Wheel monitoring system and method for shore bridge trolley based on fusion of imu and encoder

Abstract

The present application belongs to the technical field of intelligent monitoring of port hoisting equipment, and proposes a shore-to-bridge trolley wheel monitoring system based on IMU and encoder fusion, which comprises: an IMU sensor group for collecting attitude information of the trolley wheel; an encoder combination for collecting running speed and displacement data of the trolley; a data processing unit for calculating the roll angle and the angle of attack of the trolley wheel based on the data collected by the IMU sensor group and the encoder combination; and a pre-warning and linkage execution subsystem. The present application also proposes a shore-to-bridge trolley wheel monitoring method based on the above shore-to-bridge trolley wheel monitoring system, which comprises: collecting attitude information, speed information and displacement information of the trolley; calculating the roll angle and the angle of attack of the shore-to-bridge trolley; and executing corresponding safety operations. The present application can realize pre-warning and alarm intelligent linkage of the monitoring process of the traction type shore-to-bridge trolley, thereby ensuring the safe operation of the port equipment under the unmanned operation mode.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology for port crane equipment, and relates to a quay crane trolley wheel monitoring system and method based on the fusion of IMU and encoder. Background Technology

[0002] In port container terminals, quay cranes are the core equipment for loading and unloading operations. With the advancement of intelligent and unmanned trends, quay crane trolleys are gradually realizing remote control and automatic operation. The effective travel of the trolley of the traction quay crane is as long as 120 meters and the running speed is as high as 240 meters per minute. The quay crane trolley wheels are in a state of high-speed operation for a long time, which makes them prone to deviation and rail wear. In severe cases, it can cause abnormal wear of the wheel flanges of the quay crane trolley, rail deformation, or even equipment derailment.

[0003] Currently, the industry generally lacks technical means to effectively monitor the real-time operation status of quay crane trolleys. Traditional methods mainly rely on manual periodic inspections or stopping the machine to measure wheel wear, which has many prominent problems: First, it is highly delayed, and the problem is usually only addressed after it appears, making it impossible to provide early warning; second, manual measurement has low accuracy and it is difficult to capture the slight changes in the angle between the quay crane trolley wheels and the track; third, inspection requires stopping the machine on the work line, which affects the overall operational efficiency of the terminal; fourth, with the popularization of unmanned operation mode, there are no operators on site, and there is a lack of effective automatic sensing and interlocking protection means.

[0004] Although existing quay crane trolleys are equipped with speed encoders at the tail of the travel drive motor and absolute encoders at the tail of the drum, which can accurately obtain the trolley's position and speed information, these sensors cannot directly collect the wheel's attitude parameters and cannot reflect the relative position and posture of the wheel and the track.

[0005] Therefore, the industry urgently needs a system that can monitor the relative position of the wheels and rails of the quay crane in real time and automatically reduce speed or stop when parameters exceed limits, in order to fill the technological gap and ensure the safe operation of equipment in unmanned operation mode. Summary of the Invention

[0006] In view of this, it is necessary to address the shortcomings and deficiencies in existing technologies by proposing a quay crane trolley wheel monitoring system and method based on the fusion of IMU and encoder. The aim is to achieve intelligent linkage of early warning and alarm during the monitoring process of the quay crane trolley based on the monitoring information of the towing steel cable and the wheel posture of the trolley, thereby ensuring the safe operation of port equipment in unmanned operation mode.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] This invention proposes a quay crane trolley wheel monitoring system based on IMU and encoder fusion, applied to a port traction quay crane. The port traction quay crane includes a trolley, a crane body, a drum, and a spreader. The drum is used to wind steel wire ropes to achieve the lifting and lowering movement of the spreader. The quay crane trolley wheel monitoring system includes:

[0009] IMU sensor arrays are mounted on the wheels of the vehicle to collect the attitude information of the wheels;

[0010] The encoder assembly is used to collect the running speed and displacement data of the trolley, including a speed encoder installed at the detection end of the trolley's drive motor and an absolute encoder installed at the detection end of the drum.

[0011] The data processing unit is used to calculate the roll angle of the car wheels based on the data collected by the IMU sensor group and encoder combination, and through an intelligent detection model. and corner This is to enable real-time attitude monitoring of the quay crane trolley;

[0012] The early warning and coordinated execution subsystem is used to, based on at least a three-level early warning mechanism, determine the roll angle. , corner of attack The data processing unit collects wheel speed data, triggers corresponding alarm signals, and implements braking or adjustment operations on the gantry crane via preset safety linkage logic. The data processing unit communicates with the early warning and linkage execution subsystem and the encoder combination.

[0013] Ideally, the IMU sensor group is a MEMS-IMU sensor.

[0014] Ideally, the IMU sensor group includes a temperature compensation module and a vibration detection module. The temperature compensation module works in conjunction with the vibration detection module to eliminate the influence of environmental factors on the measurement accuracy of the IMU sensor group.

[0015] Optimally, the data processing unit also calculates the tilt angle based on the operating parameters of the quay crane trolley, including the wire rope drag force, trolley load weight, and track tilt angle. and corner Dynamic compensation is performed.

[0016] Optimally, the intelligent detection model includes:

[0017] The tilt angle algorithm engine is used to separate gravity components and transform coordinate systems based on IMU sensor group data, and dynamically compensate the angle calculation results by combining the encoder group data.

[0018] The angle of attack algorithm engine is used to comprehensively analyze the motion state of the wheels, the change in wire rope tension, and the track geometry parameters through multi-sensor fusion algorithms, so as to accurately calculate the lateral offset angle during the operation of the trolley.

[0019] Ideally, the at least three-level early warning mechanism includes the following threshold ranges:

[0020] Level 1 warning: When the angle of attack... Triggered when the threshold is exceeded, and reminder function is activated simultaneously;

[0021] Level 2 warning: When the angle of attack Triggered when the second threshold is exceeded, and speed limiting function is activated at the same time;

[0022] Level 3 Warning: When the angle of attack... Triggered when the third threshold is exceeded, and emergency braking function is activated simultaneously;

[0023] The third threshold is greater than the second threshold, and the second threshold is greater than the first threshold.

[0024] Ideally, the at least three-level early warning mechanism includes the following threshold ranges:

[0025] Level 1 warning: When the roll angle Triggered when the threshold is exceeded, and reminder function is activated simultaneously;

[0026] Level 2 warning: When the roll angle Triggered when the fifth threshold is exceeded, and speed limiting function is activated at the same time;

[0027] Level 3 warning: When the roll angle Triggered when the sixth threshold is exceeded, and emergency braking function is activated simultaneously;

[0028] The sixth threshold is greater than the fifth threshold, and the fifth threshold is greater than the fourth threshold.

[0029] This invention further proposes a method for monitoring the wheels of a quay crane trolley, based on the quay crane trolley wheel monitoring system described in any of the above claims. This method includes:

[0030] The attitude, speed, and displacement information of the vehicle are collected by the combination of the IMU sensor group and the encoder.

[0031] The intelligent detection model calculates the tilt angle of the quay crane trolley in real time. and corner ;

[0032] According to the roll angle and corner The data, combined with the vehicle's operating speed, triggers a corresponding early warning mechanism and executes the corresponding safety operation through the safety linkage logic.

[0033] The present invention further proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the quay crane trolley wheel monitoring method as described above.

[0034] The present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to implement the steps of the quay crane trolley wheel monitoring method as described above.

[0035] The beneficial effects of this invention are as follows:

[0036] This invention deeply integrates and analyzes IMU sensor arrays, encoder combinations, data processing units, and intelligent detection models. Based on the monitoring information of the towing steel cable and wheel posture of the traction quay crane trolley, it achieves intelligent linkage of early warning and alarm functions within the early warning and coordinated execution subsystems during the traction quay crane trolley monitoring process, thereby ensuring the safe operation of port equipment in unmanned operation modes. This invention also achieves a leap from "post-event maintenance" to "pre-event early warning" in equipment status monitoring, adapting to the fully automated monitoring requirements of unmanned terminals. This invention can effectively capture minute changes in wheel posture, meeting the early warning requirements for rail wear and deviation faults. The system can operate continuously for extended periods without drift or jamming. This invention significantly extends equipment lifespan and effectively reduces terminal operation and maintenance costs, adapting to the needs of smart port construction. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the structural principle of the quay crane trolley wheel monitoring system based on the fusion of IMU and encoder of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be further described clearly and completely below in conjunction with the embodiments of this invention. It should be noted that the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0039] Terms such as “first,” “second,” “third,” and “fourth” are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0040] Example 1

[0041] like Figure 1 As shown:

[0042] This embodiment proposes a quay crane trolley wheel monitoring system based on IMU and encoder fusion, applied to a port traction quay crane. The port traction quay crane includes a trolley, a crane body, a drum, and a spreader. The drum is used to wind steel wire rope to realize the lifting and lowering movement of the spreader. The quay crane trolley wheel monitoring system includes:

[0043] IMU sensor arrays are mounted on the wheels of the vehicle to collect the attitude information of the wheels;

[0044] The encoder assembly is used to collect the running speed and displacement data of the trolley, including a speed encoder installed at the detection end of the trolley's drive motor and an absolute encoder installed at the detection end of the drum.

[0045] The data processing unit is used to calculate the roll angle of the car wheels based on the data collected by the IMU sensor group and encoder combination, and through an intelligent detection model. and corner This is to enable real-time attitude monitoring of the quay crane trolley;

[0046] The early warning and coordinated execution subsystem is used to, based on at least a three-level early warning mechanism, determine the roll angle. , corner of attack The data processing unit collects wheel speed data, triggers corresponding alarm signals, and implements braking or adjustment operations on the gantry crane via preset safety linkage logic. The data processing unit communicates with the early warning and linkage execution subsystem and the encoder combination.

[0047] This embodiment of the IMU and encoder-based quay crane trolley wheel monitoring system deeply integrates and analyzes IMU sensor groups, encoder combinations, data processing units, and intelligent detection models. Based on the intelligent detection model and monitoring information of the trolley's towing steel cable and wheel posture, it achieves intelligent linkage of early warning and alarm functions within the trolley monitoring process and the associated execution subsystem, thereby ensuring the safe operation of port equipment in unmanned operation modes. This invention also achieves a leap from "post-event maintenance" to "pre-event early warning" in equipment status monitoring, adapting to the fully automated monitoring requirements of unmanned terminals. This invention can effectively capture minute changes in wheel posture, meeting the early warning requirements for rail wear and deviation faults. The system can operate continuously for extended periods without drift or jamming. This invention can significantly extend equipment lifespan and effectively reduce terminal operation and maintenance costs, adapting to the needs of smart port construction.

[0048] In some embodiments, the quay crane trolley wheel monitoring system based on IMU and encoder fusion can be composed of three parts: a sensor layer, a data processing layer, and an execution layer. Each layer is designed independently and modularly, and data interaction between layers is achieved through standardized interfaces. The overall architecture is simple and highly scalable.

[0049] In some embodiments, the speed encoder is specifically an incremental encoder; specifically, the speed encoder collects the vehicle's running speed v in real time, mainly for determining the vehicle's acceleration and deceleration state, and at the same time provides basic data for traction force state determination.

[0050] In some embodiments, specifically, the absolute encoder is used to accurately acquire the position s of the trolley throughout its entire journey (0~120 meters), with a resolution better than 1 millimeter, and is mainly used for track straight segment identification and IMU sensor group drift correction.

[0051] In some embodiments, the IMU sensor group is preferably a MEMS-IMU sensor;

[0052] Further optimized, the MEMS-IMU sensor is specifically a six-axis MEMS-IMU sensor. Specifically, the MEMS-IMU sensor adopts a single-wheel independent mounting method, fixed to the top of the wheel axle head or bearing seat of the trolley via a custom-made rigid aluminum alloy bracket (natural frequency ≥50Hz), with a vertical distance ≤100mm from the wheel's rotation center, ensuring a rigid connection with the wheel without relative displacement. During installation, the parallelism deviation between the IMU coordinate system and the wheel coordinate system is guaranteed. The equipment can collect triaxial acceleration in real time. and triaxial angular velocity It has a built-in temperature sensor for subsequent drift compensation processing.

[0053] In some embodiments, the IMU sensor group is optimized to include a temperature compensation module and a vibration detection module. The temperature compensation module works in conjunction with the vibration detection module to eliminate the influence of environmental factors on the measurement accuracy of the IMU sensor group.

[0054] In some embodiments, the data processing unit further optimizes the tilt angle based on the operating parameters of the quay crane trolley, including the wire rope drag force, the trolley load weight, and the track tilt angle. and corner Dynamic compensation is performed.

[0055] In other embodiments, specifically, the data processing unit achieves synchronization of the sensor layer through a hardware trigger pulse (1kHz), ensuring that the timestamp error of each sensor's data is less than 0.5ms. The data processing unit can use an embedded edge controller as the data processing core, with a built-in dedicated attitude calculation algorithm. It first performs preprocessing on the raw acquired data, such as removing outliers using the Grubbs criterion (significance level 0.05) and linear interpolation to compensate for lost packets. Then, it sequentially performs steps such as filtering, calibration compensation, integration, and drift correction to calculate and output the angle of attack in real time. and roll angle Simultaneously, based on preset thresholds and linkage logic, corresponding early warning levels are generated; the data processing unit has a built-in sensor fault detection mechanism, which can monitor the data transmission and acquisition status of each sensor in real time.

[0056] In some embodiments, the intelligent detection model preferably includes:

[0057] The tilt angle algorithm engine is used to separate gravity components and transform coordinate systems based on IMU sensor group data, and dynamically compensate the angle calculation results by combining the encoder group data.

[0058] The angle of attack algorithm engine is used to comprehensively analyze the motion state of the wheels, the change in wire rope tension, and the track geometry parameters through multi-sensor fusion algorithms, so as to accurately calculate the lateral offset angle during the operation of the trolley.

[0059] Furthermore, the roll angle algorithm engine can reflect the degree of tilt of the wheel plane relative to the direction of gravity, directly indicating whether the wheel has undergone "outward" or "inward" deformation. It uses gravity components collected by an IMU accelerometer for calculation, and the core formula for calculating the roll angle is:

[0060] (1)

[0061] In formula (1), This is the filtered lateral acceleration. The vertical acceleration is the result of filtering; to ensure the accuracy of the solution, a targeted dynamic processing and anti-interference scheme is designed:

[0062] ① Uniform speed determination trigger: Only when the rate of change of the trolley speed displayed by the motor speed encoder is triggered. The roll angle calculation result is only updated when the speed is zero or when the speed is constant, effectively avoiding interference from acceleration and deceleration inertial forces on the solution result.

[0063] ② Vibration interference filtering: Before data processing, the raw acceleration data is first processed by a 5Hz low-pass filter to filter out vibration acceleration interference caused by track joint impact, port wind, equipment operation vibration, etc., and only retain the pure gravity acceleration component.

[0064] ③ Installation Error Compensation: To address the unavoidable installation angle deviation during on-site installation, an installation error compensation mechanism is added. If there is an installation angle between the Z-axis and the direction of gravity... There is an installation angle between the Y-axis and the transverse direction of the track. The formula for calculating the roll angle after compensation is:

[0065] (2)

[0066] In formula (2): The mounting offset angle of the Z-axis around the X-axis. Let φ be the installation offset angle of the Y-axis around the Z-axis; assuming the actual roll angle of the trolley wheels is φ, then the components of gravitational acceleration on each axis of the measurement coordinate system can be expressed as: , Dividing the two equations, we get: Therefore, we can solve for: When installation error , When both are zero, the formula degenerates into formula (1), which is completely consistent with the physical reality.

[0067] The above compensation can completely eliminate the systematic errors caused by installation deviations and ensure the accuracy of the roll angle calculation.

[0068] Further optimization involves the angle of attack algorithm engine. The angle of attack is the horizontal angle between the wheel's direction of travel and the centerline of the track, and it is the direct cause of wheel wear on the track. The angle of attack calculation of the angle of attack algorithm engine is divided into two steps: gyroscope integration and encoder drift correction. The entire process uses a 1ms time step to achieve synchronous acquisition and calculation of the angle of attack of all four wheels.

[0069] ① The original angle of attack is obtained by the trapezoidal integral of the gyroscope:

[0070] (3)

[0071] In formula (3): The angular velocity about the Z-axis measured by the IMU. The initial angle of attack. The integration step size is 1 ms (consistent with the data synchronization frequency), and n is the number of integration iterations. To avoid significant deviations from the actual value caused by large instantaneous angular velocity interference, the integration result is set as follows: The integral limit; among which This represents the summation operation from k=1 to k=n.

[0072] Initial angle of attack Strict calibration is required: The trolley is parked on a professionally tested standard straight track section (track straightness deviation ≤ 0.1 mm / m). With the trolley stationary, 500 sets of raw gyroscope data are collected, and the average value is taken as the initial zero bias. The angle of attack at this point is then calibrated. Set the actual initial angle (not a fixed zero value) for the equipment and record the calibration time and specific track position in detail as a reference for subsequent drift correction.

[0073] ② Linear and nonlinear drift correction using an absolute encoder:

[0074] Because gyroscopes have inherent zero bias and temperature drift, the integral results need to be compensated for drift by using a known standard straight track segment. The compensation process consists of three stages:

[0075] Phase 1, Straight Track Segment Identification: The position data of the trolley corresponding to the track collected by the drum absolute encoder, combined with the track as-built drawings, automatically determines the section with a track straightness deviation ≤0.1mm / m as the standard straight track segment, and only performs drift correction within this section;

[0076] Phase 2, Linear Drift Correction: Within the standard straight track segment, the angle of attack should theoretically remain constant. All changes in the gyro integral result are considered drift amounts, which are then subtracted proportionally to the real-time position of the trolley. The calculation formula is as follows:

[0077] (4)

[0078] In formula (4), s(t) is the real-time position of the car collected by the absolute encoder, and sstart and send are the start and end positions of the standard straight section, respectively. The above linear drift correction is applicable to the car running at a constant speed in the standard straight section (at this time, the position s is approximately proportional to the time t). If there is acceleration or deceleration in the straight section, the drift amount should be processed according to the time ratio or in a segmented manner.

[0079] Phase 3, Nonlinear Temperature Compensation: The real-time operating temperature of the device is acquired using the built-in temperature sensor in the IMU sensor array. A drift-temperature fitting model is established, and secondary compensation is performed on the linearly corrected result to completely eliminate the nonlinear drift caused by temperature changes. The calculation formula is as follows:

[0080] (5)

[0081] In formula (5): Here, T(t) represents the temperature drift coefficient, and T(t) represents the real-time operating temperature of the IMU. The ambient temperature at the initial calibration; the corrected temperature. That is, the high-precision true angle of attack of the wheel, which can effectively eliminate various drift errors caused by long-term operation of the system; it should be noted that when kT is a piecewise function or polynomial fitting function that varies with temperature, formula (5) constitutes true nonlinear temperature compensation; in practical applications, kT-T fitting curves can be established based on multi-temperature point calibration data to achieve high-precision drift compensation in the entire temperature range.

[0082] In some embodiments, the data processing unit or intelligent detection model performs a mechanism for special processing of the traction structure:

[0083] 1) Execute traction force state judgment: compare the displacement difference between the speed encoder and the absolute encoder in real time, and use the difference to reflect the amount of wire rope extension and contraction; when the difference is ≤5mm, it is determined to be the steady-state operation stage of the trolley, and no filtering processing is required; when the difference is >5mm, it is determined to be the elastic torsion stage of the trolley acceleration and deceleration, and filtering processing is started immediately.

[0084] 2) Perform elastic deformation component separation: During the acceleration / deceleration elastic torsion stage, for formula (3) The second-order Butterworth low-pass filter (cutoff frequency 2Hz) is used to effectively separate the high-frequency component of the elastic deformation of the wire rope from the low-frequency component of the steady-state angle of attack caused by wheel wear and bearing clearance.

[0085] 3) Perform four-wheel collaborative discrimination: Install an independent set of IMU sensor groups at each of the four wheels of the quay crane trolley to realize the synchronous acquisition and calculation of the four-wheel attack angle / tilt angle; design differentiated early warning strategies for single-wheel and multi-wheel overload, which can accurately identify problems such as trolley unbalanced load and single-sided rail wear, and avoid local faults from being not detected in time;

[0086] The mechanism for special handling of traction structures is designed to address the problem of elastic torsion during start-stop of traction quay crane trolleys caused by wire rope dragging. Such elastic torsion is easily misjudged as a change in angle of attack, so this mechanism is designed to handle multiple stages specifically.

[0087] In some embodiments, the early warning and linkage execution subsystem is specifically an execution layer. The execution layer is seamlessly connected to the quay crane PLC system via Ethernet and can send control commands such as speed limit and shutdown to the PLC system according to the early warning level. Tiered audible and visual alarms are equipped on both sides of the trolley frame and in the quay crane operator's room / central control room to achieve dual alarms on-site and remotely. At the same time, through a visual operation interface, real-time attitude curves, historical data trends, sensor operating status and equipment maintenance suggestions are uploaded to the central control room at a frequency of 10Hz. All data can be remotely stored and retrieved.

[0088] In some embodiments, the at least three-level early warning mechanism preferably includes the following threshold ranges:

[0089] Level 1 warning: When the angle of attack... Exceeding Triggered at any time, simultaneously activating the reminder function;

[0090] Level 2 warning: When the angle of attack Exceeding When triggered, the speed limiting function is activated simultaneously;

[0091] Level 3 Warning: When the angle of attack... Exceeding When triggered, it simultaneously activates the emergency braking function.

[0092] In some embodiments, the at least three-level early warning mechanism preferably includes the following threshold ranges:

[0093] Level 1 warning: When the roll angle Exceeding Triggered at any time, simultaneously activating the reminder function;

[0094] Level 2 warning: When the roll angle Exceeding When triggered, the speed limiting function is activated simultaneously;

[0095] Level 3 warning: When the roll angle Exceeding When triggered, it simultaneously activates the emergency braking function.

[0096] In some embodiments, the control logic of the at least three-level early warning mechanism is as follows:

[0097] Preferably, a three-level early warning threshold is set, wherein the angle of attack... At °, the wear rate of the wheel flange begins to increase significantly. The wear rate increases by 5 times. This can easily cause permanent deformation of the track; tilt angle The car started to veer slightly to one side. The deviation reached 5mm. There is a risk of equipment derailment, and the threshold can be flexibly adjusted according to the actual working conditions of the dock. At the same time, a sound hysteresis logic, parameter priority and fault tolerance handling mechanism are designed.

[0098]

[0099] The control logic of the at least three-level early warning mechanism also includes linkage execution rules:

[0100] Level 1 warning: The system only automatically records the warning event and pushes maintenance reminders to the central control room, without affecting the normal operation of the trolley. All warning data is uploaded to the central control room for archiving in real time.

[0101] Level 2 warning: The system interrupts the high-speed operation command of the trolley through the PLC, forcibly reducing the trolley's speed to a low speed of 30m / min. After the trolley completes the current loading and unloading operation, it pushes the equipment maintenance prompt to the site and remote, and at the same time activates the yellow audible and visual alarm.

[0102] Level 3 alarm: The system immediately cuts off the safety circuit of the trolley running mechanism, applies emergency braking to the trolley, and activates a red audible and visual alarm. The equipment can only be released from braking and resumed operation after manual on-site reset and comprehensive testing.

[0103] In some embodiments, the control logic of the at least three-level early warning mechanism is optimized by setting a parameter priority: the angle of attack is the core monitoring parameter, the early warning level is higher than the roll angle, and if the angle of attack reaches the level three alarm standard, the system will directly perform an emergency stop operation regardless of the roll angle.

[0104] In some embodiments, the control logic of the at least three-level warning mechanism further includes multi-wheel coordination rules: when the single-wheel attack angle / roll angle reaches the first-level warning, the system only records the event; when both wheels on the same side reach the second-level warning, a speed limit command is triggered; when any three of the four wheels reach the third-level warning or both wheels on one side reach the third-level warning, an emergency stop is immediately triggered.

[0105] In some embodiments, the control logic of the at least three-level early warning mechanism further includes a sensor fault tolerance rule: if a sensor experiences data loss or data fluctuations exceeding a certain threshold... ( If the data standard deviation under normal operating conditions is not met or there is no response for 1 second, the system will immediately trigger a sensor fault alarm and automatically switch the trolley to manual remote control mode, prohibiting the trolley from running automatically.

[0106] In some embodiments, the IMU and encoder-based quay crane trolley wheel monitoring system of this embodiment also includes a host computer in the terminal control room. All monitoring data, early warning information, and sensor status are uploaded to the host computer at a frequency of 10Hz via Modbus TCP industrial Ethernet to achieve remote real-time monitoring of the data.

[0107] This embodiment further proposes a method for monitoring the wheels of a quay crane trolley, based on the quay crane trolley wheel monitoring system described in any of the above embodiments. This method for monitoring the wheels of a quay crane trolley includes:

[0108] S1, The attitude information, speed information and displacement information of the vehicle are collected through the combination of the IMU sensor group and the encoder;

[0109] S2, the tilt angle of the quay crane trolley is calculated in real time using the intelligent detection model. and corner ;

[0110] S3, according to the roll angle and corner The data, combined with the vehicle's operating speed, triggers a corresponding early warning mechanism and executes the corresponding safety operation through the safety linkage logic.

[0111] Furthermore, the method for monitoring the wheels of the gantry crane also includes:

[0112] Before S1, S100 is also performed, involving the installation of the IMU sensor assembly: The IMU sensor assembly is fixed to the top of the wheel axle end cap or bearing housing via a custom rigid bracket (natural frequency ≥ 50Hz). The bracket is rigidly connected to the wheel with no relative displacement. The vertical distance between the IMU sensor assembly and the wheel's rotation center is ≤ 100mm. During installation, ensure the parallelism deviation between the IMU coordinate system and the wheel coordinate system. Then execute S200;

[0113] S200, define the equipment coordinate system: the X-axis is parallel to the track and points in the direction of the trolley's movement; the Y-axis is perpendicular to the track and horizontal; the Z-axis is perpendicular to the ground and consistent with the direction of gravity; after the system is installed, the IMU sensor group needs to be initially calibrated for zero bias, scale factor, and installation error, and the encoder group needs to be initially calibrated for zero point and counting error. The calibration data is stored in the system and used as the basis for compensation in subsequent data processing; then execute S1.

[0114] This embodiment also proposes a method for monitoring the wheels of a quay crane trolley, which further includes performing a periodic sensor calibration procedure before S1 or after S3:

[0115] To ensure the long-term monitoring accuracy of the system, clear periodic calibration specifications and standards are established for various types of sensors, and calibration data must be recorded and archived throughout the process;

[0116] IMU sensor group: After the equipment is installed, initial calibration of zero bias, scale factor and installation error is performed, and subsequent periodic calibration is performed every 3 months through a standard turntable;

[0117] Encoder assembly: After the equipment is installed, initial calibration of zero point and counting error is performed, followed by periodic calibration every 6 months.

[0118] After all sensors have been calibrated, detailed information such as calibration time, calibration personnel, and calibration data must be recorded and stored in the system database as a basis for compensation in subsequent data processing.

[0119] This embodiment also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the quay crane trolley wheel monitoring method described above.

[0120] This embodiment also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, it implements the steps of the quay crane trolley wheel monitoring method as described above.

[0121] The processor involved in this embodiment can have various specific implementation forms. For example, the processor may include one or more combinations of a central processing unit (CPU), GPU, NPU, TPU, or DPU, etc., and this application embodiment does not impose specific limitations. The processor can also be a single-core processor or a multi-core processor. The processor can be a combination of a CPU and hardware chips. The aforementioned hardware chips can be application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or combinations thereof. The aforementioned PLDs can be complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), generic array logic (GALs), or any combination thereof. The processor can also be implemented using logic devices with built-in processing logic, such as FPGAs or digital signal processors (DSPs).

[0122] The memory involved in this embodiment can be volatile memory or non-volatile memory, or it can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DRRAM).

[0123] In some embodiments, the electronic device may be a neuromorphic chip. Since neuromorphic chips can employ vectorized computation and require external memory, such as Double Data Rate (DDR) synchronous dynamic random access memory, to load parameters such as weights of the neural network model, the batch processing method used in this embodiment offers higher computational efficiency.

[0124] In one example, the electronic device may further include a communication interface and a bus; the processor, memory, and communication interface of the electronic device are connected via the bus and communicate with each other; the communication interface is mainly used to realize communication between various modules, devices, units, and / or equipment in the embodiments of this application; the bus includes hardware, software, or both, coupling the components of the electronic device together; for example, and not as a limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, or an Industry Standard interconnect. Architecture (ISA) bus, Infinite Bandwidth Interconnect, Low Pin Count (LPC) bus, Memory Bus, Microchannel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus, or other suitable buses or combinations of two or more of these; where appropriate, the bus may include one or more buses; although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0125] Furthermore, the present invention can be implemented using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, program code or code segments (e.g., a computer program product) that perform the necessary tasks can be stored in a machine-readable medium. The processor can perform the necessary tasks. In the above embodiments, it can be implemented wholly or partially using software, hardware, firmware, or any combination thereof. When implemented using a software program, it can be implemented wholly or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function described in the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access, or it can include one or more data storage devices such as servers or data centers that can be integrated with media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0126] The system, device, and storage medium in this invention are based on multiple aspects of the same inventive concept as the method in the foregoing embodiments. The implementation process of the method has been described in detail above, so those skilled in the art can clearly understand the structure and implementation process of the system, device, and storage medium in this embodiment based on the foregoing description. For the sake of brevity, it will not be described again here.

[0127] In some embodiments, the system may be configured as a distributed system, wherein one or more components of the system are distributed across one or more networks of a cloud computing system.

[0128] In some embodiments, the steps in the method of this application can be adjusted, merged, or deleted in order according to actual needs; the modules in the system of this application can be divided, merged, or deleted according to actual needs.

[0129] The corresponding technical solutions of the present invention also have the following corresponding advantages.

[0130] The system of this invention adopts a modular design and can be flexibly deployed on various existing towing quay crane trolleys. There is no need to modify the original mechanical structure of the equipment. Installation can be completed simply by adding IMU sensor groups and matching cables at the wheel axle position. All sensors have clearly defined initial calibration and periodic calibration standards, which facilitates on-site maintenance and management.

[0131] This invention fills a technological gap in the industry: for the first time, MEMS-IMU and encoder are deeply integrated and applied to the real-time monitoring of the wheel attitude of the quay crane trolley. Combined with multiple algorithms such as installation error compensation, multi-source filtering, and drift correction, it realizes the leap from "post-event maintenance" to "pre-event early warning" in equipment status monitoring, and is fully adapted to the fully automatic monitoring needs of unmanned terminals.

[0132] The invention features high monitoring accuracy and strong operational reliability: through a dual drift correction algorithm combining automatic compensation for straight track sections and nonlinear temperature compensation, the system achieves an angle of attack detection accuracy of ±0.05° and a tilt angle detection accuracy of ±0.1°, effectively capturing minute changes in wheel attitude and meeting the early warning requirements for rail wear and deviation faults. The system can operate continuously for a long time without drifting or jamming.

[0133] This invention significantly improves the safety of unmanned operations: the system operates fully automatically throughout the entire process, with a well-designed three-level linkage, fault tolerance, and four-wheel collaborative discrimination mechanism. When equipment parameters are abnormal, the system can automatically limit speed or stop the machine without manual judgment, effectively avoiding safety accidents such as severe wheel wear, track deformation, and equipment derailment. When a sensor malfunctions, the system can automatically switch to manual control mode to ensure the continuity of dock operations.

[0134] This invention significantly extends the service life of equipment: by real-time monitoring, minor deviation problems of the trolley can be detected in time, guiding precise maintenance on site. Test results show that the average wear of the wheels in the experimental group using this system is 0.2mm / month, which reduces abnormal wear of wheels and tracks by more than 42.8% compared with the traditional manual inspection method (0.35mm / month), greatly extending the service life of wheels and tracks and reducing equipment replacement costs.

[0135] This invention can effectively reduce port operation and maintenance costs: the system's real-time early warning function can significantly reduce the frequency of manual inspections and equipment downtime for testing, avoiding long-term downtime losses due to sudden failures; at the same time, the system adopts a modular design, which does not require modification of the original mechanical structure of the equipment, resulting in low equipment modification costs and easy promotion and application in the industry;

[0136] This invention can be adapted to the needs of smart port construction: the system can transmit all monitoring data to the terminal control room via industrial Ethernet, and the visual interface can display the equipment operating status and historical data trends in real time, providing accurate data support for the full life cycle management of port equipment, which fully meets the intelligent and remote operation and maintenance development needs of smart ports.

[0137] This invention provides a system for real-time detection, analysis, and early warning of the angle between the wheel and the track (angle of attack) and the perpendicularity of the wheel (tilt angle), which is particularly suitable for remote monitoring of the operating status of trolleys in automated and unmanned terminals;

[0138] This invention enables high-precision real-time detection of wheel angle of attack and roll angle, and automatically executes graded control of "attention warning → speed limit operation → emergency stop" according to the degree of parameter exceeding limits, overcoming the technical defects of the prior art that cannot monitor the deviation of the car in real time and accurately;

[0139] This invention achieves multi-source fusion and complementary advantages: it integrates data from a six-axis MEMS-IMU, a motor speed encoder, and a drum absolute encoder, uses the high-precision position and speed information of the encoder to correct IMU drift, and combines temperature compensation to achieve nonlinear drift correction, effectively improving the long-term operational stability of the system.

[0140] This invention achieves real-time accuracy and high-speed adaptability: it realizes millisecond-level (≤0.5ms) data synchronization through hardware trigger pulse, and completes the angle of attack calculation using trapezoidal integral algorithm, which can be fully adapted to high-speed operation conditions of 240m / min. The angle of attack detection accuracy can reach ±0.05°, and the side tilt angle detection accuracy is ±0.1°.

[0141] The physical model of this invention is clear and the algorithm is reliable: the side tilt angle and the angle of attack are calculated using simple physical formulas, and combined with technologies such as automatic drift compensation for straight track sections (straightness deviation ≤ 0.1 mm / m) and 5Hz low-pass filtering to resist vibration interference, the system can be guaranteed to operate without drift for a long time.

[0142] This invention can provide automatic early warning and safety control: based on design specifications and on-site measured data, three-level thresholds are set, hysteresis logic and parameter priority are designed, and instructions are automatically output to the PLC to realize speed limiting or emergency braking without manual intervention. At the same time, a sensor fault tolerance processing module is built in.

[0143] This invention is adapted to the characteristics of traction-type quay cranes: In response to the elastic torsional interference caused by wire rope traction, the traction force state is determined by the encoder displacement difference, and a second-order Butterworth low-pass filter is designed to separate the elastic deformation component, effectively avoiding false alarms, and supporting four-wheel synchronous monitoring and collaborative discrimination;

[0144] The invention is unmanned and user-friendly: the system operates fully automatically throughout the process, transmits monitoring data to the central control room via Modbus TCP industrial Ethernet, and is equipped with a graded audible and visual alarm device, which fully meets the actual needs of remote monitoring of smart ports.

[0145] The present invention has strong engineering adaptability: it adopts a modular design and can be flexibly deployed on existing quay crane trolleys without modifying the mechanical structure of the equipment. Only IMU sensor groups and matching cables need to be installed at the wheel axle positions. The installation and calibration standards are clearly defined, which greatly reduces the equipment modification cost.

[0146] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A quay crane trolley wheel monitoring system based on IMU and encoder fusion, applied to a port traction quay crane, the port traction quay crane including a trolley, a crane body, a drum, and a spreader, wherein the drum is used to wind wire rope to realize the lifting and lowering movement of the spreader, characterized in that, The wheel monitoring system for the gantry crane includes: IMU sensor arrays are mounted on the wheels of the vehicle to collect the attitude information of the wheels; The encoder assembly is used to collect the running speed and displacement data of the trolley, including a speed encoder installed at the detection end of the trolley's drive motor and an absolute encoder installed at the detection end of the drum. The data processing unit is used to calculate the roll angle of the car wheels based on the data collected by the IMU sensor group and encoder combination, and through an intelligent detection model. and corner This is to enable real-time attitude monitoring of the quay crane trolley; The early warning and coordinated execution subsystem is used to, based on at least a three-level early warning mechanism, determine the roll angle. , corner of attack The data processing unit collects wheel speed data, triggers corresponding alarm signals, and implements braking or adjustment operations on the gantry crane via preset safety linkage logic. The data processing unit communicates with the early warning and linkage execution subsystem and the encoder combination.

2. The quay crane trolley wheel monitoring system according to claim 1, characterized in that, The IMU sensor group is a MEMS-IMU sensor.

3. The quay crane trolley wheel monitoring system according to claim 2, characterized in that, The IMU sensor group includes a temperature compensation module and a vibration detection module. The temperature compensation module works in conjunction with the vibration detection module to eliminate the influence of environmental factors on the measurement accuracy of the IMU sensor group.

4. The quay crane trolley wheel monitoring system according to any one of claims 1-3, characterized in that, The data processing unit also calculates the tilt angle based on the operating parameters of the quay crane trolley, including the wire rope drag force, trolley load weight, and track tilt angle. and corner Dynamic compensation is performed.

5. The quay crane trolley wheel monitoring system according to any one of claims 1-3, characterized in that, The intelligent detection model includes: The tilt angle algorithm engine is used to separate gravity components and transform coordinate systems based on IMU sensor group data, and dynamically compensate the angle calculation results by combining the encoder group data. The angle of attack algorithm engine is used to comprehensively analyze the motion state of the wheels, the change in wire rope tension, and the track geometry parameters through multi-sensor fusion algorithms, so as to accurately calculate the lateral offset angle during the operation of the trolley.

6. The quay crane trolley wheel monitoring system according to claim 1, characterized in that, The at least three-level early warning mechanism includes the following threshold ranges: Level 1 warning: When the angle of attack... Triggered when the threshold is exceeded, and reminder function is activated simultaneously; Level 2 warning: When the angle of attack Triggered when the second threshold is exceeded, and speed limiting function is activated at the same time; Level 3 Warning: When the angle of attack... Triggered when the third threshold is exceeded, and emergency braking function is activated simultaneously; The third threshold is greater than the second threshold, and the second threshold is greater than the first threshold.

7. The quay crane trolley wheel monitoring system according to claim 1 or 6, characterized in that, The at least three-level early warning mechanism includes the following threshold ranges: Level 1 warning: When the roll angle Triggered when the threshold is exceeded, and reminder function is activated simultaneously; Level 2 warning: When the roll angle Triggered when the fifth threshold is exceeded, and speed limiting function is activated at the same time; Level 3 warning: When the roll angle Triggered when the sixth threshold is exceeded, and emergency braking function is activated simultaneously; The sixth threshold is greater than the fifth threshold, and the fifth threshold is greater than the fourth threshold.

8. A method for monitoring the wheels of a quay crane trolley, characterized in that, Based on the quay crane trolley wheel monitoring system as described in any one of claims 1-6, the quay crane trolley wheel monitoring method includes: The attitude, speed, and displacement information of the vehicle are collected by the combination of the IMU sensor group and the encoder. The intelligent detection model calculates the tilt angle of the quay crane trolley in real time. and corner ; According to the roll angle and corner The data, combined with the vehicle's operating speed, triggers a corresponding early warning mechanism and executes the corresponding safety operation through the safety linkage logic.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it implements the steps of the quay crane trolley wheel monitoring method as described in claim 8.

10. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor executes the computer program to implement the steps of the quay crane trolley wheel monitoring method as described in claim 8.

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