A magnetic coupling toothed belt runout detection method, device, equipment and medium

Abstract

The method comprises the following steps: collecting first motion data of a conveying belt based on master-slave double detection modules on both sides of a guide groove; determining displacement gradient and fluctuation intensity of the conveying belt according to magnetic grating data and Hall array data in the first motion data; determining second motion data of the conveying belt within a predicted time length according to the displacement gradient and the fluctuation intensity; determining guide wheel adjustment attributes related to the conveying belt based on a relationship between the second motion data and a preset offset threshold, and adjusting the offset of the conveying belt based on the guide wheel adjustment attributes. The technical scheme of the embodiment of the present disclosure adjusts the offset of the conveying belt based on the magnetic grating data collected by the master detection module and the Hall array data collected by the slave detection module, realizes early prediction of the offset trend, effectively avoids wear and tear and jamming and other faults caused by the offset of the conveying belt, and achieves the effect of improving the running stability of the equipment.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus, device, and medium for detecting misalignment of a magnetically coupled toothed belt. Background Technology

[0002] During long-term operation, toothed belts are prone to lateral deviation due to installation errors, uneven loads, or belt wear, which can lead to belt jamming, tearing, or even equipment shutdown.

[0003] Currently, the main method for detecting misalignment of toothed belts is laser detection technology. However, laser detection technology has high requirements for the operating environment, especially in dusty and impurity-rich environments. Dust easily adheres to the laser emitter and receiver, obstructing the laser signal and leading to a significant decrease in detection accuracy and detection failure. Summary of the Invention

[0004] This disclosure provides a magnetically coupled toothed belt misalignment detection method, device, equipment, and medium to enable early prediction of misalignment trends, effectively avoid wear and jamming caused by conveyor belt misalignment, and improve equipment operational stability.

[0005] In a first aspect, embodiments of this disclosure provide a method for detecting misalignment of a magnetically coupled toothed belt, the method comprising: Based on the master-slave dual detection modules deployed on both sides of the guide groove, the first motion data of the conveyor belt located in the guide groove is collected, wherein the first motion data includes at least the magnetic scale data collected by the master detection module and the Hall array data collected by the slave detection module. Based on the magnetic scale data and the Hall array data in the first motion data, the displacement gradient and fluctuation intensity of the conveyor belt are determined; Based on the displacement gradient and the fluctuation intensity, determine the second motion data of the conveyor belt within the predicted time period; Based on the relationship between the second motion data and the preset offset threshold, the guide wheel adjustment attribute related to the conveyor belt is determined, so as to adjust the offset of the conveyor belt based on the guide wheel adjustment attribute.

[0006] Secondly, embodiments of the present invention also provide a magnetically coupled toothed belt misalignment detection device, the device comprising: The first motion data acquisition module is used to acquire first motion data of the conveyor belt located in the guide groove based on the master-slave dual detection modules deployed on both sides of the guide groove. The first motion data includes at least magnetic scale data acquired by the master detection module and Hall array data acquired by the slave detection module. The wave intensity determination module is used to determine the displacement gradient and wave intensity of the conveyor belt based on the magnetic scale data and the Hall array data in the first motion data. The second motion data determination module is used to determine the second motion data of the conveyor belt within the predicted time period based on the displacement gradient and the fluctuation intensity. The offset adjustment module is used to determine the guide wheel adjustment attributes related to the conveyor belt based on the relationship between the second motion data and the preset offset threshold, so as to adjust the offset of the conveyor belt based on the guide wheel adjustment attributes.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the magnetically coupled toothed belt misalignment detection method as described in any embodiment of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the magnetically coupled toothed belt misalignment detection method as described in any embodiment of the present invention.

[0009] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program, characterized in that, when executed by a processor, the computer program implements the magnetically coupled toothed belt misalignment detection method as described in any embodiment of the present invention.

[0010] The technical solution of this disclosure embodiment is based on master-slave dual detection modules deployed on both sides of the guide groove to collect first motion data of the conveyor belt located in the guide groove. The first motion data includes at least magnetic scale data collected by the master detection module and Hall array data collected by the slave detection module. Then, based on the magnetic scale data and the Hall array data in the first motion data, the displacement gradient and fluctuation intensity of the conveyor belt are determined. Further, based on the displacement gradient and fluctuation intensity, second motion data of the conveyor belt within a predicted time period is determined. Finally, based on the relationship between the second motion data and a preset offset threshold, the guide wheel adjustment attributes related to the conveyor belt are determined, and the conveyor belt is offset adjusted based on the guide wheel adjustment attributes. This solves the problem of significantly reduced detection accuracy and detection failure when using laser detection technology for belt misalignment detection in the prior art. This disclosure embodiment realizes the offset adjustment of the conveyor belt based on the magnetic scale data collected by the master detection module and the Hall array data collected by the slave detection module, achieving early prediction of misalignment trends, effectively avoiding wear and jamming caused by conveyor belt misalignment, and improving the operational stability of the equipment. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of exemplary embodiments of the present invention, the accompanying drawings used in describing the embodiments are briefly introduced below. Obviously, the accompanying drawings described are only a portion of the drawings of the embodiments to be described in this invention, and not all of the drawings. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0012] Figure 1 This is a schematic flowchart of a magnetically coupled toothed belt misalignment detection method provided in an embodiment of this disclosure; Figure 2 This is a schematic flowchart of a magnetically coupled toothed belt misalignment detection method provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of the structure of a magnetically coupled toothed belt misalignment detection device provided in an embodiment of this disclosure; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0013] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0014] Before introducing the technical solutions provided in the embodiments of this disclosure, the application scenarios can be illustrated by example. The technical solutions provided in the embodiments of this disclosure can be applied to scenarios involving the detection of toothed belt misalignment. For example, the lifting mechanism of a high-bay warehouse stacker crane often uses synchronous toothed belt drive, which is usually wrapped with metal steel wire to improve tensile strength. Based on the technical solutions provided in the embodiments of this disclosure, the magnetic coupling generated by the magnetic field and the steel wire inside the toothed belt is used to detect whether the toothed belt is misaligned laterally in real time.

[0015] It should be noted that the toothed belt contains metal wires. When a constant magnetic field is applied externally, the wires are magnetized, forming a stable and regular magnetic signal distribution. When the conveyor belt moves laterally, i.e., it deviates from its designated path, the magnetic field distribution will shift and become distorted. Based on the technical solution of this disclosure embodiment, the conveyor belt offset adjustment is achieved based on the magnetic scale data collected by the main detection module and the Hall array data collected from the detection module. This enables early prediction of the deviation trend, effectively avoiding wear and jamming caused by conveyor belt deviation, and ultimately improving the stability of equipment operation.

[0016] Example 1 Figure 1 This is a flowchart illustrating a magnetically coupled toothed belt misalignment detection method provided in this embodiment. This embodiment is applicable to the situation of detecting misalignment of toothed belts. The method can be executed by a magnetically coupled toothed belt misalignment detection device, which can be implemented in the form of software and / or hardware. The hardware can be a mobile electronic device, which can execute the magnetically coupled toothed belt misalignment detection method provided in this technical solution.

[0017] like Figure 1 As shown, the method includes: S110: Based on the master-slave dual detection modules deployed on both sides of the guide groove, the first motion data of the conveyor belt located in the guide groove is collected.

[0018] The first motion data includes at least the magnetic scale data collected by the main detection module and the Hall array data collected by the detection module.

[0019] It should be noted that the guide groove refers to the guiding structure of the conveyor belt during operation, equivalent to a track. The guide groove is used to limit the running trajectory of the conveyor belt and prevent its irregular deviation. Furthermore, the guide groove is the installation reference for the master-slave dual detection module. The master-slave dual detection module consists of two cooperating detection units, including hardware and matching acquisition circuitry. The master and slave dual detection modules have clearly defined roles and are mutually calibrated, which can improve detection accuracy and reliability. The core component of the master detection module is a magnetic scale, responsible for acquiring magnetic scale data, i.e., absolute position data. The magnetic scale data acquired by the master detection module has high accuracy and strong stability, and can be used as the detection reference. The core component of the slave detection module is a Hall array, responsible for acquiring Hall array data, i.e., magnetic signal data related to relative displacement. The slave detection module has a fast response and can capture subtle changes, assisting the master detection module in achieving real-time tracking.

[0020] It should be noted that, in this embodiment of the invention, the conveyor belt used for deviation detection can be a toothed belt. The toothed belt contains a steel wire reinforcement layer and can be used in the lifting mechanism of a high-bay warehouse stacker crane. The first motion data refers to the real-time running status data of the conveyor belt, primarily consisting of magnetic scale data and Hall array data. The magnetic scale data is collected by the magnetic scale in the main detection module, representing the absolute lateral displacement of the conveyor belt relative to a reference position. For example, the magnetic scale data can show how many millimeters the conveyor belt has deviated from the reference position. The Hall array data is the magnetic signal data of the steel wire layer inside the conveyor belt, collected from a Hall array composed of multiple Hall sensors in the detection module. The steel wire generates a specific magnetic signal in a magnetic field, and this magnetic signal changes when the conveyor belt deviates from its intended path.

[0021] It should also be noted that the magnetization parameters of the steel wires in the conveyor belt can be analyzed through finite element simulation optimization, and the optimal magnetization intensity is determined to be 0.5-1.2T. This allows the steel wires to generate a stable and uniform magnetic field signal without reducing the flexibility of the conveyor belt, providing a reliable magnetic field basis for magnetic scale detection and Hall array detection.

[0022] Specifically, two main detection modules and a slave detection module, with different functions but working together, are installed on the left and right sides of the guide trough where the conveyor belt is located, i.e., the guide trough during conveyor belt operation. The main and slave dual detection modules collect the initial motion data of the conveyor belt during operation. The two most critical types of data in the initial motion data are the magnetic scale data from the main detection module and the Hall array data from the slave detection module, respectively.

[0023] S120. Based on the magnetic scale data and Hall array data in the first motion data, determine the displacement gradient and fluctuation intensity of the conveyor belt.

[0024] The displacement gradient is a physical quantity reflecting the speed and trend of conveyor belt deviation. A positive displacement gradient indicates that the conveyor belt is accelerating outwards; a negative displacement gradient indicates that the conveyor belt is decelerating or deviating in the opposite direction. The wave intensity is a physical quantity reflecting the uniformity of the magnetic signal distribution within the steel wire layer of the conveyor belt, that is, reflecting the dispersion of the magnetic signals collected by multiple Hall sensors. Before deviation occurs, the magnetic signal becomes non-uniform, and the wave intensity increases suddenly; therefore, the wave intensity is an early warning signal for deviation.

[0025] Specifically, after collecting the initial motion data of the conveyor belt located in the guide trough, the collected magnetic scale data and Hall array data are processed and calculated to obtain two characteristic quantities that reflect the conveyor belt's deviation state: displacement gradient and fluctuation intensity. Displacement gradient and fluctuation intensity are key to determining the deviation trend and achieving prediction.

[0026] S130. Based on the displacement gradient and wave intensity, determine the second motion data of the conveyor belt within the predicted time period.

[0027] The prediction duration is a pre-set timeframe used to predict the future deviation of the conveyor belt. For example, the prediction duration could be 3 seconds. Setting the prediction duration allows for advance prediction of deviation trends, preventing the deviation from worsening. The second motion data is the predicted operating status data of the conveyor belt within the predicted future timeframe. The second motion data includes the predicted displacement offset, representing how many millimeters it will deviate from the baseline at a future moment, and the predicted displacement gradient, representing the future speed trend of the deviation. The second motion data is the core basis for subsequent judgments on whether and how to correct the deviation.

[0028] Optionally, the second motion data of the conveyor belt within the prediction time can be determined based on a pre-created discrete state-space prediction model and the optimal solution of each parameter in the pre-determined discrete state-space prediction model.

[0029] The second motion data includes at least the predicted displacement offset and predicted displacement gradient of the conveyor belt within the predicted time period.

[0030] It should be noted that the pre-created discrete state-space prediction model is a mathematical model established through simulation and experimentation before actual detection. This pre-created model is specifically designed to describe and predict the misalignment state of the toothed conveyor belt, providing a mathematical formula to extrapolate future states based on current conditions. Features such as displacement gradient, fluctuation intensity, and lateral offset, which can be calculated in real time, are input into the discrete state-space prediction model. Lateral offset refers to the distance the conveyor belt deviates to the left or right from its ideal, centered position, without deviation. The optimal solution for each parameter in the discrete state-space prediction model is the best value for all parameters determined through experimentation or identification. The optimal solution for each parameter is a set of fixed values ​​with the smallest error and the most accurate prediction, obtained after fitting or identifying a large amount of data.

[0031] By substituting the displacement gradient, wave intensity, lateral offset, and the optimal solution for each parameter into the discrete state-space prediction model, the second motion data of the conveyor belt within the prediction time can be calculated. For example, the formula for determining the second motion data of the conveyor belt within the prediction time can be: ; in, The current moment; For the next moment; This represents the horizontal offset at the current moment. This is the predicted displacement offset for the next moment; This represents the displacement gradient at the current moment. This is the predicted displacement gradient for the next time step; The fluctuation intensity at the current moment; , , , , as well as These are the parameters in the discrete space prediction model. This indicates the influence of the lateral offset at the current moment on the predicted displacement offset at the next moment; This indicates the influence of the current displacement gradient on the predicted displacement offset at the next time step; This indicates the influence of the fluctuation intensity at the current moment on the predicted displacement offset at the next moment; This indicates the influence of the lateral offset at the current moment on the predicted displacement gradient at the next moment; This indicates the influence of the current displacement gradient on the predicted displacement gradient at the next time step; This indicates the influence of the fluctuation intensity at the current moment on the predicted displacement gradient at the next moment; and This refers to pre-set process noise or measurement error.

[0032] Among them, the predicted displacement offset can predict how many millimeters the conveyor belt will deviate from the center at a future moment. The predicted displacement gradient can predict the future speed and trend of deviation, whether it is accelerating or decelerating.

[0033] In this embodiment, a first warning message is generated when the difference between the predicted displacement offset and the preset offset threshold is within a first range; a second warning message is generated when the difference between the predicted displacement offset and the preset offset threshold is within a second range; and a third warning message is generated when the difference between the predicted displacement offset and the preset offset threshold is within a third range.

[0034] It should be noted that the predicted displacement offset is the distance the conveyor belt will deviate from its center position within a certain future period, determined by a discrete state-space prediction model. The preset offset threshold is a pre-set critical value used to determine whether the deviation is dangerous. Based on the predicted displacement offset and the preset offset threshold, the difference between the two can be determined. The first, second, and third ranges are three intervals divided according to the severity of the deviation, from minor to severe, corresponding to slight deviation, deviation requiring correction, and dangerous deviation, respectively. When the difference between the predicted displacement offset and the preset offset threshold is within the first range, it indicates a small deviation, close to the threshold but not yet dangerous. When the difference is within the second range, it indicates a large deviation, reaching a level requiring automatic adjustment. When the difference is within the third range, the deviation is extremely large, and continued operation will damage the equipment.

[0035] It should also be noted that the warning information is based on the difference between the predicted displacement offset and the preset offset threshold, outputting different levels of prompts or actions. The first, second, and third warning information are graded warning commands generated according to different deviation levels, used for prompting alarms, automatic correction, and emergency shutdown protection, respectively. The first warning information can be an audible and visual prompt and logging. The second warning information can initiate automatic correction. The third warning information can be emergency braking and shutdown protection.

[0036] Specifically, based on a pre-created discrete state-space prediction model, the measured displacement gradient and wave intensity are substituted into the model to predict how much and how fast the conveyor belt will deviate in the future.

[0037] S140. Based on the relationship between the second motion data and the preset offset threshold, determine the guide wheel adjustment attributes related to the conveyor belt, so as to adjust the offset of the conveyor belt based on the guide wheel adjustment attributes.

[0038] It should be noted that the guide rollers are components that support the conveyor belt and control its running direction. By adjusting the angle or position of the guide rollers, the running trajectory of the conveyor belt can be changed, thereby achieving deviation correction. The guide roller adjustment attributes are the specific parameters that need to be adjusted for the guide rollers, such as the adjustment angle and adjustment speed, which are the core basis for driving the servo motor to adjust the guide rollers. Deviation adjustment, or deviation correction, involves adjusting the guide rollers to pull the misaligned conveyor belt back to the reference position, avoiding problems such as belt jamming, tearing, and equipment downtime.

[0039] Optionally, when the difference between the predicted displacement offset and the preset offset threshold in the second motion data is within a second range, the guide wheel adjustment attribute for controlling the conveyor belt offset is calculated based on the predicted displacement offset and the predicted displacement gradient in the second motion data.

[0040] It should be noted that when the difference between the predicted displacement offset contained in the second motion data and the preset offset threshold is within the second range, it indicates that the conveyor belt deviation has reached the critical state requiring the initiation of automatic correction. At this time, the guide wheel adjustment attributes controlling the conveyor belt deviation need to be calculated using a preset algorithm based on the predicted displacement offset and predicted displacement gradient in the second motion data.

[0041] It should also be noted that when calculating the guide wheel adjustment attributes to control conveyor belt deviation, firstly, the predicted displacement offset and predicted displacement gradient are extracted from the second motion data. Based on the predicted displacement offset, the required adjustment range of the guide wheel is determined. The larger the predicted displacement offset, the larger the required adjustment range of the guide wheel. Simultaneously, based on the predicted displacement gradient, the adjustment rate of the guide wheel is determined. The larger the absolute value of the predicted displacement gradient, i.e., the faster the deviation speed, the faster the adjustment rate of the guide wheel, in order to achieve rapid correction and avoid worsening of deviation. Through a preset control algorithm, such as a proportional-derivative control algorithm, the predicted displacement offset and predicted displacement gradient are fused and calculated to finally obtain the guide wheel adjustment attributes such as the adjustment angle and adjustment rate.

[0042] In this embodiment, a control signal for driving the servo motor is generated based on the guide wheel adjustment properties; based on the control signal, the servo motor is driven to adjust the guide wheel offset value, so as to perform deviation correction processing on the transmission belt based on the adjusted guide wheel offset value.

[0043] Among them, the servo motor is the motor that drives the conveyor belt; the guide wheel is the hardware information that supports the conveyor belt.

[0044] It should be noted that the guide wheel adjustment attributes are a set of calculated correction parameters used to guide the guide wheel in which direction, by how much, and at what speed to adjust. The servo motor is a high-precision motor capable of precisely controlling position or angle, used to perform the adjustment action. The control signal driving the servo motor is an electrical command sent to the servo motor, instructing it on how much and how fast to rotate. The guide wheel offset value is the specific offset amount after the guide wheel is moved or deflected, used to push the conveyor belt back to the center. The correction process pulls the misaligned conveyor belt back to its normal track, ensuring it stays on track, doesn't jam, and doesn't wear out.

[0045] Specifically, the predicted second motion data is compared with a preset offset threshold. Based on the comparison results, the specific parameters that need to be adjusted for the guide rollers are determined. By adjusting the guide rollers, the conveyor belt deviation is corrected, forming a closed-loop control from detection to prediction to correction. This achieves precise and dynamic correction adjustment, improving the stability, reliability, and automation of the conveyor belt operation.

[0046] The technical solution of this disclosure embodiment is based on master-slave dual detection modules deployed on both sides of the guide groove to collect first motion data of the conveyor belt located in the guide groove. The first motion data includes at least magnetic scale data collected by the master detection module and Hall array data collected by the slave detection module. Then, based on the magnetic scale data and the Hall array data in the first motion data, the displacement gradient and fluctuation intensity of the conveyor belt are determined. Further, based on the displacement gradient and fluctuation intensity, second motion data of the conveyor belt within a predicted time period is determined. Finally, based on the relationship between the second motion data and a preset offset threshold, the guide wheel adjustment attributes related to the conveyor belt are determined, and the conveyor belt is offset adjusted based on the guide wheel adjustment attributes. This solves the problem of significantly reduced detection accuracy and detection failure when using laser detection technology for belt misalignment detection in the prior art. This disclosure embodiment realizes the offset adjustment of the conveyor belt based on the magnetic scale data collected by the master detection module and the Hall array data collected by the slave detection module, achieving early prediction of misalignment trends, effectively avoiding wear and jamming caused by conveyor belt misalignment, and improving the operational stability of the equipment.

[0047] Example 2 Figure 2 This is a flowchart illustrating the magnetically coupled toothed belt misalignment detection method provided in this embodiment of the invention. Based on the aforementioned embodiments, it provides a more detailed explanation of determining the displacement gradient and fluctuation intensity of the conveyor belt according to the magnetic scale data and Hall array data in the first motion data. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0048] like Figure 2 As shown, the method specifically includes the following steps: S210: Based on the master-slave dual detection modules deployed on both sides of the guide groove, the first motion data of the conveyor belt located in the guide groove is collected.

[0049] S220. Based on the magnetic scale data, determine the lateral offset of the conveyor belt relative to the reference position, and determine the displacement gradient of the conveyor belt according to the lateral offset.

[0050] It should be noted that, when determining the lateral offset of the conveyor belt relative to the reference position based on the magnetic scale data, magnetic scales are pre-installed along the longitudinal direction on the side of the conveyor belt, and magnetic scale detection units are symmetrically positioned on both sides of the guide groove. The magnetic scale signal collected by the magnetic scale detection units when the conveyor belt is at the center of the guide groove is used as the reference signal. Then, during the operation of the conveyor belt, the corresponding magnetic scale data is collected in real time by the magnetic scale detection units on both sides. The magnetic scale data on both sides is compared with the reference signal to obtain the signal difference between the two sides. According to the preset correspondence between the signal difference and the lateral offset, the lateral offset of the conveyor belt relative to the reference position is calculated from the signal difference.

[0051] Optionally, the displacement gradient of the conveyor belt can be determined based on the lateral offset at the current moment, the lateral offset at the previous moment, and the interval duration.

[0052] It should be noted that the displacement gradient of the conveyor belt can be obtained by dividing the difference between the lateral offset at the current moment and the lateral offset at the previous moment by the interval duration information.

[0053] Specifically, based on the magnetic grating data collected by the magnetic grating ruler, the lateral offset of the conveyor belt relative to the center reference position of the guide groove is determined. Then, based on the lateral offset obtained at different times, the displacement gradient of the conveyor belt during operation is calculated to reflect the rate of change and trend of the lateral offset of the conveyor belt.

[0054] S230. Based on the Hall array data, determine the magnetic field distribution information, and based on the magnetic field distribution information, determine the wave intensity.

[0055] Among them, the magnetic field distribution information refers to the magnetic field strength, spatial distribution, symmetry, peak position, and waveform changes formed at the location of the Hall array by the magnetic structure on the conveyor belt during operation.

[0056] Specifically, the magnetic field strength signals at corresponding locations are collected by each Hall sensor unit in the Hall array. These signals are then integrated and arranged according to their spatial location to obtain the magnetic field distribution information within the conveyor belt's operating area. Signal stability analysis is performed on this magnetic field distribution information, calculating the peak difference, variance, or standard deviation of the magnetic field strength. This quantifies the fluctuation intensity during conveyor belt operation, reflecting the degree of magnetic field signal jitter and operational stability.

[0057] S240. Based on the displacement gradient and wave intensity, determine the second motion data of the conveyor belt within the predicted time period.

[0058] S250. Based on the relationship between the second motion data and the preset offset threshold, determine the guide wheel adjustment attributes related to the conveyor belt, so as to adjust the offset of the conveyor belt based on the guide wheel adjustment attributes.

[0059] The technical solution of this disclosure embodiment is based on master-slave dual detection modules deployed on both sides of the guide groove to collect the first motion data of the conveyor belt located in the guide groove. Then, based on the magnetic scale data, the lateral offset of the conveyor belt relative to the reference position is determined, and the displacement gradient of the conveyor belt is determined according to the lateral offset. Then, based on the Hall array data, the magnetic field distribution information is determined, and the fluctuation intensity is determined according to the magnetic field distribution information. Further, based on the displacement gradient and fluctuation intensity, the second motion data of the conveyor belt within the predicted time period is determined. Finally, based on the relationship between the second motion data and the preset offset threshold, the guide wheel adjustment attributes related to the conveyor belt are determined. The offset adjustment of the conveyor belt based on the guide wheel adjustment attributes can predict the deviation trend in advance, realize advanced correction control, and effectively avoid problems such as jamming and wear caused by excessive conveyor belt offset.

[0060] Example 3 Figure 3 This is a schematic diagram of the structure of the magnetically coupled toothed belt misalignment detection device provided in the embodiments of this disclosure, as shown below. Figure 3 As shown, the device includes: a first motion data acquisition module 310, a wave intensity determination module 320, a second motion data determination module 330, and an offset adjustment module 340.

[0061] A first motion data acquisition module is used to acquire first motion data of the conveyor belt located in the guide groove based on master-slave dual detection modules deployed on both sides of the guide groove. The first motion data includes at least magnetic scale data acquired by the master detection module and Hall array data acquired by the slave detection module. A fluctuation intensity determination module is used to determine the displacement gradient and fluctuation intensity of the conveyor belt based on the magnetic scale data and the Hall array data in the first motion data. A second motion data determination module is used to determine the second motion data of the conveyor belt within a predicted time period based on the displacement gradient and the fluctuation intensity. An offset adjustment module is used to determine the guide wheel adjustment attributes related to the conveyor belt based on the relationship between the second motion data and a preset offset threshold, so as to adjust the offset of the conveyor belt based on the guide wheel adjustment attributes.

[0062] The technical solution of this disclosure embodiment is based on master-slave dual detection modules deployed on both sides of the guide groove to collect first motion data of the conveyor belt located in the guide groove. The first motion data includes at least magnetic scale data collected by the master detection module and Hall array data collected by the slave detection module. Then, based on the magnetic scale data and the Hall array data in the first motion data, the displacement gradient and fluctuation intensity of the conveyor belt are determined. Further, based on the displacement gradient and fluctuation intensity, second motion data of the conveyor belt within a predicted time period is determined. Finally, based on the relationship between the second motion data and a preset offset threshold, the guide wheel adjustment attributes related to the conveyor belt are determined, and the conveyor belt is offset adjusted based on the guide wheel adjustment attributes. This solves the problem of significantly reduced detection accuracy and detection failure when using laser detection technology for belt misalignment detection in the prior art. This disclosure embodiment realizes the offset adjustment of the conveyor belt based on the magnetic scale data collected by the master detection module and the Hall array data collected by the slave detection module, achieving early prediction of misalignment trends, effectively avoiding wear and jamming caused by conveyor belt misalignment, and improving the operational stability of the equipment.

[0063] Based on the above technical solutions, the wave intensity determination module 320 further includes: a displacement gradient determination submodule and a magnetic field distribution information processing submodule.

[0064] The displacement gradient determination submodule is used to determine the lateral offset of the conveyor belt relative to the reference position based on the magnetic scale data, and to determine the displacement gradient of the conveyor belt based on the lateral offset. The magnetic field distribution information processing submodule is used to determine the magnetic field distribution information based on the Hall array data, and to determine the wave intensity based on the magnetic field distribution information.

[0065] Based on the above technical solutions, the displacement gradient determination submodule is also used to determine the displacement gradient of the conveyor belt according to the lateral offset at the current moment, the lateral offset at the previous moment, and the interval duration information.

[0066] Based on the above technical solutions, the second motion data determination module 330 is further used to determine the second motion data of the conveyor belt within the prediction time based on the pre-created discrete state space prediction model and the optimal solution of each parameter in the pre-determined discrete space prediction model; wherein the second motion data includes at least the predicted displacement offset and predicted displacement gradient of the conveyor belt within the prediction time.

[0067] Based on the above technical solutions, the device further includes: a warning information generation module, used to generate a first warning information when the difference between the predicted displacement offset and the preset offset threshold is within a first range; generate a second warning information when the difference between the predicted displacement offset and the preset offset threshold is within a second range; and generate a third warning information when the difference between the predicted displacement offset and the preset offset threshold is within a third range.

[0068] Based on the above technical solutions, the offset adjustment module 340 is further configured to calculate the guide wheel adjustment attribute for controlling the conveyor belt offset when the difference between the predicted displacement offset and the preset offset threshold in the second motion data is within a second range.

[0069] Based on the above technical solutions, the device further includes: a deviation correction processing module, used to generate a control signal for driving a servo motor based on the adjustment attributes of the guide wheel, wherein the servo motor is a motor that drives the conveyor belt to move; based on the control signal, driving the servo motor to adjust the guide wheel offset value of the guide wheel, so as to perform deviation correction processing on the transmission belt based on the adjusted guide wheel offset value; wherein the guide wheel is hardware information supporting the conveyor belt.

[0070] The magnetically coupled toothed belt misalignment detection device provided in this disclosure can execute the magnetically coupled toothed belt misalignment detection method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of the method.

[0071] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this disclosure.

[0072] Example 4 Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Refer to the following... Figure 4 It illustrates an electronic device suitable for implementing embodiments of the present disclosure (e.g., Figure 4 The diagram below shows the structure of the terminal device or server 500. The terminal device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and vehicle terminals (e.g., vehicle navigation terminals). Figure 4The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0073] like Figure 4 As shown, electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An edit / output (I / O) interface 505 is also connected to bus 504.

[0074] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0075] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0076] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0077] The electronic device provided in this embodiment and the magnetically coupled toothed belt deviation detection method provided in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0078] Example 5 This disclosure provides a computer storage medium storing a computer program that, when executed by a processor, implements the magnetically coupled toothed belt misalignment detection method provided in the above embodiments.

[0079] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0080] In some implementations, the server may communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and may interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0081] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0082] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: Based on the master-slave dual detection modules deployed on both sides of the guide groove, the first motion data of the conveyor belt located in the guide groove is collected, wherein the first motion data includes at least the magnetic scale data collected by the master detection module and the Hall array data collected by the slave detection module. Based on the magnetic scale data and the Hall array data in the first motion data, the displacement gradient and fluctuation intensity of the conveyor belt are determined; Based on the displacement gradient and the fluctuation intensity, determine the second motion data of the conveyor belt within the predicted time period; Based on the relationship between the second motion data and the preset offset threshold, the guide wheel adjustment attribute related to the conveyor belt is determined, so as to adjust the offset of the conveyor belt based on the guide wheel adjustment attribute.

[0083] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0085] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0086] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0087] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0088] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0089] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0090] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for detecting misalignment of a magnetically coupled toothed belt, characterized in that, include: Based on the master-slave dual detection modules deployed on both sides of the guide groove, the first motion data of the conveyor belt located in the guide groove is collected, wherein the first motion data includes at least the magnetic scale data collected by the master detection module and the Hall array data collected by the slave detection module. Based on the magnetic scale data and the Hall array data in the first motion data, the displacement gradient and fluctuation intensity of the conveyor belt are determined; Based on the displacement gradient and the fluctuation intensity, determine the second motion data of the conveyor belt within the predicted time period; Based on the relationship between the second motion data and the preset offset threshold, the guide wheel adjustment attribute related to the conveyor belt is determined, so as to adjust the offset of the conveyor belt based on the guide wheel adjustment attribute.

2. The method according to claim 1, characterized in that, The step of determining the displacement gradient and fluctuation intensity of the conveyor belt based on the magnetic scale data and the Hall array data in the first motion data includes: Based on the magnetic scale data, the lateral offset of the conveyor belt relative to the reference position is determined, and the displacement gradient of the conveyor belt is determined according to the lateral offset. Based on the Hall array data, the magnetic field distribution information is determined, and based on the magnetic field distribution information, the wave intensity is determined.

3. The method according to claim 2, characterized in that, Determining the displacement gradient of the conveyor belt based on the lateral offset includes: The displacement gradient of the conveyor belt is determined based on the lateral offset at the current moment, the lateral offset at the previous moment, and the interval duration.

4. The method according to claim 1, characterized in that, The step of determining the second motion data of the conveyor belt within the predicted time period based on the displacement gradient and the fluctuation intensity includes: Based on a pre-created discrete state space prediction model and the optimal solution for each parameter in the pre-determined discrete state space prediction model, the second motion data of the conveyor belt within the prediction time is determined. The second motion data includes at least the predicted displacement offset and the predicted displacement gradient of the conveyor belt within the predicted time period.

5. The method according to claim 1, characterized in that, The method further includes: If the difference between the predicted displacement offset and the preset offset threshold is within a first range, a first warning message is generated. When the difference between the predicted displacement offset and the preset offset threshold falls within a second range, a second warning message is generated. When the difference between the predicted displacement offset and the preset offset threshold is within a third range, a third warning message is generated.

6. The method according to claim 1, characterized in that, The step of determining the guide wheel adjustment attributes related to the conveyor belt based on the relationship between the second motion data and the preset offset threshold includes: When the difference between the predicted displacement offset and the preset offset threshold in the second motion data is within a second range, the guide wheel adjustment attribute for controlling the conveyor belt offset is calculated based on the predicted displacement offset and the predicted displacement gradient in the second motion data.

7. The method according to claim 1, characterized in that, The method further includes: Based on the adjustment properties of the guide wheel, a control signal is generated to drive the servo motor, wherein the servo motor is a motor that drives the conveyor belt to move. Based on the control signal, the servo motor is driven to adjust the guide wheel offset value, so as to perform deviation correction processing on the transmission belt based on the adjusted guide wheel offset value; Among them, the guide wheels are hardware components that support the conveyor belt.

8. A magnetically coupled toothed belt misalignment detection device, characterized in that, include: The first motion data acquisition module is used to acquire first motion data of the conveyor belt located in the guide groove based on the master-slave dual detection modules deployed on both sides of the guide groove. The first motion data includes at least magnetic scale data acquired by the master detection module and Hall array data acquired by the slave detection module. The wave intensity determination module is used to determine the displacement gradient and wave intensity of the conveyor belt based on the magnetic scale data and the Hall array data in the first motion data. The second motion data determination module is used to determine the second motion data of the conveyor belt within the predicted time period based on the displacement gradient and the fluctuation intensity. The offset adjustment module is used to determine the guide wheel adjustment attributes related to the conveyor belt based on the relationship between the second motion data and the preset offset threshold, so as to adjust the offset of the conveyor belt based on the guide wheel adjustment attributes.

9. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs. When one or more programs are executed by one or more processors, the one or more processors implement the magnetically coupled toothed belt misalignment detection method as described in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the magnetically coupled toothed belt misalignment detection method as described in any one of claims 1-7.

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