A kind of stepless magnetic control system for intelligentizing headgear

Abstract

The application discloses a kind of for intelligentization headgear stepless magnetic control system, belong to electromagnetic control technical field, it includes the core microvibration signal of electromagnet core being acquired by vibration signal acquisition module;Dynamic flux linkage compensation module generates magnetic flux compensation signal;Inertia trend prediction module generates load inertia change trend information;Multi-dimensional attitude perception module obtains inclination data and tension data;Space-time collaborative decision control module carries out space-time weighted fusion to magnetic flux compensation signal, load inertia change trend information, inclination data and tension data, generates collaborative control parameter;Instruction generation module analyzes collaborative control parameter and generates magnetic field intensity adjustment instruction;Collaborative execution module executes magnetic field intensity adjustment instruction and collaboratively adjusts electromagnetic force.The application adopts multi-dimensional perception data fusion and space-time collaborative decision control strategy, can effectively compensate magnetic field fluctuation, actively suppress load swing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electromagnetic control, in particular to a stepless magnetic control system for intelligentized overhead traveling crane. BACKGROUND

[0002] The overhead traveling crane is a key tool in the metallurgical, mechanical and warehousing industries, and its basic principle is to use the strong magnetic field generated by the coil to attract and transport steel plates, steel coils and other magnetically conductive materials. The performance of the overhead traveling crane directly affects the efficiency and safety of the operation, so it is very important to accurately and effectively control the magnetic field.

[0003] In the prior art, the control method of the overhead traveling crane is relatively simple. Usually, contactor control or simple frequency converter open-loop voltage regulation is used to adjust the excitation current in steps to achieve rough control of the magnetic force. In the operation control of the overhead traveling crane, the acceleration and deceleration control and the magnetic force control are two independent systems, which are operated by the operator according to experience, and there is lack of information interaction and coordinated operation between the two. The mechanical braking system is only used as an independent execution unit in parking or emergency situations.

[0004] However, the magnetic force regulation precision of the prior art is low, and it cannot be continuously and smoothly adjusted steplessly according to the load weight and running state, resulting in excessive force causing energy waste and thin material damage, or insufficient force causing the risk of magnetic drop. Secondly, the mechanical vibration generated by the overhead traveling crane when starting, braking or passing through the track joint will cause the relative displacement of the electromagnetic iron core and the coil, and then cause the change of the magnetic resistance of the magnetic circuit, resulting in the fluctuation of the actual magnetic force. This fluctuation cannot be effectively compensated. Finally, the electromagnetic force control and the overhead traveling crane operation control are independent of each other, and cannot coordinate to suppress the load swing caused by acceleration, deceleration or external disturbance, resulting in low operation efficiency, difficult positioning, and lack of intelligent comprehensive response and protection ability in emergency situations. SUMMARY

[0005] To solve the above problems, the present application provides a stepless magnetic control system for intelligentized overhead traveling crane, which adopts a control strategy of multi-dimensional perception data fusion and space-time collaborative decision-making, and can coordinate the adjustment of electromagnetic force to realize accurate, stable and safe control of the load.

[0006] The above object can be achieved by the following scheme:

[0007] The application discloses a kind of for intelligentizing headgear stepless magnetic control system, including vibration signal acquisition module, for obtaining the core micro-vibration signal of electromagnet core;Dynamic flux compensation module, for the core micro-vibration signal is carried out vibration-magnetic hysteresis coupling analysis, generates magnetic flux compensation signal;Inertial trend prediction module, for obtaining the real-time current of headgear walking motor, and based on the real-time current carries out inertia prediction analysis, generates load inertia change trend information;Multi-dimensional attitude perception module, for obtaining the inclination data of characterizing load space posture and the tension data of characterizing load stress state;Space-time collaborative decision control module, for the magnetic flux compensation signal, load inertia change trend information, inclination data and tension data are space-time weighted fusion, generates collaborative control parameter;Instruction generation module, for generating magnetic field intensity regulation instruction according to the collaborative control parameter;Collaborative execution module, for executing the magnetic field intensity regulation instruction collaborative regulation electromagnetic force.

[0008] Optionally, the dynamic flux compensation module comprises: a feature extraction unit configured to perform time-frequency analysis on the core micro-vibration signal to extract frequency features and amplitude features; an offset calculation unit configured to obtain a magnetic flux offset based on the frequency features and the amplitude features; and a compensation signal generation unit configured to calculate a current compensation value for offsetting non-linear changes of a magnetic field based on the magnetic flux offset, and generate a magnetic flux compensation signal.

[0009] Optionally, the inertia trend prediction module comprises: a current data acquisition unit configured to acquire real-time current of the headgear walking motor; a trend monitoring unit configured to monitor a current rising slope in an acceleration stage and a current falling slope in a deceleration stage of the real-time current; a trend data processing unit configured to determine a load inertia instantaneous change rate based on a difference between the current rising slope and the current falling slope through a preset inertia-current mapping relationship; and a trend prediction unit configured to perform time series smoothing processing on the load inertia instantaneous change rate to filter out noise and generate load inertia change trend information.

[0010] Optionally, the system further comprises: filtering the inclination data to generate a filtered hook inclination vector; comparing the filtered hook inclination vector with a preset inclination safety threshold; and generating a swing suppression instruction for correcting the collaborative control parameter when the filtered hook inclination vector exceeds the inclination safety threshold.

[0011] Optionally, the system further comprises: performing frequency domain decomposition on the tension data to separate out a steel wire rope inherent oscillation frequency component and an external disturbance frequency component; generating an emergency suppression signal when energy of the external disturbance frequency component exceeds a preset disturbance threshold; and adjusting the mechanical braking force timing instruction based on the emergency suppression signal.

[0012] Optionally, the space-time synergistic decision control module comprises: a data processing unit configured to normalize the magnetic flux compensation signal, the load inertia change trend information, the inclination data and the tension data to generate a multi-dimensional state feature vector; and a space-time fusion unit configured to perform matrix operation on the multi-dimensional state feature vector by using a preset space-time synergistic weight matrix to output a synergistic control parameter containing an electromagnetic force priority coefficient and a mechanical brake force delay time.

[0013] Optionally, the space-time synergistic decision control module further comprises: obtaining a real-time temperature value of the electromagnetic coil; generating a thermal protection adjustment signal when the real-time temperature value exceeds a preset temperature threshold based on material characteristics; and reducing the electromagnetic force priority coefficient and synchronously increasing the mechanical brake force delay time length according to the thermal protection adjustment signal.

[0014] Optionally, the step of executing the magnetic field strength adjustment instruction and the mechanical brake force timing instruction to synergistically adjust the electromagnetic force and the mechanical brake force comprises: analyzing the mechanical brake force timing instruction to obtain a brake force distribution ratio and an action delay time; determining a load swing direction based on the inclination data; adjusting the action of a swing same-direction brake by using the action delay time, and synergistically adjusting the electromagnetic force of a swing opposite-direction brake by using the magnetic field strength adjustment instruction to perform timing synergistic swing suppression; and dynamically adjusting the mechanical brake force output of a plurality of brakes according to the brake force distribution ratio.

[0015] Optionally, the system further comprises: calculating a time change rate of the tension data; generating a preset priority brake strengthening signal when the time change rate exceeds a preset emergency safety limit value; improving the response level of the mechanical brake force timing instruction by using the brake strengthening signal; and synchronously activating a preset magnetic force safety release program to reduce the electromagnetic coil current and transfer the load brake responsibility to the mechanical brake system.

[0016] Based on the same inventive concept, the application further provides a method for intelligently controlling the stepless magnetic adjustment of a head sheave, which comprises the following steps: obtaining a core micro-vibration signal of an electromagnet core; performing vibration-magnetic hysteresis coupling analysis on the core micro-vibration signal to generate a magnetic flux compensation signal; obtaining a real-time current of a head sheave traveling motor and performing inertia prediction analysis based on the real-time current to generate load inertia change trend information; obtaining inclination data representing the spatial posture of a load and tension data representing the force state of the load; performing space-time weighted fusion on the magnetic flux compensation signal, the load inertia change trend information, the inclination data and the tension data to generate a synergistic control parameter; analyzing and generating a magnetic field strength adjustment instruction according to the synergistic control parameter; and executing the magnetic field strength adjustment instruction to synergistically adjust the electromagnetic force.

[0017] Compared with the prior art, the application has the following advantages:

[0018] 1、The present application effectively offsets the nonlinear interference of mechanical vibration on the magnetic field by analyzing the micro-vibration signal of the electromagnet core and generating a magnetic flux compensation, ensuring the constancy and precision of the holding force of the electromagnet under dynamic working conditions, and significantly improving the stability and reliability of electromagnetic force control.

[0019] 2、The present application realizes intelligent linkage and optimized cooperation of electromagnetic force and mechanical braking force through spatiotemporal collaborative decision-making by fusing multi-dimensional information such as load inertia, spatial attitude and stress state; this collaborative control strategy not only actively suppresses the swing of the load during operation, improving positioning accuracy and work efficiency, but also enhances the dynamic response capability and running smoothness of the entire system;

[0020] 3、The present application constructs a comprehensive safety guarantee system based on multi-source perception information, which can predict the change of load inertia in real time and accurately identify dangerous working conditions such as external disturbance and sudden hooking through deep analysis of tension data, and then trigger a rapid and graded emergency response program to safely transfer the braking responsibility between the electromagnetic system and the mechanical system, thereby greatly improving the intrinsic safety level of the headgear operation and effectively preventing the occurrence of serious accidents.

[0021] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and obtained by the structure indicated in the specification, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 is a framework diagram of an intelligent headgear stepless magnetic control system according to an embodiment of the present application.

[0024] Figure 2 is a structural schematic diagram of an intelligent headgear stepless magnetic control system according to an embodiment of the present application.

[0025] Figure 3 is a flowchart of an intelligent headgear stepless magnetic control method according to an embodiment of the present application.

[0026] Figure 4 is a collaborative swing suppression control effect comparison curve diagram according to an embodiment of the present application.

[0027] Figure 5 is a system response timing diagram of a load hooking event of an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0029] With reference to Figure 1 An embodiment of the present application provides a kind of for intelligentizing headgear stepless magnetic control system, using multi-dimensional perception data fusion and space-time collaborative decision control strategy, can be coordinated to adjust electromagnetic force and mechanical braking force, realize the accurate, stable and safe control of load.

[0030] The system of the embodiment specifically includes:

[0031] Vibration signal acquisition module, for obtaining the core micro-vibration signal of electromagnet core;

[0032] Dynamic flux compensation module, for vibration-magnetic hysteresis coupling analysis to the core micro-vibration signal, generate magnetic flux compensation signal;

[0033] Inertial trend prediction module, for obtaining the real-time current of headgear running motor, and based on the real-time current, inertial prediction analysis is generated, and load inertia change trend information is generated;

[0034] Multi-dimensional attitude perception module, for obtaining the inclination data representing the spatial attitude of load and the tension data representing the stress state of load;

[0035] Space-time collaborative decision control module, for space-time weighted fusion of the magnetic flux compensation signal, the load inertia change trend information, the inclination data and the tension data, generates collaborative control parameters;

[0036] Instruction generation module, for generating magnetic field intensity adjustment instruction according to the collaborative control parameters;

[0037] Collaborative execution module, for executing the magnetic field intensity adjustment instruction to coordinate the adjustment of electromagnetic force.

[0038] Specifically, by real-time compensation of the micro-vibration of the electromagnet, the nonlinear interference of mechanical impact on magnetic force is effectively overcome, ensuring the stability and precision of the electromagnetic holding force, and avoiding safety hazards caused by fluctuation of the suction force. Secondly, by fusing multi-dimensional information such as load inertia, attitude and stress, the system can predict the dynamic trend of the load and actively intervene, and collaboratively adjust the electromagnetic force and mechanical braking force to actively suppress the load swing, thereby greatly improving the smoothness of the head sheave operation and the accuracy of positioning, and improving the overall operation efficiency. Finally, the intelligent collaborative control of electromagnetic force and mechanical force enables the system to adapt to complex working conditions as a whole, realizing a leap from simple independent control to intelligent collaborative control, and greatly enhancing the stability, adaptability and intrinsic safety level of the head sheave lifting operation.

[0039] Optionally, as shown in Figure 2 the dynamic magnetic chain compensation module comprises:

[0040] a feature extraction unit for performing time-frequency analysis on the iron core micro-vibration signal and extracting frequency features and amplitude features;

[0041] Specifically, the feature extraction unit of the dynamic magnetic chain compensation module performs time-frequency analysis on the electromagnet iron core micro-vibration signal obtained by the vibration signal acquisition module. Time-frequency analysis is a signal processing technique that can reveal the frequency composition of a signal at different time points. Compared with traditional Fourier transform, it can capture transient changes in the vibration signal more precisely. Through this analysis, the system can extract two key feature parameters, namely the frequency feature representing the main frequency component of the vibration and the amplitude feature representing the vibration intensity.

[0042] a shift calculation unit for calculating a magnetic flux shift based on the frequency feature and the amplitude feature;

[0043] Specifically, the shift calculation unit calculates a key physical quantity, i.e., the magnetic flux shift, based on the extracted frequency feature and amplitude feature. The calculation process reveals how mechanical vibrations of different frequencies and amplitudes affect the motion of magnetic domains inside the electromagnet, and further cause the actual magnetic flux to deviate from the theoretical set value. The calculation of this magnetic flux shift can be represented by the following formula:

[0044] ,

[0045] wherein, represents the magnetic flux shift that needs to be compensated, and its value is obtained by calculating a function . represents the frequency feature obtained from time-frequency analysis, while represents the corresponding amplitude feature. This function The magnetic flux offset is established according to the magnetostriction effect and hysteresis characteristics of the electromagnet material through experimental data fitting, and maps a specific vibration state to a determined magnetic flux change value.

[0046] The compensation signal generation unit is configured to calculate a current compensation value for offsetting the nonlinear change of the magnetic field based on the magnetic flux offset, and generate a magnetic flux compensation signal.

[0047] Specifically, the compensation signal generation unit receives the calculated magnetic flux offset and calculates a current compensation value for offsetting the nonlinear change of the magnetic field based on the magnetic flux offset. The target of the current compensation value is to generate a magnetic flux with the same magnitude and opposite direction of the magnetic flux offset, so as to stabilize the total magnetic flux at the target value. The calculation formula of the current compensation value is:

[0048] ,

[0049] wherein, is the final generated current compensation value, is the calculated magnetic flux offset, is a magnetic flux current conversion coefficient, which is determined by the inherent physical parameters such as the number of turns of the electromagnet coil, the core material and the geometric structure, and represents the size of the magnetic flux generated by a unit current, which is obtained through design or experimental calibration. The calculated current compensation value will be output as a magnetic flux compensation signal to the space-time collaborative decision control module for subsequent control instruction generation.

[0050] Optionally, the inertia trend prediction module comprises:

[0051] The current data acquisition unit is configured to acquire real-time current of the head sheave traveling motor.

[0052] The trend monitoring unit is configured to monitor a current rising slope in the acceleration stage and a current falling slope in the deceleration stage of the real-time current.

[0053] The trend data processing unit is configured to determine a load inertia instantaneous change rate through a preset inertia-current mapping relationship according to a difference between the current rising slope and the current falling slope.

[0054] The trend prediction unit is configured to perform time series smoothing processing on the load inertia instantaneous change rate, filter out noise and generate load inertia change trend information.

[0055] Specifically, first of all, the current data acquisition unit is started, which continuously obtains real-time current data representing the motor load torque from the driver or sensor of the traveling motor of the overhead traveling crane. After obtaining the continuous real-time current signal, the trend monitoring unit processes it, identifies the acceleration stage and deceleration stage in the operation of the overhead traveling crane, and calculates the rate of change of the current signal with time, i.e., the current rising slope and the current falling slope, in the two stages. The two slopes reflect the speed at which the motor adjusts the torque output to overcome the total inertia of the system. Subsequently, the trend data processing unit determines the instantaneous change rate of the load inertia by using the difference between the two slopes through a preset inertia-current mapping relationship. This mapping relationship is established through offline experiments, i.e., when carrying loads with different known weights or inertias, the current slope under standard acceleration and deceleration conditions is recorded, thereby constructing a corresponding model between the slope difference and the load inertia. The calculation process can be represented by the following function relationship:

[0056] ,

[0057] wherein, is the calculated instantaneous change rate of the load inertia, which is an index for quantifying the size of the current load inertia, is the acceleration stage current rising slope measured by the trend monitoring unit, and is the current falling slope in the deceleration stage, both of which are obtained by differentiating the real-time current data. The function represents the preset inertia-current mapping relationship. Finally, since the instantaneously calculated value may contain electrical noise or disturbances caused by control instruction fine-tuning, the trend prediction unit performs time series smoothing processing on the series of discrete load inertia instantaneous change rate data, such as applying moving average or exponential smoothing filtering algorithm to filter out high-frequency noise and extract the real and smooth dynamic trend of the load inertia change, finally generates the load inertia change trend information, and transmits it to the space-time collaborative decision control module.

[0058] Optionally, the system further comprises:

[0059] filtering the inclination data to generate a filtered hook inclination vector;

[0060] comparing the filtered hook inclination vector with a preset inclination safety threshold;

[0061] when the filtered hook inclination vector exceeds the inclination safety threshold, generating a swing suppression instruction for correcting the collaborative control parameter.

[0062] Specifically, the intelligent hoist stepless magnetic control system integrates a closed-loop control logic based on real-time attitude sensing to achieve active load swing suppression. The process begins with filtering the original inclination data collected by the multi-dimensional attitude sensing module. Due to the interference of sensor structure vibration and electromagnetic noise in actual working conditions, the original inclination data contains a large amount of burrs and noise, which cannot be directly used for accurate control. Therefore, the system uses digital filtering algorithms such as Kalman filtering or complementary filtering to smooth and denoise the original data, extracts a signal that truly reflects the macro swing attitude of the hook and load, and generates a pure filtered hook inclination vector. This vector accurately describes the angle and direction of the load deviation from the vertical line. Then, the system continuously compares the real-time filtered hook inclination vector with a preset inclination safety threshold. This threshold is a critical safety angle preset according to the design specifications of the hoist, safety regulations of the working environment, and load characteristics. The comparison process can be characterized by the following conditional expression:

[0063] ,

[0064] where, represents the modulus of the filtered hook inclination vector, i.e., the actual swing angle of the load, which is obtained by filtering the inclination sensor data. represents the preset inclination safety threshold, which is a constant value preset according to safety regulations. When the above condition is met, i.e., the system determines that the swing amplitude of the load has exceeded the safe controllable range, the system will immediately generate a swing suppression command. This command is not a simple on-off signal, but a dynamic adjustment signal containing the swing direction and amplitude, which will be sent to the space-time collaborative decision control module to real-time correct the collaborative control parameters of its output, thereby changing the subsequent magnetic field strength adjustment command and mechanical braking force timing command to generate control force that can offset the current swing.

[0065] Optionally, the system further comprises:

[0066] performing frequency domain decomposition on the tension data to separate the inherent oscillation frequency component and the external disturbance frequency component of the steel wire rope;

[0067] when the energy of the external disturbance frequency component exceeds a preset disturbance threshold, an emergency suppression signal is generated;

[0068] adjust the mechanical braking force timing command based on the emergency suppression signal.

[0069] Specifically, the intelligent hoist stepless magnetic control system is embedded with a safety protection mechanism for responding to sudden external disturbances, and the implementation process is based on in-depth analysis of the load stress state. First, the continuous tension data collected by the multi-dimensional attitude perception module is subjected to frequency domain decomposition. Frequency domain decomposition converts the signal changing over time into the frequency domain for analysis, revealing various frequency components contained in the signal. By applying algorithms such as fast Fourier transform, the system can decompose the complex tension signal into two key parts: the inherent oscillation frequency component of the wire rope caused by the acceleration and deceleration of the hoist itself and the load swing, and the external disturbance frequency component caused by external sudden events such as collision, hook or strong wind, etc. After successfully separating the external disturbance frequency component, the system calculates the energy of this component to quantify the severity of the external disturbance. The energy calculation can be done by integrating the power spectral density of the signal spectrum in the specific frequency band representing the external disturbance. This process can be characterized by the following judgment formula:

[0070] ,

[0071] wherein, represents the total energy of the calculated external disturbance frequency component, which is obtained by accumulating or integrating the square of the signal spectrum amplitude in the specific frequency band representing the external disturbance. represents a preset disturbance threshold, which is set based on historical safety data and risk assessment analysis, and is used to define the boundary between normal disturbance and dangerous disturbance. Once the calculated energy exceeds this preset disturbance threshold, the system will determine that the current working condition is potentially dangerous and immediately generate a high-priority emergency suppression signal. This signal serves as a trigger instruction and is sent to the instruction generation module to adjust the timing instruction of the mechanical braking force, the core function of which is to start a fast and powerful braking response program to respond to the detected sudden danger.

[0072] Optionally, the spatio-temporal collaborative decision control module comprises:

[0073] a data processing unit configured to normalize the magnetic flux compensation signal, the load inertia change trend information, the inclination data, and the tension data to generate a multi-dimensional state feature vector;

[0074] a space-time fusion unit configured to perform matrix operation on the multi-dimensional state feature vector using a preset space-time collaborative weight matrix to output a collaborative control parameter containing an electromagnetic force priority coefficient and a mechanical braking force delay time.

[0075] Specifically, first, the data processing unit receives four heterogeneous input signals, which are the magnetic flux compensation signal representing the stability of the electromagnet magnetic field, the load inertia trend information reflecting the dynamic characteristics of the load, the inclination data describing the spatial posture of the load, and the tension data indicating the force state of the load. Since the physical units, numerical ranges, and change rates of these signals are different, direct comparison and fusion do not have physical meaning, so normalization processing must be performed. Normalization is a mathematical transformation that scales data in proportion to fall within a certain interval, such as the [0, 1] interval. Through this processing, all input signals are converted into dimensionless scalar values, so that they can be compared and weighted in a unified framework, and finally a multi-dimensional state feature vector is generated. Subsequently, the spatio-temporal fusion unit uses this multi-dimensional state feature vector to make decisions. The core of this unit is a pre-set spatio-temporal coordination weight matrix. This matrix is not a simple constant set, but encapsulates the control strategy knowledge base obtained from expert experience or through a large amount of experimental data and machine learning algorithms. Each weight value in the matrix defines the degree of influence of a certain state feature on the final control output. The spatio-temporal fusion unit performs matrix operations on the multi-dimensional state feature vector and the weight matrix, which can be represented by the following relationship:

[0076] ,

[0077] wherein, is the output of the synergistic control parameter vector, which contains the key parameters required for subsequent instruction generation. is the pre-set spatio-temporal coordination weight matrix, whose internal values determine the preference and response mode of the control strategy. is the multi-dimensional state feature vector generated by the data processing unit, each element of which corresponds to the normalized magnetic flux compensation signal, load inertia trend information, inclination data, and tension data. The output of this matrix operation, the synergistic control parameter vector , is directly analyzed into two core control quantities, the electromagnetic force priority coefficient and the mechanical brake force delay duration. The electromagnetic force priority coefficient is a regulating factor that determines the dominant position of electromagnetic force and mechanical brake force in control tasks, while the mechanical brake force delay duration precisely defines the action timing of mechanical brake relative to electromagnetic force regulation.

[0078] Optionally, the spatio-temporal synergistic decision control module further comprises:

[0079] obtaining the real-time temperature value of the electromagnet coil;

[0080] when the real-time temperature value exceeds the temperature threshold value preset based on material characteristics, generating a thermal protection adjustment signal;

[0081] According to the thermal protection adjustment signal, the electromagnetic force priority coefficient is reduced and the mechanical brake force delay duration is simultaneously increased.

[0082] Specifically, the space-time collaborative decision control module in the intelligentized hoist stepless magnetic control system also integrates a key device self-protection mechanism, i.e., a thermal protection function. The implementation of this function starts from the health state monitoring of the core components of the electromagnet. The system continuously obtains the real-time temperature value of the electromagnet coil through the temperature sensor arranged inside or close to the surface of the electromagnet coil. This temperature value is the most direct physical quantity representing the working load and heat dissipation state of the electromagnet. The system internally presets a key safety parameter, i.e., a temperature threshold value based on material properties. This threshold value is a critical safety temperature determined through scientific calculation and experimental verification according to the insulation class of the wire used in the electromagnet coil, the Curie point of the core material, and the reliability requirements of long-term operation. After the space-time collaborative decision control module obtains the real-time temperature value, it performs a continuous comparison and judgment. The logic can be expressed as:

[0083]

[0084] Among them, represents the real-time temperature value of the electromagnet coil collected by the temperature sensor and transmitted to the module. represents the temperature threshold value based on material properties. If this condition is met, the system determines that the electromagnet coil is at risk of overheating, and the module will immediately generate an internal logic signal, i.e., a thermal protection adjustment signal. This signal will directly act on the output result of the space-time fusion unit and dynamically correct it. Specifically, according to the thermal protection adjustment signal, the system will sequentially reduce the electromagnetic force priority coefficient calculated in the collaborative control parameters and simultaneously increase the mechanical brake force delay duration. Reducing the electromagnetic force priority coefficient will directly result in a reduction in the subsequent generated magnetic field strength adjustment instruction, thereby reducing the coil current and reducing heat generation from the root. Increasing the delay duration of the mechanical brake force is to prepare for the smooth transfer of control rights to the mechanical brake system, ensuring that the stability of the overall control system will not be severely impacted while the electromagnetic force is weakened.

[0085] Optionally, the execution of the magnetic field strength adjustment instruction and the mechanical brake force timing instruction cooperatively adjusts the electromagnetic force and the mechanical brake force includes:

[0086] Analyzing the mechanical brake force timing instruction to obtain a brake force distribution ratio and an action delay time;

[0087] Combining the load swing direction determined based on the inclination angle data, adjusting the action of the swing same-direction brake using the action delay time, and cooperatively enhancing the swing opposite-direction electromagnetic force using the magnetic field strength adjustment instruction to perform timing collaborative swing suppression; ​

[0088] According to the brake force distribution ratio, the mechanical brake force output of the plurality of brake groups is dynamically adjusted.

[0089] Specifically, first, the received mechanical brake force timing instruction is analyzed, and two key control parameters, i.e., the brake force distribution ratio and the action delay time, are extracted. The brake force distribution ratio is a dimensionless parameter that specifies how the total mechanical brake force should be distributed among the plurality of brake groups of the crown block to achieve precise torque control. The action delay time is a time parameter accurate to milliseconds that defines the phase relationship of the mechanical brake system relative to the electromagnetic force adjustment action and is the key to achieving timing coordination control. After obtaining the above parameters, the system enters the core timing coordination swing suppression stage. In this stage, the inclination data provided by the multi-dimensional attitude perception module is fused in real time, which accurately indicates the real-time swing direction of the current load. Based on this direction information, the cooperative execution module adopts an asymmetric cooperative control strategy. On the one hand, it uses the analyzed action delay time to accurately trigger and adjust the action of the brake with the same swing direction as the load, i.e., the swing same direction brake, which aims to generate a mechanical damping torque that counteracts the swing trend. On the other hand, the module will strictly follow the magnetic field strength adjustment instruction to synchronously enhance the electromagnetic force of the electromagnet in the opposite direction of the load swing, i.e., to enhance the swing opposite direction electromagnetic force, thereby generating a restoring force that actively pulls the load back to the equilibrium position. These two forces with different properties but the same goal, through the accurate coordination of the action delay time, achieve timing optimization, avoid conflict or effect cancellation, and constitute an efficient timing coordination swing suppression closed loop. Finally, the cooperative execution module issues specific brake torque instructions to the plurality of brake groups of the crown block according to the analyzed brake force distribution ratio, dynamically adjusting the mechanical brake force output of each brake unit. This dynamic adjustment based on the ratio ensures that the application of the mechanical brake force is smooth and evenly distributed, avoiding the impact and secondary oscillation that may be caused by single-point braking.

[0090] Optionally, the system further comprises:

[0091] The time rate of change of the tension data is calculated, and when the time rate of change exceeds a preset emergency safety limit, a brake reinforcement signal of a preset priority is generated;

[0092] The response level of the mechanical brake force timing instruction is improved using the brake reinforcement signal;

[0093] A preset magnetic force safety release program is activated synchronously to reduce the current of the electromagnet and transfer the load braking responsibility to the mechanical brake system.

[0094] Specifically, the intelligent hoist stepless magnetic control system integrates an emergency safety protection mechanism for dealing with sudden dangerous working conditions. The implementation of the mechanism begins with dynamic monitoring of the load stress state. The system continuously obtains real-time tension data from the multi-dimensional attitude perception module and performs differential operation on the data to calculate the time rate of change. The calculation process aims to capture the instantaneous change rate of the tension data, which quantifies the severity of the tension on the steel wire rope. This process can be characterized by the following relationship:

[0095] ,

[0096] wherein, is the calculated time rate of change of the tension data, which represents the speed of change of the load tension, is the real-time tension data measured by the tension sensor and transmitted to the system, represents the extremely short time interval used in the differential operation. The system continuously compares the absolute value of the real-time calculated time rate of change with a pre-set emergency safety limit value. The emergency safety limit value is a critical threshold value pre-set according to the hoist structure strength, the steel wire rope safety factor and the analysis of historical accident data, to distinguish between normal operation tension fluctuations and catastrophic tension mutations caused by load hooking, collision or sudden falling, etc. When the calculated time rate of change exceeds the limit value, the system determines that an emergency has occurred and immediately generates a brake enhancement signal with the highest execution priority, which directly intervenes the command generation module to increase the response level of the mechanical brake force timing command. This means that the system will bypass the conventional time-space coordination decision control logic and force the coordination execution module to output the mechanical brake force at the fastest speed and maximum capacity, ensuring strong locking of the load movement. On the other hand, the brake enhancement signal will simultaneously activate a pre-set magnetic force safety release program, which will quickly and smoothly reduce the electromagnetic coil current according to the pre-set control curve. This controlled current reduction aims to safely weaken or eliminate the electromagnetic force, smoothly transferring the entire load braking responsibility to the already activated mechanical braking system.

[0097] Based on the same inventive concept, as shown in Figure 3 , the present application also provides a method for intelligent hoist stepless magnetic control, which comprises:

[0098] obtaining the core micro-vibration signal of the electromagnet core;

[0099] performing vibration-magnetic hysteresis coupling analysis on the core micro-vibration signal to generate a magnetic flux compensation signal;

[0100] obtaining the real-time current of the hoist walking motor, and performing inertia prediction analysis based on the real-time current to generate load inertia change trend information;

[0101] obtaining inclination data representing a posture of the load space and tension data representing a force state of the load;

[0102] spatiotemporally weighting and fusing the magnetic flux compensation signal, the load inertia change trend information, the inclination data and the tension data to generate a cooperative control parameter;

[0103] generating a magnetic field strength adjustment instruction according to the cooperative control parameter;

[0104] cooperatively adjusting the electromagnetic force according to the magnetic field strength adjustment instruction.

[0105] In order to verify the feasibility of the application in implementation, the application is applied to the No. 3 finished product warehouse crane of a certain large special steel group hot rolling branch. The crane is a double-beam bridge type electromagnetic crane, with a rated lifting capacity of 20 tons, mainly responsible for lifting high-strength automobile plate steel coils with a diameter of 1.2-1.8 meters and a weight of 10-18 tons. The finished product warehouse has a fast operation rhythm, requires accurate positioning of the crane with an error of ±10 mm, and has extremely strict requirements on the stability of the running process and the safety of sudden conditions due to ground vehicles and personnel flow. Before the transformation, the crane used a traditional frequency converter plus PLC control, which had problems such as large acceleration and deceleration impact, severe load swing, rough electromagnetic attraction adjustment, and inability to adapt to steel coils of different weights.

[0106] In this embodiment, the crane is intelligently transformed, and the control system of the application is deployed. On the hardware side, an IMU inertial measurement unit of the Swiss Baumer Company is installed at the hook beam to obtain inclination data, a U10M series tension and compression force sensor of the German HBM Company with a range of 50 tons is installed at the root of the steel wire rope to obtain tension data, and a high-frequency MEMS accelerometer and a PT100 platinum resistance temperature sensor are pasted on the electromagnet shell. All sensor data and driver data of the crane running are collected into a Delta embedded industrial PC at a sampling frequency of 100 Hz through Profinet industrial Ethernet, the PC runs the core algorithm of the application, and the cooperative control instruction is issued to the crane PLC, i.e. Siemens S7-1500, and the electromagnet special controller for execution. The system has been continuously running and recording data for 6 months since January 2024.

[0107] In daily operation, the weight of steel coils varies greatly. The inertia trend prediction module realizes online identification of load inertia by analyzing walking motor current data in real time. On March 15, 2024 at 10:12, the overhead crane hoisted a steel coil with a standard weight of 15.8 tons. During the acceleration stage of 0-1.5 m / s, the system monitored that the motor current rose from 12 A of no-load to a peak of 87 A, and the calculated current rise slope was 75 A / s. On March 15, 2024 at 10:25, the overhead crane hoisted a repaired coil with a standard weight of 5.2 tons. During the same acceleration stage, the motor current peak was only 40 A, and the calculated current rise slope was only 28 A / s.

[0108] Based on the slope difference, the system instantaneously determines the change in load inertia through a pre-set inertia-current mapping model and automatically adjusts the control strategy. For the 15.8-ton heavy load, the system extends the time constant of the S-shaped acceleration and deceleration curve from the default 1.8 s to 2.5 s, and optimizes the PID control parameters, with the integral term I increased by 30% and the derivative term D increased by 18%, ensuring smooth and impact-free startup. For the 5.2-ton light load, the time constant is shortened to 1.2 s, ensuring smoothness while shortening the single handling cycle by nearly 6 seconds.

[0109] In a long-distance handling test with a span of 30 meters, when the overhead crane travels through the track joint at a maximum speed of 2.5 m / s, the MEMS accelerometer deployed on the electromagnet monitors an instantaneous impact vibration with a main frequency of 80 Hz and a peak amplitude of 1.5 g. The dynamic magnetic flux compensation module calculates in real time that this impact will cause the magnetic flux to instantaneously decrease by about 3.5% through a pre-set vibration-magnetic hysteresis coupling model. The system immediately generates a pulse current compensation signal with a peak of 1.2 A and a duration of 150 ms, which is superimposed into the main excitation current, ensuring the stability of the electromagnetic attraction under severe vibration, and the actual measured attraction fluctuation is suppressed within ±0.8%.

[0110] In the above joint passing condition, the impact causes the load to swing, and the IMU sensor data shows that the load swing angle reaches 8.3 degrees within 1.2 seconds. The system determines that it exceeds the pre-set 5-degree safety threshold, and the space-time collaborative decision control module immediately intervenes. It analyzes the generated collaborative control parameters and instructs the collaborative execution module to adopt asymmetric collaborative control, applying 40% of the rated braking torque of A1 and A2 brakes that swing in the same direction, and precisely delaying the action by 0.15 seconds, forming precise dynamic damping. At the same time, the magnetic field strength adjustment instruction asymmetrically increases the electromagnet current in the area opposite to the swing by 8%, generating a horizontal restoring force of about 2000 N, actively "pulling back" the load to the balance position. Through the timing collaboration of electromagnetic force and mechanical force, as shown in Figure 4 , the entire swing is suppressed to within 1.5 degrees within 2.9 seconds, avoiding large swings.

[0111] On April 22, 2024, at 15:47, a load hooking event occurred. When the crown was lowering a 15.8-ton steel coil, the outer packaging iron accidentally hooked a ground tool clamp that had not been cleaned in time. As shown in the figure, the tension sensor data shows that the total tension of the steel wire rope increases from 155kN, i.e. the load weight, to 258kN in 50 milliseconds, and the time rate of change is about 2060kN / s, which is much higher than the preset emergency safety limit of 200kN / s. The system determines that it is an emergency hooking event within 75 milliseconds, and generates a brake reinforcement signal with the highest priority. The signal bypasses the conventional decision logic and forces the mechanical brake system to apply 120% of the rated brake torque within 180 milliseconds, completely locking the crown car. At the same time, the magnetic safety release program is activated, and within the next 500 milliseconds, the excitation current is smoothly and linearly reduced from 25A to 5A (only maintaining basic adsorption), successfully transferring the huge braking responsibility from the electromagnet and steel wire rope to the more reliable mechanical brake system, avoiding the serious accident of the steel wire rope being pulled off or the crown structure being damaged. Figure 5

[0112] Through the above application, the intelligent crown infinitely variable magnetic control system of the present application has a qualitative improvement in various performances.

[0113] Table 1 Comparison table of cooperative sway suppression control effect

[0114]

[0115] Table 2 Comparison table of variable load working condition adaptability data

[0116]

[0117] Table 3 Comparison table of emergency brake response

[0118]

[0119] From the data in Tables 1-3 above, it can be seen that the application has a significant effect. In terms of sway control, the system can actively intervene to reduce the swing amplitude and suppression time. In terms of variable load adaptability, through inertia prediction, not only is the heavy load operation efficiency improved, but also the overshoot and positioning inaccuracy problems caused by mismatched control curves during light load are solved, and the positioning accuracy is improved by several times. Most importantly, in terms of emergency safety response, the system response time is shortened from seconds to sub-seconds, achieving instantaneous protection of dangerous working conditions and greatly improving the intrinsic safety level of operation. These data results fully prove the advancement, efficiency and high reliability of the present application in the field of intelligent crown control.

[0120] ​It should be noted that the electrical connection between the various units described above does not necessarily indicate a direct connection, and the indirect connection mode can also be applied to the embodiments of the present application as long as the purpose of the present application is achieved. The above is only an exemplary embodiment of the present application, and cannot limit the scope of the present application.

[0121] That is, any equivalent changes and modifications made in accordance with the teachings of the present application are still within the scope of the present application. Other embodiments of the present application will be readily apparent to those skilled in the art upon considering the description and practice of the principles disclosed herein. The present application is intended to cover any variations, uses, or adaptive changes of the present application that follow the general principles of the present application and include common knowledge or conventional techniques in the art that are not described in the present application.

Claims

1. A stepless magnetic adjustment control system for intelligent overhead cranes, characterized in that, The system includes: The vibration signal acquisition module is used to acquire the micro-vibration signal of the electromagnet core. The dynamic flux compensation module is used to perform vibration-hysteresis coupling analysis on the micro-vibration signal of the iron core and generate a magnetic flux compensation signal. The inertial trend prediction module is used to acquire the real-time current of the crane's traveling motor and perform inertial prediction analysis based on the real-time current to generate load inertia change trend information. The multi-dimensional attitude perception module is used to acquire tilt angle data that characterizes the spatial attitude of the load and tension data that characterizes the stress state of the load. The spatiotemporal collaborative decision-making and control module is used to perform spatiotemporal weighted fusion of the magnetic flux compensation signal, the load inertia change trend information, the tilt angle data and the tension data to generate collaborative control parameters. The instruction generation module is used to parse and generate a magnetic field strength adjustment instruction based on the cooperative control parameters. The collaborative execution module is used to execute the magnetic field strength adjustment command to collaboratively adjust the electromagnetic force.

2. The stepless magnetic adjustment control system for an intelligent overhead crane according to claim 1, characterized in that, The dynamic magnetic flux compensation module includes: The feature extraction unit is used to perform time-frequency analysis on the micro-vibration signal of the iron core and extract frequency and amplitude features. The offset calculation unit is used to obtain the magnetic flux offset based on the frequency characteristics and the amplitude characteristics; The compensation signal generation unit is used to calculate the current compensation value to offset the nonlinear change of the magnetic field based on the magnetic flux offset, and generate a magnetic flux compensation signal.

3. The stepless magnetic adjustment control system for an intelligent overhead crane according to claim 1, characterized in that, The inertial trend prediction module includes: The current data acquisition unit is used to acquire the real-time current of the overhead crane's traveling motor; A trend monitoring unit is used to monitor the current rise slope during the acceleration phase and the current fall slope during the deceleration phase of the real-time current. The trend data processing unit is used to determine the instantaneous rate of change of load inertia based on the difference between the current rising slope and the current falling slope, through a preset inertia-current mapping relationship. The trend prediction unit is used to perform time series smoothing on the instantaneous rate of change of the load inertia, filter out noise, and generate load inertia change trend information.

4. The stepless magnetic adjustment control system for an intelligent overhead crane according to claim 1, characterized in that, The system also includes: The tilt angle data is filtered to generate a filtered hook tilt angle vector; The filtered hook tilt angle vector is compared with the preset tilt angle safety threshold. When the filtered hook tilt angle vector exceeds the tilt angle safety threshold, a swing suppression command is generated to correct the cooperative control parameters.

5. The stepless magnetic adjustment control system for an intelligent overhead crane according to claim 1, characterized in that, The system also includes: The tension data is decomposed in the frequency domain to separate the inherent oscillation frequency component of the wire rope from the external disturbance frequency component. When the energy of the external disturbance frequency component exceeds a preset disturbance threshold, an emergency suppression signal is generated; Adjust the timing command of mechanical braking force based on the emergency suppression signal.

6. The stepless magnetic adjustment control system for an intelligent overhead crane according to claim 1, characterized in that, The spatiotemporal collaborative decision-making and control module includes: The data processing unit is used to normalize the magnetic flux compensation signal, the load inertia change trend information, the tilt angle data and the tension data to generate a multi-dimensional state feature vector. The spatiotemporal fusion unit is used to perform matrix operations on the multidimensional state feature vector using a preset spatiotemporal collaborative weight matrix, and output collaborative control parameters including electromagnetic force priority coefficient and mechanical braking force delay.

7. A stepless magnetic adjustment control system for an intelligent overhead crane according to claim 6, characterized in that, The spatiotemporal collaborative decision control module also includes: Obtain the real-time temperature value of the electromagnetic coil; When the real-time temperature value exceeds a preset temperature threshold based on material properties, a thermal protection adjustment signal is generated; Based on the thermal protection adjustment signal, the priority coefficient of the electromagnetic force is reduced and the delay time of the mechanical braking force is increased simultaneously.

8. A stepless magnetic adjustment control system for an intelligent overhead crane according to claim 5, characterized in that, The step of executing the magnetic field strength adjustment command and the mechanical braking force timing command to coordinately adjust the electromagnetic force and the mechanical braking force includes: Analyze the mechanical braking force timing command to obtain the braking force distribution ratio and action delay time; Based on the load swing direction determined by the tilt angle data, the action of the swing-in-the-straight brake is adjusted using the action delay time, and the electromagnetic force in the opposite direction of the swing is enhanced in coordination with the magnetic field strength adjustment command to perform time-coordinated swing suppression. The mechanical braking force output of multiple brakes is dynamically adjusted according to the braking force distribution ratio.

9. A stepless magnetic adjustment control system for an intelligent overhead crane according to claim 5, characterized in that, The system also includes: Calculate the time change rate of the tension data, and when the time change rate exceeds a preset emergency safety limit, generate a braking enhancement signal with a preset priority; The braking enhancement signal is used to improve the response level of the mechanical braking force timing command; The preset magnetic safety release procedure is activated simultaneously to reduce the electromagnetic coil current and transfer the load braking responsibility to the mechanical braking system.

10. A method for stepless magnetic adjustment control of an intelligent overhead crane, employing the stepless magnetic adjustment control system for an intelligent overhead crane as described in any one of claims 1-9, characterized in that, The method includes: Obtain the micro-vibration signal of the electromagnet core; Vibration-hysteresis coupling analysis was performed on the micro-vibration signal of the iron core to generate a magnetic flux compensation signal; The real-time current of the overhead crane traveling motor is obtained, and inertial prediction analysis is performed based on the real-time current to generate load inertia change trend information. Acquire tilt angle data characterizing the spatial attitude of the load and tension data characterizing the load's stress state; The magnetic flux compensation signal, the load inertia change trend information, the tilt angle data and the tension data are spatiotemporally weighted and fused to generate collaborative control parameters. Based on the aforementioned coordinated control parameters, a magnetic field strength adjustment command is generated. The electromagnetic force is adjusted in coordination by executing the magnetic field strength adjustment command.

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