A friction coefficient intelligent measurement system, method, electronic device and storage medium

Abstract

This application discloses an intelligent friction coefficient measurement system, method, electronic device, and storage medium, relating to the field of friction coefficient measurement. The system includes an intelligent traction trolley, a detection block of known weight, a traction rope, a tension sensor, and an image acquisition device. The intelligent traction trolley includes a vehicle body, a main control module, a drive motor, and a human-machine interface module. The intelligent traction trolley moves at a constant speed via the drive motor, causing the detection block to move synchronously. The tension sensor measures the tension in real time, and the image acquisition device acquires images of the target to determine the spatial attitude parameters of the traction rope. The main control module calculates the actual effective tension based on the spatial attitude parameters and the tension signal, and then calculates and displays the maximum static and dynamic friction coefficients in conjunction with the weight of the detection block. This solves the problems of insufficient measurement accuracy, poor operational stability, and errors caused by the attitude of the traction rope in existing technologies, achieving automated, high-precision intelligent measurement.

Description

Technical Field

[0001] This application relates to the field of friction coefficient measurement, specifically to an intelligent friction coefficient measurement system, method, electronic device, and storage medium. Background Technology

[0002] Accurate measurement of the coefficient of friction of object surfaces is of paramount importance in many fields, including mechanical engineering and materials science. The coefficient of friction is a key parameter reflecting the characteristics of frictional force between two object surfaces, directly impacting mechanical design, material selection, and process optimization. For example, in mechanical transmission systems, a suitable coefficient of friction ensures efficient power transmission; in material processing, understanding the coefficient of friction of material surfaces helps improve machining accuracy and quality. With continuous technological advancements, higher demands are being placed on the accuracy and efficiency of friction coefficient measurement, and related technologies are constantly being improved and perfected.

[0003] In the past, the coefficient of friction was typically measured using relatively traditional methods. One common approach was to manually apply a pulling force and use simple tools such as a spring scale to measure the maximum pulling force when the object began to slide and the stable pulling force during the sliding process, then combine this with the object's weight to calculate the coefficient of friction. Another method involved using more complex experimental setups, where a motor drove the object to move, measuring parameters such as pulling force and displacement during the movement, and then calculating the coefficient of friction.

[0004] Traditional manual measurement methods suffer from significant errors due to the instability of human operation, making it difficult to guarantee uniform application of tension and accurate measurement. While using a testing device avoids human interference, it is difficult to precisely control the object's motion during measurement and often ignores the influence of the traction rope's own posture changes on the measurement results, leading to substantial errors in the final calculated coefficient of friction. Summary of the Invention

[0005] This application provides an intelligent friction coefficient measurement system, method, electronic device, and storage medium, which solves the problems of insufficient friction coefficient measurement accuracy, poor operational stability, and measurement errors introduced by changes in the attitude of the traction rope in the prior art, and realizes automated, high-precision intelligent measurement of the friction coefficient.

[0006] In a first aspect, this application provides an intelligent friction coefficient measurement system, the system comprising an intelligent traction trolley, a detection block of known weight, a traction rope, a tension sensor, and an image acquisition device. The intelligent traction trolley comprises a vehicle body and a drive motor, a human-machine interaction module, and a main control module installed in the vehicle body. The drive motor, the human-machine interaction module, the tension sensor, and the image acquisition device are all electrically connected to the main control module. The bottom of the vehicle body is equipped with a set of wheels, and the drive motor is connected to the set of wheels for driving the vehicle body to move at a constant speed along a preset path. The tension sensor is installed on the intelligent traction trolley to form the trolley traction point. The tension sensor is used to measure the tension on the traction rope in real time, generate a tension signal, and transmit it to the main control module. The detection block is placed on the contact surface to be tested and is located behind the intelligent traction vehicle in the forward direction. A connector of known weight is provided on the detection block to form a slider traction point. One end of the traction rope is connected to the tension sensor, and the other end of the traction rope is connected to the connector. At least one mark is provided on the traction rope to form a rope feature point. The image acquisition device is located at the rear end of the top of the intelligent traction trolley, and the acquisition direction of the image acquisition device is towards the rear of the intelligent traction trolley. It is used to acquire target images containing the trolley traction point, as well as the slider traction point and / or rope feature points and transmit them to the main control module. The main control module is electrically connected to the drive motor and is used to control the running speed of the intelligent traction trolley; it is also used to receive the target image and the tension signal, and determine the spatial attitude parameters of the traction rope based on the target image; it is also used to calculate the actual effective tension based on the spatial attitude parameters and the tension signal, and calculate the maximum static friction coefficient and dynamic friction coefficient based on the actual effective tension, the weight of the detection block and the weight of the connector, and display the calculated maximum static friction coefficient and dynamic friction coefficient through the human-machine interaction module.

[0007] By adopting the above technical solution, the intelligent traction trolley moves at a constant speed along a preset path via a drive motor and a set of wheels, pulling a detection block placed on the contact surface to be measured. A tension sensor measures the tension on the traction rope in real time, generating a tension signal that is transmitted to the main control module. An image acquisition device captures target images including the trolley's traction point, the slider's traction point, and / or rope feature points, which are also transmitted to the main control module. The main control module determines the spatial attitude parameters of the traction rope based on the received target images, and then calculates the actual effective tension based on the tension signal. Finally, it calculates the maximum static and dynamic friction coefficients based on the actual effective tension, the weight of the detection block, and the weight of the connecting parts, and displays the results through a human-machine interface module. The intelligent traction trolley achieves constant-speed traction, avoiding the instability of manual operation. Utilizing the image acquisition device to obtain the spatial attitude parameters of the traction rope allows for precise correction of the influence of the tension direction on the measurement results, thereby significantly improving the accuracy and repeatability of friction coefficient measurement.

[0008] Optionally, the central axis of the connector coincides with the central horizontal axis of the detection block, and the trolley traction point and the slider traction point are at the same horizontal height.

[0009] By adopting the above technical solution, it can be ensured that when the traction rope applies tension to the detection block in the horizontal direction, no additional torque is generated that would cause the detection block to tilt or flip. It also ensures that the bottom surface of the detection block can make uniform contact with the surface to be measured, avoiding the impact of uneven force on the accuracy of friction coefficient measurement due to local pressure changes. At the same time, the setting of traction points at the same horizontal height simplifies the subsequent calculation of spatial attitude parameters, reduces the complexity of vector decomposition, and improves the computing efficiency of the main control module.

[0010] Optionally, the detection block includes a standard block, a mounting plate, a positioning component, and at least three sliders. The positioning component is disposed on the top surface of the mounting plate. The bottom surface of the standard block has a groove that matches the mounting plate, and the mounting plate has a positioning hole that matches the positioning component at the groove. The standard block is embedded in the groove and is detachably connected to the mounting plate through the positioning component. Each slider is symmetrically arranged on the bottom surface of the mounting plate with the central axis of the mounting plate as the axis of symmetry to form a support protrusion. Each slider is in direct contact with the surface to be tested. The center of the side wall of the standard block has a mounting hole for mounting a connector.

[0011] By adopting the above technical solution, the detection block adopts a modular design. The standard block and the mounting plate are detachably connected through positioning components, which facilitates the quick replacement of standard blocks of different specifications according to different measurement needs, improving the versatility and adaptability of the system. At least three sliders are symmetrically arranged around the central axis of the mounting plate to form a stable support structure, ensuring that the detection block remains stable during movement and avoiding measurement deviations caused by single-point contact. At the same time, the direct contact between the slider and the surface to be measured ensures the authenticity and reliability of friction measurement. The mounting hole design at the center of the side wall of the standard block makes the installation position of the connector precise and controllable, further ensuring the alignment of the traction force application point with the center of gravity of the detection block, which is conducive to improving measurement accuracy.

[0012] Optionally, the system further includes a transfer mechanism, which includes a base, a guide rod, and a guide assembly. The base has a insertion hole that matches the guide rod. The guide assembly includes a guide wheel, a wheel frame, and an adjusting bolt. The guide wheel is rotatably mounted on the wheel frame. The outer circumference of the guide wheel has a guide groove that matches the diameter of the traction rope. The wheel frame is connected to the guide rod by the adjusting bolt, and the central axis of the guide wheel is perpendicular to the line connecting the trolley traction point and the slider traction point.

[0013] By adopting the above technical solution, the transfer mechanism allows the direction of the traction rope to be flexibly adjusted according to the actual measurement scenario. When there is an angle between the contact surface to be measured and the preset path of the intelligent traction trolley, the traction direction is changed by the guide wheel to ensure that the detection block is always pulled along the specific direction of the contact surface to be measured. The guide groove on the outer circumference of the guide wheel matches the diameter of the traction rope, which can effectively constrain the lateral swing of the traction rope relative to the guide wheel and reduce measurement interference caused by rope swaying. The wheel frame is connected to the guide rod by adjusting bolts, so that the height of the guide wheel can be adjusted to adapt to measurement scenarios with different height differences. The setting of the guide wheel's central axis perpendicular to the line connecting the trolley traction point and the slider traction point ensures that the turning angle of the traction rope at the guide wheel is minimized, reducing the friction loss between the rope and the guide groove, and ensuring that the tension value measured by the tension sensor more accurately reflects the traction force on the detection block.

[0014] A second aspect of this application provides an intelligent method for measuring the coefficient of friction, applied to the main control module of the intelligent friction coefficient measurement system as described in the first aspect, the method comprising: Control the intelligent traction trolley to move at a constant speed along a preset path, so as to drive the detection block to move synchronously through the traction rope; The tension signal is generated by the tension sensor collecting the tension of the traction rope in real time, and the target image is collected in real time by the image acquisition device. The target image includes the traction point of the trolley, the slider traction point of the detection block and / or the rope feature points on the traction rope. The spatial attitude parameters of the traction rope are determined based on the target image. The spatial attitude parameters include the pitch angle of the traction rope relative to the horizontal plane and the lateral deflection angle of the traction rope on the horizontal plane. The actual effective tensile force is calculated based on the spatial attitude parameters and the tensile signal. Based on the actual effective tensile force, the weight of the detection block, and the weight of the connector, the maximum static friction coefficient and dynamic friction coefficient are calculated and displayed through the human-machine interface module.

[0015] By adopting the above technical solution, the main control module controls the intelligent traction trolley to move at a constant speed, achieving stable traction of the detection block and avoiding the problem of uneven force application in traditional manual operation. Utilizing an image acquisition device to acquire the spatial attitude parameters of the traction rope in real time, it can accurately capture the angular changes of the traction rope in three-dimensional space, thereby performing accurate vector decomposition of the tension signal and effectively eliminating measurement errors caused by traction rope attitude deviations. By distinguishing the calculation methods of maximum static friction and sliding friction, the instantaneous tension value at the critical moment and the average tension value during the stable fluctuation phase are extracted respectively, and vector decomposition is performed in conjunction with the spatial attitude parameters at the corresponding moments, ensuring the calculation accuracy of the maximum static friction coefficient and dynamic friction coefficient. Finally, the measurement results are intuitively displayed through the human-computer interaction module, realizing full automation and intelligence of the friction coefficient measurement process, significantly improving measurement efficiency and data reliability.

[0016] Optionally, before controlling the intelligent traction trolley to move at a constant speed along a preset path, the method further includes: Acquire a reference image under ideal, unbiased conditions, and extract the original pixel coordinates of the trolley traction point, slider traction point, or rope feature points in the reference image; A standard three-dimensional rectangular coordinate system is established with the trolley traction point as the origin; The original pixel coordinates of the slider traction point or rope feature point are projected onto the standard three-dimensional rectangular coordinate system to obtain the standard pixel coordinates of the slider traction point or rope feature point. Determining the spatial attitude parameters of the traction rope based on the target image includes: Feature recognition technology is used to extract the traction point of the trolley, the traction point of the slider, or the rope feature points in the target image, and the current pixel coordinates of the traction point of the trolley, the traction point of the slider, or the rope feature points in the target image are determined. The current pixel coordinates are projected onto a standard three-dimensional rectangular coordinate system. Based on the deviation between the current pixel coordinates and the standard pixel coordinates, the lateral deflection angle of the traction rope in the horizontal plane and the pitch angle relative to the horizontal plane are calculated. The calculation of the actual effective tensile force based on the spatial attitude parameters and the tensile signal includes: The tension signal is vector-decomposed based on the lateral deflection angle and pitch angle to obtain the actual effective tension of the traction rope in the horizontal direction.

[0017] By adopting the above technical solution, before conducting formal measurements, a reference image under ideal, unbiased conditions is acquired, and a standard three-dimensional Cartesian coordinate system is established based on the trolley traction point, slider traction point, and rope feature points in the reference image. This provides a reliable reference for subsequent calculation of spatial attitude parameters. During the measurement process, the target image is identified, and feature recognition technology is used to extract the trolley traction point and slider traction point or rope feature points from the target image. The current pixel coordinates of each point in the target image are determined, and then the pixel coordinates are projected onto the standard three-dimensional Cartesian coordinate system, thus realizing the identification of the trolley traction point and slider traction point in the target image acquired during the measurement process. Alternatively, a comparative analysis of the rope feature points with the trolley traction point and slider traction point or rope feature points in the reference image under the same coordinate system can be performed. By comparing the deviation between the current pixel coordinates and the standard pixel coordinates, the lateral deflection angle and the pitch angle relative to the horizontal plane of the traction rope can be accurately calculated, thus accurately reflecting the actual attitude of the traction rope in three-dimensional space. Based on the calculated lateral deflection angle and pitch angle, the tension signal is vector decomposed, and the tension value measured by the sensor is accurately converted into the actual effective tension in the horizontal direction. This effectively eliminates the tension measurement error caused by the change in the attitude of the traction rope, ensuring the accuracy and reliability of the tension data on which the friction coefficient is based.

[0018] Optionally, the step of performing vector decomposition on the tension signal based on the lateral deflection angle and pitch angle to obtain the actual effective tension of the traction rope in the horizontal direction includes: The magnitude of the tension on the traction rope and its time variation characteristics are determined based on the tension signal. The critical moment when the detection block changes from a static state to a sliding state is identified. The tension value corresponding to the critical moment is extracted as the maximum static friction force, and the average value of the tension signal after the critical moment is extracted as the sliding friction force after a preset period of stable fluctuation. The maximum static friction force is vector-decomposed based on the lateral deflection angle and the pitch angle to obtain the first actual effective tension of the traction rope in the horizontal direction. The sliding friction force is appropriately decomposed based on the average lateral deviation angle and average pitch angle within the preset time period to obtain the second actual effective tension of the traction rope in the horizontal direction. The calculation of the maximum static friction coefficient and dynamic friction coefficient based on the actual effective tensile force, the weight of the detection block, and the weight of the connecting parts includes: The maximum static friction coefficient is calculated based on the first actual effective tensile force, the weight of the detection block, and the weight of the connector. The coefficient of dynamic friction is calculated based on the second actual effective tensile force, the weight of the detection block, and the weight of the connecting parts.

[0019] By adopting the above technical solution and accurately analyzing the characteristics of the tensile signal changing over time, the critical moment when the detection block changes from rest to sliding can be accurately identified, thereby distinguishing the measurement range of maximum static friction and sliding friction, avoiding the subjective error caused by human judgment of the critical state in traditional methods. Different vector decomposition strategies are adopted for maximum static friction and sliding friction. For the instantaneously changing maximum static friction, the real-time attitude parameters at the critical moment are used for decomposition, while for the relatively stable sliding friction, the average attitude parameters within a preset time period are used for decomposition, ensuring instantaneous measurement accuracy and effectively suppressing the influence of random fluctuations on stable measurement results. Finally, the maximum static friction coefficient and dynamic friction coefficient are calculated separately, realizing a comprehensive characterization of the static and dynamic friction characteristics of the material.

[0020] Optionally, controlling the intelligent traction trolley to move at a constant speed along a preset path includes: Before the detection block reaches its maximum static friction, control the intelligent traction trolley to move at a first preset speed; After the detection block reaches its maximum static friction, the intelligent traction trolley is controlled to move at a second preset speed, wherein the first preset speed is different from the second preset speed.

[0021] By adopting the above technical solution, a first preset speed is used before the detection block reaches its maximum static friction force. This allows the detection block to slowly accumulate traction force during the static friction stage, facilitating the accurate capture of the sudden change in tension at the critical sliding moment and avoiding difficulties in identifying the critical moment or insufficient data sampling due to excessive speed. After the detection block enters the sliding state, the speed is switched to a second preset speed. An appropriate sliding speed can be selected according to the dynamic friction characteristics of different materials, ensuring the measurement stability during the sliding friction stage and simulating various motion conditions in actual working conditions. Through the phased speed control strategy, the optimized separation of static friction measurement and dynamic friction measurement is achieved, taking into account both the sensitivity of critical state capture and the stability of sliding state measurement, further improving the system's adaptability to the friction characteristics of different types of materials and its measurement accuracy.

[0022] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described in the second aspect and any possible implementation thereof. A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in the second aspect and any possible implementation thereof.

[0023] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By using an intelligent traction trolley to achieve uniform traction, and in conjunction with an image acquisition device to acquire and accurately correct the spatial attitude parameters of the traction rope in real time, the influence of human operation instability and tension direction deviation on the measurement results is effectively eliminated, and the accuracy and repeatability of friction coefficient measurement are significantly improved. 2. Through the flexible setting of the transfer mechanism, the traction direction can be easily adjusted and the system can adapt to multiple scenarios. At the same time, by utilizing the precise constraint and position adjustment function of the guide wheel, the interference of rope swaying and friction loss on tension measurement is effectively reduced, further improving the measurement reliability of the system. 3. Based on the spatial attitude parameter extraction and vector decomposition algorithm of the target image, combined with the phased speed control strategy and differentiated static and dynamic friction measurement methods, the entire process of friction coefficient measurement is automated and intelligent, which can accurately distinguish and calculate the maximum static friction coefficient and dynamic friction coefficient, and comprehensively characterize the friction characteristics of the material. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the architecture of an intelligent friction coefficient measurement system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an intelligent friction coefficient measurement system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a detection block in an intelligent friction coefficient measurement system provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a standard block in an intelligent friction coefficient measurement system provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a mounting plate in an intelligent friction coefficient measurement system provided in an embodiment of this application; Figure 6 This is a schematic diagram of the transfer mechanism of an intelligent friction coefficient measurement system provided in an embodiment of this application; Figure 7 This is a schematic flowchart of a smart friction coefficient measurement method disclosed in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0025] Explanation of reference numerals in the attached drawings: 1. Intelligent traction trolley; 11. Vehicle body; 12. Main control module; 13. Drive motor; 14. Walking wheel set; 15. Human-machine interaction module; 2. Detection block; 21. Standard block; 211. Slot; 212. Positioning hole; 22. Mounting plate; 23. Positioning component; 24. Slider; 3. Traction rope; 4. Tension sensor; 5. Image acquisition device; 6. Connector; 7. Transfer mechanism; 71. Base; 72. Guide rod; 73. Guide wheel; 731. Guide groove; 74. Wheel frame; 75. Adjusting bolt; 500. Electronic equipment; 501. Processor; 502. Communication bus; 503. User interface; 504. Network interface; 505. Memory. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0028] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Reference Figures 1-6 This is a schematic diagram of the system framework of the intelligent friction coefficient measurement system disclosed in an embodiment of this application. Figure 1As shown, the system includes an intelligent traction trolley 1, a detection block 2 of known weight, a traction rope 3, a tension sensor 4, and an image acquisition device 5. The intelligent traction trolley 1 includes a body 11 and a drive motor 13, a human-machine interface module 15, and a main control module 12 installed in the body 11. The drive motor 13, the human-machine interface module 15, the tension sensor 4, and the image acquisition device 5 are all electrically connected to the main control module 12 to achieve data transmission. The bottom of the body 11 of the intelligent traction trolley 1 is equipped with a set of wheels 14. The drive motor 13 is connected to the set of wheels 14 for transmission, so that the main control module 12 can drive the set of wheels 14 to rotate through the drive motor 13, so that the intelligent traction trolley 1 can move at a constant speed along a preset path. The intelligent traction trolley 1 drives the detection block 2 to move on the contact surface to be tested through the traction rope 3. The tension sensor 4 collects the tension on the traction rope 3 in real time, forms a tension signal, and transmits it to the main control module 12. The image acquisition device 5 collects the intelligent traction trolley in real time. The target images of the trolley 1, traction rope 3, and detection block 2 are transmitted to the main control module 12. The main control module 12 analyzes and calculates the tension signal collected by the tension sensor 4 and the target image collected by the image acquisition device 5 to obtain the maximum static friction coefficient and dynamic friction coefficient of the contact surface to be measured. The results are then displayed through the human-machine interaction module 15, realizing the automated measurement of the friction coefficient. There is no human intervention in the measurement process, ensuring the consistency and repeatability of the measurement process and effectively eliminating random errors caused by human operation. At the same time, the image acquisition device 5 collects the target images of the intelligent traction trolley 1, traction rope 3, and detection block 2 in real time during the measurement process. The target images are used to analyze whether the posture of the traction rope 3 has deviated during the measurement process. If a deviation occurs, the tension signal is corrected through vector decomposition to compensate for the measurement error caused by the change in the spatial posture of the traction rope 3, further improving the accuracy of the friction coefficient measurement.

[0030] The transmission connection between the drive motor 13 and the walking wheel set 14 can be achieved by means of gear transmission, belt transmission or chain transmission, etc. The specific transmission method can be selected according to the load requirements and running accuracy requirements of the intelligent traction trolley 1.

[0031] The preset path can be a path input by the monitoring personnel through the human-machine interaction module 15 before the measurement begins; or it can be a default straight path in the main control module 12, which turns left / right when encountering an obstacle. In this embodiment, in order to ensure that the intelligent traction vehicle 1 does not collide during its movement, an obstacle avoidance sensor is installed on the intelligent traction vehicle 1. The obstacle avoidance sensor is electrically connected to the main control module 12 and is used to detect obstacle information in the direction of travel of the intelligent traction vehicle 1 in real time and transmit it to the main control module 12. The main control module 12 dynamically adjusts the travel path of the intelligent traction vehicle 1 according to the received obstacle information to ensure the continuity and safety of the measurement process.

[0032] A tension sensor 4 is installed on the intelligent traction trolley 1 to form a traction point. The tension sensor 4 is used to measure the tension force on the traction rope 3 in real time, generate a tension signal, and transmit it to the main control module 12. The tension sensor 4 is installed on the intelligent traction trolley 1 in a detachable or fixed manner. One end of the tension sensor 4 is connected to the intelligent traction trolley 1, and the other end is connected to the traction rope 3, so that the tension force of the traction rope 3 acts directly on the force-receiving end of the tension sensor 4. The end of the traction rope 3 away from the tension sensor 4 extends to the detection slider 24 and is connected to the traction point of the slider 24. Thus, when the intelligent traction trolley 1 moves, the tension sensor 4 can collect the traction force signal of the traction rope 3 on the detection slider 24 in real time and accurately.

[0033] The tension sensor 4 includes, but is not limited to, S-type tension sensor 4, column-type tension sensor 4, spoke-type tension sensor 4, miniature tension sensor 4, and thin-film tension sensor 4.

[0034] The detection block 2 is placed on the contact surface to be tested and is located behind the intelligent traction trolley 1 in the forward direction. A connector 6 of known weight is provided on the detection block 2 to form a traction point for the slider 24. The connector 6 is connected to the end of the traction rope 3 away from the intelligent traction trolley 1. In this embodiment, the detection block 2 includes a standard block 21, a mounting plate 22, a positioning component 23, and at least three sliders 24. The positioning component 23 is disposed on the top surface of the mounting plate 22. The bottom surface of the standard block 21 has a groove 211 that matches the mounting plate 22, and the mounting plate 22 has a positioning hole 212 that matches the positioning component 23 at the groove 211. The standard block 21 is embedded in the groove 211 and is detachably connected to the mounting plate 22 through the positioning component 23. The sliders 24 are symmetrical about the central axis of the mounting plate 22. The sliders 24 are arranged on the bottom surface of the mounting plate 22 to form a support protrusion on the bottom surface of the mounting plate 22. Each slider 24 is in direct contact with the contact surface to be tested. The standard block 21 has a mounting hole at the center of its side wall for mounting the connector 6. The connector 6 can be detachably mounted on the standard block 21 through the mounting hole, ensuring that the central axis of the connector 6 coincides with the central horizontal axis of the detection block 2. The traction point of the trolley and the traction point of the slider 24 are at the same horizontal height, so that when the intelligent traction trolley 1 moves, it can apply a horizontal traction force to the detection block 2 through the traction rope 3.

[0035] The weights of the detection block 2 and the connector 6 are known quantities and are pre-stored in the main control module 12 so that they can be directly called when calculating the friction coefficient later.

[0036] In this embodiment, at least one marker is provided on the traction rope 3 to form rope feature points. These rope feature points are uniformly distributed along the axial direction of the traction rope 3. The marking methods include, but are not limited to, fluorescent markers, reflective patches, or knots of specific colors. The color of the marker contrasts sharply with the body of the traction rope 3, so that the image acquisition device 5 can accurately identify the position of the rope feature points even under complex lighting conditions. The number of rope feature points can be set according to the length of the traction rope 3 and the measurement accuracy requirements, with the goal of meeting the calculation needs of spatial attitude parameters.

[0037] Image acquisition device 5 is mounted on top of the intelligent traction trolley 1 at the rear end, i.e., at the top of the rear of the trolley. The field of view of image acquisition device 5 covers the traction point of the traction rope 3, the traction point of the slider 24 of the detection block 2, and / or a section of the traction rope 3 including at least one rope feature point. This ensures complete capture of the spatial attitude changes of the traction rope 3. Specifically, the target image must contain at least the information of the traction point and the slider 24 traction point, or the traction point and the rope feature point, or the traction point, the slider 24 traction point, and the telescopic feature point. This ensures that the target image can determine the spatial attitude changes of the traction rope 3 based on two specific marker points. Image acquisition device 5 includes, but is not limited to, industrial cameras, high-speed cameras, depth cameras, or binocular stereo vision cameras. Image acquisition device 5 is mounted on top of the intelligent traction trolley 1 via a bracket. The height and angle of the bracket are adjustable to adapt to the field of view requirements in different measurement scenarios. Image acquisition device 5 is connected to the main control module 12 via wired or wireless means, transmitting the real-time acquired target images to the main control module 12 for processing.

[0038] The main control module 12 controls the running speed of the intelligent traction trolley 1, receives data transmitted from the tension sensor 4 and the image acquisition device 5, processes and analyzes the data, and calculates the maximum static friction coefficient and dynamic friction coefficient of the contact surface to be measured. The main control module 12 can adopt a hardware platform with data processing capabilities, such as an embedded processor, industrial control computer, or programmable logic controller. It runs specially developed friction coefficient measurement software, which integrates image processing algorithms, signal filtering algorithms, vector decomposition calculation modules, and friction coefficient calculation models, and can automatically complete the entire process from data acquisition to result output.

[0039] After receiving the target image transmitted by the image acquisition device 5, the main control module 12 first performs preprocessing on the image, including noise reduction, contrast enhancement, and edge detection, to improve the accuracy of feature point recognition. Then, it uses an image recognition algorithm based on color threshold segmentation or template matching to extract the pixel coordinates of the trolley traction point, the slider 24 traction point, and the rope feature point from the preprocessed image. It establishes a coordinate system with the trolley traction point as the origin and determines the coordinates of the slider 24 traction point or the rope feature point as the target pixel coordinates. Then, based on the pre-stored standard pixel coordinates of the corresponding slider 24 traction point or the rope feature point, it calculates the pitch angle of the traction rope 3 relative to the horizontal plane and the lateral deflection angle on the horizontal plane. Meanwhile, the main control module 12 performs comprehensive analysis based on the tension signal collected in real time by the tension sensor 4. According to the tension signal, the main control module 12 identifies the change in motion state of the detection block 2. By monitoring the sudden change characteristics of the tension signal, the main control module 12 determines the critical moment when the detection block 2 changes from stationary to sliding. The peak tension corresponding to this moment is the maximum static friction force. After the critical moment, the tension signal enters a stable fluctuation stage. The main control module 12 extracts the average value within a preset time period of this stage as the sliding friction force.

[0040] Considering that the spatial orientation of the traction rope 3 may cause a deviation between the direction of the tension measured by the tension sensor 4 and the actual movement direction of the detection block 2, the main control module 12 performs vector decomposition of the tension and friction forces based on the calculated pitch and lateral tilt angles. This projects the tension vector in three-dimensional space onto the horizontal plane, eliminating the measurement error introduced by the tilt of the traction rope 3 and ensuring that the force value used for calculating the friction coefficient accurately reflects the force on the contact surface of the detection block 2. After obtaining the actual effective tension in the horizontal direction, the main control module 12 calls the pre-stored weight data of the detection block 2 and the connector 6, and calculates the friction coefficient according to Coulomb's law of friction. After the calculation is completed, the main control module 12 transmits the friction coefficient result to the human-machine interface module 15 for real-time display. Simultaneously, the measured data can be stored in local memory or uploaded to a remote server via a network interface for subsequent data traceability and statistical analysis.

[0041] The human-machine interface module 15 includes a display screen and an operation input device. The display screen is used to display the tension curve, the change curve of the attitude parameters of the traction rope 3, and the finally calculated friction coefficient value in real time during the measurement process. The operation input device can be a touch screen, buttons, or knobs, allowing monitoring personnel to set measurement parameters, start or stop the measurement process, and view historical measurement records. The human-machine interface module 15 also supports the export function of measurement results, which can output the data to an external storage device in the form of tables or charts.

[0042] In this embodiment, a transfer mechanism 7 is also included. The transfer mechanism 7 includes a base 71, a guide rod 72, and a guide assembly. The base 71 has a insertion hole for mounting the guide rod 72, so that the guide rod 72 can be detachably mounted on the base 71. The guide assembly includes a guide wheel 73, a wheel frame 74, and an adjusting bolt 75. The guide wheel 73 is rotatably mounted on the wheel frame 74, and the outer circumferential surface of the guide wheel 73 has a guide groove 731 that matches the diameter of the traction rope 3. When the traction rope 3 passes around the guide wheel 73 to change the traction direction, the guide groove 731 limits the traction direction. The lateral swing of the traction rope 3 relative to the guide wheel 73 is controlled. The wheel frame 74 is detachably connected to the guide rod 72 via the adjusting bolt 75, so that the height of the guide wheel 73 on the guide rod 72 is adjustable to adapt to measurement scenarios with different height differences. The setting of the central axis of the guide wheel 73 perpendicular to the line connecting the traction point of the trolley and the traction point of the slider 24 ensures that the turning angle of the traction rope 3 at the guide wheel 73 is minimized, reducing the friction loss between the rope and the guide groove 731, and ensuring that the tension value measured by the tension sensor 4 more accurately reflects the traction force on the detection block 2.

[0043] In this embodiment, when not in use, the detection block 2 and the transfer mechanism 7 can be stored in the intelligent traction trolley 1. The trolley 1 has a storage chamber inside its body 11, which is separated from core components such as the drive motor 13 and the main control module 12 by a partition to prevent collisions or interference with the precision components during storage. A cushioning layer made of rubber or sponge is laid at the bottom of the storage chamber to absorb vibrations and impacts during transportation, protecting the flatness of the detection block 2 surface and the integrity of the slider 24. The side walls of the storage chamber have slots or elastic fixing straps that match the shape of the detection block 2 and the transfer mechanism 7. After the detection block 2 and the transfer mechanism 7 are placed inside, they can be securely positioned using a snap-fit ​​structure or magnetic attraction, preventing shaking or displacement of the intelligent traction trolley 1 during movement.

[0044] In this embodiment, the intelligent traction trolley 1 moves at a constant speed along a preset path via the drive motor 13 and the walking wheel set 14, pulling the detection block 2 placed on the contact surface to be measured. The tension sensor 4 can measure the tension on the traction rope 3 in real time, generating a tension signal and transmitting it to the main control module 12. The image acquisition device 5 acquires a target image including the traction point of the trolley, the traction point of the slider 24, and / or the feature points of the rope, and also transmits it to the main control module 12. The main control module 12 determines the spatial attitude parameters of the traction rope 3 based on the received target image, and then calculates the actual effective tension based on the tension signal. Finally, it calculates the maximum static friction coefficient and dynamic friction coefficient based on the actual effective tension, the weight of the detection block 2, and the weight of the connector 6, and displays them through the human-machine interaction module 15. The intelligent traction trolley 1 achieves constant speed traction, avoiding the instability of manual operation. By using the image acquisition device 5 to acquire the spatial attitude parameters of the traction rope 3, the influence of the tension direction on the measurement results can be accurately corrected, thereby significantly improving the accuracy and repeatability of friction coefficient measurement.

[0045] like Figure 7 The diagram shown is a flowchart illustrating an intelligent friction coefficient measurement method provided in an embodiment of this application. Figure 7 As shown, this method can be executed by the main control module in the above-described intelligent friction coefficient measurement system embodiment, and includes at least the following steps: S1 controls the intelligent traction trolley to move at a constant speed along a preset path, so as to drive the detection block to move synchronously through the traction rope.

[0046] Specifically, the monitoring personnel place the detection block 2 and the intelligent traction trolley 1 at the test location. After placing the detection block 2 on the test contact surface, they connect both ends of the traction rope 3 to the traction point of the trolley and the traction point of the slider 24, respectively, ensuring a stable connection and that the traction rope 3 is in a naturally taut state. Subsequently, the monitoring personnel input preset path parameters, including the moving speed, moving distance, and starting direction, through the human-machine interaction module 15, or select the system's default straight-line mode. After receiving the start command, the main control module 12 sends a control signal to the drive motor 13. The drive motor 13 starts smoothly according to the preset speed curve, driving the walking wheel set 14 to rotate, causing the intelligent traction trolley 1 to gradually accelerate from a stationary state to the target uniform speed state.

[0047] In one feasible approach, during the uniform speed movement of the intelligent traction trolley 1, the main control module 12 monitors the actual operating speed of the intelligent traction trolley 1 in real time through an encoder or inertial measurement unit, and uses a closed-loop control algorithm to fine-tune the drive motor 13 to ensure that speed fluctuations are controlled within a preset threshold range, such as within ±5%, thereby ensuring the stability of the traction process.

[0048] To improve the measurement accuracy and stability of the maximum static friction coefficient and dynamic friction coefficient, the intelligent traction trolley 1 is controlled to move at a first preset speed before the detection block 2 reaches the maximum static friction force; after the detection block 2 reaches the static friction force, the intelligent traction trolley 1 is controlled to move at a second preset speed, which are different from each other. In this embodiment, the first preset speed is lower than the second preset speed. In the critical stage when the detection block 2 is about to slide from a standstill, the lower first preset speed allows the tension to increase slowly, making it easier for the main control module 12 to accurately capture the sudden change point of the tension signal, thereby accurately identifying the peak value of the maximum static friction force. After the detection block 2 enters the sliding state, switching to the higher second preset speed can ensure the speed stability during the sliding friction force measurement stage and reduce the friction force measurement deviation caused by speed fluctuations. The specific values ​​of the first and second preset speeds can be set according to the material characteristics of the contact surface to be measured and the measurement accuracy requirements. For example, the first preset speed can be set to 5 cm to 10 cm per second, and the second preset speed can be set to 20 cm to 30 cm per second. The main control module 12 automatically switches between the two speeds according to the real-time monitored changes in the tension signal.

[0049] In one feasible approach, the input of preset path parameters can also be achieved through the mobile terminal of the monitoring personnel. The mobile terminal of the monitoring personnel is connected to the intelligent traction vehicle 1 via wireless communication. The mobile terminal of the monitoring personnel is equipped with a matching control application. The monitoring personnel can view the real-time status information of the intelligent traction vehicle 1 on the application interface, including the current position, running speed, battery level and working status of each sensor, and send start, pause or emergency stop commands through simple touch operation.

[0050] The wireless communication method can utilize WiFi, Bluetooth, or 4G / 5G mobile networks. The main control module 12 has a built-in corresponding communication module to support multiple connection methods, ensuring stable remote control capabilities under different measurement environments. When monitoring personnel send a start command via a mobile terminal, the main control module 12 first verifies the legality and completeness of the command. After confirming that it is correct, it executes the same start process as the local human-machine interaction module 15. At the same time, it provides real-time feedback on the execution progress and system response status on the mobile terminal interface, allowing monitoring personnel to fully control the measurement process even when they are not near the intelligent traction trolley 1.

[0051] S2, acquire the tension signal generated by the tension sensor 4 in real time on the tension of the traction rope 3, and acquire the target image in real time by the image acquisition device 5. The target image includes the traction point of the trolley, the traction point of the slider 24 of the detection block 2 and / or the rope feature points on the traction rope 3.

[0052] Specifically, as the intelligent traction trolley 1 starts moving, the main control module 12 simultaneously triggers the data acquisition process of the tension sensor 4 and the image acquisition device 5. The tension sensor 4 measures the tension on the traction rope 3 in real time at a preset sampling frequency to ensure that the transient change characteristics of the tension signal can be completely captured. The image acquisition device 5 acquires images at a frame rate that matches the tension sampling to ensure that each frame of the image is precisely aligned with the corresponding tension data in time, facilitating subsequent joint analysis and processing. The tension sensor 4 amplifies and filters the measured analog tension signal through a signal conditioning circuit, and then converts it into a digital signal through an analog-to-digital converter before transmitting it to the main control module 12. The image acquisition device 5 transmits the captured image frames to the main control module 12 in real time in either compressed format or as a raw data stream.

[0053] In one feasible approach, to ensure the time synchronization accuracy between the tension signal and the target image, the main control module 12 sends a unified hardware trigger signal to the tension sensor 4 and the image acquisition device 5, causing both to begin data acquisition at the same time and embed the same timestamp information in the data frame. When using a software trigger method, the main control module 12 marks the acquisition time for both types of data using a high-precision clock source, and uses a time interpolation algorithm in the subsequent data processing stage to compensate for minor time deviations, ensuring that the tension signal and the target image can accurately correspond to the same physical moment during analysis, providing a data foundation for subsequent motion state recognition and vector correction.

[0054] During the acquisition process, the image acquisition device 5 automatically adjusts the exposure time and gain parameters according to the ambient lighting conditions, or uses an adaptive image enhancement algorithm to optimize the acquired images in real time, ensuring that the trolley traction point, slider 24 traction point, and rope feature points all have clear visual features under different lighting conditions. For rope feature points that use fluorescent markers or reflective patches as identifiers, the image acquisition device 5 can be configured with corresponding filters or auxiliary light sources to enhance the contrast between the markers and the background and improve the reliability of feature point recognition.

[0055] S3. Determine the spatial attitude parameters of the traction rope 3 based on the target image. The spatial attitude parameters include the pitch angle of the traction rope 3 relative to the horizontal plane and the lateral deflection angle of the traction rope 3 on the horizontal plane.

[0056] Specifically, the main control module 12 analyzes the target image acquired by the image acquisition device 5 to determine the pitch angle of the traction rope 3 relative to the horizontal plane and the lateral deflection angle of the traction rope 3 on the horizontal plane.

[0057] In this embodiment, to improve the accuracy of angle calculation and the repeatability of measurement results, a reference image is acquired and a standard coordinate system is established before measurement begins. This is used to unify the measurement reference, eliminate system installation errors, and provide a fixed reference for subsequent deviation calculation of the spatial attitude of the traction rope 3. Specifically, this includes: acquiring a reference image under ideal, unbiased conditions; extracting the original pixel coordinates of the trolley traction point, slider 24 traction point, or rope feature point in the reference image; establishing a standard three-dimensional rectangular coordinate system with the trolley traction point as the origin; and projecting the original pixel coordinates of the slider 24 traction point or rope feature point into the standard three-dimensional rectangular coordinate system to obtain the standard pixel coordinates of the slider 24 traction point or rope feature point.

[0058] Specifically, after the intelligent traction trolley 1, detection block 2, and traction rope 3 are placed at their corresponding detection points, before measurement begins, the monitoring personnel input a benchmark acquisition command on the human-machine interaction module 15 or a mobile terminal. The main control module 12 then controls the image acquisition device 5 to acquire at least one set of benchmark images. The benchmark images are taken under ideal conditions, i.e., the traction rope 3 is horizontally taut, without pitch or lateral deflection. At this time, the line connecting the trolley traction point and the slider 24 traction point is parallel to the preset movement direction of the intelligent traction trolley 1. The main control module 12 extracts features from the benchmark images, identifies the pixel positions of the trolley traction point, the slider 24 traction point, and the rope feature points, and determines the original pixel coordinates in the image coordinate system. Using the pixel position of the trolley traction point as the origin, the line connecting the trolley traction point and the slider 24 traction point or the trolley traction point and the corresponding rope feature point as the reference X-axis, the horizontal direction perpendicular to the X-axis and parallel to the contact surface to be measured as the Y-axis, and the vertical direction perpendicular to the contact surface to be measured and upward as the Z-axis, a standard three-dimensional rectangular coordinate system is established. The original pixel coordinates of the slider 24 traction point and the rope feature point are projected into the standard three-dimensional rectangular coordinate system to obtain the corresponding standard pixel coordinates, which are then stored in the non-volatile memory of the main control module 12 as a reference for subsequent angle calculations.

[0059] In the actual measurement process, the method for determining the spatial attitude parameters of the traction rope 3 in the target image specifically includes: using feature recognition technology to extract the traction point of the trolley, the traction point of the slider 24, or the rope feature point in the target image, and determining the current pixel coordinates of the traction point of the trolley, the traction point of the slider 24, or the rope feature point in the target image; projecting the current pixel coordinates into a standard three-dimensional rectangular coordinate system, and calculating the lateral deflection angle and the pitch angle relative to the horizontal plane of the traction rope 3 based on the deviation between the current pixel coordinates and the standard pixel coordinates.

[0060] Specifically, the main control module 12 uses feature recognition technology to perform feature point recognition processing on the real-time acquired target image, obtains the current pixel coordinates of the trolley traction point, slider 24 traction point, or rope feature point in the target image, projects the current pixel coordinates onto the established standard three-dimensional rectangular coordinate system, calculates the positional deviation between the current pixel coordinates and the pre-stored standard pixel coordinates, calculates the lateral deflection angle of the traction rope 3 on the horizontal plane based on the components of this deviation in the X and Y axes in the horizontal plane through the arctangent function, and calculates the pitch angle of the traction rope 3 relative to the horizontal plane based on the ratio of the component of this deviation in the vertical Z axis to the horizontal projection distance through the arctangent function.

[0061] In one feasible approach, the feature recognition technology employs a deep learning-based convolutional neural network model. This model is pre-trained with a large number of labeled samples and can accurately identify the traction points of the trolley, the traction points of the slider 24, and the rope feature points under different lighting conditions and background complexity. The main control module 12 inputs the target image into the trained feature recognition model, and the model outputs the pixel coordinates of each feature point in the image and its confidence score. The main control module 12 filters the feature point detection results with confidence scores higher than a preset threshold, removes outliers, and then uses the least squares method or Kalman filter algorithm to smooth the feature point trajectory to improve the stability of coordinate extraction.

[0062] For the identification of rope feature points, when multiple equally spaced fluorescent markers or reflective patches are set on the traction rope 3, the main control module 12 identifies the positional distribution of multiple rope feature points, fits the spatial linear equation of the traction rope 3 in the image, and then calculates the overall spatial attitude of the traction rope 3. The combined use of multiple feature points can effectively reduce the impact of single-point identification errors on angle calculation and improve the measurement accuracy and robustness of spatial attitude parameters.

[0063] Taking the traction points of the trolley and slider 24 identified in the target image as an example, when calculating the lateral deflection angle, the main control module 12 first obtains the pixel coordinates of the trolley and slider 24 in the target image at the current moment. These two pixel coordinates are then projected onto the XOY horizontal plane of the standard three-dimensional rectangular coordinate system to obtain the horizontal projection coordinates. The angle between the horizontal projection line and the standard X-axis reference direction is calculated; this angle is the lateral deflection angle of the traction rope 3 on the horizontal plane. That is, the lateral deflection angle is the arctangent of the ratio of the offset of the horizontal projection line in the Y-axis direction to the projection length in the X-axis direction. ,in, It is a lateral deflection angle. This represents the difference in coordinates between the current slider traction point and the standard slider traction point along the Y-axis. This represents the coordinate difference between the current slider traction point and the trolley traction point along the X-axis. When... When the value is zero, it indicates that the traction rope is perpendicular to the preset direction of movement in the horizontal plane, with a lateral deviation angle of 90 degrees or -90 degrees, the specific symbol depending on... The positive or negative sign is determined.

[0064] When calculating the pitch angle, the main control module also calculates the height difference between the two points in the vertical Z-axis direction based on the current pixel coordinates of the trolley traction point and the slider traction point. And the projected distance between the two points in the horizontal plane, that is, the projected length in the X-axis direction. Projected length along the Y-axis The sum of squares and the square root of the values. The pitch angle is equal to the arctangent of the ratio of the height difference to the horizontal projected distance, where the horizontal projected distance is... The pitch angle is When the slider traction point is higher than the trolley traction point, the pitch angle is positive, indicating that the traction rope 3 is tilted upwards; when the slider traction point is lower than the trolley traction point, the pitch angle is negative, indicating that the traction rope 3 is tilted downwards; when the two are at the same height, the pitch angle is zero, indicating that the traction rope 3 is in a horizontal state. When the rope feature point is located between the slider traction point and the trolley traction point, the main control module 12 can also calculate the spatial curve shape of the traction rope through a multi-point fitting method to more accurately describe the actual posture of the rope.

[0065] In one feasible approach, to eliminate the impact of image distortion on the accuracy of angle calculation, the main control module 12 first calibrates the image acquisition device 5 before establishing a standard three-dimensional rectangular coordinate system, obtains the camera's intrinsic parameter matrix and distortion coefficients, uses the calibration parameters to perform distortion correction on the reference image and target image, converts the corrected image coordinates into normalized image coordinates, and then uses the camera's extrinsic parameter matrix to transform the normalized image coordinates into the world coordinate system, thereby obtaining the positional relationship of feature points in the real three-dimensional space, further improving the accuracy of calculating the lateral tilt angle and pitch angle.

[0066] In another feasible approach, when the image acquisition device 5 employs a binocular stereo vision system, the main control module 12 directly calculates the three-dimensional spatial coordinates of feature points using the binocular parallax principle, without relying on a pre-established projection relationship between two-dimensional pixel coordinates and three-dimensional spatial coordinates. The binocular stereo vision system simultaneously acquires target images using two cameras. The main control module 12 performs feature point matching on the left and right images, calculates the three-dimensional spatial coordinates of each feature point based on the triangulation principle, and then directly determines the spatial attitude of the traction rope 3. This method effectively avoids projection errors caused by the lack of depth information in monocular vision systems and is particularly suitable for measurement scenarios where the traction rope 3 is long and the pitch angle is large.

[0067] During angle calculation, the main control module 12 also verifies the validity of the calculation results and removes outliers. Specifically, the main control module 12 compares the currently calculated lateral tilt and pitch angles with historical data sequences. If the difference between the current angle value and the angle value at adjacent times exceeds a preset reasonable change threshold, the data is considered to be potentially affected by image noise or feature recognition errors and is marked as suspicious data. For suspicious data, the main control module 12 uses a sliding window filtering algorithm or a median filtering algorithm to process it, replacing outliers with statistical values ​​of valid data within the window to ensure the continuity and stability of angle parameters. Simultaneously, the main control module 12 pre-stores reasonable value ranges for the lateral tilt and pitch angles; for example, the lateral tilt angle is limited to ±30°, and the pitch angle is limited to ±20°. Angle calculation results exceeding these ranges will be deemed invalid, and the system will trigger an alarm to prompt monitoring personnel to check the connection status of the traction rope 3 or the installation position of the image acquisition device 5.

[0068] S4 calculates the actual effective tension based on spatial attitude parameters and tension signal.

[0069] Specifically, by analyzing the spatial attitude parameters and tension signal of the traction rope 3 in the target image, the apparent tension measured by the tension sensor 4 is converted into an effective tension component along the tangential direction of the contact surface, so as to eliminate the systematic error introduced by the spatial attitude of the traction rope 3 deviating from the ideal horizontal state.

[0070] In this embodiment, the calculation of the actual effective tension is based on the principle of mechanical vector decomposition, specifically including: performing vector decomposition on the tension signal according to the lateral deflection angle and pitch angle to obtain the actual effective tension of the traction rope 3 in the horizontal direction. The tension sensor 4 measures the tension inside the traction rope 3, which acts along the axial direction of the traction rope 3. Since the traction rope 3 has lateral deflection angle and pitch angle, the tension can be decomposed into a horizontal traction component along the preset movement direction of the intelligent traction trolley 1, a horizontal lateral component perpendicular to the preset movement direction, and a vertical lifting component. Among them, only the horizontal traction component along the preset movement direction is the effective tension that is actually used to overcome the friction between the detection block 2 and the contact surface to be measured; the other two components are sources of system error and need to be eliminated in the calculation.

[0071] In the actual calculation of the maximum static friction and sliding friction, the maximum static friction and sliding friction need to be determined first. The specific calculation process includes: determining the magnitude of the tension on the traction rope 3 and its time-varying characteristics based on the tension signal; identifying the critical moment when the detection block 2 changes from a static state to a sliding state; extracting the tension value corresponding to the critical moment as the maximum static friction; and extracting the average value of the tension signal after the critical moment during a preset period of stable fluctuation as the sliding friction; performing vector decomposition of the maximum static friction based on the lateral deflection angle and pitch angle to obtain the first actual effective tension of the traction rope 3 in the horizontal direction; and performing appropriate decomposition of the sliding friction based on the average lateral deflection angle and average pitch angle within the preset time to obtain the second actual effective tension of the traction rope 3 in the horizontal direction.

[0072] Specifically, the main control module 12 performs time-domain analysis on the tension signal acquired in real time by the tension sensor 4, monitoring the trend of tension value change over time. In the initial measurement stage, the detection block 2 is stationary, and the tension gradually increases to overcome static friction. When the tension reaches a certain peak, it suddenly drops. This peak point is the critical moment when the detection block 2 begins to slide. The main control module 12 automatically identifies this critical moment and records the corresponding tension peak as the apparent measurement value of the maximum static friction. After the critical moment, the detection block 2 enters a uniform sliding state, and the tension signal exhibits relatively stable small fluctuations. The main control module 12 extracts the tension data within a preset time period (e.g., 2 to 5 seconds) after the critical moment and calculates its arithmetic mean as the apparent measurement value of the sliding friction.

[0073] After obtaining the apparent values ​​of the maximum static friction and sliding friction, the main control module 12 performs vector decomposition by combining the lateral deflection and pitch angles calculated at the same time. For the maximum static friction, the lateral deflection and pitch angles at the critical moment are used to calculate the effective horizontal tension component through trigonometric relationships. Let the apparent value of the maximum static friction measured by the tension sensor 4 be... The lateral deflection angle is The pitch angle is Then the angle between the axis of the traction rope 3 and the horizontal plane is . The angle between the projection of the traction rope 3 on the horizontal plane and the preset direction of movement is... .pull The size of the projection on the horizontal plane is The horizontal projection is then decomposed along the preset direction of movement to obtain the first actual effective tensile force. .in, The term eliminates the influence of the vertical component caused by the pitch angle. The term eliminates the influence of the lateral component caused by the lateral deflection angle, and the final result is... This refers to the effective horizontal traction force used purely to overcome the static friction of the contact surface.

[0074] For sliding friction, since the attitude of the traction rope 3 may undergo slight dynamic changes during the sliding of the detection block 2, the main control module 12 uses the average lateral deflection angle and average pitch angle within a preset time period for vector decomposition to improve calculation stability. After determining that the detection slider 24 has entered a uniform sliding state based on the tension signal, a preset time period with stable tension and smooth movement is selected as the data acquisition interval. Within this preset time period, multiple sets of lateral deflection angle and pitch angle data of the traction rope 3 are continuously acquired. The arithmetic mean of the acquired multiple sets of lateral deflection angles is calculated to obtain the average lateral deflection angle within the preset time period; the arithmetic mean of the acquired multiple sets of pitch angles is calculated to obtain the average pitch angle within the preset time period. By averaging the angle data within the preset time period, random errors caused by instantaneous jitter, image noise, and brief disturbances can be effectively eliminated, making the obtained spatial attitude parameters more stable and representative, thereby improving the accuracy of subsequent tension compensation and friction coefficient calculation.

[0075] Let the apparent average value of sliding friction be The average lateral deflection angle is The average pitch angle is Then the second actual effective tensile force Using average values ​​instead of instantaneous values ​​can effectively smooth out the angle measurement noise caused by rope shaking or vibration due to the movement of the trolley, ensuring the representativeness and repeatability of the sliding friction calculation results.

[0076] In one feasible approach, when multiple rope feature points are set on the traction rope 3, the main control module 12 can also calculate the spatial curve shape of the traction rope 3 segment by segment based on the local attitude differences at each feature point, and then use an integration method to calculate the tension transmission loss distributed along the rope length. For example, if the traction rope 3 has obvious sag or bend, the pitch angle and lateral deflection angle at each feature point are not consistent. The main control module 12 divides the rope into several micro-segments, calculates the local attitude angle and the corresponding effective tension component for each micro-segment, and then integrates along the rope length direction to obtain the true effective tension at the end of the detection block 2, so as to compensate for the tension attenuation caused by the rope's own weight or flexible deformation, and further improve the accuracy of the friction coefficient calculation.

[0077] In summary, the formula for calculating the actual effective tensile force is: This formula shows that when the pitch angle... and lateral deflection When both are zero, that is, when the traction rope 3 is in an ideal horizontal taut state and completely consistent with the preset direction of movement, the effective tension is... Equal to apparent tensile force At this point, no correction is needed. As the pitch or yaw angle increases, the cosine function value decreases, and the ratio of effective tension to apparent tension decreases. If the original tension signal is not corrected for attitude, the effective tension acting on the friction measurement will be systematically overestimated, leading to a significant deviation in the calculated friction coefficient.

[0078] S5 calculates the maximum static friction coefficient and dynamic friction coefficient based on the actual effective tensile force, the weight of the detection block, and the weight of the connector, and displays the maximum static friction coefficient and dynamic friction coefficient through the human-machine module.

[0079] Specifically, after calculating the actual effective tensile force, the main control module 12, in conjunction with the total gravitational load of the detection block 2 and the connecting piece 6, calculates the maximum static friction coefficient and dynamic friction coefficient according to Coulomb's law of friction. That is, the formula for calculating the friction coefficient is: The combined weight of the detection block 2 and the connector 6 constitutes the total normal force acting perpendicularly on the contact surface to be tested. Let the mass of detection block 2 be... The equivalent mass of connector 6 is The acceleration due to gravity is Then the total positive pressure The direction of the normal force is perpendicular to the contact surface to be tested and downwards, and is perpendicular to the horizontal plane where the actual effective tensile force is located, forming a complete tribological analysis system.

[0080] The maximum static friction coefficient is calculated based on the first actual effective tensile force, the weight of the detection block 2, and the weight of the connecting piece 6. The main control module 12 then calculates the first actual effective tensile force F1 and the total normal force. Substituting into Coulomb's law of friction, we obtain the maximum coefficient of static friction. The calculation formula is: The maximum static friction coefficient characterizes the frictional properties between the test block 2 and the contact surface under test during the instantaneous transition from a static state to a sliding state, reflecting the maximum anti-slip resistance of the contact interface. The maximum static friction coefficient is usually greater than the dynamic friction coefficient, and its value is closely related to factors such as the surface roughness, cleanliness, temperature, and presence of lubricating media of the contact materials.

[0081] The dynamic friction coefficient is calculated based on the second actual effective tensile force, the weight of the detection block 2, and the weight of the connecting piece 6. The main control module 12 then calculates the second actual effective tensile force... With total positive pressure Substituting into Coulomb's law of friction, we obtain the coefficient of kinetic friction. The calculation formula is: The dynamic friction coefficient characterizes the frictional properties of the test block 2 when it is sliding at a constant speed on the contact surface to be tested. It is usually more stable than the maximum static friction coefficient and is less affected by the sliding speed.

[0082] In actual measurements, the main control module 12 calculates... Then, the calculated maximum coefficient of friction can be compared with the preset material property database. If the calculation result exceeds the typical range of the material under standard conditions, the operator will be prompted to check the contact surface condition or the working conditions of the measurement system. The main control module can also further analyze the variation law of the dynamic friction coefficient with sliding speed and sliding distance, and evaluate the frictional stability of the contact surface and the dynamic friction characteristics such as whether there is stick-slip phenomenon.

[0083] In one feasible approach, to obtain more statistically significant friction coefficient measurement results, the main control module 12 supports a multiple-repetition measurement mode. The operator can set the number of repetitions, n, through the human-machine interface. The main control module 12 automatically controls the intelligent traction trolley 1 to complete n independent friction measurement cycles. Each cycle re-executes the complete process of static loading, critical slip identification, uniform slip maintenance, and data acquisition. After completing all measurement cycles, the main control module 12 performs statistical analysis on the maximum static and dynamic friction coefficients obtained from the n measurements, calculating their respective arithmetic mean as the final measurement result. It also calculates the standard deviation and coefficient of variation to assess the dispersion of the measurement data. If the coefficient of variation exceeds a preset threshold (e.g., 5%), it indicates poor measurement repeatability, suggesting checking the contact surface uniformity or system stability. The statistical analysis results, along with the single measurement data, are presented through the human-machine interface, allowing the operator to fully understand the measurement quality.

[0084] After calculating the maximum static friction coefficient and dynamic friction coefficient, the results are displayed through the human-computer interaction module 15. The display can be done on the human-computer interaction module 15, or by broadcasting the calculation results via voice, or by pushing the calculation results to the mobile terminal of the monitoring personnel.

[0085] The human-machine interface (HMI) serves as the core interface for interaction between the system and the operator, integrating a high-definition touchscreen display, data storage unit, and network communication interface. During measurement, the HMI displays the tension curve, angle change curve, currently calculated friction coefficient value, and system operating status indicator lights in real time. After measurement, it automatically generates a complete measurement report including measurement time, environmental parameters, device number, single measurement data, and statistical results, supporting various data management methods such as local storage, USB flash drive export, or cloud upload. Operators can also set measurement parameters through the HMI, including preset movement speed, target tension range, number of repeated measurements, filter algorithm selection, and alarm threshold adjustment, to meet personalized measurement needs in different application scenarios.

[0086] In one feasible implementation, the system is also equipped with an environmental parameter monitoring module to synchronously collect temperature, humidity, and air pressure data during the measurement process. The main control module 12 associates and stores the environmental parameters with the friction coefficient measurement results and establishes an environmental compensation model. When the measurement environment deviates from standard conditions, the main control module 12 can compensate for the friction coefficient calculation results according to the pre-stored environmental impact correction coefficient, eliminating or reducing the interference of environmental factors on the measurement accuracy and ensuring the comparability of measurement results at different times and locations.

[0087] In summary, the intelligent friction coefficient measurement system provided in this application acquires the spatial attitude parameters of the traction rope 3 in real time through image recognition technology, and accurately corrects the tension signal by combining the principle of mechanical vector decomposition, effectively eliminating the system error introduced by the non-ideal attitude of the traction rope 3. It employs multiple data quality control methods, such as sliding window filtering, median filtering, angle rationality verification, and statistical analysis of repeated measurements, to ensure the accuracy and reliability of the measurement results. The integrated environmental parameter monitoring and compensation function expands the system's applicability. The fully automated measurement and intelligent data processing significantly improve the efficiency and standardization of friction coefficient measurement, and it can be widely applied in fields such as material surface performance testing, engineering quality inspection, and scientific research experiments.

[0088] It should be noted that the methods and system embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the system embodiments, which will not be repeated here.

[0089] This application also discloses an electronic device 500. (See reference...) Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device 500 disclosed in an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0090] The communication bus 502 is used to enable communication between these components.

[0091] The user interface 503 may include a display screen and a camera. Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0092] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0093] The processor 501 may include one or more processing cores. The processor 501 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 505, and by calling data stored in memory 505. Optionally, the processor 501 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 501 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 501 and may be implemented as a separate chip.

[0094] The memory 505 may include random access memory (RAM) or read-only memory. Optionally, the memory 505 may include a non-transitory computer-readable storage medium. The memory 505 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 505 may also be at least one storage device located remotely from the aforementioned processor 501. (Refer to...) Figure 8 The memory 505, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for an intelligent friction coefficient measurement system.

[0095] exist Figure 8 In the illustrated electronic device 500, the user interface 503 is mainly used to provide an input interface for the user and acquire user input data; while the processor 501 can be used to call the application program of the intelligent friction coefficient measurement system stored in the memory 505. When executed by one or more processors 501, the electronic device 500 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0096] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0097] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0098] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0099] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0100] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 505 and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory 505 includes various media capable of storing program code, such as a USB flash drive, external hard drive, magnetic disk, or optical disk.

[0101] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0102] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A coefficient of friction intelligent measurement system, characterized in that, The system includes an intelligent traction trolley (1), a detection block of known weight (2), a traction rope (3), a tension sensor (4), and an image acquisition device (5). The intelligent traction trolley (1) includes a vehicle body (11) and a drive motor (13), a human-machine interaction module (15), and a main control module (12) installed in the vehicle body (11). The drive motor (13), the human-machine interaction module (15), the tension sensor (4), and the image acquisition device (5) are all electrically connected to the main control module (12). The bottom of the vehicle body (11) is provided with a set of walking wheels (14), and the drive motor (13) is connected to the set of walking wheels (14) for driving the vehicle body (11) to move at a constant speed along a preset path; The tension sensor (4) is set on the intelligent traction trolley (1) to form the traction point of the trolley. The tension sensor (4) is used to measure the tension of the traction rope (3) in real time to generate a tension signal and transmit it to the main control module (12). The detection block (2) is placed on the contact surface to be tested and is located behind the intelligent traction trolley (1) in the forward direction. A connector (6) of known weight is provided on the detection block (2) to form a traction point of the slider (24). One end of the traction rope (3) is connected to the tension sensor (4), and the other end of the traction rope (3) is connected to the connector (6). At least one mark is provided on the traction rope (3) to form a rope feature point. The image acquisition device (5) is located at the rear end of the top of the intelligent traction vehicle (1), and the acquisition direction of the image acquisition device (5) is towards the tail of the intelligent traction vehicle (1). It is used to acquire target images containing the traction point of the vehicle, the traction point of the slider (24) and / or the feature point of the rope and transmit them to the main control module (12). The main control module (12) is electrically connected to the drive motor (13) and is used to control the running speed of the intelligent traction trolley (1); it is also used to receive the target image and the tension signal, and determine the spatial attitude parameters of the traction rope (3) according to the target image; it is also used to calculate the actual effective tension according to the spatial attitude parameters and the tension signal, and calculate the maximum static friction coefficient and dynamic friction coefficient according to the actual effective tension, the weight of the detection block (2) and the weight of the connector (6), and display the calculated maximum static friction coefficient and dynamic friction coefficient through the human-machine interaction module (15).

2. The intelligent friction coefficient measurement system according to claim 1, characterized in that, The central axis of the connector (6) coincides with the central horizontal axis of the detection block (2), and the traction point of the trolley and the traction point of the slider (24) are at the same horizontal height.

3. The intelligent friction coefficient measurement system according to claim 1, characterized in that, The detection block (2) includes a standard block (21), a mounting plate (22), a positioning component (23), and at least three sliders (24). The positioning component (23) is disposed on the top surface of the mounting plate (22). The bottom surface of the standard block (21) is provided with a groove (211) that matches the mounting plate (22). The mounting plate (22) is provided with a positioning hole (212) that matches the positioning component (23) at the groove (211). The standard block (21) is embedded in the groove (211) and is detachably connected to the mounting plate (22) through the positioning component (23). Each slider (24) is symmetrically arranged on the bottom surface of the mounting plate (22) with the central axis of the mounting plate (22) as the axis of symmetry to form a support protrusion. Each slider (24) is in direct contact with the contact surface to be tested. The center of the side wall of the standard block (21) is provided with a mounting hole for installing the connector (6).

4. The intelligent friction coefficient measurement system according to claim 1, characterized in that, The system also includes a transfer mechanism (7), which includes a base (71), a guide rod (72), and a guide assembly. The base (71) has a plug hole that matches the guide rod (72). The guide assembly includes a guide wheel (73), a wheel frame (74), and an adjusting bolt (75). The guide wheel (73) is rotatably mounted on the wheel frame (74). The outer circumference of the guide wheel (73) has a guide groove (731) that matches the diameter of the traction rope (3). The wheel frame (74) is connected to the guide rod (72) through the adjusting bolt (75). The central axis of the guide wheel (73) is perpendicular to the line connecting the traction point of the trolley and the traction point of the slider (24).

5. A smart method for measuring the coefficient of friction, characterized in that, The main control module (12) applied to the intelligent friction coefficient measurement system according to any one of claims 1-4, the method comprising: Control the intelligent traction trolley (1) to move at a constant speed along a preset path so as to drive the detection block (2) to move synchronously through the traction rope (3); The tension signal is generated by the tension sensor (4) collecting the tension of the traction rope (3) in real time, and the target image is collected by the image acquisition device (5) in real time. The target image includes the traction point of the trolley, the traction point of the slider (24) of the detection block (2) and / or the rope feature points on the traction rope (3). The spatial attitude parameters of the traction rope (3) are determined based on the target image. The spatial attitude parameters include the pitch angle of the traction rope (3) relative to the horizontal plane and the lateral deflection angle of the traction rope (3) on the horizontal plane. The actual effective tensile force is calculated based on the spatial attitude parameters and the tensile signal. Based on the actual effective tensile force, the weight of the detection block (2) and the weight of the connector (6), the maximum static friction coefficient and the dynamic friction coefficient are calculated and displayed through the human-machine module.

6. The intelligent friction coefficient measurement method according to claim 5, characterized in that, Before the intelligent traction trolley (1) moves at a constant speed along a preset path, the following steps are also included: Acquire a reference image under ideal, unbiased conditions, and extract the original pixel coordinates of the trolley traction point, slider (24) traction point, or rope feature point in the reference image; A standard three-dimensional rectangular coordinate system is established with the trolley traction point as the origin; The original pixel coordinates of the slider (24) traction point or rope feature point are projected into the standard three-dimensional rectangular coordinate system to obtain the standard pixel coordinates of the slider (24) traction point or rope feature point; The determination of the spatial attitude parameters of the traction rope (3) based on the target image includes: The feature recognition technology is used to extract the traction point of the trolley, the traction point of the slider (24) or the rope feature point in the target image, and the current pixel coordinates of the traction point of the trolley, the traction point of the slider (24) or the rope feature point in the target image are determined; The current pixel coordinates are projected into a standard three-dimensional rectangular coordinate system. Based on the deviation between the current pixel coordinates and the standard pixel coordinates, the lateral deflection angle of the traction rope (3) in the horizontal plane and the pitch angle relative to the horizontal plane are calculated. The calculation of the actual effective tensile force based on the spatial attitude parameters and the tensile signal includes: The tension signal is vector decomposed based on the lateral deviation angle and pitch angle to obtain the actual effective tension of the traction rope (3) in the horizontal direction.

7. The intelligent friction coefficient measurement method according to claim 6, characterized in that, The vector decomposition of the tension signal based on the lateral deflection and pitch angles, yielding the actual effective tension of the traction rope (3) in the horizontal direction, includes: Based on the tension signal, determine the magnitude of the tension on the traction rope (3) and its change characteristics over time, identify the critical moment when the detection block (2) changes from a static state to a sliding state, extract the tension value corresponding to the critical moment as the maximum static friction force, and extract the average value of the tension signal after the critical moment when it is in a stable fluctuation for a preset time as the sliding friction force. The maximum static friction force is vector decomposed based on the lateral deflection angle and pitch angle to obtain the first actual effective tension of the traction rope (3) in the horizontal direction; The sliding friction force is appropriately decomposed based on the average lateral deviation angle and average pitch angle within the preset time period to obtain the second actual effective tension of the traction rope (3) in the horizontal direction. The calculation of the maximum static friction coefficient and dynamic friction coefficient based on the actual effective tensile force, the weight of the detection block (2), and the weight of the connector (6) includes: The maximum static friction coefficient is calculated based on the first actual effective tensile force, the weight of the detection block (2) and the weight of the connector (6); The coefficient of dynamic friction is calculated based on the second actual effective tensile force, the weight of the detection block (2), and the weight of the connector (6).

8. The intelligent friction coefficient measurement method according to claim 5, characterized in that, The control of the intelligent traction trolley (1) to move at a constant speed along a preset path includes: Before the detection block (2) reaches the maximum static friction, control the intelligent traction trolley (1) to move at a first preset speed; After the detection block (2) reaches the maximum static friction, the intelligent traction trolley (1) is controlled to move at a second preset speed, wherein the first preset speed is different from the second preset speed.

9. An electronic device, characterized in that, The device includes a processor (501), a memory (505), a user interface (503), and a network interface (504). The memory (505) is used to store instructions. The user interface (503) and the network interface (504) are used to communicate with other devices. The processor (501) is used to execute the instructions stored in the memory (505) to cause the electronic device (500) to perform any of the methods described in claims 5-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform any one of the methods described in claims 5-8.

Images