time operating speed of the crane as the basis for calculation, after determining the type parameters of the monorail joint, the impact coefficient table can be consulted according to the joint type, and the theoretical impact duration when passing the joint can be calculated in combination with the real-time speed. The theoretical impact duration is added to the response delay time of torque adjustment to obtain the advance adjustment time. In this way, it can be ensured that the crane completes the pre-adjustment of the drive wheel torque and stabilizes the torque state before reaching the joint, thereby effectively buffering the impact when passing the joint and improving the smoothness of operation.

[0133] Step S102: When the distance information is less than the preset warning threshold, calculate the advance adjustment time required for impact buffering based on the current running speed of the monorail crane and the position characteristics of the monorail joint.

[0134] Here, the preset warning threshold can be a distance value set in advance. When the distance between the monorail crane and the monorail joint in front is less than the preset warning threshold, it means that the monorail crane is about to approach the joint and the impact buffer preparation needs to be activated.

[0135] Location characteristics can refer to the structural features of a monorail joint, which may include structural information such as the size of the joint gap and whether there are protrusions.

[0136] In this embodiment of the application, when the distance information obtained by the position detection device shows that the distance between the crane and the monorail joint ahead is less than the preset warning threshold, that is, it has entered the range that requires advance preparation, the system can combine the current running speed of the crane and the position characteristics of the monorail joint to calculate the advance adjustment time required to buffer the impact. That is, how long in advance before the crane reaches the joint should the drive wheel torque be adjusted to ensure that the impact is minimized when passing the joint and to ensure smooth operation.

[0137] Step S103: Based on the advance adjustment time, the output torque of multiple sets of drive wheels is pre-adjusted in stages to ensure that the monorail crane and monorail joint are properly connected.

[0138] Here, phased pre-adjustment can refer to dividing the adjustment process of the drive wheel output torque into multiple small stages and carrying it out step by step, rather than completing it all at once, so as to make the torque change more stable.

[0139] Therefore, the torque can be adjusted in advance to cope with the impact at the joint. Based on the previously calculated advance adjustment time, the output torque of multiple sets of drive wheels is pre-adjusted in stages before the monorail crane reaches the monorail joint. Through this staged adjustment, the torque state can be stabilized at the appropriate value before the crane contacts the joint, thereby effectively buffering the impact when passing through the joint, reducing vibration or slippage, and ensuring smooth operation.

[0140] As an example, the following detailed implementation provides an exemplary description of the third embodiment of the drive force distribution control method for monorail crane operating systems proposed in this application, and is not intended to limit the drive force distribution control method for monorail crane operating systems proposed in this application.

[0141] In actual operation scenarios of monorail cranes, the track is not a continuous connection but rather a segmented splicing structure. Due to factors such as installation errors and thermal expansion and contraction caused by changes in ambient temperature, there will be tiny gaps at the track joints. When the drive wheel passes through this gap, there will be a sudden change in the working condition, resulting in a momentary disconnection from the track, which will cause a sudden fluctuation in the speed of the drive wheel. If the torque distribution strategy under normal working conditions is still used at this time, the sudden change in the contact state between the drive wheel and the track will cause a torque shock, which will manifest in two ways: First, after the drive wheel disconnects from the track, it is in an unloaded state, and the original torque can easily cause its speed to rise sharply; second, when the drive wheel re-contacts the track, the speed difference will generate a mechanical shock, which will not only aggravate the wear of the drive wheel and track joints but may also cause vehicle body vibration, affecting the stability of material transportation, and causing a short-term torque distribution deviation.

[0142] To address the aforementioned issues, this solution utilizes infrared reflection sensors deployed beneath the monorail crane body to predict and regulate the torque buffering of track joints. By collecting infrared signals from the track surface using these sensors, a scanned profile of the track surface can be generated. Due to the gap at the track joint, the scanned profile will exhibit momentary discontinuities, significantly differing from the continuous profile of a normal track surface. Based on preset threshold values ​​for the instantaneous discontinuity signal intensity gradient and continuous discontinuity length, the precise location of the track joint can be identified and marked in real time. Specific technical details of this location identification process can be found in the description of the aforementioned solution and will not be repeated here.

[0143] After completing the identification of the track joint positions, the gap length of each joint can be obtained, which is the length of the continuous breakpoints in the scan profile. Combined with the current running speed of the monorail crane, the disengagement time of the drive wheel when passing through the gap is calculated by dividing the gap length by the running speed, and denoted as t. At the same time, based on the distance between the current position of the drive wheel and the joint position and the real-time running speed of the crane, the time for the drive wheel to reach the joint is predicted by dividing the distance by the running speed, and denoted as ty, so as to reserve sufficient preparation time for subsequent torque adjustment.

[0144] To avoid a sudden increase in speed due to no load when the drive wheels disengage, the torque output of the drive wheels can be gradually reduced within the predicted arrival time ty.

[0145] Specifically, the actual torque reduction value T2 of the previous adjustment cycle, collected in real time by the torque sensor, is used as the current torque reduction benchmark to ensure continuous torque adjustment without jumps. Simultaneously, the current real-time load torque of the drive wheel is acquired and denoted as T1. The maximum allowable angular velocity Δw and the moment of inertia J of the drive wheel are retrieved from the equipment technical manual. The upper limit of the safe torque T3 is then calculated based on mechanical formulas.

[0146] Here, T3 = J × (△w / t).

[0147] Based on the comparison between the real-time angular velocity of the drive wheel before it disengages from contact and the unloaded safe angular velocity Δw, the torque reduction coefficient k1 is dynamically adjusted: if the real-time angular velocity is greater than Δw, the torque reduction amplitude needs to be increased, so k1=1.2; if the real-time angular velocity is less than Δw, the torque reduction amplitude needs to be decreased, so k1=0.8; if the absolute value of the difference between the two is divided by Δw and normalized, and the result is ≤1%, then the torque is reduced at the normal amplitude, so k1=1.

[0148] Based on the above parameters, the unit torque output value within the predicted arrival time is calculated using the formula T_drop = T2 - [(T1 - T3) / ty] × k1. During the ty period, the output torque of the drive wheel is gradually reduced according to this unit torque, so that the torque smoothly transitions from the current load torque T1 to the no-load safe torque T3, effectively avoiding the speed fluctuations caused by the traditional average torque reduction method, and ensuring that the torque reduction process is synchronized with the stable speed.

[0149] When the infrared reflection sensor detects that the track profile has returned to continuity, it can be confirmed that the drive wheel has passed the joint and re-engaged with the track. At this point, the torque output needs to be gradually increased to the normal operating level.

[0150] Specifically, the actual torque increase value T2 of the previous adjustment cycle collected in real time by the torque sensor is used as the current torque increase benchmark. 1.5 times the contact time t of the drive wheel is disengaged is set as the buffer time after contact, denoted as tq, to ​​provide a buffer period for the smooth recovery of torque. At the same time, the average torque of the drive wheel before the torque reduction is retrieved as the target recovery torque, denoted as T4.

[0151] Adjust the torque increase coefficient k2 according to the stability of the real-time speed of the drive wheel: If the real-time speed of the drive wheel is stable, it is shown that the standard deviation of three consecutive speed values ​​divided by the average value is ≤3%, then increase the torque at the normal speed, and set k2=1; If there is a fluctuation in speed, it can be shown that the above calculation result is >3%, and the torque increase speed needs to be slowed down to avoid impact, and set k2=0.7.

[0152] The unit torque output value during the buffer time is calculated using the formula T_up = T2 + [(T4-T1) / tq] × k2. During the tq period, the output torque of the drive wheel is gradually increased according to this unit torque until the torque is restored to the target recovery torque T4, ensuring that there is no mechanical impact when the drive wheel re-contacts the track.

[0153] Therefore, through the aforementioned predictive torque buffering scheme, the torque of the drive wheel of the monorail crane can be dynamically adapted and adjusted under both conventional rail and rail joint conditions. This ensures that the torque distribution is always synchronized with the changes in the contact state between the drive wheel and the rail, effectively eliminating impact and speed fluctuation problems, and significantly improving the operational safety and material transportation efficiency of the monorail crane.

[0154] This application effectively solves the impact problem of monorail cranes passing through rail joints through a predictive torque buffering scheme, bringing multiple practical benefits. By using infrared reflection sensors to accurately identify the rail joint location and pre-calculate the time for the drive wheel to disengage and arrive, torque can be gradually reduced before contact with the joint and smoothly increased afterward. This avoids sudden speed increases under no-load conditions and mechanical impacts upon re-contact, reducing wear on the drive wheel and rail. Furthermore, by dynamically adjusting the torque reduction and increase coefficients and optimizing the adjustment rhythm based on real-time speed and torque data, compared to traditional average adjustment methods, vehicle vibration is significantly reduced, ensuring stable material transport. Finally, the entire solution requires no manual intervention, automatically adapts to rail joint conditions, and seamlessly integrates with the torque adjustment logic of conventional rails, improving both the safety of monorail crane operation and further optimizing transport efficiency.

[0155] Please see Figure 4 , Figure 4 This is a schematic diagram of a drive force distribution control system 40 for a monorail crane operating system provided in this application. The drive force distribution control system 40 is applied to a monorail crane operating system, which includes multiple sets of drive wheels. The drive force distribution control system 40 includes: The dynamic sensing module 41 is used to collect the working environment parameters and operating status parameters corresponding to each group of drive wheels in the monorail crane operating system; The load-bearing calculation module 42 is used to calculate the maximum load-bearing torque of the drive wheels based on the working environment parameters and operating status parameters, and to determine the target distribution ratio of the driving force of multiple sets of drive wheels based on the maximum load-bearing torque. The dynamic adjustment module 43 is used to collect the rotational speed data of the drive wheels, determine the real-time rotational speed deviation of the drive wheels, and dynamically adjust each group of drive wheels according to the maximum load torque and the target distribution ratio of driving force to correct the real-time rotational speed deviation.

[0156] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A drive force distribution control method applied to a monorail crane running system, characterized by, The driving force distribution control method is applied to a monorail crane running system, the monorail crane running system comprising a plurality of groups of driving wheels, the driving force distribution control method comprising: Collecting working condition environment parameters and running state parameters corresponding to each group of driving wheels in the monorail crane running system; According to the working condition environment parameters and the running state parameters, calculating the maximum bearing torque of the driving wheels, and determining the driving force target distribution ratio of the plurality of groups of driving wheels based on the maximum bearing torque; Collecting the rotation speed data of the driving wheels, determining the real-time rotation speed deviation of the driving wheels, and dynamically adjusting the driving wheels according to the real-time rotation speed deviation, the maximum bearing torque and the driving force target distribution ratio to correct the real-time rotation speed deviation.

2. The drive force distribution control method for a monorail crane running system according to claim 1, characterized by, The working condition environment parameters include wear morphology data and infrared reflection signals, and the running state parameters include real-time torque values and acceleration values; the monorail crane running system further comprises a laser profile sensor, an infrared reflection sensor, a torque sensor and a longitudinal acceleration sensor; The step of collecting the working condition environment parameters and the running state parameters corresponding to each group of driving wheels in the monorail crane running system comprises: Scanning the surface profile of the driving wheel by the laser profile sensor to generate profile point cloud information of the driving wheel, thereby obtaining the wear morphology data; wherein the wear morphology data is used to represent the wear degree of the surface of the driving wheel; The infrared reflection sensor emits and receives reflection signals to the monorail surface of the driving wheel to generate infrared reflection signals; wherein the infrared reflection signals are used to represent the roughness and medium adhesion state of the monorail surface; The torque sensor detects the output torque of the driving wheel to obtain the real-time torque value; wherein the real-time torque value is used to represent the driving force size currently output by the driving wheel; The longitudinal acceleration sensor detects the longitudinal motion acceleration of the vehicle body of the monorail crane to obtain the acceleration value; wherein the acceleration value is used to represent the actual motion state of the vehicle body of the monorail crane.

3. The drive force distribution control method for a monorail crane running system according to claim 2, characterized by, The monorail crane running system further comprises a monorail, and the step of calculating the maximum bearing torque of the driving wheels according to the working condition environment parameters and the running state parameters comprises: Based on the wear morphology data, determining the actual contact area ratio between the driving wheel and the monorail; According to the infrared reflection signals, calculating the real-time friction coefficient between the driving wheel and the monorail surface; According to the deviation degree of the real-time torque value and the acceleration value, adaptively adjusting the clamping force of the driving wheel to the monorail; Combining the actual contact area ratio, the real-time friction coefficient, the adjusted clamping force and the inherent parameters of the driving wheel, the maximum bearing torque of the driving wheel is calculated through a preset torque calculation formula.

4. The drive force distribution control method for a monorail crane running system according to claim 3, characterized by, The step of determining the actual contact area ratio between the driving wheel and the monorail based on the wear morphology data comprises: The point cloud information of the driving wheel surface profile obtained by scanning the laser profile sensor is compared with the reference profile based on the annular working surface profile of the driving wheel in a brand new state, and the wear of the driving wheel is calculated; According to the wear, the driving wheel is classified to determine the wear type of the driving wheel; In the case where the wear type of the driving wheel is a concentrated wear region with wear greater than the average wear, the actual contact area ratio of the actual contact area between the driving wheel and the monorail is calculated according to the ratio of the actual contact area to the relative rated area.

5. The drive force distribution control method for a monorail crane running system according to claim 3, characterized by, The step of calculating the real-time friction coefficient between the driving wheel and the monorail surface according to the infrared reflection signal comprises: Obtain the infrared signal intensity and reflectivity distribution data reflected by the monorail surface; Compare the infrared signal intensity with the preset reference signal to calculate the signal attenuation rate; Based on the reflectivity distribution data, identify whether there is an attached medium on the monorail surface and the coverage area ratio; According to the signal attenuation rate, the attached medium coverage area ratio, the real-time friction coefficient between the driving wheel and the monorail surface is calculated.

6. The drive force distribution control method for a monorail crane running system according to claim 3, characterized by, The step of adaptively adjusting the clamping force of the driving wheel on the monorail according to the deviation degree of the real-time torque value and the acceleration value comprises: The real-time torque value and the acceleration value are respectively subjected to maximum value normalization processing to calculate the difference value between the real-time torque value and the acceleration value, and the deviation degree is obtained. The deviation degree is used to reflect the matching state of the driving wheel output driving force and the actual motion demand of the vehicle body, and represents the loss in the driving force transmission process; A continuous time sequence corresponding to the deviation degree is generated, and the deviation change trend is judged through the continuous time sequence: If the deviation degree continuously expands at multiple time nodes, the preset proportion of the basic clamping force set by the driving wheel factory is used as the adjustment step to gradually increase the clamping force; If the deviation degree continuously shrinks at multiple time nodes, the adjustment step of the clamping force is reduced; If the deviation degree is stable within a preset minimum range, stop adjusting the clamping force; If the deviation degree expands to more than a preset proportion of the sensor detection range, increase the clamping force while simultaneously reducing the output torque of the driving wheel until the deviation degree decreases to a preset safe range.

7. The drive force distribution control method for a monorail crane running system according to claim 1, characterized by, The step of dynamically adjusting the driving wheel to correct the real-time speed deviation according to the real-time speed deviation, the maximum bearing torque and the driving force target distribution ratio comprises: If the real-time speed deviation meets the preset deviation threshold of the rotating wheel, the target adjustment torque is calculated according to the maximum bearing torque and the driving force target distribution ratio; According to the target adjustment torque, the output torque of the driving wheel is controlled to decrease at a uniform rate within a preset time until the adjustment of the target adjustment torque is completed.

8. The driving force distribution control method for a monorail crane running system according to claim 1, characterized by, The monorail crane running system further comprises a position detection device, and the driving force distribution control method further comprises: The position detection device is used to acquire distance information between the monorail crane and the front monorail joint in real time; When the distance information is less than a preset warning threshold, an advance adjustment time required for impact buffering is calculated based on a current running speed of the monorail crane and a position feature of the monorail joint; According to the advance adjustment time, the output torque of the multiple groups of driving wheels is pre-adjusted in stages to make the monorail crane and the monorail joint.

9. The drive force distribution control method for a monorail crane running system according to claim 8, characterized by, The step of calculating the advance adjustment time required for impact buffering based on the current running speed of the monorail crane and the position feature of the monorail joint comprises: acquiring a type parameter of the monorail joint and a real-time running speed of the monorail crane; According to the type parameter, a preset impact coefficient table is queried, and a theoretical impact duration when passing through the monorail joint is calculated in combination with the real-time running speed; The theoretical impact duration and the response delay time of the driving wheel torque adjustment are superimposed to obtain the advance adjustment time required for impact buffering; wherein the advance adjustment time is the time for completing the pre-adjustment of the driving wheel output torque and stabilizing the torque state before the monorail crane reaches the monorail joint.

10. A drive force distribution control system for a monorail crane running system, characterized by, The driving force distribution control system is applied to a monorail crane running system, the monorail crane running system comprising multiple groups of driving wheels, and the driving force distribution control system comprising: A dynamic perception module is configured to collect working condition environment parameters and running state parameters corresponding to each group of driving wheels in the monorail crane running system; A calculation bearing module is configured to calculate a maximum bearing torque of the driving wheels according to the working condition environment parameters and the running state parameters, and determine a driving force target distribution ratio of the multiple groups of driving wheels based on the maximum bearing torque; A dynamic adjustment module is configured to collect rotation speed data of the driving wheels, determine a real-time rotation speed deviation of the driving wheels, and dynamically adjust each group of driving wheels according to the maximum bearing torque and the driving force target distribution ratio to correct the real-time rotation speed deviation.

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