A lifting mechanism gear regulation intelligent control method and system

Abstract

The embodiment of the application provides a hoisting mechanism gear regulation and control intelligent control method and system, relates to the technical field of hoisting mechanism control, and the method comprises the following steps: acquiring the running state information of a hoisting mechanism and acquiring the swing state information of the load of the hoisting mechanism, wherein the swing state information comprises an absolute inclination angle and high-frequency transient acceleration; the swing state information of the load is subjected to time alignment processing to determine the real-time swing phase of the load; based on the real-time swing phase, the swing phase evolution of the load in a future preset time period is predicted; based on the swing phase evolution, the impact risk of triggering gear switching to the transmission chain of the hoisting mechanism at different moments is evaluated, and an impact risk evaluation result is obtained; according to the running state information of the hoisting mechanism and the impact risk evaluation result, the execution mode of the gear switching action is determined, and a corresponding gear regulation and control instruction is generated. The application can realize the regulation and control balance between efficiency and impact.

Description

Technical Field

[0001] This application relates to the field of crane control technology, and more specifically, to a method and system for intelligent control of crane gear position adjustment. Background Technology

[0002] In hoisting operations of crane main hooks, gear shifting is typically triggered by speed deviation to achieve smooth acceleration from low to high speed. However, in actual working conditions, the load often experiences periodic small-amplitude oscillations due to sudden lifting, rope elasticity, or airflow disturbances. Existing shifting criteria rely solely on time or speed conditions, neglecting oscillation phase information. When the operation requires rapid gear shifting, the shifting command may coincide with the phase interval where the load oscillates in the opposite direction to the lifting direction. At this point, the motor torque and the load's reverse additional torque are superimposed, causing the wire rope or coupling to experience instantaneous impact loads far exceeding steady-state values. Simultaneously, the impact exacerbates the load oscillation, creating a vicious cycle. Traditional control logic, lacking phase awareness, cannot avoid this risk. If operators artificially delay shifting to reduce impact, it significantly reduces lifting efficiency, rendering the balance between efficiency and impact ineffective. The core problem arising from this is that during acceleration and upshifting, due to the mismatch between the switching timing and the swing phase, the gear shift is prone to falling into an unfavorable range, causing overload of the transmission chain and deterioration of the swing. Existing methods cannot automatically optimize the switching phase, nor can they balance efficiency and safety. There is an urgent need to introduce a real-time monitoring and dynamic decision-making mechanism for the swing phase to solve this problem. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes an intelligent control method and system for hoist gear shifting, aiming to solve the problem that in existing hoist gear shifting systems, improper gear switching timing under load swing conditions leads to excessive impact and aggravated swaying in the transmission chain, thereby affecting lifting efficiency and safety.

[0004] In a first aspect, embodiments of this application provide an intelligent control method for adjusting the gear position of a crane mechanism, including:

[0005] The system acquires the operating status information of the crane mechanism and the swing status information of the load of the crane mechanism, wherein the swing status information includes the absolute tilt angle and high-frequency transient acceleration.

[0006] The oscillation state information of the load is time-aligned to determine the real-time oscillation phase of the load.

[0007] Based on the real-time swing phase, predict the evolution of the swing phase of the load over a future preset time period;

[0008] Based on the evolution of the swing phase, the impact risk of triggering gear switching on the transmission chain of the crane mechanism at different times is evaluated, and the impact risk assessment result is obtained.

[0009] Based on the operating status information of the hoisting mechanism and the impact risk assessment results, the execution method of the gear shifting action is determined, and the corresponding gear control command is generated.

[0010] According to some embodiments of this application, obtaining the swing state information of the load of the hoisting mechanism includes:

[0011] The absolute tilt angle of the hook of the hoisting mechanism relative to the direction of gravity in space is measured by a tilt sensor.

[0012] The high-frequency transient acceleration of the load during the oscillation process is obtained by using an accelerometer.

[0013] According to some embodiments of this application, the time alignment processing of the load's oscillation state information to determine the real-time oscillation phase of the load includes:

[0014] Establish a data preprocessing and time alignment mechanism;

[0015] The absolute tilt angle and the high-frequency transient acceleration are processed based on the data preprocessing and time alignment mechanism to obtain preprocessed swing state information, wherein the preprocessed swing state information includes the preprocessed absolute tilt angle and the preprocessed high-frequency transient acceleration.

[0016] The preprocessed oscillation state information is predicted based on the extended Kalman filter to determine the real-time oscillation phase of the load.

[0017] According to some embodiments of this application, the step of predicting the preprocessed swing state information based on an extended Kalman filter to determine the real-time swing phase of the load includes:

[0018] Obtain the actual rotational speed of the hoisting motor and the vertical acceleration of the drum of the hoisting mechanism;

[0019] A state prior estimate is obtained by predicting based on the extended Kalman filter, the actual rotational speed of the hoisting motor, and the vertical acceleration of the drum.

[0020] The observation residual is obtained by calculating the preprocessed oscillation state information and the state prior estimate based on the extended Kalman filter;

[0021] The state prior estimate is corrected based on the observed residuals to obtain the target swing angle and the velocity time series of the target swing angle to determine the real-time swing phase of the load.

[0022] According to some embodiments of this application, predicting the evolution of the load's swing phase over a future preset time period based on the real-time swing phase includes:

[0023] Based on the real-time swing phase and the preset forward time prediction model, the phase angle and swing amplitude of the load in the future preset time period are predicted;

[0024] The oscillation phase evolution is generated based on the phase angle and the oscillation amplitude.

[0025] According to some embodiments of this application, the step of assessing the impact risk on the transmission chain of the crane mechanism caused by triggering gear switching at different times based on the oscillation phase evolution, and obtaining the impact risk assessment result, includes:

[0026] Based on the evolution of the swing phase, the instantaneous peak load that the transmission chain of the crane mechanism will bear when the upshift is triggered at each moment is calculated point by point;

[0027] The impact risk assessment result is obtained based on the instantaneous peak load and the preset safety threshold.

[0028] According to some embodiments of this application, the step of determining the execution method of the gear shifting action based on the operating status information of the hoisting mechanism and the impact risk assessment result, and generating a corresponding gear control command, includes:

[0029] Obtain the mechanical inertia parameters and electrical response time of the hoisting mechanism;

[0030] Calculate the total delay time from generation to actual execution based on the mechanical inertia parameters and the electrical response time;

[0031] The total delay time is superimposed on the swing phase evolution to obtain the predicted swing phase at the actual execution time of the instruction;

[0032] Based on the impact risk assessment results, preset conditions are set for the gear shifting action;

[0033] Based on the predicted swing phase at the actual execution time of the instruction, and under the condition that the preset conditions are met, a mechanically bearable window that matches the predicted swing phase is selected.

[0034] Based on the operating status information of the hoisting mechanism and the mechanically bearable window, the execution mode of the gear shifting action is determined, and the corresponding gear control command is generated.

[0035] According to some embodiments of this application, the step of selecting a mechanically acceptable window that matches the predicted swing phase based on the actual execution time of the instruction, under the condition of satisfying the preset conditions, includes:

[0036] Continuously monitor the trend of the predicted swing phase at the actual execution time of the instruction within each mechanically bearable window;

[0037] Based on the predicted swing phase change trend within each mechanically bearable window at the actual execution time of the instruction, calculate the swing phase change rate within each mechanically bearable window and the sensitivity of the swing phase at the edge of each mechanically bearable window to deviate from the bearable range.

[0038] Based on the swing phase change rate, the stability of the swing state inside the mechanically bearable window is identified, and a stability evaluation result is obtained.

[0039] Based on the sensitivity, the stability of the mechanically supported window edge is evaluated to obtain the stability evaluation result;

[0040] If the stability assessment results and the stability evaluation results meet the preset conditions, a mechanically acceptable window that matches the predicted oscillation phase is selected.

[0041] According to some embodiments of this application, after determining the execution method of the gear shifting action based on the operating status information of the hoisting mechanism and the impact risk assessment result, and generating the corresponding gear control command, the process includes:

[0042] Execute the gear control command to achieve gear switching;

[0043] Within a preset monitoring window period after gear shifting, the residual swing angle peak value of the load is collected in real time as the monitoring result;

[0044] The monitoring results are compared with the predicted peak swing angle before gear shifting to obtain the phase matching deviation.

[0045] Based on the phase matching deviation, a phase compensation amount is generated and superimposed on the subsequent predicted swing phase calculation to correct the triggering timing of the mechanically bearable window.

[0046] Secondly, this application also discloses an intelligent control system for adjusting the gear position of a crane mechanism, comprising:

[0047] The acquisition module is used to acquire the operating status information of the crane mechanism and the swing status information of the load of the crane mechanism, wherein the swing status information includes the absolute tilt angle and high-frequency transient acceleration.

[0048] The determination module is used to perform time alignment processing on the swing state information of the load in order to determine the real-time swing phase of the load.

[0049] The prediction module is used to predict the evolution of the load's swing phase over a future preset time period based on the real-time swing phase.

[0050] The impact risk assessment module is used to assess the impact risk to the transmission chain of the crane mechanism caused by triggering gear switching at different times based on the evolution of the swing phase, and obtain the impact risk assessment result.

[0051] The instruction generation module is used to determine the execution mode of the gear shifting action based on the operating status information of the hoisting mechanism and the impact risk assessment results, and to generate the corresponding gear control instruction.

[0052] According to the technical solution of the embodiments of this application, at least the following beneficial effects are achieved: This application provides an intelligent control method for gear shifting of a crane mechanism. By acquiring the operating status information of the crane mechanism and the swing status information of the load, and performing time alignment processing on the swing status information to determine the real-time swing phase, the method predicts the evolution of the swing phase of the load within a preset time period in the future. Based on this, the method can assess the impact risk to the crane mechanism's transmission chain caused by triggering gear shifting at different times, and determine the execution mode of the gear shifting action and generate the corresponding gear shifting control command based on the operating status information and the impact risk assessment results. By introducing real-time perception, prediction, and impact risk assessment of the load swing phase, this application can intelligently select the optimal gear shifting time, avoiding shifting under unfavorable swing phases, thereby significantly reducing the instantaneous peak load borne by the transmission chain and effectively suppressing the aggravation of load swing. Through accurate acquisition and time alignment processing of the load swing status information, the real-time swing phase of the load can be accurately captured, laying the foundation for subsequent prediction and decision-making. Secondly, by predicting the future swing phase evolution and assessing the impact risk, the system becomes forward-looking, able to identify potential impact risks in advance and select the optimal switching window. Finally, based on the operating status information and the impact risk assessment results, the system intelligently generates gear control commands to ensure that the gear switching action is executed within a mechanically bearable window. This ensures lifting efficiency while maximizing the protection of the transmission chain, extending equipment life, and improving operational safety.

[0053] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0054] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0055] Figure 1 A flowchart illustrating an intelligent control method for adjusting the gear position of a crane mechanism according to an embodiment of this application;

[0056] Figure 2 A schematic flowchart illustrating the process of determining the real-time swing phase of a load according to an embodiment of this application;

[0057] Figure 3 A flowchart illustrating the process of determining the real-time swing phase of a load, provided for another embodiment of this application;

[0058] Figure 4 This is a schematic diagram of an intelligent control system for adjusting the gear position of a crane mechanism, provided in one embodiment of this application. Detailed Implementation

[0059] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0060] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0061] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: the existence of a alone, the existence of b alone, the existence of c alone, the simultaneous existence of a and b, the simultaneous existence of a and c, the simultaneous existence of b and c, or the simultaneous existence of a, b, and c, where a, b, and c can be single or multiple.

[0062] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0063] The intelligent control method for hoisting gear position adjustment provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application that implements the intelligent control method for hoisting gear position adjustment, but is not limited to the above forms.

[0064] The embodiments of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0065] See Figure 1 , Figure 1 This is a flowchart illustrating an intelligent control method for adjusting the gear position of a crane mechanism according to an embodiment of this application. The intelligent control method for adjusting the gear position of a crane mechanism provided in this embodiment includes, but is not limited to, steps S110 to S150, which are described in detail below.

[0066] Step S110: Obtain the operating status information of the crane mechanism and the swing status information of the load of the crane mechanism, wherein the swing status information includes the absolute tilt angle and high-frequency transient acceleration.

[0067] Step S120: Perform time alignment processing on the load swing state information to determine the real-time swing phase of the load;

[0068] Step S130: Based on the real-time swing phase, predict the evolution of the load's swing phase over a preset time period in the future;

[0069] Step S140: Based on the evolution of the swing phase, assess the impact risk on the transmission chain of the crane mechanism caused by triggering gear switching at different times, and obtain the impact risk assessment results.

[0070] Step S150: Based on the operating status information of the crane mechanism and the impact risk assessment results, determine the execution method of the gear shifting action and generate the corresponding gear control command.

[0071] It should be noted that operating status information typically refers to parameters such as the current speed, acceleration, motor torque, and gear position of the crane mechanism. This information can be obtained through the crane mechanism's built-in sensors or control system. Load oscillation status information is a key input in this application, including absolute tilt angle and high-frequency transient acceleration. The absolute tilt angle refers to the load's tilt angle relative to the direction of gravity in space, reflecting the overall oscillation trend of the load; high-frequency transient acceleration captures more precise and rapid dynamic changes in the load during oscillation. Using these two types of information together provides a more comprehensive and accurate description of the load's oscillation. Real-time oscillation phase refers to the load's position within its oscillation cycle at a given moment, such as the highest point, lowest point, or midpoint of the oscillation, as well as its oscillation direction. Oscillation phase evolution is a prediction of the load's oscillation phase over a future period, providing a basis for advance decision-making. Impact risk refers to the instantaneous load impact that gear shifting at a specific moment may cause to the crane mechanism's transmission chain (such as wire ropes, couplings, gears, etc.). Excessive impact may lead to mechanical fatigue or even damage. The execution method of gear shifting includes parameters such as the timing, speed, and acceleration curve of the shift, aiming to optimize the shifting process and reduce impact. The gear control command is the final command sent to the crane control system to execute the specific gear shifting operation. This method is typically deployed in the intelligent control unit or industrial computer of the crane, achieving closed-loop control through data interaction with the crane's sensors and actuators.

[0072] In one embodiment, firstly, the process involves acquiring the operating status information of the crane mechanism and the swing state information of the load on the crane mechanism. The operating status information of the crane mechanism can be acquired in various ways. For example, the motor speed can be measured using an encoder installed on the crane mechanism motor, thereby calculating the crane mechanism's operating speed; alternatively, the motor current can be measured using a current sensor, and the output torque can be estimated using a motor model. For the swing state information of the load, namely the absolute tilt angle and high-frequency transient acceleration, different sensors can be used for measurement. Secondly, the swing state information of the load is time-aligned to determine the real-time swing phase of the load. In practical applications, data collected by different sensors may have inconsistent sampling frequencies and asynchronous timestamps. Therefore, these raw data need to be preprocessed and time-aligned. For example, interpolation algorithms (such as linear interpolation and spline interpolation) can be used to unify data with different sampling frequencies onto the same time base. Alternatively, time consistency of data from different sensors can be ensured by synchronizing clock signals or using high-precision timestamps. After time alignment, this processed swing state information can be used to determine the real-time swing phase of the load using a specific algorithm model. A sway state estimator based on a physical model, such as a Kalman filter or an extended Kalman filter, can be constructed. The preprocessed absolute tilt angle and high-frequency transient acceleration are used as observation inputs. Combined with the dynamic model of the load, the sway angle, angular velocity and other state variables of the load are estimated in real time, and then its real-time phase in the sway period is determined.

[0073] In one embodiment, after determining the real-time swing phase of the load, to achieve forward-looking control, it is necessary to predict the swing trend of the load over a future period. For example, a predictive model based on historical data and the real-time swing phase can be established. This model can be based on time series analysis methods, such as ARIMA models or neural network models, which learn patterns in historical swing data and combine them with the current real-time swing phase to predict the swing angle and swing speed for several future time steps. Alternatively, it can be based on a physical model prediction. If the load swing can be approximated as simple harmonic motion, then given the current swing phase, amplitude, and period, the swing phase at any future time can be directly calculated using the simple harmonic motion equations. The predicted output is typically a series of swing phase points within a preset future time period, collectively forming the swing phase evolution curve. After predicting the evolution of the load swing phase, this information needs to be used to assess the potential impact risks of gear shifting at different times. For example, an impact load model can be established, which takes the characteristics of the crane's transmission chain (such as stiffness and damping), motor torque changes, and the load swing phase as inputs. When a gear shift is triggered at a specific moment in the simulation, the model calculates the instantaneous peak load that the transmission chain may bear. By performing this simulation point-by-point on the predicted swing phase evolution curve, a series of instantaneous peak loads at different switching moments can be obtained. Comparing these peak loads with preset safety thresholds yields the impact risk assessment result for each moment. For example, if the predicted peak load exceeds the safety threshold, the impact risk at that moment is assessed as high risk; otherwise, it is low risk. Finally, after obtaining the impact risk assessment result, the control system needs to comprehensively consider the current operating status information of the crane (such as speed, load size, current gear, etc.) to make the final decision. For example, if the crane is currently operating at low speed, and the impact risk assessment result shows that the risk of upshifting at a future moment is low, the system can decide to perform the upshifting operation at that moment. A series of decision rules or a rule-based expert system can be set to select an optimal gear shifting timing and strategy (e.g., smooth acceleration curve, segmented acceleration, etc.) based on the operating status information and the impact risk assessment result.

[0074] It should be noted that this application, through accurate prediction of the load swing phase and assessment of impact risk, can select the moment of lowest impact risk for gear switching, thereby significantly reducing the impact load on the transmission chain and improving the smoothness and safety of the crane operation. For example, when the load swing is predicted to be in the phase range consistent with the lifting direction, the system will preferentially select to switch gears within this range. At this time, the motor torque is consistent with the swing direction, which can effectively reduce the impact. This forward-looking intelligent control strategy not only avoids the contradiction between efficiency and impact in traditional methods, but also ensures that while maintaining lifting efficiency, it maximizes the protection of the mechanical structure and extends the equipment life.

[0075] Specifically, obtaining the swing state information of the load on the hoisting mechanism can include the following steps:

[0076] The absolute tilt angle of the hook of the crane mechanism relative to the direction of gravity in space is measured by an tilt sensor.

[0077] The high-frequency transient acceleration of the load during the oscillation process is obtained by using an accelerometer.

[0078] An inclinometer is a device that measures the angle of inclination of an object relative to a horizontal plane or the direction of gravity. In this application, it is configured to directly measure the absolute inclination angle of a crane hook, which reflects the overall tilting attitude of the load in space. The inclinometer can be implemented using microelectromechanical systems (MEMS) technology, offering advantages such as small size, low power consumption, and fast response, providing high-precision angle data. Furthermore, an accelerometer is a sensor used to measure the acceleration of an object. In this application, the accelerometer is used to acquire the high-frequency transient acceleration of the load during its oscillation. High-frequency transient acceleration can capture the rapidly changing dynamic characteristics of the load during oscillation, such as small but rapid oscillation components caused by wind, operational shocks, or structural vibrations. By appropriately filtering and processing the accelerometer's output signal, these high-frequency transient components can be effectively extracted, thus providing a more comprehensive description of the load's oscillation state.

[0079] See Figure 2 , Figure 2 This is a flowchart illustrating the process of determining the real-time swing phase of a load according to one embodiment of this application. This application provides an intelligent control method for adjusting the gear position of a crane mechanism, including but not limited to steps S210 to S230, which are described below.

[0080] Step S210: Establish a data preprocessing and time alignment mechanism;

[0081] Step S220: Based on the data preprocessing and time alignment mechanism, the absolute tilt angle and high-frequency transient acceleration are processed to obtain the preprocessed swing state information, wherein the preprocessed swing state information includes the preprocessed absolute tilt angle and the preprocessed high-frequency transient acceleration.

[0082] Step S230: Predict the preprocessed swing state information based on the extended Kalman filter to determine the real-time swing phase of the load.

[0083] Specifically, establishing a data preprocessing and time alignment mechanism refers to designing a set of rules and algorithms for processing raw sensor data (i.e., absolute tilt angle and high-frequency transient acceleration). The aim is to eliminate or reduce noise in the data, correct sensor biases, and ensure consistency of data from different sensors across the time dimension. For example, this mechanism may include digital filtering techniques such as low-pass filtering and median filtering to remove high-frequency noise, and methods such as resampling and interpolation to unify the sampling frequency and timestamps of different sensors. Processing the absolute tilt angle and high-frequency transient acceleration based on the data preprocessing and time alignment mechanism to obtain preprocessed oscillation state information can be understood as cleaning and synchronizing the raw sensor data, which may contain noise and time biases, through the aforementioned mechanism to obtain a more reliable and consistent dataset. The preprocessed oscillation state information, namely the preprocessed absolute tilt angle and preprocessed high-frequency transient acceleration, provides the foundation for high-quality input for subsequent accurate state estimation. Predicting the real-time swing phase of a load based on preprocessed swing state information using an Extended Kalman Filter (EKF) involves leveraging the powerful nonlinear state estimation algorithm of the EKF to fuse and predict preprocessed and time-aligned swing state information. The EKF effectively handles nonlinear system models and measurement noise, fusing data from tilt sensors and accelerometers through an iterative prediction and update process to provide accurate estimates of the load's real-time swing phase (e.g., swing angle and swing velocity). Its aim is to overcome the limitations of single-sensor data and improve the accuracy and robustness of swing phase estimation.

[0084] It should be noted that the scheme in this application establishes a data preprocessing and time alignment mechanism. Firstly, it cleans and synchronizes the raw absolute tilt angles and high-frequency transient accelerations from different sensors, effectively solving problems such as noise interference, inconsistent sampling frequencies, and time deviations that may exist in the raw data, providing high-quality input for subsequent accurate estimation. Based on this, an extended Kalman filter is introduced to fuse the preprocessed absolute tilt angles and high-frequency transient accelerations. The extended Kalman filter can use its prediction-update loop to optimally estimate the load's oscillation state, considering the system dynamic model and measurement noise. The absolute tilt angle provided by the tilt sensor has high accuracy in the low-frequency range but is susceptible to vibration and shock; while the high-frequency transient acceleration provided by the accelerometer reflects the dynamic characteristics of the oscillation but is susceptible to integral drift and noise. The extended Kalman filter cleverly fuses these two complementary data sources, using the long-term stability of the tilt sensor to correct the accelerometer drift, and simultaneously using the transient response of the accelerometer to compensate for the tilt sensor's hysteresis, thereby providing smooth, accurate, and robust real-time oscillation phase estimation across the entire frequency range.

[0085] In some preferred embodiments, it is assumed that the tilt sensor of the crane mechanism outputs absolute tilt angle data at a frequency of 10 Hz, while the accelerometer outputs high-frequency transient acceleration data at a frequency of 100 Hz. First, a data preprocessing and time alignment mechanism is activated. For the tilt sensor data, a low-pass filter can be applied to smooth its output and remove high-frequency noise; for the accelerometer data, a band-pass filter can be performed to extract the swing-related frequency components, and integration is performed to obtain velocity or displacement information. Subsequently, the tilt sensor data is upsampled to 100 Hz using a resampling or interpolation algorithm, precisely aligned with the accelerometer data on the time axis, ensuring that each time stamp has corresponding data from both sensors. These preprocessed absolute tilt angle and high-frequency transient acceleration data are input into an extended Kalman filter. The extended Kalman filter internally establishes a nonlinear dynamic model describing the load swing state, such as a motion equation based on a simple pendulum model, whose state variables may include swing angle, swing angular velocity, etc. At each time step, the filter first predicts the current state based on the state estimate from the previous time step and the dynamic model. Then, it compares the predicted state with the preprocessed and time-aligned sensor measurements (absolute tilt angle and high-frequency transient acceleration) at the current moment to calculate the observation residual. Finally, the filter uses Kalman gain to correct the predicted state based on the observation residual (update step), thus obtaining a more accurate real-time swing phase estimate, including the real-time swing angle and swing angular velocity. In this way, even with noise in the sensor data or uncertainties in the system model, an accurate and robust real-time estimate of the load swing state can be obtained.

[0086] See Figure 3 , Figure 3 This is a flowchart illustrating the determination of the real-time swing phase of a load, as provided in another embodiment of this application. This application provides an intelligent control method for adjusting the gear position of a crane mechanism, including but not limited to steps S310 to S340, which are described below.

[0087] Step S310: Obtain the actual rotational speed of the hoisting motor and the vertical acceleration of the drum of the hoisting mechanism;

[0088] Step S320: Based on the extended Kalman filter, the actual rotational speed of the hoisting motor, and the vertical acceleration of the drum, a state prior estimate is obtained.

[0089] Step S330: Calculate the preprocessed oscillation state information and state prior estimate based on the extended Kalman filter to obtain the observation residual;

[0090] Step S340: Correct the state prior estimate based on the observation residual to obtain the target swing angle and the velocity time series of the target swing angle to determine the real-time swing phase of the load.

[0091] It is important to note that the actual rotational speed of the hoisting motor and the vertical acceleration of the drum are crucial input parameters used to assist the Extended Kalman Filter (EKF) in state estimation. The actual rotational speed of the hoisting motor reflects the vertical motion trend of the crane mechanism, while the vertical acceleration of the drum provides dynamic information about the load in the vertical direction. This information helps to more accurately predict the load's sway state. State prior estimation refers to the EKF's preliminary prediction of the system state (e.g., load sway angle and sway velocity) at the current moment, based on its internal model and the state estimate from the previous moment, before receiving new observation data. The observation residual can be understood as the difference between the preprocessed sway state information actually observed and the observed values ​​predicted through state prior estimation. This residual reflects the inconsistency between the model prediction and the actual situation and is key to correcting the state estimation. By correcting the observation residual, the EKF can incorporate actual observation data into the state estimation, thereby obtaining a more accurate target sway angle and its velocity time series. The target sway angle refers to the actual tilt angle of the load relative to the vertical direction at a certain moment, while the velocity time series of the target sway angle describes the rate of change of this sway angle over time.

[0092] Specifically, in some implementations of the above method, predicting the evolution of the load's swing phase over a preset time period based on the real-time swing phase may include the following steps:

[0093] Based on a real-time swing phase and a preset forward time prediction model, the phase angle and swing amplitude of the load are predicted within a preset time period in the future.

[0094] The oscillation phase evolution is generated based on the phase angle and oscillation amplitude.

[0095] The preset forward time prediction model refers to a mathematical model or algorithm used to infer the future swing state of the load based on the current real-time swing phase. This model can be constructed based on physical dynamics principles (e.g., simple harmonic motion model, damped vibration model) or data-driven methods (e.g., time series prediction model, neural network model), with the aim of accurately predicting the load's trajectory within a preset time period. The preset forward time refers to a specific duration extending into the future from the current moment. This duration is set based on factors such as the crane's response time, control cycle, and the characteristics of the load's swing, ensuring sufficient time for subsequent impact risk assessment and gear adjustment decisions. The phase angle refers to the instantaneous angle of the load relative to its equilibrium position during the swing process; it describes the load's specific position within the swing cycle. The swing amplitude refers to the maximum angle or displacement of the load from its equilibrium position during the swing process; it reflects the severity of the load's swing. By predicting the phase angle and swing amplitude, the swing state of the load within the preset future time period can be comprehensively characterized. Therefore, the swing phase evolution can be understood as a sequence or curve of the load's phase angle and swing amplitude changing over time within the preset future time period, providing a complete view of the load's future movement trend. This application's solution utilizes real-time swing phase as input, combined with a pre-defined forward time prediction model, to accurately predict the phase angle and swing amplitude of the load within a preset future time period. This prediction mechanism allows the system to anticipate the load's dynamic behavior, thereby generating detailed swing phase evolution. In this way, the system can not only understand the load's current state but also predict its future movement trends, providing crucial forward-looking information for subsequent impact risk assessment and gear shifting decisions.

[0096] It should be noted that the above technical solution can provide a more detailed and comprehensive prediction of the load's oscillation state over a preset time period, including its specific phase angle and oscillation amplitude. This detailed information on the evolution of the oscillation phase allows subsequent impact risk assessments to more accurately identify potential risk moments, thus providing a more reliable basis for gear control of the crane mechanism, effectively avoiding transmission chain impacts caused by load oscillations, and improving the smoothness and safety of the crane mechanism's operation.

[0097] In response, this application further proposes the above-mentioned assessment of the impact risk on the transmission chain of the crane mechanism caused by gear shifting at different times. The steps to obtain the impact risk assessment results include:

[0098] The instantaneous peak load that the transmission chain of the crane mechanism will bear when the upshift is triggered at each moment is calculated point by point based on the oscillation phase evolution.

[0099] The impact risk assessment results are obtained based on the instantaneous peak load and the preset safety threshold.

[0100] Specifically, calculating the instantaneous peak load that the crane's transmission chain will bear when upshifting is triggered at each moment based on the swing phase evolution refers to simulating and analyzing each potential gear shifting moment within a preset time period using previously predicted swing phase evolution data of the load over that time period. This analysis precisely calculates the maximum instantaneous mechanical load that the crane's transmission chain (e.g., gears, shafts, couplings, etc.) needs to bear when upshifting is triggered at each specific moment. This calculation typically involves a complex dynamic model, considering parameters such as the load's mass, swing velocity, acceleration, and the crane's own inertia and stiffness. The instantaneous peak load can be understood as the maximum stress or force applied to the transmission chain at the moment of gear shifting due to a sudden change in speed or torque. Obtaining the impact risk assessment result based on the instantaneous peak load and a preset safety threshold involves comparing the calculated instantaneous peak load at each moment with a pre-set safety threshold. The preset safety threshold is the maximum allowable load determined based on factors such as the crane's transmission chain's design strength, material properties, fatigue life, and safety factor. If the instantaneous peak load at a certain moment exceeds the preset safety threshold, then triggering a gear shift at that moment is considered to pose a high risk of impact; conversely, if the instantaneous peak load is below the safety threshold, then the risk is considered low or acceptable. This allows for a quantitative assessment of impact risk, such as high risk, medium risk, or low risk, or directly indicates whether a gear shift is permitted at that moment.

[0101] It should be noted that the above technical solution enables precise quantitative assessment of the impact risk in the crane's transmission chain, significantly improving the scientific rigor and safety of gear shifting decisions. This solution effectively avoids transmission chain overload and damage caused by improper gear shifting, extending equipment lifespan and reducing maintenance costs. Furthermore, by identifying safe gear shifting windows, it can optimize crane operation efficiency and ensure smooth and reliable operation even under dynamic load conditions.

[0102] In this regard, this application further proposes the following steps for determining the execution method of the gear shifting action and generating the corresponding gear control command:

[0103] Obtain the mechanical inertia parameters and electrical response time of the lifting mechanism;

[0104] The total delay from generation to actual execution is calculated based on mechanical inertia parameters and electrical response time.

[0105] The total delay time is superimposed on the swing phase evolution to obtain the predicted swing phase at the actual execution time of the instruction;

[0106] Based on the impact risk assessment results, preset conditions are set for the gear shifting action;

[0107] Based on the predicted swing phase at the actual execution time of the instruction, a mechanically bearable window that matches the predicted swing phase is selected under preset conditions.

[0108] Based on the operating status information and mechanical bearing capacity of the crane mechanism, the execution method of gear switching action is determined, and the corresponding gear control command is generated.

[0109] Specifically, mechanical inertia parameters refer to the physical characteristics of each component in the crane's transmission chain (such as the mass and moment of inertia) and the crane's dynamic characteristics, directly affecting the crane's response speed and dynamic characteristics to gear shifting commands. Electrical response time refers to the time required from the control system issuing an electrical command to the actuator (such as a frequency converter or contactor) beginning to respond and generate actual action; it includes the inherent delays in signal transmission, controller processing, and actuator movement. The total delay time is the result of the combined effect of the aforementioned mechanical inertia parameters and electrical response time, characterizing the time interval required from the generation of the gear shifting command to the crane actually starting to execute the gear shifting action. This total delay time can be obtained through system identification, experimental testing, or theoretical calculation. Superimposing the total delay time onto the swing phase evolution means shifting the predicted load swing phase evolution curve on the time axis to reflect the time difference between the command issuance time and the actual execution time. Thus, the predicted swing phase of the load at the actual execution time of the command can be obtained. Preset conditions may include, but are not limited to, impact risk below a certain safety threshold, load swing amplitude within an acceptable range, and crane operating speed within a specific range. These conditions collectively define the safe operating range suitable for gear shifting. The mechanically bearable window refers to a specific time period or phase range during the load swing cycle where the drivetrain can mechanically withstand the instantaneous load changes caused by gear shifting. For example, when the load swings to near its lowest point with a low speed, or swings to near its highest point with an instantaneous speed of zero, the impact on the drivetrain is relatively small; these moments can be considered the mechanically bearable window. Selecting a mechanically bearable window that matches the predicted swing phase aims to ensure that the actual execution time of the gear shifting action falls within these more mechanically stable ranges.

[0110] In one embodiment, this application significantly improves the intelligence and safety of gear shifting control in crane mechanisms. By accurately calculating and compensating for system delays, it ensures that the actual execution time of the gear shifting command precisely matches the phase of the load's oscillation, thereby avoiding transmission chain impact and increased load oscillation caused by inaccurate timing. Furthermore, by selecting a mechanically bearable window for gear shifting under preset conditions, the shifting timing is further optimized, minimizing the instantaneous load on the transmission chain during gear adjustment, greatly reducing impact risk and extending equipment life. This solution not only improves the stability and efficiency of lifting operations but also provides a safer working environment for operators.

[0111] Specifically, when selecting a mechanically acceptable window that matches the predicted swing phase based on the actual execution time of the instruction, under the condition that preset conditions are met, the following method can be adopted.

[0112] Continuously monitor the trend of the predicted swing phase at the actual execution time of the instruction within each mechanically acceptable window;

[0113] Based on the predicted swing phase change trend within each mechanically bearable window at the actual execution time of the instruction, calculate the swing phase change rate within each mechanically bearable window and the sensitivity of the swing phase deviation from the bearable range at the edge of each mechanically bearable window.

[0114] Based on the rate of change of the oscillation phase, the stability of the oscillation state inside the mechanically acceptable window is identified, and the stability evaluation result is obtained.

[0115] Based on the degree of sensitivity, the stability of the mechanically supported window edge is evaluated, and the stability evaluation result is obtained;

[0116] If the stability assessment results and the steadyness assessment results meet the preset conditions, a mechanically acceptable window that matches the predicted oscillation phase is selected.

[0117] Specifically, continuous monitoring refers to the system acquiring and tracking the predicted swing phase data at the actual execution time of the command in real time, and analyzing its dynamic changes within multiple preset mechanically bearable windows. The mechanically bearable window can be understood as the time interval during crane operation where the load swing is within a relatively stable or controllable range; shifting gears within this interval carries a low risk of impact on the transmission chain. The swing phase change rate refers to the rate at which the predicted swing phase changes over time within each mechanically bearable window, reflecting the dynamic characteristics of the swing state. Sensitivity refers to the probability of the predicted swing phase deviating from the bearable range when it approaches the boundary of the mechanically bearable window, and its potential impact on system stability. In practical applications, the stability assessment result is determined by analyzing the swing phase change rate to judge the degree of fluctuation in the swing state within the mechanically bearable window; for example, a lower change rate usually indicates higher stability. The stability assessment result is determined by analyzing the sensitivity to judge the robustness at the edge of the mechanically bearable window, i.e., whether it is easy to exceed the bearable range when the predicted swing phase experiences small fluctuations.

[0118] In this regard, this application further proposes, following the above method, the following:

[0119] Execute gear control commands to achieve gear shifting;

[0120] Within the preset monitoring window period after gear shifting, the peak value of the residual swing angle of the load is collected in real time as the monitoring result;

[0121] The monitoring results are compared with the predicted peak swing angle before gear shifting to obtain the phase matching deviation.

[0122] Based on the phase matching deviation, a phase compensation amount is generated and superimposed on the subsequent calculation of the predicted oscillation phase to correct the triggering timing of the mechanically bearable window.

[0123] Specifically, after the gear shifting command is generated and executed, the gear shifting action of the crane mechanism is realized. To evaluate the effect of this shift and optimize subsequent operations, the system continuously collects the load's oscillation status information in real time within a preset monitoring window period after the gear shift is completed, and extracts the residual oscillation peak value. The preset monitoring window period refers to a specific time during which the system continuously observes the load's oscillation behavior after the gear shift is completed. Its length can be set according to the dynamic characteristics of the crane mechanism and the load's oscillation cycle to ensure that the residual oscillation after the shift is fully captured. The residual oscillation peak value refers to the maximum oscillation angle reached by the load relative to the vertical direction within the monitoring window period after the gear shift is completed. This value is a key indicator for measuring the smoothness of the gear shift. Subsequently, the real-time collected residual oscillation peak value (i.e., the monitoring result) is compared with the oscillation peak value predicted by the system before the gear shift. The predicted oscillation peak value refers to the minimum or acceptable oscillation peak value that the load should reach at the ideal gear shifting moment, as expected by the system based on the predicted oscillation phase evolution when generating the gear shifting command. This comparison yields the phase matching deviation, which quantifies the difference between the actual and predicted oscillations, reflecting potential errors in the prediction model or execution timing. This phase compensation is a correction value used to adjust subsequent predicted oscillation phase calculations. By adding this compensation to subsequent predicted oscillation phase calculations, the triggering timing of the mechanically bearable window can be dynamically corrected.

[0124] This application's solution introduces a closed-loop feedback mechanism to monitor and evaluate the actual effect of gear shifting in real time. Once the gear shifting command is executed and the shift is achieved, the system no longer relies solely on pre-prediction but actively collects the peak value of the residual load swing angle after the shift. By comparing the actual monitoring results with the predicted swing angle peak value before the shift, the deviation between prediction and reality can be quantified, i.e., the phase matching deviation. This deviation directly reflects the accuracy of the current prediction model or the triggering timing of the mechanically bearable window. Based on this deviation, the system can generate a phase compensation amount and feed it back into subsequent predicted swing phase calculations. Thus, the triggering timing of the mechanically bearable window can be dynamically corrected, enabling future gear shifts to more accurately match the actual swing state of the load, thereby effectively reducing shifting impact.

[0125] In some preferred embodiments, assuming the crane mechanism is performing a gear shift, the system issues a gear control command when the load swings to a specific phase based on the predicted swing phase and impact risk assessment results. After the command is executed, the system immediately initiates a preset monitoring window, collecting load swing data in real time using tilt sensors and accelerometers. At the end of the monitoring period, the system calculates the peak residual swing angle of the load to be 0.8 degrees. Simultaneously, the system predicts an ideal peak residual swing angle of 0.2 degrees before the gear shift. By comparison, a phase matching deviation of 0.6 degrees is obtained. Based on this deviation, the system generates a phase compensation amount, for example, advancing the subsequent predicted swing phase by 50 milliseconds. When the next gear shift is required, this compensation amount is added to the predicted swing phase calculation, causing the triggering timing of the mechanically bearable window to be advanced accordingly, thereby more accurately capturing the load's swing state. It is expected that the peak residual swing angle can be reduced to 0.3 degrees or even lower, further improving the smoothness of gear shifting.

[0126] See Figure 4 , Figure 4 This is a schematic diagram of a crane gear position control system according to an embodiment of this application. The crane gear position control system 400 includes:

[0127] The acquisition module 410 is used to acquire the operating status information of the crane mechanism and the swing status information of the load of the crane mechanism, wherein the swing status information includes the absolute tilt angle and high-frequency transient acceleration.

[0128] The determination module 420 is used to perform time alignment processing on the load swing state information in order to determine the real-time swing phase of the load.

[0129] Prediction module 430 is used to predict the evolution of the swing phase of the load within a preset time period based on the real-time swing phase.

[0130] The impact risk assessment module 440 is used to assess the impact risk on the transmission chain of the crane mechanism caused by triggering gear switching at different times based on the evolution of the swing phase, and obtain the impact risk assessment results.

[0131] The instruction generation module 450 is used to determine the execution mode of gear switching action based on the operating status information of the crane mechanism and the impact risk assessment results, and to generate corresponding gear control instructions.

[0132] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0133] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0134] The above provides a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A method for intelligent control of hoisting mechanism gear position adjustment, characterized in that, include: The system acquires the operating status information of the crane mechanism and the swing status information of the load of the crane mechanism, wherein the swing status information includes the absolute tilt angle and high-frequency transient acceleration. The oscillation state information of the load is time-aligned to determine the real-time oscillation phase of the load. Based on the real-time swing phase, predict the evolution of the swing phase of the load over a future preset time period; Based on the evolution of the swing phase, the impact risk of triggering gear switching on the transmission chain of the crane mechanism at different times is evaluated, and the impact risk assessment result is obtained. Based on the operating status information of the hoisting mechanism and the impact risk assessment results, the execution method of the gear shifting action is determined, and the corresponding gear control command is generated.

2. The method according to claim 1, characterized in that, The acquisition of the swing state information of the load of the hoisting mechanism includes: The absolute tilt angle of the hook of the hoisting mechanism relative to the direction of gravity in space is measured by a tilt sensor. The high-frequency transient acceleration of the load during the oscillation process is obtained by using an accelerometer.

3. The method according to claim 2, characterized in that, The step of performing time alignment processing on the oscillation state information of the load to determine the real-time oscillation phase of the load includes: Establish a data preprocessing and time alignment mechanism; The absolute tilt angle and the high-frequency transient acceleration are processed based on the data preprocessing and time alignment mechanism to obtain preprocessed swing state information, wherein the preprocessed swing state information includes the preprocessed absolute tilt angle and the preprocessed high-frequency transient acceleration. The preprocessed oscillation state information is predicted based on the extended Kalman filter to determine the real-time oscillation phase of the load.

4. The method according to claim 3, characterized in that, The step of predicting the preprocessed oscillation state information based on an extended Kalman filter to determine the real-time oscillation phase of the load includes: Obtain the actual rotational speed of the hoisting motor and the vertical acceleration of the drum of the hoisting mechanism; A state prior estimate is obtained by predicting based on the extended Kalman filter, the actual rotational speed of the hoisting motor, and the vertical acceleration of the drum. The observation residual is obtained by calculating the preprocessed oscillation state information and the state prior estimate based on the extended Kalman filter; The state prior estimate is corrected based on the observed residuals to obtain the target swing angle and the velocity time series of the target swing angle to determine the real-time swing phase of the load.

5. The method according to claim 1, characterized in that, The method of predicting the evolution of the load's swing phase over a preset time period based on the real-time swing phase includes: Based on the real-time swing phase and the preset forward time prediction model, the phase angle and swing amplitude of the load in the future preset time period are predicted; The oscillation phase evolution is generated based on the phase angle and the oscillation amplitude.

6. The method according to claim 1, characterized in that, Based on the evolution of the swing phase, the impact risk on the transmission chain of the crane mechanism caused by triggering gear switching at different times is assessed, and the impact risk assessment results are obtained, including: Based on the evolution of the swing phase, the instantaneous peak load that the transmission chain of the crane mechanism will bear when the upshift is triggered at each moment is calculated point by point; The impact risk assessment result is obtained based on the instantaneous peak load and the preset safety threshold.

7. The method according to claim 1, characterized in that, The step of determining the execution method of gear shifting action based on the operating status information of the hoisting mechanism and the impact risk assessment results, and generating corresponding gear control commands, includes: Obtain the mechanical inertia parameters and electrical response time of the hoisting mechanism; Calculate the total delay time from generation to actual execution based on the mechanical inertia parameters and the electrical response time; The total delay time is superimposed on the swing phase evolution to obtain the predicted swing phase at the actual execution time of the instruction; Based on the impact risk assessment results, preset conditions are set for the gear shifting action; Based on the predicted swing phase at the actual execution time of the instruction, and under the condition that the preset conditions are met, a mechanically bearable window that matches the predicted swing phase is selected. Based on the operating status information of the hoisting mechanism and the mechanically bearable window, the execution mode of the gear shifting action is determined, and the corresponding gear control command is generated.

8. The method according to claim 7, characterized in that, The predicted swing phase based on the actual execution time of the instruction, under the condition that the preset conditions are met, selects a mechanically acceptable window that matches the predicted swing phase, including: Continuously monitor the trend of the predicted swing phase at the actual execution time of the instruction within each mechanically bearable window; Based on the predicted swing phase change trend within each mechanically bearable window at the actual execution time of the instruction, calculate the swing phase change rate within each mechanically bearable window and the sensitivity of the swing phase at the edge of each mechanically bearable window to deviate from the bearable range. Based on the swing phase change rate, the stability of the swing state inside the mechanically bearable window is identified, and a stability evaluation result is obtained. Based on the sensitivity, the stability of the mechanically supported window edge is evaluated to obtain the stability evaluation result; If the stability assessment results and the stability evaluation results meet the preset conditions, a mechanically acceptable window that matches the predicted oscillation phase is selected.

9. The method according to claim 7 or 8, characterized in that, After determining the execution method of the gear shifting action based on the operating status information of the hoisting mechanism and the impact risk assessment results, and generating the corresponding gear control command, the process includes: Execute the gear control command to achieve gear switching; Within a preset monitoring window period after gear shifting, the residual swing angle peak value of the load is collected in real time as the monitoring result; The monitoring results are compared with the predicted peak swing angle before gear shifting to obtain the phase matching deviation. Based on the phase matching deviation, a phase compensation amount is generated and superimposed on the subsequent predicted swing phase calculation to correct the triggering timing of the mechanically bearable window.

10. A smart control system for adjusting the gear position of a crane mechanism, characterized in that, include: The acquisition module is used to acquire the operating status information of the crane mechanism and the swing status information of the load of the crane mechanism, wherein the swing status information includes the absolute tilt angle and high-frequency transient acceleration. The determination module is used to perform time alignment processing on the swing state information of the load in order to determine the real-time swing phase of the load. The prediction module is used to predict the evolution of the load's swing phase over a future preset time period based on the real-time swing phase. The impact risk assessment module is used to assess the impact risk to the transmission chain of the crane mechanism caused by triggering gear switching at different times based on the evolution of the swing phase, and obtain the impact risk assessment result. The instruction generation module is used to determine the execution mode of the gear shifting action based on the operating status information of the hoisting mechanism and the impact risk assessment results, and to generate the corresponding gear control instruction.

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