Intelligent control method and system for crane in aerial work condition

Abstract

The present application relates to the technical field of crane control, in particular to a crane intelligent control method and system under aerial work condition. According to the distance fluctuation degree between the actual load center of gravity position and the theoretical load center of gravity position in the historical analysis period of each moment, the control response lag factor is obtained, and combined with the load swing angle change degree in the historical analysis period, the error accumulation factor is obtained; according to the environment index, the confusion degree and fluctuation degree of rigging vibration displacement, and the load swing angle change degree, the disturbance load swing mapping value is obtained; according to the error accumulation factor, the disturbance load swing mapping value, the control response lag factor and the correlation degree of the two in the historical analysis period of the current moment, the compensation coefficient of the current moment is obtained, and the crane is controlled. The present application adaptively generates compensation coefficient and corrects control from three angles of risk trend, problem severity and problem root, and improves the accuracy of crane control.

Description

Technical Field

[0001] This invention relates to the field of crane control technology, specifically to an intelligent control method and system for cranes operating at height. Background Technology

[0002] Aerial work cranes are widely used in construction, bridge building, energy, and other fields, and their stable control is a crucial factor in ensuring project safety and efficiency. With the development of sensor technology, automatic control technology, and information technology, the intelligentization of cranes has become an inevitable trend.

[0003] Currently, intelligent control methods for cranes operating at height mainly employ PID control strategies based on predetermined trajectory tracking and load sway measurement. However, in the high-altitude working environment, the system is continuously affected by time-varying wind disturbances, flexible vibrations of lifting slings, and uncertainties in load parameters. This leads to significant perturbations in the controlled object model, causing adaptive failure of the PID control law based on fixed parameters. This results in a dynamic coupling contradiction between anti-sway control and trajectory tracking, causing system response lag and decreased stability. Consequently, it becomes difficult to eliminate low-frequency residual oscillations and steady-state positioning errors during the precision positioning stage, severely restricting the accuracy and operational efficiency of high-altitude operations. Summary of the Invention

[0004] To address the technical problem of fixed-parameter PID control models failing to adapt to complex high-altitude working environments, leading to model adaptability failure, this invention aims to provide an intelligent control method and system for cranes under high-altitude working conditions. The specific technical solution adopted is as follows:

[0005] In a first aspect, one embodiment of the present invention provides an intelligent control method for a crane under high-altitude operation conditions, the method comprising:

[0006] Real-time acquisition of the crane's load swing angle, environmental indicators, rigging vibration displacement, actual load center of gravity position, and theoretical load center of gravity position under control commands at every moment;

[0007] Based on the distance fluctuation of the theoretical load center of gravity position from the actual load center of gravity position at each time point within the historical analysis period, the control response hysteresis factor at each time point is obtained; based on the control response hysteresis factor at the current time point and the load swing angle change at each time point within the historical analysis period, the error accumulation factor at the current time point is obtained.

[0008] Based on the historical analysis of environmental indicators, the degree of disorder and fluctuation of rigging vibration displacement, and the degree of load swing angle change at each time point, the disturbance load swing mapping value at each time point is obtained.

[0009] Based on the error accumulation factor, the disturbance load swing mapping value, the control response hysteresis factor at the current moment, and the correlation between the disturbance load swing mapping value and the control response hysteresis factor at each moment in the historical analysis period at the current moment, the compensation coefficient at the current moment is obtained; the crane is controlled using the compensation coefficient.

[0010] Furthermore, obtaining the control response hysteresis factor at each moment includes:

[0011] Arrange the theoretical load centroid positions and actual load centroid positions of all times within the historical analysis period of each time step according to the time sequence to obtain the theoretical load position sequence and the actual load position sequence.

[0012] The theoretical load position sequence is matched with the actual load position sequence. The distance between the actual load centroid position in the actual load position sequence and the theoretical load centroid position matched in the theoretical load position sequence is calculated. The distances corresponding to all actual load centroid positions in the actual load position sequence are arranged to obtain the load error sequence at each time moment.

[0013] Calculate the variance of all elements within a preset window for each element in the load error sequence, and denot it as the neighborhood error fluctuation of each element; arrange the neighborhood error fluctuations of all elements in the load error sequence to obtain the error fluctuation sequence.

[0014] The error fluctuation sequence is integrated over the historical analysis period at each moment, and the ratio of the integration result to the duration of the historical analysis period is used as the control response hysteresis factor at each moment.

[0015] Further, obtaining the error accumulation factor at the current moment includes:

[0016] Based on the differences in the rate of change of load swing angle between each time period and its neighboring time periods in the historical analysis of each time period, as well as the differences in load swing angle between adjacent time periods, the load swing chaos coefficient at each time period is obtained.

[0017] Obtain the first-order difference sequence of the load error sequence at the current moment, and use the mean of the elements in the first-order difference sequence as the cumulative error intensity at the current moment;

[0018] The error accumulation factor at the current moment is obtained based on the current load swing chaos coefficient, the control response hysteresis factor, and the error accumulation intensity.

[0019] Furthermore, obtaining the load swing chaos coefficient at each moment includes:

[0020] Obtain the maximum value of the load swing angle at all times within the historical analysis period for each time moment, and record the time corresponding to the maximum value point as the analysis time.

[0021] Calculate the slope of the load swing angle at each moment, and average the absolute difference between the slopes of the load swing angle at each analysis moment and the slopes at other moments in its neighboring time period to obtain the local swing angle abrupt change degree at each analysis moment; the mean of the local swing angle abrupt change degrees in all analysis time periods is denoted as the overall swing angle abrupt change degree.

[0022] The oscillation fluctuation is obtained by averaging the absolute difference between the load swing angles at any two adjacent analysis times.

[0023] The load swing chaos coefficient at each moment is obtained based on the overall swing angle abrupt change and the swing fluctuation.

[0024] Furthermore, obtaining the disturbance load swing mapping value at each moment includes:

[0025] Environmental indicators and rigging vibration amplitude are recorded as analysis indicators;

[0026] Curve fitting is performed on the same analytical index for all times within the historical analysis period for each time point to obtain two envelope lines of the obtained fitted curve; phase extraction is performed on the envelope lines to obtain phase values; the absolute difference of the phase values ​​of the two envelope lines of the fitted curve corresponding to each analytical index is recorded as the phase difference at each time point.

[0027] Calculate the variance of each analytical indicator across all times within the historical analysis period for each time point, as the data volatility at each time point;

[0028] Based on the phase difference and data volatility of all types of analytical indicators, the comprehensive perturbation degree at each moment is obtained;

[0029] Based on the load swing chaos coefficient and the comprehensive disturbance degree, the disturbance load swing mapping value at each moment is obtained.

[0030] Furthermore, obtaining the compensation coefficient at the current moment includes:

[0031] Arrange the disturbance load swing mapping value and the control response hysteresis factor of all times within the historical analysis period at the current time in chronological order to obtain the swing mapping sequence and the hysteresis factor sequence in sequence. Obtain the correlation coefficient between the two sequences and record it as the compensation value degree at the current time.

[0032] The product of the current disturbance load swing mapping value and the control response hysteresis factor is used as the problem severity.

[0033] The compensation coefficient for the current moment is obtained based on the severity of the problem, the degree of compensation value, and the error accumulation factor.

[0034] Furthermore, the control of the crane using the compensation coefficient includes:

[0035] Obtain the ideal PID control parameters of the crane, and use the sum of constant 1 and the compensation coefficient at the current moment to weight the ideal PID control parameters to obtain the corrected PID control parameters at the current moment; use the corrected PID control parameters to control the crane in real time.

[0036] Furthermore, obtaining the comprehensive perturbation degree at each moment includes:

[0037] Calculate the product of the phase difference and the data volatility for each analytical indicator at each time step, and use it as the local perturbation degree; sum the local influence degrees of all types of analytical indicators at each time step, and use it as the comprehensive perturbation degree at each time step.

[0038] Furthermore, the least squares method is used to perform curve fitting on the same analytical index for all times within the historical analysis period at each time point.

[0039] Secondly, another embodiment of the present invention provides an intelligent control system for cranes operating at height, the system comprising:

[0040] The data acquisition module is used to acquire the load swing angle, environmental indicators, rigging vibration displacement, actual load center of gravity position, and theoretical load center of gravity position of the crane at each moment in real time under control commands.

[0041] The error accumulation analysis module is used to obtain the control response hysteresis factor at each moment based on the distance fluctuation between the actual load center of gravity position and the theoretical load center of gravity position at each moment in the historical analysis period; and to obtain the error accumulation factor at the current moment based on the control response hysteresis factor at the current moment and the load swing angle change at each moment in the historical analysis period.

[0042] The disturbance load swing analysis module is used to obtain the disturbance load swing mapping value at each moment based on the environmental indicators, the degree of disorder and fluctuation of the rigging vibration displacement, and the degree of load swing angle change at each moment in the historical analysis period.

[0043] The crane control module is used to obtain the compensation coefficient at the current moment based on the error accumulation factor, the disturbance load swing mapping value, the control response hysteresis factor, and the correlation between the disturbance load swing mapping value and the control response hysteresis factor at each moment in the historical analysis period at the current moment; and to control the crane using the compensation coefficient.

[0044] The present invention has the following beneficial effects:

[0045] In this embodiment of the invention, the control response hysteresis factor quantifies the degree of hysteresis in the execution effect of the PID controller in a real dynamic environment, directly revealing the inadequacy of fixed parameters. Combined with the degree of load swing angle change during the historical analysis period that presents the chaos of the system, the dynamic cumulative effect of the control system error is analyzed, and the error accumulation factor is obtained. The environmental indicators are coupled with the disorder and volatility of the rigging vibration displacement, and combined with the degree of load swing angle change, the causal relationship of how external disturbances are transmitted through the structure and affect the load stability is clearly quantified, and the significance of external disturbances causing internal instability of the load is measured, and the disturbance load swing mapping value is obtained. By integrating the error accumulation factor, the disturbance load swing mapping value, the control response hysteresis factor, and the correlation between the two, the necessity of compensation for the control system is analyzed from three perspectives: risk trend, problem severity, and problem root cause. The compensation coefficient is adaptively generated, which can effectively cope with the model perturbation caused by the complex high-altitude working environment such as load, attitude, and external wind disturbance changes in high-altitude operations. This fundamentally solves the adaptive failure problem of fixed parameter PID and enhances the operational accuracy of cranes in high-altitude working conditions. Attached Figure Description

[0046] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart illustrating the steps of an intelligent crane control method under high-altitude operation conditions provided in an embodiment of the present invention;

[0048] Figure 2 A flowchart illustrating a method for obtaining an error accumulation factor according to an embodiment of the present invention;

[0049] Figure 3 This is a system structure diagram of a crane intelligent control system under high-altitude operation conditions provided in one embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of a computer device for a crane intelligent control system under high-altitude operation conditions, provided as an embodiment of the present invention. Detailed Implementation

[0051] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a crane intelligent control method and system for high-altitude operations proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0053] The following description, in conjunction with the accompanying drawings, details a specific scheme for an intelligent crane control method and system under high-altitude operation conditions provided by the present invention.

[0054] Example 1:

[0055] This invention proposes an intelligent control method for cranes operating at height. Please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of a crane intelligent control method under high-altitude operation conditions according to an embodiment of the present invention. The method includes:

[0056] Step S1: Real-time acquisition of the crane's load swing angle, environmental indicators, rigging vibration displacement, actual load center of gravity position, and theoretical load center of gravity position under control commands at each moment.

[0057] High-precision wind speed and direction sensors are used to collect instantaneous wind speeds in the crane's high-altitude operation area at any given moment, recording them as environmental indicators. A combination module of LiDAR and industrial vision sensors is deployed. Specifically, the LiDAR generates 3D point cloud data of the operation area by emitting laser beams and receiving reflected signals. Images are captured by a camera, and a deep learning model is used to identify the 3D boundaries of crane components and loads, determining the load's center of gravity position at any given moment, which is recorded as the actual load center of gravity position. Inertial measurement units are installed at key nodes of the lifting rigging to collect the vibration displacement values ​​of the rigging at any given moment, recording them as rigging vibration displacement. Simultaneously, the attitude parameters of the crane boom at any given moment are acquired, including pitch angle, slewing angle, and extension length. Here, the load refers to the object being lifted by the crane, and the rigging refers to flexible components such as wire ropes and hooks connecting the crane and the load, which are susceptible to wind-induced vibration.

[0058] A timestamp synchronization protocol is employed to align the timelines of heterogeneous data collected by various sensors, ensuring temporal consistency. The sensor self-diagnostic module verifies data validity, removes outliers and missing data, and performs real-time interpolation to complete missing data. The point cloud data from the LiDAR uses the radar coordinate system as a reference, the image pixel coordinates from the visual sensor use the camera coordinate system, and the vibration data from the inertial measurement unit uses the rigging local coordinate system. Through coordinate transformations such as rotation and translation matrices, these data are unified to a global coordinate system, such as a geodetic coordinate system with the crane base as the origin, achieving spatial consistency calibration. The processed multi-source data is then uniformly converted into a standardized numerical format.

[0059] Control commands describe the target state of the crane mechanism itself, including luffing commands, slewing commands, and hoisting commands, which sequentially control the boom attitude data of the boom's pitch angle, slewing angle, and telescopic length. The pitch angle refers to the angle between the crane boom and the horizontal plane, the slewing angle refers to the angle by which the crane boom rotates around its vertical axis, and the telescopic length refers to the extension of the crane boom's telescopic section.

[0060] A kinematic model of the crane, such as a forward kinematic model, is established. The crane boom's pitch angle, slewing angle, telescopic length, and known geometric parameters are input into the model. The model outputs the three-dimensional coordinates of the crane load, which are denoted as the theoretical load center of gravity position, thus transforming the boom attitude data and load position to the same coordinate system. The crane boom may execute multiple control commands simultaneously. The boom attitude data for all control commands at each moment is input into the crane kinematic model, and the model outputs the theoretical load center of gravity position for the corresponding moment.

[0061] In one implementation of this invention, all sensors have the same data acquisition frequency, which is set to 10 Hz.

[0062] Step S2: Based on the distance fluctuation of the theoretical load center of gravity position from the actual load center of gravity position at each time point within the historical analysis period, obtain the control response hysteresis factor at each time point; based on the control response hysteresis factor at the current time point and the load swing angle change at each time point within the historical analysis period, obtain the error accumulation factor at the current time point.

[0063] The distance between the actual load center of gravity and the theoretical load center of gravity of the crane at each moment reflects the accuracy of the control system in tracking commands. It includes the net effect of all system delays, flexible vibrations, external disturbances, and other factors. The fluctuation of the corresponding distance at each moment in the historical analysis period reflects the inconsistency of the control system's response in the historical analysis period. It can measure the degree of response lag of the control system in the historical analysis period and obtain the control response hysteresis factor.

[0064] In a chaotic oscillation process, the load swing angle is quite drastic. The degree of load swing angle variation at each moment within the historical analysis period reflects the inherent chaos of the control system. The inherent chaotic characteristics of the system determine its sensitivity to or amplification capability of errors, affecting the error accumulation rate. The control response hysteresis factor reflects the degree of response lag of the control system within the historical analysis period. A control system with a hysteresis response cannot correct errors in a timely manner; that is, the worse the control system's ability to correct errors, the more severe the error accumulation. Therefore, by combining the control response hysteresis factor with the degree of load swing angle variation at each moment within the historical analysis period, the dynamic accumulation effect of the control system error is analyzed, resulting in the error accumulation factor.

[0065] In one implementation of this invention, the minute preceding each moment is recorded as its historical analysis period, and the duration of the historical analysis period can be set according to specific circumstances.

[0066] Step S3: Based on the environmental indicators, the degree of disorder and fluctuation of rigging vibration displacement, and the degree of load swing angle change at each moment in the historical analysis period, obtain the disturbance load swing mapping value at each moment.

[0067] In high-altitude operations, the effects of environmental factors such as wind disturbance and rigging vibration on load sway are not independent but coupled. Environmental factors can induce rigging vibration, which in turn amplifies the impact of wind disturbance on load sway angle. Among these factors, environmental factors are the primary source of external disturbance, and rigging vibration is the direct path through which external disturbance and mechanical motion are transmitted to the load.

[0068] The environmental indicators and the degree of disorder and fluctuation of rigging vibration displacement at each moment within the historical analysis period collectively demonstrate that the external environment is chaotic, which is the cause of load swaying. The degree of load swing angle change at each moment within the historical analysis period reflects the inherent chaos of the control system, which is the effect, revealing the inherent instability of the load swaying. By combining these two factors and linking environmental excitation with structural response, the significance of external disturbances causing internal instability of the load is measured, i.e., the effective portion of the external disturbance is transmitted to the load, thus obtaining the disturbance load sway mapping value.

[0069] Step S4: Based on the error accumulation factor, disturbance load swing mapping value, control response hysteresis factor at the current moment, and the correlation between the disturbance load swing mapping value and the control response hysteresis factor at each moment in the historical analysis period at the current moment, obtain the compensation coefficient at the current moment; use the compensation coefficient to control the crane.

[0070] In high-altitude operations, errors accumulate over time. For example, uncompensated errors caused by hysteresis will be compounded by new errors generated by subsequent disturbances. Therefore, it is necessary to analyze the future risks of the control system through error accumulation factor analysis to ensure that the compensation strategy can offset the cumulative effect. The disturbance load swing mapping value at the current moment and the control response hysteresis factor together reflect the severity of the control problem caused by the disturbance. The correlation between the disturbance load swing mapping value and the control response hysteresis factor in the historical analysis period at the current moment can analyze the main contradiction of control hysteresis and determine whether the hysteresis problem is worth compensating for. The above three factors, from the perspectives of the severity of the control problem, whether the control problem is worth compensating for, and the urgency of future risks, jointly analyze the necessity of compensation for the control system, obtain the compensation coefficient, and thus enable real-time control of the crane.

[0071] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the control response hysteresis factor includes: arranging the theoretical load centroid positions and actual load centroid positions of all times within the historical analysis period at each time step according to time sequence to obtain a theoretical load position sequence and an actual load position sequence; matching the theoretical load position sequence and the actual load position sequence, calculating the distance between each actual load centroid position in the actual load position sequence and the matched theoretical load centroid position in the theoretical load position sequence, arranging the distances corresponding to all actual load centroid positions in the actual load position sequence to obtain a load error sequence at each time step; calculating the variance of all elements within a preset window for each element in the load error sequence, denoted as the neighborhood error fluctuation of each element; arranging the neighborhood error fluctuations of all elements in the load error sequence to obtain an error fluctuation sequence; integrating the error fluctuation sequence within the historical analysis period at each time step, and using the ratio of the integration result to the duration of the historical analysis period as the control response hysteresis factor at each time step.

[0072] It should be noted that the error fluctuation sequence measures the severity of error fluctuations within the neighborhood of each element at any given time, reflecting the inconsistency of the control system's response at different times. Integrating the error fluctuation sequence over the historical analysis period involves summing the error fluctuation intensity at each instant within the historical analysis period, i.e., the neighborhood error fluctuation degree, to obtain the overall cumulative response hysteresis. To eliminate the influence of different analysis durations, the integral result is divided by the duration of the historical analysis period to obtain the control response hysteresis factor, which represents the overall hysteresis level. A larger control response hysteresis factor means that the control system's response lag is more significant within the historical analysis period, and the tracking accuracy of the load trajectory is lower; conversely, a smaller factor indicates that the control system responds promptly within the historical analysis period.

[0073] In one implementation of this invention, the elements in the theoretical load position sequence and the actual load position sequence are matched by a dynamic time warping algorithm to determine the theoretical load centroid position that matches the actual load centroid position in the theoretical load position sequence for each actual load centroid position in the actual load position sequence.

[0074] In one implementation of this invention, each element in the load error sequence is located at the center of its preset window, which contains 11 elements. The implementer can set these elements according to specific circumstances.

[0075] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the error accumulation factor is described in [reference needed]. Figure 2 The diagram illustrates a flowchart of a method for obtaining an error accumulation factor according to an embodiment of the present invention, the method comprising:

[0076] Step S210: Based on the differences in the rate of change of load swing angle between each time period and its neighboring time periods within the historical analysis period, as well as the differences in load swing angle between adjacent time periods, obtain the load swing chaos coefficient for each time period.

[0077] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the load swing chaos coefficient includes: obtaining the maximum point of the load swing angle among all times in the historical analysis period at each time moment, and recording the time corresponding to the maximum point as the analysis time moment; calculating the slope of the load swing angle at each time moment, averaging the absolute difference between the slope of the load swing angle at each analysis time moment and the slope of the load swing angle at other times in its neighboring period, to obtain the local swing angle abrupt change degree at each analysis time moment; recording the average of the local swing angle abrupt change degrees of all analysis periods as the overall swing angle abrupt change degree; averaging the absolute difference between the load swing angles of every two adjacent analysis times moment to obtain the swing fluctuation degree; and obtaining the load swing chaos coefficient at each time moment based on the overall swing angle abrupt change degree and the swing fluctuation degree.

[0078] It should be noted that the maximum points of the load swing angle are the most critical manifestations of the load's swing energy and dynamic characteristics. Analyzing the information at these points can capture the most significant features of the swing. The slope of the load swing angle at each moment refers to the ratio of the difference between the load swing angle at each moment and the adjacent previous moment to the time interval, representing the load swing rate. In an ideal, smooth pendulum, the velocity change at the crest is gradual, but when affected by irregular external forces, the shape of the load swing angle crest becomes sharp, and the velocity undergoes abrupt changes. The local swing angle abruptness measures the drastic degree of change in the load's swing velocity near the analysis moment, while the overall swing angle abruptness reflects the dynamic abruptness of the load swing angle at the maximum amplitude. A chaotic swing process often contains both drastic state abrupt changes, i.e., inherent instability, and exhibits a high degree of overall disorder, i.e., inherent randomness. The greater the abrupt change in the overall swing angle, the stronger the dynamic impact experienced by the surface load during the switching of swing direction, and the worse the system stability. Swing fluctuation reflects the stability of the crane load swing angle; the smaller the swing fluctuation, the more stable the load swing amplitude. Conversely, the greater the swing amplitude, the stronger the randomness, and the more prone to low-frequency residual oscillations. Therefore, both the abrupt change in the overall swing angle and the swing fluctuation are positively correlated with the load swing chaos coefficient. In this embodiment of the invention, the product of the abrupt change in the overall swing angle and the swing fluctuation at each moment is used as the load swing chaos coefficient.

[0079] The larger the load swing chaos coefficient, the worse the stability of the load during the swing angle switching process in the historical analysis period. This will exacerbate the low-frequency residual oscillation caused by the irregular amplitude of the load swing. The more significant the perturbation of the crane load, the weaker the adaptability of the traditional control law, and the stronger the subsequent adaptive compensation is required.

[0080] In one implementation of this invention, a zero-point detection algorithm is used to identify the maximum value of the load swing angle at all times within the historical analysis period.

[0081] In one implementation of this invention, the middle time of each analysis time within its neighboring time period is set to 2 seconds, and the implementer can set it according to the specific situation.

[0082] Step S220: Obtain the first-order difference sequence of the load error sequence at the current moment, and use the mean of the elements in the first-order difference sequence as the cumulative error intensity at the current moment.

[0083] It should be noted that since the elements in the load error sequence include the combined effects of all system delays, flexible vibrations, external disturbances, and other factors, the load error sequence can be used to analyze error accumulation. The intensity of error accumulation reflects the rate of error accumulation over the historical analysis period at the current moment, and is direct evidence of error accumulation. If the intensity of error accumulation is greater, the system control error is changing and accumulating rapidly; conversely, the system control error changes slowly, and the accumulation effect is weak.

[0084] Step S230: Obtain the error accumulation factor at the current moment based on the load swing chaos coefficient, control response hysteresis factor and error accumulation intensity at the current moment.

[0085] It should be noted that if the error accumulation intensity is high, the system control error is changing and accumulating rapidly. The load oscillation chaos coefficient represents the inherent instability of the control system at the current moment. The inherent chaotic characteristics of the system determine its sensitivity to or amplification ability to errors. When the load oscillation chaos coefficient is large, the state of the control system itself is more divergent, and any small error is more easily amplified, accelerating the error accumulation process, thus making the error more likely to accumulate. The control response hysteresis factor presents the degree of response lag of the control system within the historical analysis period. When the control response hysteresis factor is large, the control system cannot correct errors in time, resulting in overshoot and oscillations. The more difficult it is to eliminate errors, the worse the control system's ability to correct errors, thus making the error accumulation more severe. Therefore, the load oscillation chaos coefficient, the control response hysteresis factor, and the error accumulation intensity are all positively correlated with the error accumulation factor.

[0086] In this embodiment of the invention, the product of the current load swing chaos coefficient, the control response hysteresis factor, and the error accumulation intensity is used as the error accumulation factor. The larger the error accumulation factor, the stronger the dynamic accumulation effect of the control system error. In the crane control system, strong compensation is required to prevent the load trajectory error from continuously expanding and affecting the crane's precise positioning.

[0087] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the disturbance load swing mapping value includes: denoting environmental indicators and rigging vibration amplitude as analysis indicators; performing curve fitting on the same analysis indicator for all times within the historical analysis period at each time moment to obtain two envelope lines of the obtained fitted curve; extracting the phase of the envelope lines to obtain phase values; denoting the absolute difference between the phase values ​​of the two envelope lines of the fitted curve corresponding to each analysis indicator as the phase difference at each time moment; calculating the variance of each analysis indicator for all times within the historical analysis period at each time moment as the data volatility at each time moment; obtaining the comprehensive disturbance degree at each time moment based on the phase difference and data volatility of all types of analysis indicators; and obtaining the disturbance load swing mapping value at each time moment based on the load swing chaos coefficient and the comprehensive disturbance degree.

[0088] It should be noted that the envelope of the fitted curves of the analytical indicators over a historical analysis period reflects the energy modulation process and dynamic characteristics of the system better than the analytical indicators at a single moment. To analyze the regularity and synchronicity of the changes in the envelope of the fitted curves, the phase value of the envelope is extracted. Phase difference is a measure of the internal coordination of the signal; a larger phase difference means that the amplitude changes of the fitted curves of each analytical indicator are more chaotic and irregular. Data volatility measures the signal strength of the fitted curves of each analytical indicator. Therefore, when both phase difference and data volatility are large, the fitted curves of each analytical indicator exhibit chaotic and strong disturbances, representing the overall level of external disturbance threats by integrating threats from two different sources: wind and cable vibration. In this embodiment of the invention, the product of the phase difference and data volatility corresponding to each analytical indicator at each moment is calculated as the local disturbance degree; the sum of the local influence degrees of all types of analytical indicators at each moment is used as the comprehensive disturbance degree at each moment. The comprehensive disturbance degree represents the overall level of external disturbance threats from wind and cable vibration.

[0089] The overall disturbance degree is the cause, reflecting the chaotic characteristics of the external environment, while the load swing chaos coefficient is the effect, reflecting the inherent instability of the load swing. When the overall disturbance degree is large, the external disturbance is more severe. In this case, a larger load swing chaos coefficient means that the external disturbance causes more significant instability within the load, effectively amplifying and transmitting the external disturbance to the load, resulting in a larger disturbance load swing mapping value. Therefore, both the load swing chaos coefficient and the overall disturbance degree are positively correlated with the disturbance load swing mapping value. In this embodiment of the invention, the product of the load swing chaos coefficient and the overall disturbance degree at the example time is used as the disturbance load swing mapping value. The larger the disturbance load swing mapping value, the more significant the swing error caused by external disturbances, and the more dangerous the system, which should be prioritized for suppression in subsequent crane control.

[0090] In one implementation of this invention, the envelope of the fitted curve is generated using the Hilbert transform method.

[0091] In one implementation of this invention, for each analytical indicator, a two-dimensional coordinate system is established with time as the horizontal axis and the analytical indicator as the vertical axis. The analytical indicators of all times within the historical analysis period for each time moment are mapped to the two-dimensional coordinate system to obtain coordinate points. The least squares method is used to perform curve fitting on the coordinate points in the two-dimensional coordinate system to obtain the fitted curve.

[0092] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the compensation coefficient includes: arranging the disturbance load swing mapping value and the control response hysteresis factor of all times within the historical analysis period at the current time in chronological order to obtain the swing mapping sequence and the hysteresis factor sequence in sequence; obtaining the correlation coefficient between the two sequences and recording it as the compensation value degree at the current time; taking the product of the disturbance load swing mapping value and the control response hysteresis factor at the current time as the problem severity; and obtaining the compensation coefficient at the current time based on the problem severity, the compensation value degree, and the error accumulation factor.

[0093] It should be noted that if the correlation coefficient is closer to 1, it means the external disturbance is larger and the control system hysteresis is more severe. In this case, the external disturbance is the main factor causing the system hysteresis and is more worthy of compensation. If the correlation coefficient is closer to 0, it means that the external disturbance is basically unrelated to the degree of control system hysteresis. In this case, the inherent characteristics of the mechanism, such as friction and structural flexibility, are the main factors causing the system hysteresis and cannot be avoided, so compensation is basically not worthwhile. If the correlation coefficient is close to -1, it indicates that the increase in external disturbance reduces the hysteresis, which is extremely rare in high-altitude operations and represents abnormal data. Therefore, when the correlation coefficient is less than 0, the compensation value is directly set to 0. If both the disturbance load swing mapping value and the control response hysteresis factor are larger, it means that the system is simultaneously subjected to stronger external disturbances and more severe internal response hysteresis. This indicates that the control problem caused by the disturbance is more severe, that is, the comprehensive threat faced by the system at the current moment is stronger, making the problem more severe, and the system needs a stronger compensation force. Therefore, both the disturbance load swing mapping value and the control response hysteresis factor are positively correlated with the severity of the problem. A larger error accumulation factor indicates that the system is rapidly deviating from its stable state, the future risk is higher, the situation is more urgent, and stronger intervention is needed to reverse the trend. Therefore, stronger compensation control must be applied to reverse the deteriorating trend. Thus, the problem severity, the compensation value, and the error accumulation factor are all positively correlated with the compensation coefficient. In this embodiment of the invention, the product of the problem severity, the compensation value, and the error accumulation factor at the current moment is normalized to obtain the compensation coefficient at the current moment.

[0094] In this embodiment of the invention, the Sigmoid function is used for normalization. However, other normalization methods such as function transformation or max-min normalization can also be used, and no limitation is made here.

[0095] Preferably, in some possible implementations of the embodiments of the present invention, the crane control method includes: obtaining the ideal PID control parameters of the crane, using the sum of a constant 1 and the compensation coefficient at the current moment to weight the ideal PID control parameters to obtain the corrected PID control parameters at the current moment; and using the corrected PID control parameters to perform real-time control of the crane.

[0096] It should be noted that the ideal PID control parameters are basic parameters obtained through debugging under certain standard or relatively ideal operating conditions. When the system is stable, i.e., the compensation coefficient is close to 0, the controller operates using the basic parameters. When the system faces disturbances, hysteresis, or error accumulation, i.e., the compensation coefficient is larger, the controller will proportionally enhance the control action, i.e., all parameters increase synchronously, resulting in a faster response speed and stronger anti-interference capability to suppress oscillations and errors. The controller calculates based on the corrected PID control parameters, outputs a comprehensive control quantity, and decomposes it into three specific instructions that can be recognized by the three actuators according to rules: luffing command, slewing command, and hoisting command. These converted instructions are synchronously sent to each actuator of each crane site through the programmable logic controller, realizing multi-mechanism coordinated control.

[0097] This invention is now complete.

[0098] Example 2:

[0099] This invention proposes an intelligent control system for cranes operating at height. Please refer to [link / reference]. Figure 3 This diagram illustrates a system structure of an intelligent control system for cranes operating at height, provided by an embodiment of the present invention. The system includes:

[0100] The data acquisition module 510 is used to acquire in real time the load swing angle, environmental indicators, rigging vibration displacement, actual load center of gravity position and theoretical load center of gravity position of the crane at each moment;

[0101] The error accumulation analysis module 520 is used to obtain the control response hysteresis factor at each moment based on the distance fluctuation of the theoretical load center of gravity position from the actual load center of gravity position at each moment in the historical analysis period; and to obtain the error accumulation factor at the current moment based on the control response hysteresis factor at the current moment and the load swing angle change at each moment in the historical analysis period.

[0102] The disturbance load swing analysis module 530 is used to obtain the disturbance load swing mapping value at each moment based on the environmental indicators, the degree of disorder and fluctuation of the rigging vibration displacement, and the degree of load swing angle change at each moment during the historical analysis period.

[0103] The crane control module 540 is used to obtain the compensation coefficient at the current moment based on the error accumulation factor, disturbance load swing mapping value, control response hysteresis factor, and the correlation between the disturbance load swing mapping value and the control response hysteresis factor at each moment in the historical analysis period at the current moment; and to control the crane using the compensation coefficient.

[0104] It should be noted that the equipment provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the intelligent control system for cranes under high-altitude operation conditions and the intelligent control method for cranes under high-altitude operation conditions provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0105] Example 3:

[0106] Figure 4 This is a schematic diagram of a computer device for an intelligent control system of a crane operating at height, provided as an embodiment of the present invention. For example,... Figure 4 As shown, the computer device includes: a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602. When the processor 602 executes the computer program 603, the computer device can execute any of the crane intelligent control methods described above for high-altitude operation conditions.

[0107] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute a crane intelligent control method for high-altitude operations provided in embodiments of this application.

[0108] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0109] It should be understood that the device provided in this embodiment is used to execute the above-described intelligent control method for cranes under high-altitude operation conditions, and therefore can achieve the same effect as the above-described implementation method.

[0110] When using integrated units, the device may include a processing module and a storage module. When applied to a workpiece, the processing module can be used to control and manage the workpiece's operations. The storage module can be used to support the execution of program code by the workpiece.

[0111] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits contained in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and microprocessors, etc., and the storage module may be a memory.

[0112] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0113] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0114] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent control of a crane under high-altitude operation conditions, characterized in that, The method includes: Real-time acquisition of the crane's load swing angle, environmental indicators, rigging vibration displacement, actual load center of gravity position, and theoretical load center of gravity position under control commands at every moment; Based on the distance fluctuation of the theoretical load center of gravity position from the actual load center of gravity position at each time point within the historical analysis period, the control response hysteresis factor at each time point is obtained; based on the control response hysteresis factor at the current time point and the load swing angle change at each time point within the historical analysis period, the error accumulation factor at the current time point is obtained. Based on the historical analysis of environmental indicators, the degree of disorder and fluctuation of rigging vibration displacement, and the degree of load swing angle change at each time point, the disturbance load swing mapping value at each time point is obtained. Based on the error accumulation factor, the disturbance load swing mapping value, the control response hysteresis factor at the current moment, and the correlation between the disturbance load swing mapping value and the control response hysteresis factor at each moment in the historical analysis period at the current moment, the compensation coefficient at the current moment is obtained; the crane is controlled using the compensation coefficient. The process of obtaining the control response hysteresis factor at each moment includes: Arrange the theoretical load centroid positions and actual load centroid positions of all times within the historical analysis period of each time step according to the time sequence to obtain the theoretical load position sequence and the actual load position sequence. The theoretical load position sequence is matched with the actual load position sequence. The distance between the actual load centroid position in the actual load position sequence and the theoretical load centroid position matched in the theoretical load position sequence is calculated. The distances corresponding to all actual load centroid positions in the actual load position sequence are arranged to obtain the load error sequence at each time moment. Calculate the variance of all elements within a preset window for each element in the load error sequence, and denot it as the neighborhood error fluctuation of each element; arrange the neighborhood error fluctuations of all elements in the load error sequence to obtain the error fluctuation sequence. The error fluctuation sequence is integrated over the historical analysis period at each moment, and the ratio of the integration result to the duration of the historical analysis period is used as the control response hysteresis factor at each moment. The step of obtaining the error accumulation factor at the current moment includes: Based on the differences in the rate of change of load swing angle between each time period and its neighboring time periods in the historical analysis of each time period, as well as the differences in load swing angle between adjacent time periods, the load swing chaos coefficient at each time period is obtained. Obtain the first-order difference sequence of the load error sequence at the current moment, and use the mean of the elements in the first-order difference sequence as the cumulative error intensity at the current moment; The error accumulation factor at the current moment is obtained based on the current load swing chaos coefficient, the control response hysteresis factor, and the error accumulation intensity.

2. The intelligent control method for a crane under high-altitude operation conditions according to claim 1, characterized in that, The acquisition of the load swing chaos coefficient at each moment includes: Obtain the maximum value of the load swing angle at all times within the historical analysis period for each time moment, and record the time corresponding to the maximum value point as the analysis time. Calculate the slope of the load swing angle at each moment, and average the absolute difference between the slopes of the load swing angle at each analysis moment and the slopes at other moments in its neighboring time period to obtain the local swing angle abrupt change degree at each analysis moment; the mean of the local swing angle abrupt change degrees in all analysis time periods is denoted as the overall swing angle abrupt change degree. The oscillation fluctuation is obtained by averaging the absolute difference between the load swing angles at any two adjacent analysis times. The load swing chaos coefficient at each moment is obtained based on the overall swing angle abrupt change and the swing fluctuation.

3. The intelligent control method for a crane under high-altitude operation conditions according to claim 2, characterized in that, The process of obtaining the disturbance load swing mapping value at each moment includes: Environmental indicators and rigging vibration amplitude are recorded as analysis indicators; Curve fitting is performed on the same analytical index for all times within the historical analysis period for each time point to obtain two envelope lines of the obtained fitted curve; phase extraction is performed on the envelope lines to obtain phase values; the absolute difference of the phase values ​​of the two envelope lines of the fitted curve corresponding to each analytical index is recorded as the phase difference at each time point. Calculate the variance of each analytical indicator across all times within the historical analysis period for each time point, as the data volatility at each time point; Based on the phase difference and data volatility of all types of analytical indicators, the comprehensive perturbation degree at each moment is obtained; Based on the load swing chaos coefficient and the comprehensive disturbance degree, the disturbance load swing mapping value at each moment is obtained.

4. The intelligent control method for a crane under high-altitude operation conditions according to claim 1, characterized in that, The process of obtaining the compensation coefficient at the current moment includes: Arrange the disturbance load swing mapping value and the control response hysteresis factor of all times within the historical analysis period at the current time in chronological order to obtain the swing mapping sequence and the hysteresis factor sequence in sequence. Obtain the correlation coefficient between the two sequences and record it as the compensation value degree at the current time. The product of the current disturbance load swing mapping value and the control response hysteresis factor is used as the problem severity. The compensation coefficient for the current moment is obtained based on the severity of the problem, the degree of compensation value, and the error accumulation factor.

5. The intelligent control method for a crane under high-altitude operation conditions according to claim 1, characterized in that, The method of controlling the crane using the compensation coefficient includes: Obtain the ideal PID control parameters of the crane, and use the sum of constant 1 and the compensation coefficient at the current moment to weight the ideal PID control parameters to obtain the corrected PID control parameters at the current moment; use the corrected PID control parameters to control the crane in real time.

6. The intelligent control method for a crane under high-altitude operation conditions according to claim 3, characterized in that, The process of obtaining the comprehensive perturbation degree at each moment includes: Calculate the product of the phase difference and the data volatility for each analytical indicator at each time step, and use it as the local perturbation degree; sum the local perturbation degrees of all types of analytical indicators at each time step, and use it as the comprehensive perturbation degree at each time step.

7. The intelligent control method for a crane under high-altitude operation conditions according to claim 3, characterized in that, The least squares method is used to perform curve fitting on the same analytical index for all times within the historical analysis period at each time point.

8. A crane intelligent control system for high-altitude operation, implementing the crane intelligent control method for high-altitude operation as described in claim 1, characterized in that, The system includes: The data acquisition module is used to acquire the load swing angle, environmental indicators, rigging vibration displacement, actual load center of gravity position, and theoretical load center of gravity position of the crane at each moment in real time under control commands. The error accumulation analysis module is used to obtain the control response hysteresis factor at each moment based on the distance fluctuation between the actual load center of gravity position and the theoretical load center of gravity position at each moment in the historical analysis period; and to obtain the error accumulation factor at the current moment based on the control response hysteresis factor at the current moment and the load swing angle change at each moment in the historical analysis period. The disturbance load swing analysis module is used to obtain the disturbance load swing mapping value at each moment based on the environmental indicators, the degree of disorder and fluctuation of the rigging vibration displacement, and the degree of load swing angle change at each moment in the historical analysis period. The crane control module is used to obtain the compensation coefficient at the current moment based on the error accumulation factor, the disturbance load swing mapping value, the control response hysteresis factor, and the correlation between the disturbance load swing mapping value and the control response hysteresis factor at each moment in the historical analysis period at the current moment; and to control the crane using the compensation coefficient.

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