Crown block intelligent operation method and system based on multi-dimensional data

Abstract

The invention relates to the technical field of intelligent control of cranes, and discloses an intelligent crown block operation method and system based on multi-dimensional data, and the method comprises the following steps: outputting a dynamic inertia coefficient based on load mass and rope length; outputting a motion state coefficient based on the current synthesis speed, the residual distance and the path curvature; according to the two coefficients and the load swing angle / angular velocity, a working condition matching coefficient is output; a safety margin coefficient is output based on the collision time, the crown block distance and the distance from the end; and finally, the operation acceleration is regulated and controlled according to the working condition matching coefficient, the real-time wind speed, the safety margin coefficient and the reference acceleration. The system comprises a corresponding evaluation and regulation module. According to the method, through multi-dimensional data fusion and dynamic coefficient evaluation, self-adaptive and refined intelligent regulation and control of the operation acceleration are realized, the defects of fixed parameters, single dimension and module splitting of a traditional method are overcome, and the working efficiency, the operation stability and the safety are effectively balanced.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for cranes, and particularly relates to an intelligent operation method and system for overhead cranes based on multi-dimensional data. Background Technology

[0002] Overhead cranes, as core equipment for material handling in industrial production, play a crucial role in steel smelting, port loading and unloading, large-scale warehousing, and manufacturing assembly. Their operational efficiency, stability, and safety directly determine the continuity of the production line, energy consumption levels, and overall risk control capabilities. Traditional overhead crane operation relies heavily on human experience, requiring operators to constantly assess load status, environmental changes, and equipment dynamics. This can lead to significant fluctuations in operational quality, unstable production cycles, and a high risk of accidents due to human negligence. Although modern overhead cranes have integrated basic sensors such as encoders and limit switches to achieve basic automation of position and speed, under complex dynamic conditions, such as sudden changes in load quality, path curvature, or environmental interference, the system still lacks adaptive decision-making capabilities, making it difficult to maintain efficient and stable operation.

[0003] In recent years, the rapid development of sensor networks and industrial IoT technologies has driven the intelligent upgrade of overhead cranes, but existing solutions mostly focus on optimizing single functions. For example, some systems use preset programs to execute automatic operation along fixed paths, which can only handle simple repetitive tasks; other solutions use weight or position sensors to achieve basic anti-sway control, but cannot cope with multi-variable coupled scenarios; and some technologies rely on single sensors such as laser rangefinders for obstacle detection, which has limited functional coverage. These methods suffer from three fundamental flaws: First, they lack adaptability. With fixed control parameters, they cannot dynamically adjust the inertial response based on changes in load mass and rope length, nor can they correct motion strategies in real time based on composite velocity, remaining distance, and path curvature, requiring frequent manual intervention during periods of fluctuating operating conditions. Second, the data dimensions are too limited, focusing only on a few variables such as speed or distance, neglecting the synergistic effects of multi-dimensional information such as load swing angle, swing angle angular velocity, collision time, coordination distance, and wind speed. This makes it difficult for the system to simultaneously optimize anti-sway, obstacle avoidance, and precise stopping during high-speed operation, failing to achieve a dynamic balance between efficiency, stability, and safety. Third, the control modules are fragmented, with safety protection, efficiency improvement, and stable operation operating independently. The lack of a unified framework integrating operating condition assessment, safety margin, and environmental factors often leads to conservative operation or risk accumulation due to conflicting objectives. Especially in multi-crane collaborative operations or strong wind environments, existing technologies cannot achieve precise control of operating acceleration, hindering the overall performance improvement of the crane system. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent crane operation method and system based on multi-dimensional data, in order to solve the above-mentioned problems.

[0005] This invention is implemented as follows: an intelligent overhead crane operation method based on multi-dimensional data, comprising the following steps: based on the load mass and rope length, a dynamic inertia evaluation model is invoked to output the dynamic inertia coefficient; based on the current composite velocity, the remaining distance to the target stopping point, and the instantaneous radius of curvature of the path, a motion state evaluation model is invoked to output the motion state coefficient; based on the load swing angle angular velocity and load swing angle under the dynamic inertia coefficient and motion state coefficient, a working condition evaluation model is invoked to output the working condition matching coefficient; based on the shortest expected collision time, the spacing between cooperating overhead cranes, and the distance to the end of the track, a safety margin evaluation model is invoked to output the safety margin coefficient; based on the working condition matching coefficient, real-time lateral wind speed, safety margin coefficient, and reference acceleration, a running acceleration control model is used to output the target running acceleration and adjust the current running acceleration to the target running acceleration.

[0006] A further technical solution involves the following operational flow of the acceleration control model: acquiring the operating condition matching coefficient, real-time lateral wind speed, safety margin coefficient, and reference acceleration; calculating the wind speed influence factor using a nonlinear attenuation function based on the relative magnitude of the real-time lateral wind speed and the preset maximum allowable operating wind speed, wherein the wind speed influence factor decreases as the real-time wind speed increases; multiplying the reference acceleration by the safety margin coefficient, the wind speed influence factor, and an adjustment term weighted by the operating condition matching coefficient to generate the target operating acceleration, wherein the adjustment term aims to balance the influence of the degree of operating condition matching on acceleration and avoid complete loss of acceleration capability when the oscillation state is poor; and calculating and outputting the acceleration adjustment amount for a single control based on the difference between the target operating acceleration and the current operating acceleration, according to a preset adjustment rate ratio.

[0007] A further technical solution involves the following operation process for the safety margin assessment model: The shortest predicted collision time, the coordinated crane spacing, and the distance to the track end are compared with the collision time threshold, the coordinated operation safety distance threshold, and the track end safety distance threshold, respectively. After applying a min function with an upper limit of 1, the collision time index, the coordinated crane spacing index, and the track end distance index are obtained. The minimum value among these three indices is taken as the safety margin coefficient. A larger safety margin coefficient indicates a higher environmental safety margin, and a correspondingly larger upper limit for the system's allowable operating acceleration.

[0008] A further technical solution involves the following operation flow of the working condition evaluation model: Based on the dynamic inertia coefficient and motion state coefficient, obtain the dynamic maximum swing angle and dynamic maximum angular velocity; calculate the ratio of the absolute value of the load swing angle to the dynamic maximum swing angle to obtain the load swing angle index; calculate the ratio of the absolute value of the load swing angle angular velocity to the dynamic maximum angular velocity to obtain the load swing angle angular velocity index; and calculate the working condition matching coefficient using a monotonically decreasing function with the larger of the load swing angle index and the angular velocity index as the independent variable. The larger the coefficient value, the more the actual swing state matches the ideal state under the current working condition.

[0009] A further technical solution, the specific method for obtaining the dynamic maximum swing angle and dynamic maximum angular velocity based on the dynamic inertia coefficient and motion state coefficient is as follows: the dynamic inertia coefficient and the motion state coefficient are linearly weighted and fused to obtain a comprehensive working condition coefficient that comprehensively reflects the severity of the current operation; the preset benchmark maximum allowable swing angle and angular velocity are multiplied by the comprehensive working condition coefficient respectively to obtain the maximum allowable swing angle and angular velocity thresholds after dynamic adjustment under the current working condition, that is, the dynamic maximum swing angle and dynamic maximum angular velocity.

[0010] A further technical solution is that the operation process of the dynamic inertia evaluation model is as follows: the current load mass and rope length are both normalized to obtain the load mass index and rope length index; the load mass index and rope length index are weighted and summed, and the summation result is performed with complement to obtain the dynamic inertia coefficient. The larger the coefficient value, the smaller the system inertia and the greater the allowable acceleration potential.

[0011] A further technical solution involves the following operation flow of the motion state evaluation model: The current synthesized speed is compared with the maximum allowed operating speed of the system, and the complement of the ratio is taken as the synthesized speed index; the remaining distance from the target parking point is compared with the sum of the remaining distance from the target parking point and the distance adjustment constant to obtain the remaining distance index; the instantaneous curvature radius of the path is normalized to obtain the instantaneous curvature radius index; the synthesized speed index, the remaining distance index, and the instantaneous curvature radius index are multiplied consecutively to obtain the motion state coefficient. The larger the motion state coefficient, the more favorable the current motion state is for acceleration operations.

[0012] A method for intelligent overhead crane operation based on multi-dimensional data includes: a dynamic inertia assessment module, which outputs dynamic inertia coefficients based on load mass and rope length by calling a dynamic inertia assessment model; a motion state assessment module, which outputs motion state coefficients based on the current composite velocity, the remaining distance to the target parking point, and the instantaneous radius of curvature of the path by calling a motion state assessment model; a working condition assessment module, which outputs a working condition matching coefficient based on the load swing angle angular velocity and load swing angle under the dynamic inertia coefficients and motion state coefficients by calling a working condition assessment model; a safety margin assessment module, which outputs a safety margin coefficient based on the shortest expected collision time, the spacing between cooperating overhead cranes, and the distance to the end of the track by calling a safety margin assessment model; and a running acceleration control module, which outputs a target running acceleration and adjusts the current running acceleration to the target running acceleration based on the working condition matching coefficient, real-time lateral wind speed, safety margin coefficient, and reference acceleration by using a running acceleration control model.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Achieved adaptive and refined intelligent control of operating acceleration: By integrating multi-dimensional heterogeneous data such as load characteristics, motion state, real-time oscillation, ambient wind speed, and multiple safety risks, and transforming them into five core evaluation coefficients, the target operating acceleration is dynamically generated based on these coefficients, completely changing the traditional fixed parameter or single variable control mode. This enables the overhead crane to perceive changes in operating conditions in real time and achieve a dynamic optimal balance among multiple sometimes conflicting objectives such as efficiency, stability, and safety.

[0014] 2. Significantly improved operational safety and risk foresight: By comprehensively considering the shortest expected collision time, collaborative operation spacing, and track end distance through a safety margin assessment model, and using the "barrel principle" to generate a safety factor by taking the minimum value, a comprehensive, conservative, and quantifiable safety defense line is constructed. This mechanism can identify complex risks in advance and automatically limit acceleration when risks escalate, greatly reducing the incidence of collisions, over-limit accidents, and other incidents, achieving an upgrade from passive protection to proactive prevention.

[0015] 3. Significantly improved operational efficiency and economy: The motion state assessment model generates smooth and efficient acceleration and deceleration curves through comprehensive optimization of speed, distance, and curvature. The dynamic inertia assessment and operating condition assessment models ensure that the system's acceleration potential is fully explored but not exceeded under different load and oscillation conditions. This avoids unnecessary conservative operations, shortens work cycle time, reduces start-stop shock and energy loss, thereby improving overall production cycle time and energy efficiency.

[0016] 4. Significantly enhanced system adaptability to different operating conditions and environments: The core of this method lies in "dynamic evaluation" and "coefficient fusion." The dynamic inertia coefficient enables the system to automatically adapt to changes from no-load to full-load and from short rope to long rope; the operating condition matching coefficient enables dynamic adjustment of the swing allowable threshold; and the wind speed influence factor provides real-time compensation for external disturbances. This design allows the system to maintain excellent performance in a wide range of operating conditions and changing working environments without manual parameter readjustment.

[0017] 5. Effectively ensures operational stability and positioning accuracy: A high-frequency, dynamic evaluation of load swing angle and angular velocity is conducted using a working condition assessment model. The impact of these factors is then incorporated into the acceleration control closed loop through a matching coefficient, achieving proactive and intelligent suppression of load sway. Combined with precise handling of the remaining distance during motion state assessment, this ensures the crane can smoothly and accurately reach the target position, reducing material damage and subsequent adjustment time. This is particularly suitable for lifting precision equipment or processes requiring high stability.

[0018] 6. A modular and scalable intelligent framework is provided: This invention decomposes complex control problems into multiple clearly defined modules, such as dynamic inertia, motion state, operating conditions, safety margin assessment, and final control. This architecture is not only clear and reliable, facilitating engineering implementation and debugging, but also reserves interfaces for the subsequent integration of more dimensional data (such as equipment health and energy consumption indicators) or more advanced algorithms (such as machine learning optimizers), exhibiting good scalability and driving the continuous evolution of overhead crane systems towards higher levels of intelligence. Attached Figure Description

[0019] Figure 1 The flowchart of an intelligent crane operation method and system based on multidimensional data provided by the present invention is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] In traditional overhead crane operations, control parameters are mostly fixed values, unable to be dynamically adjusted according to load characteristics, motion status, and real-time environment, leading to performance degradation when operating conditions change. Furthermore, existing technologies only consider single or limited parameters, failing to integrate multi-dimensional data for collaborative analysis and decision-making, resulting in poor control performance when facing complex dynamic processes with multiple coupled variables. In addition, safety control, efficiency optimization, and stability control modules are independent, lacking a unified decision-making framework to coordinate multiple conflicting control objectives, making it difficult to achieve an optimal balance between efficiency, stability, and safety, thus affecting key performance indicators such as operational efficiency, operational stability, and safety.

[0022] For example, in a steel smelting workshop, overhead cranes need to transport molten steel ladles at high temperatures. The load mass fluctuates with the temperature of the molten steel, and the rope length changes due to adjustments in the hook position. Multiple overhead cranes operate collaboratively within the workshop, their paths needing to bypass fixed equipment to form curved trajectories, and plant ventilation causes variations in lateral wind speed. In this scenario, a control system with fixed parameters cannot adapt to changes in load inertia, leading to increased load sway amplitude during high-speed operation. Motion state assessment relies solely on speed sensors, ignoring remaining distance and radius of curvature, resulting in insufficient stopping accuracy. The safety module operates independently, triggering an emergency stop upon detecting nearby equipment without considering the overall operating conditions, causing unnecessary operational interruptions and consequently affecting the continuous operation of the production line and the reliability of material handling.

[0023] If the above problems are not resolved, load fluctuations may exceed safety thresholds, increasing the risk of collisions with surrounding equipment; frequent manual intervention will lead to unstable work rhythms, affecting the continuous operation of the production line; the conflict between safety and efficiency may force operators to adopt conservative strategies, reducing overall work efficiency, or to take risks, threatening the safety of personnel and equipment.

[0024] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0025] like Figure 1 As shown, an intelligent crane operation method and system based on multi-dimensional data is provided in one embodiment of the present invention, including the following steps: Based on the load mass and rope length, the dynamic inertia evaluation model is invoked to output the dynamic inertia coefficient. Load mass refers to the actual weight of the object being lifted by the overhead crane, and its magnitude directly affects the inertial characteristics and motion response of the overhead crane system. Rope length refers to the effective length of the wire rope connecting the lifting device and the load; changes in this length alter the oscillation period and amplitude of the load, thus affecting the dynamic characteristics of the system. In practical applications, the load mass can be acquired in real time using a load cell installed on the lifting device, while the rope length can be obtained by measuring the change in height of the lifting device relative to the drum using an encoder or laser rangefinder.

[0026] Based on the current synthesized speed, the remaining distance to the target parking point, and the instantaneous radius of curvature of the path, the motion state evaluation model is invoked to output motion state coefficients. The current synthesized speed refers to the vector synthesis of the actual horizontal running speed of the overhead crane and the running speed of the trolley, representing the overall motion speed of the load. The remaining distance to the target parking point is the straight-line distance between the current position of the overhead crane and the preset parking target point; this parameter is used to evaluate the accuracy of parking and braking requirements. The instantaneous radius of curvature of the path refers to the curvature of the overhead crane's trajectory at the current point; a smaller radius of curvature indicates a greater degree of path curvature, requiring higher operational stability. The current synthesized speed can be calculated by vector synthesis using the encoder data of both the overhead crane and the trolley. The remaining distance to the target parking point can be calculated in real time by the positioning module in the overhead crane control system. The instantaneous radius of curvature of the path can be calculated by interpolation or fitting based on the preset running path data and the current position of the overhead crane.

[0027] Based on the load swing angular velocity and load swing angle under dynamic inertia coefficient and motion state coefficient, the working condition evaluation model outputs the working condition fit coefficient. Load swing angular velocity refers to the angular velocity of the load relative to the vertical direction, and is an important indicator of the severity of load swing. Load swing angle refers to the angle of deviation of the load relative to the vertical direction, reflecting the stability of the load. Load swing angular velocity and load swing angle can be measured in real time using an inertial measurement unit or vision sensor installed on the lifting device or load.

[0028] Based on the shortest predicted collision time, the spacing between cooperating cranes, and the distance to the track end, a safety margin assessment model is invoked to output a safety margin coefficient. The shortest predicted collision time refers to the shortest possible time for a collision, predicted based on the crane's own motion state and the motion state and position of surrounding obstacles (such as other cranes and structures). The cooperating crane spacing refers to the real-time distance between adjacent cranes operating collaboratively; it is a crucial safety parameter for avoiding collisions. The distance to the track end refers to the distance between the crane's current position and the extreme positions at both ends of the running track, used to prevent the crane from exceeding the track's boundaries. The shortest predicted collision time can be calculated using the crane's own motion prediction algorithm combined with detection data from surrounding environmental sensors (such as lidar and millimeter-wave radar). The cooperating crane spacing can be obtained through the communication or positioning systems between multiple cranes. The distance to the track end is obtained by comparing the crane's positioning system with track boundary data.

[0029] Based on the operating condition fit coefficient, real-time lateral wind speed, safety margin coefficient, and reference acceleration, the operating acceleration control model outputs the target operating acceleration and adjusts the current operating acceleration to the target operating acceleration. Real-time lateral wind speed refers to the speed of the lateral wind acting on the overhead crane and its load. Lateral wind speed can cause load swaying, affecting operational stability and safety. Real-time lateral wind speed can be obtained through a wind speed sensor installed on the overhead crane. Reference acceleration refers to the maximum permissible operating acceleration preset by the overhead crane system under ideal and safe operating conditions, serving as a reference basis for acceleration control. The target operating acceleration refers to the operating acceleration calculated by the operating acceleration control model based on the current multi-dimensional data evaluation results, which balances efficiency, stability, and safety.

[0030] The overhead crane system can dynamically adjust its operating acceleration based on various real-time data, including load characteristics, motion state, load sway, ambient wind speed, and potential safety risks. When the load mass is large and the rope length is long, the dynamic inertia coefficient decreases, and the system tends to output a more conservative acceleration. When approaching the target stopping point or when the path curvature is large, the motion state coefficient decreases, and the acceleration is adjusted accordingly to ensure smooth stopping and trajectory tracking. When the load sways violently or the lateral wind speed is high, the working condition matching coefficient decreases, and the acceleration is limited to suppress swaying. When there is a potential collision risk or when approaching the end of the track, the safety margin coefficient decreases, and the acceleration is also limited to ensure safety. This multi-dimensional data fusion and adaptive control mechanism enables the overhead crane to achieve a dynamic balance between efficiency, stability, and safety in a dynamically changing environment, avoiding the inefficiencies, uncontrolled swaying, or safety accidents that may occur under traditional fixed parameter control.

[0031] The intelligent crane operation method proposed in this embodiment demonstrates significant technological contributions. Traditional crane operation methods often rely on the operator's experience or preset fixed parameters for control. For example, in the aforementioned port container loading and unloading scenario, traditional methods may only set a fixed maximum acceleration based on the load weight, or use a simple anti-sway algorithm, failing to fully consider the dynamic effects of rope length, real-time motion status, ambient wind speed, and multiple safety factors.

[0032] This application introduces a dynamic inertial evaluation model to achieve real-time quantification of the coupling effects of load mass and rope length. Compared with traditional methods that may use a single load weight or fixed inertial parameters, this method can more accurately reflect the dynamic response of the system under different load conditions, thereby avoiding control deviations caused by inertial parameter mismatch.

[0033] Furthermore, the introduction of the motion state evaluation model enables the overhead crane to comprehensively consider the current synthesized speed, the remaining distance to the target stopping point, and the instantaneous radius of curvature of the path. This transcends the limitations of traditional methods that may only focus on a single parameter such as speed or position, allowing the overhead crane to perform more refined motion planning and control during high-speed operation, precise stopping, and complex path tracking.

[0034] The operating condition assessment model further integrates dynamic inertia coefficient, motion state coefficient, and load swing angle angular velocity and load swing angle to provide a multi-dimensional assessment of the stability of overhead crane operations. This differs from traditional methods that may only make simple judgments based on swing angle thresholds. This method can gain a deeper understanding of the root causes of load swaying and the complexity of the current operating conditions, thereby achieving more effective anti-sway control.

[0035] The safety margin assessment model establishes a multi-dimensional safety risk quantification system by comprehensively considering the shortest expected collision time, the spacing between cooperating cranes, and the distance from the track end. This is significantly superior to traditional methods that may rely solely on limit switches or a single obstacle avoidance sensor for separation safety judgment. This method can provide more comprehensive safety warnings and more sufficient safety redundancy.

[0036] Ultimately, the acceleration control model integrates all the above evaluation results with real-time lateral wind speed and baseline acceleration, dynamically outputting the target operating acceleration. This holistic technical concept of multi-dimensional data fusion and adaptive control enables the overhead crane to intelligently adjust its operating strategy according to real-time changes in working conditions and the environment. This overcomes the problems of insufficient adaptability, efficiency fluctuations, and numerous safety hazards caused by fixed parameters, single data dimensions, and separation of decision-making in traditional methods. Through this method, overhead crane operations can improve operational efficiency and operational stability while ensuring safety, achieving intelligent and adaptive control of overhead crane operations.

[0037] like Figure 1 As shown, in a preferred embodiment of the present invention, the operation flow of the acceleration control model is as follows: The system acquires the operating condition fit coefficient, real-time lateral wind speed, safety margin coefficient, and reference acceleration. These parameters can be obtained in real time through the sensor network and evaluation module within the overhead crane control system. For example, the operating condition fit coefficient and safety margin coefficient can be calculated by the corresponding evaluation model, the real-time lateral wind speed can be acquired by meteorological sensors installed on the overhead crane or in the work area, and the reference acceleration is stored as a preset constant in the controller.

[0038] Based on the relative magnitudes of the real-time lateral wind speed and the preset maximum allowable working wind speed, a wind speed influence factor is calculated using a nonlinear attenuation function, wherein the wind speed influence factor decreases as the real-time wind speed increases; the specific calculation method for the wind speed influence factor can be as follows: [The text abruptly ends here, so the translation stops as well.] Import formula Obtain wind speed influencing factors ,in, The wind speed influence coefficient, To define the maximum permissible operating wind speed, the maximum wind speed that the overhead crane can withstand under safe conditions is defined. This step quantifies the impact of real-time lateral wind speed on the crane's acceleration, generating a wind speed influence factor. As the wind speed increases, the wind speed influence factor decreases, thereby limiting the crane's acceleration to ensure operational stability and safety. The formula used to adjust the sensitivity of wind speed to acceleration can be calculated in the crane's main controller or a dedicated signal processing unit. Real-time lateral wind speed is collected by a wind speed sensor, converted from analog to digital, and then substituted into a preset value. and The parameters are calculated. The calculation results are... This will serve as a multiplication factor in subsequent target acceleration calculations. Alternatively, a lookup table method can be used. Wind speed influence factors are pre-calculated and stored for different wind speed ranges, forming a lookup table. Once the real-time lateral wind speed is obtained, the corresponding wind speed influence factor can be directly retrieved by querying this table, thus avoiding complex real-time floating-point operations and improving computational efficiency.

[0039] The target operating acceleration is generated by multiplying the baseline acceleration by the safety margin coefficient, the wind speed influence factor, and an adjustment term weighted by the operating condition fit coefficient. This adjustment term aims to balance the influence of the degree of operating condition fit on the acceleration, preventing a complete loss of acceleration capability when the oscillation condition is poor. The specific formula for calculating the target operating acceleration can be: multiplying the wind speed influence factor... Operating condition compatibility coefficient Safety margin coefficient and reference acceleration Import formula Obtain the target running acceleration ,in, This is the adaptation weighting coefficient; this step is the core of the acceleration control model, which dynamically calculates the target operating acceleration that the crane should achieve at the current moment by comprehensively considering multiple dimensions such as environment, safety, and operating condition adaptability. This formula uses the baseline acceleration... As a foundation, through multiplication factors and The safety margin and the impact of wind speed are reduced respectively, while the weighted average term is used. Integrating the working condition matching coefficient ,in The adaptation weighting coefficient is used to balance the importance of operational condition adaptability in the target acceleration calculation. This calculation process can be executed in the crane's central processing unit (CPU) or digital signal processor (DSP). After all input parameters are acquired, floating-point multiplication and weighted operations are performed according to the formula to finally obtain the target running acceleration. Considering real-time requirements, the calculation of this formula can also be implemented using hardware accelerators or programmable gate arrays (FPGAs). Hardware parallel computing can significantly shorten the computation cycle, ensuring real-time updates of the target acceleration to adapt to rapidly changing operating environments.

[0040] Based on the difference between the target running acceleration and the current running acceleration, the acceleration adjustment amount for a single control is calculated and output according to a preset adjustment rate ratio; the acceleration adjustment amount can be calculated as follows: the current running acceleration... and target acceleration Import formula , obtain Calculate the acceleration adjustment amount, where, Adjust the rate coefficient for acceleration. This step calculates the difference between the current running acceleration and the target running acceleration, and adjusts the rate coefficient accordingly. A smooth acceleration adjustment is generated. The purpose is to avoid abrupt changes in acceleration, thereby reducing load sway and improving the smoothness and comfort of crane operation. The coefficient determines how quickly the acceleration adjusts; a larger coefficient results in a faster adjustment. This will lead to faster adjustments, but may cause a larger shock; smaller ones... This makes the adjustment smoother. This calculation is typically performed in the crane's motion controller. The motion controller monitors the crane's current acceleration in real time. It also receives the target acceleration calculated from the previous step. Then, the acceleration adjustment amount is calculated through simple subtraction and multiplication operations. This adjustment is then used to drive the crane's motors or actuators to gradually approach the target acceleration. Alternatively, a PID (Proportional-Integral-Derivative) controller or other advanced control algorithms can be used to calculate the acceleration adjustment. In this case, the above formula can be viewed as the proportional term in a PID controller, or as part of a more complex control strategy to achieve more precise and robust acceleration smoothing.

[0041] For example, the aforementioned acceleration control model can be deployed on the overhead crane's programmable logic controller (PLC) or industrial PC. Regarding parameter acquisition, the operating condition matching coefficient and safety margin coefficient can be calculated and provided in real time by the software module within the overhead crane control system; real-time lateral wind speed can be collected by ultrasonic wind speed sensors installed on the top of the overhead crane or at the edge of the working area and transmitted to the controller via industrial Ethernet; the reference acceleration is stored as a constant set during the system commissioning phase in the PLC's parameter register. In the calculation of the wind speed influence factor, after receiving the wind speed data, the controller substitutes it into a preset formula for floating-point calculation. The wind speed influence coefficient and the maximum allowable working wind speed can be configured according to the overhead crane model, operating environment, and safety regulations. For example, It can be set to 0.8, and It can be set to 15 m / s. In the calculation of the target running acceleration, the controller substitutes the wind speed influence factor, operating condition fit coefficient, safety margin coefficient, and reference acceleration into the main formula. (Fitness weighting coefficient) It can be adjusted according to actual operational needs. For example, in scenarios where high stability is required, the setting can be appropriately increased. The value is adjusted to emphasize the fit of operating conditions. Ultimately, in calculating the acceleration adjustment, the controller continuously monitors the current operating acceleration of the crane drive system and compares it with the calculated target operating acceleration, adjusting the acceleration adjustment rate coefficient accordingly. (For example, set to 0.5) Calculate the acceleration increment or decrement for each step, and then send this adjustment to the frequency converter or servo drive to smoothly adjust the motor speed, thereby achieving dynamic and smooth adjustment of the crane's running acceleration.

[0042] Through the above technical solution, this application enables precise and adaptive control of the overhead crane's acceleration. Specifically, by introducing real-time lateral wind speed and calculating the wind speed influence factor, the system can dynamically respond to changes in the external environment, effectively avoiding increased swaying or safety risks caused by excessive acceleration under strong wind conditions, significantly improving the robustness of the overhead crane in complex environments. Simultaneously, by integrating the working condition matching coefficient, safety margin coefficient, and wind speed influence factor with the benchmark acceleration in a multi-dimensional calculation, the generation of the target acceleration becomes more comprehensive and intelligent, balancing efficiency, stability, and safety, avoiding the limitations of single-parameter decision-making in traditional methods. Furthermore, by calculating the acceleration adjustment amount and performing a smooth transition, load swaying caused by sudden acceleration changes is effectively suppressed, greatly improving the stability and operational accuracy of the overhead crane, reducing the risk of cargo damage, and enhancing operator comfort. This dynamic and precise acceleration control mechanism allows the overhead crane to maintain high-efficiency operation while better adapting to changing working conditions and environments, thereby comprehensively improving the level of intelligent operation of the overhead crane.

[0043] like Figure 1 As shown, in a preferred embodiment of the present invention, the operation flow of the safety margin assessment model is as follows: The system obtains the shortest estimated collision time, the spacing between coordinating overhead cranes, and the distance to the track end. The shortest estimated collision time can be calculated using a prediction algorithm by real-time monitoring of the environment in front of or to the side of the cranes using sensing devices such as radar, lidar, and visual sensors, combined with the cranes' own motion and the motion of obstacles. Alternatively, it can be achieved by pre-defined work area divisions and path planning, combined with the cranes' current position and speed, to estimate the arrival time at the boundary of a pre-defined danger zone or the intersection of the path. The spacing between coordinating overhead cranes can be calculated in real-time using UWB (Ultra-Wideband) positioning systems, GPS / BeiDou positioning systems combined with RTK (Real-Time Kinematic) technology, or based on visual recognition and ranging technology, to obtain the position information of each crane. Another method is to exchange position and speed information between cranes via data link communication, allowing each crane to autonomously calculate its distance from the track end. The distance to the track end can be measured in real-time using limit switches, encoders, laser rangefinders, or ultrasonic sensors installed on the track. Another approach is to calculate the distance from the crane to the end of the track using the crane's absolute position sensor and a preset value for the track length.

[0044] The shortest predicted collision time, the coordinated crane spacing, and the distance to the track end are compared with the collision time threshold, the coordinated operation safety distance threshold, and the track end safety distance threshold, respectively. After applying a min function with an upper limit of 1, the collision time index, the coordinated crane spacing index, and the track end distance index are obtained. The purpose of this ratio processing is to quantify safety parameters with different physical dimensions, making them comparable and reflecting the degree of deviation between the current state and the safety standard. For example, when the actual value is much greater than the threshold, the ratio may be very small, indicating safety; when the actual value is close to or less than the threshold, the ratio may be close to or greater than 1, indicating increased risk. This is achieved by dividing the current measured value of each safety parameter by the corresponding preset safety threshold. Based on this, the collision time index, the coordinated crane spacing index, and the track end distance index are obtained after applying a min function with an upper limit of 1. The purpose of this limiting process is to ensure that the obtained index value does not exceed 1. That is, when the actual safety parameter is much larger than its threshold, the corresponding index value is still limited to 1, avoiding an unreasonable "over-safety" effect in subsequent calculations due to excessively high index values, thereby maintaining the conservatism and effectiveness of the assessment. This is achieved by comparing each ratio calculated with the value 1, and taking the smaller of the two as the final index.

[0045] The minimum value among the collision time index, the cooperative crane spacing index, and the end distance index is taken as the safety margin coefficient. , The value range is 0-1. A higher index indicates a higher environmental safety margin, and a correspondingly larger upper limit for the system's allowable acceleration. This operation reflects the "barrel effect," meaning that the overall safety margin of the system is determined by its weakest link. If the index of any safety dimension (collision time, coordination distance, track end distance) is low, even if other dimensions perform well, the overall safety margin coefficient will be lowered, thus prompting the system to adopt a more conservative control strategy. This is achieved by inputting the calculated three indices into a min function, directly outputting the minimum of these three values ​​as the safety margin coefficient.

[0046] For example, the safety margin assessment model can be implemented as follows: First, a lidar system deployed on the overhead crane continuously scans the area in front of and to the sides of the crane, identifying potential obstacles and measuring their distance and relative speed. Combined with the crane's own kinematic model, the shortest predicted collision time is calculated. Simultaneously, the crane is equipped with a high-precision GNSS receiver, which, combined with RTK technology, acquires its own precise position information and that of the cooperating cranes, thereby calculating the distance between the cooperating cranes in real time. For the distance to the track end, ultrasonic sensors can be installed at both ends of the track, and corresponding reflectors can be installed on the crane to accurately measure the distance between the crane and the track end. After acquiring this raw data, the system performs ratio processing. For example, if the collision time threshold is set to 5 seconds, the cooperative operation safety distance threshold to 10 meters, and the track end safety distance threshold to 5 meters, and the current shortest predicted collision time is 8 seconds, the cooperating crane distance is 15 meters, and the distance to the track end is 3 meters, then the collision time ratio is calculated as 1.6, the cooperating crane distance ratio as 1.5, and the end distance ratio as 0.6. Next, these ratios are processed using a min function with an upper limit of 1, resulting in a collision time index of 1, a coordinated crane spacing index of 1, and an end-point distance index of 0.6. Finally, the smallest value among these three indices is taken as the safety margin coefficient, which is 0.6. This calculated safety margin coefficient value will be passed to the operation acceleration control model. Since 0.6 is a moderately low value, the operation acceleration control model will, based on this coefficient, appropriately reduce the crane's operating acceleration from the baseline acceleration to address the safety risk of being too close to the track end, thereby preventing the crane from approaching the track end at excessive speed and ensuring operational safety.

[0047] Through the above technical solution, this application effectively addresses the problem that existing safety margin assessments fail to effectively integrate multiple key safety parameters, resulting in inaccurate quantification of safety risks and an inability to dynamically adapt to the safety requirements of different operating environments. Specifically, by comprehensively considering three core safety dimensions—the shortest expected collision time, the distance between cooperating cranes, and the distance from the track end—and quantifying them into a unified index, a safety margin coefficient is generated by taking the minimum value. This makes the safety assessment of crane operations more comprehensive, accurate, and conservative. This multi-dimensional integrated assessment method avoids the limitations of single-parameter assessments and can more accurately reflect the true safety status of the crane in complex dynamic environments. When the safety margin coefficient is input into the operating acceleration control model, it can directly affect the generation of the target operating acceleration, thereby achieving adaptive and refined control of the crane's operating acceleration. This allows the crane to reduce its operating acceleration in a timely and effective manner when the safety risk is high, avoiding potential accidents; while when the safety margin is sufficient, the crane can be allowed to operate at a higher acceleration, thereby maximizing operational efficiency and production cycle while ensuring operational safety.

[0048] like Figure 1 As shown, in a preferred embodiment of the present invention, the operation flow of the working condition evaluation model is as follows: The system acquires dynamic inertia coefficient, motion state coefficient, load swing angle angular velocity, and load swing angle. Load swing angle angular velocity refers to the angular velocity of the load relative to the vertical direction during swing; it is a key dynamic parameter for measuring the severity of load swing. It can be calculated by an inertial measurement unit (IMU) mounted on the lifting device or load, or by tracking the load position through a vision recognition system. Load swing angle refers to the angle by which the load deviates from the vertical direction during swing; it is a key static parameter for measuring the amplitude of load swing. It can be calculated by an tilt sensor mounted on the lifting device or load, or by tracking the load position through a vision recognition system.

[0049] The dynamic inertia coefficient and the motion state coefficient are linearly weighted and fused to obtain a comprehensive working condition coefficient that fully reflects the severity of the current operation. The comprehensive working condition coefficient can be calculated as follows: the dynamic inertia coefficient... and motion state coefficient Import formula Obtain the comprehensive working condition coefficient ,in, This is the inertia weighting coefficient, used to adjust the relative importance of the dynamic inertia coefficient and the motion state coefficient in the comprehensive working condition assessment. For example, when... A value of 0.5 indicates that the two have equal weights; when... A value of 0.7 indicates a greater emphasis on the load's inertial characteristics. This coefficient can be preset based on the actual application scenario, crane type, or operational experience, or it can be optimized and adjusted using machine learning algorithms based on historical data. This step aims to integrate two key coefficients reflecting the inherent characteristics of the load and the crane's operating characteristics to generate a more comprehensive overall operating condition coefficient.

[0050] The preset maximum permissible swing angle and angular velocity are multiplied by the comprehensive operating condition coefficient to obtain the dynamically adjusted maximum permissible swing angle and angular velocity thresholds under the current operating condition, i.e., the dynamic maximum swing angle and dynamic maximum angular velocity. The purpose of this step is to dynamically adjust the maximum permissible swing angle of the load based on the current comprehensive operating conditions. The preset maximum permissible swing angle is the maximum swing angle allowed by the load under ideal or standard operating conditions, for example, it can be set to 5 degrees or 10 degrees. By multiplying it by the comprehensive operating condition coefficient, adaptive adjustment of the maximum permissible swing angle can be achieved. Similarly, the preset maximum permissible angular velocity is multiplied by the comprehensive operating condition coefficient to obtain the dynamic maximum angular velocity. The preset maximum permissible angular velocity is the maximum swing angular velocity allowed by the load under ideal or standard operating conditions, for example, it can be set to 0.5 rad / s or 1 rad / s. By multiplying it by the comprehensive operating condition coefficient, adaptive adjustment of the maximum permissible angular velocity can be achieved. When the overall operating conditions are good, the dynamic maximum swing angle and angular velocity are close to the reference values; when the overall operating conditions are poor, they will decrease accordingly, thereby more strictly limiting load swing and improving safety.

[0051] The load swing angle index is obtained by comparing the absolute value of the load swing angle with the dynamic maximum swing angle; the load swing angle angular velocity index is obtained by comparing the absolute value of the load swing angle angular velocity with the dynamic maximum angular velocity. Based on the larger of the load swing angle index and the angular velocity index, a working condition matching coefficient is calculated using a monotonically decreasing function with this larger value as the independent variable. The larger the coefficient, the closer the actual swing state is to the ideal state under the current working condition. Specifically, the calculation method involves importing the load swing angle index and the load swing angle angular velocity index into the formula. Obtain the working condition consistency coefficient , Output range 0-1, The larger the value, the greater the allowable acceleration. The load swing angle index, This is the load swing angle angular velocity exponent. This step is crucial for generating the final operating condition fit coefficient, as it comprehensively considers the degree of deviation of the load swing in both angle and velocity. The `max` function is used to take the larger value between the load swing angle exponent and the load swing angle angular velocity exponent, meaning the system will prioritize the most severe deviation in the load swing.

[0052] The following is a concrete example. As a specific implementation, the operational condition assessment model can run in the central controller of the overhead crane. This controller can be a high-performance industrial PC or an embedded system. First, the controller acquires the dynamic inertia coefficient and motion state coefficient from the sensor interface. For example, the dynamic inertia coefficient can be calculated by a separate module based on data from the mass sensor and rope length sensor on the spreader; the motion state coefficient can be calculated by another module based on speed, position information, and preset path data provided by the crane encoder, GPS, or laser rangefinder. Simultaneously, the controller receives real-time load swing angle and load swing angle angular velocity data from vision sensors or inertial measurement units (IMUs) installed below the spreader. After acquiring this data, the controller processes the dynamic inertia coefficient... and motion state coefficient Substitute into the formula Among them, the inertia weighting coefficient It can be preset to 0.6, indicating that in the comprehensive working condition evaluation, the load inertia characteristics are slightly more important than the motion state characteristics. The calculated comprehensive working condition coefficient... This will be used for subsequent dynamic limit adjustments. For example, if the reference maximum permissible swing angle is set to 5 degrees, the reference maximum permissible angular velocity is set to 0.8 rad / s, and the calculated comprehensive operating coefficient is used... If the value is 0.75, then the maximum dynamic swing angle will be adjusted to 3.75 degrees, and the maximum dynamic angular velocity will be adjusted to 0.6 rad / s. Subsequently, the controller will ratio the absolute value of the real-time measured load swing angle (e.g., currently 2 degrees) with the maximum dynamic swing angle (3.75 degrees) to obtain a load swing angle exponent of 0.53. Similarly, it will ratio the absolute value of the load swing angle angular velocity (e.g., currently 0.3 rad / s) with the maximum dynamic angular velocity (0.6 rad / s) to obtain a load swing angle angular velocity exponent of 0.5. Finally, the controller will substitute these two exponents into the formula to obtain 0.65. This calculated operating condition fit coefficient of 0.65 will be used as input to the operating acceleration control model to guide the adjustment of the crane's current operating acceleration.

[0053] Through the above technical solution, this application effectively solves the problem of how to dynamically generate a working condition matching coefficient based on the dynamic inertia coefficient, motion state coefficient, load swing angle and angular velocity, and load swing angle in overhead crane operations, so as to ensure accurate reflection of the load state and adaptive adjustment of limit values ​​under complex working conditions. This solution generates a comprehensive working condition coefficient by integrating the dynamic inertia coefficient and motion state coefficient, making the working condition assessment more comprehensive and accurate. Based on this, the maximum allowable swing angle and angular velocity are dynamically adjusted to generate dynamic limit values ​​adapted to the current working conditions, avoiding the limitations of traditional fixed thresholds under variable working conditions. Furthermore, by comparing the actual load swing with the dynamic limit value, the degree of swing deviation is quantified, and finally, a conversion formula is used to generate the working condition matching coefficient. This coefficient can accurately reflect the stability of the current working condition, providing a reliable basis for subsequent acceleration control. This enables the overhead crane to achieve more precise and safer acceleration control under different loads, different motion states, and different swing degrees, significantly improving the intelligence level, operational stability, and safety of overhead crane operations.

[0054] like Figure 1 As shown, in a preferred embodiment of the present invention, the operation flow of the dynamic inertial evaluation model is as follows: The system acquires load mass and rope length. Load weight data is collected in real-time by load cells mounted on the crane hook or spreading device, or by using a pre-defined load type database combined with operator input. Rope length is obtained by measuring the released length of the wire rope in real-time using an encoder on the winch mechanism, or by measuring the distance from the bottom of the spreading device to the ground using a laser rangefinder and calculating the length based on the crane height.

[0055] The current load mass and rope length are both normalized to obtain the load mass index and rope length index. This normalization process aims to eliminate the influence of different physical dimensions on the calculation results and map the original data to a unified range, usually from 0 to 1.

[0056] The load mass index and rope length index are weighted and summed, and the sum is then complemented to obtain the dynamic inertia coefficient. A larger coefficient indicates lower system inertia and greater allowable acceleration potential. Specifically, the load mass index and rope length index are imported into the formula... , obtain Dynamic inertia coefficient The output range is 0-1, where, This refers to the mass weighting coefficient, which can be determined through prior experimental calibration and empirical setting. For example, when it is believed that the load mass has a more significant impact on inertia, the mass weighting coefficient can be... Set it to 0.6 to 0.8; when the change in rope length has a greater impact on inertia, it can be... The value is set to between 0.2 and 0.4. Furthermore, this coefficient can be dynamically optimized or adaptively adjusted using machine learning algorithms, combining historical operating data and actual swing conditions, to more accurately reflect the relative importance of mass and rope length under different working conditions. The load quality index, This is the rope length index.

[0057] The following is a concrete example to illustrate this. Suppose an overhead crane is lifting a load. The load mass is measured to be 12 tons in real time by a load cell, and the current rope length is measured to be 8 meters by the encoder of the hoisting mechanism. Assume the maximum permissible load mass of the overhead crane system is set to 20 tons, and the minimum to 1 ton; the maximum rope length is 30 meters, and the minimum to 2 meters. First, normalization is performed: the load mass index is 0.579; the rope length index is 0.214. Next, assume the mass weighting coefficient... Set it to 0.6. Substitute these values ​​into the formula to calculate the dynamic inertia coefficient as 0.567. This calculation yields... The value (approximately 0.567) will be used as input to the operating condition assessment model for subsequent calculation of the comprehensive operating condition coefficient, thereby affecting the final target operating acceleration.

[0058] Through the above technical solution, this application can accurately reflect the impact of dynamic changes in load mass and rope length on the inertia of the overhead crane, solving the control accuracy problem caused by inaccurate coefficient calculation in traditional methods. This enables the overhead crane system to adaptively adjust its operating strategy according to real-time load characteristics, avoiding reduced work efficiency due to conservative estimation or safety risks caused by aggressive operation, thereby significantly improving the stability and safety of overhead crane operation while ensuring work efficiency.

[0059] like Figure 1 As shown, in a preferred embodiment of the present invention, the operation flow of the motion state evaluation model is as follows: The system acquires the current composite velocity, the remaining distance to the target parking point, and the instantaneous radius of curvature of the path. The composite velocity can be obtained by real-time measurement using an encoder or speed sensor mounted on the crane's drive mechanism, combined with calculations based on the crane's kinematic model; or by real-time acquisition of the crane's position information using a high-precision positioning system (such as RTK-GPS or a laser positioning system), followed by differential processing of the position data. The remaining distance to the target parking point can be obtained in real-time using the crane's own positioning system (such as a laser rangefinder, visual positioning system, or encoder combined with an odometer), combined with the preset target parking point coordinates; or by real-time updates of the remaining travel distance on the predetermined path by the path planning system. The instantaneous radius of curvature of the path can be queried in real-time using preset running path data; that is, the path planning system pre-stores the geometric information of the path, and the crane queries the corresponding radius of curvature based on its current position during operation; or by using an inertial measurement unit (IMU) or visual sensor mounted on the crane to perceive the crane's attitude changes and trajectory in real-time, and by combining geometric calculation methods to estimate the radius of curvature of the current path in real-time.

[0060] The composite speed index is calculated by comparing the current composite speed with the system's maximum permissible operating speed, and then taking the complement of this ratio (1 minus the ratio) as the composite speed index. The system's maximum permissible operating speed refers to the highest operating speed that the overhead crane can achieve under design or safety specifications. This speed is usually a fixed parameter determined comprehensively based on factors such as the crane's mechanical structural strength, electrical system capabilities, load characteristics, and safety requirements of the operating environment. Alternatively, it can be a dynamically adjusted upper limit value based on current operating conditions (such as load type and ambient temperature). The ratio calculation unifies the dimensions of the current composite speed and the maximum permissible speed, reflecting the relative level of the current speed within the permissible range. Taking the complement (1 minus the ratio) aims to make the index value smaller as the speed approaches the maximum permissible speed, thus playing a limiting role in subsequent acceleration control to avoid the risk of overspeeding. The composite speed index is a dimensionless value, typically ranging from 0 to 1. This index intuitively reflects the "safety margin" between the current speed and the system's maximum permissible speed; the smaller the index, the closer the current speed is to the maximum permissible speed, requiring more conservative acceleration control.

[0061] The remaining distance to the target parking point is ratioed to the sum of the remaining distance to the target parking point and the distance adjustment constant to obtain the remaining distance index. The distance adjustment constant is a preset positive constant used to adjust the sensitivity of the remaining distance index. This constant can be empirically set according to the crane's braking performance, parking accuracy requirements, and operational efficiency needs. For example, it can be set as the distance required for the crane to decelerate from a certain speed to zero at maximum deceleration, or it can be optimized based on actual operational experience. This ratio processing method ensures that when the remaining distance is large, the index is close to 1; as the remaining distance gradually decreases and approaches 0, the index also decreases. Its function is to smoothly reduce acceleration when the crane approaches the target parking point, avoiding sudden stops, thereby improving parking accuracy and operational stability. The remaining distance index is a dimensionless value, and its value range is usually between 0 and 1. This index reflects the distance of the crane from the target parking point. The smaller the index, the closer the crane is to the target parking point, and the smaller the acceleration required to achieve precise and smooth parking.

[0062] The instantaneous radius of curvature of the path is subjected to max-min normalization to obtain the instantaneous radius of curvature exponent. Max-min normalization is a common data processing method that linearly maps the raw data to a specified range. For the instantaneous radius of curvature, this means converting its raw value into a uniform, bounded exponent. Specifically, a minimum effective radius of curvature and a maximum effective radius of curvature can be set, and then the actual radius of curvature is mapped to the 0-1 interval, so that the smaller the radius of curvature (the more curved the path), the smaller the exponent value. The instantaneous radius of curvature exponent is a dimensionless value, and its value range is usually between 0 and 1. This exponent reflects the curvature of the current path; the smaller the exponent, the more curved the path, and the smaller the acceleration required to ensure smooth operation and prevent excessive load fluctuations.

[0063] The motion state coefficients are obtained by continuously multiplying the synthetic velocity exponent, the remaining distance exponent, and the instantaneous radius of curvature exponent of the path. Output range 0-1 The larger the value, the more favorable the current motion state is for acceleration. This mechanism ensures that the system conservatively limits the permissible acceleration when there is a potential risk in any dimension of motion. The motion state coefficient is a comprehensive dimensionless coefficient with a value range of 0 to 1. This coefficient comprehensively quantifies the impact of the crane's current motion state on the permissible acceleration.

[0064] As a specific implementation, the overhead crane can be equipped with a system integrating sensors and control units. For example, a high-precision encoder can be installed on the crane to measure the rotational speed of the drive wheels in real time, calculate the instantaneous linear velocity of the crane by combining the wheel diameter, and acquire the crane's attitude and angular velocity information through an inertial measurement unit (IMU) to comprehensively calculate the current composite velocity. Simultaneously, a laser rangefinder or RTK-GPS positioning module can be used to obtain the crane's precise position on the track in real time, and combined with the coordinates of the target parking point in the preset task, calculate the remaining distance to the target parking point. For the instantaneous radius of curvature of the path, a digital map or B-spline curve model of the work path can be pre-stored in the crane control system. When the crane is running, it queries the corresponding radius of curvature value in the model based on its current position. After acquiring this real-time data, the control unit (e.g., a high-performance industrial controller or embedded system) will perform the corresponding calculations. Finally, the control unit will continuously multiply the calculated composite velocity index, remaining distance index, and instantaneous radius of curvature index to obtain the final motion state coefficients. For example, if the three indices are 0.8, 0.9, and 0.7 respectively, then... It is 0.504. The value is then passed to the working condition assessment model as one of its input parameters for subsequent acceleration control decisions. The entire process is carried out in real time and continuously during crane operation to ensure dynamic perception and response to motion status.

[0065] The motion state evaluation model of this application effectively solves the problem of insufficient dimensions and inability to comprehensively and accurately reflect the actual motion state in existing technologies by comprehensively considering three key dimensions: the current synthesized speed, the remaining distance to the target parking point, and the instantaneous radius of curvature of the path. By converting these three parameters into corresponding exponents and multiplying and fusing them, this scheme can generate a dynamically changing motion state coefficient. This coefficient can accurately capture the motion characteristics of the overhead crane in different scenarios such as high-speed operation, approaching the parking point, or passing through curves, thus providing a more refined and comprehensive basis for subsequent acceleration control. Specifically, when the synthesized speed is high, the synthesized speed exponent decreases, prompting the system to limit acceleration to avoid the risk of overspeeding; when the overhead crane approaches the target parking point, the remaining distance exponent decreases, guiding the system to decelerate smoothly to improve parking accuracy; when the path curvature is large, the instantaneous radius of curvature exponent decreases, helping the system to reduce acceleration to ensure turning smoothness and reduce load sway. This multi-dimensional comprehensive evaluation mechanism avoids the limitations of single-parameter evaluation, enabling the overhead crane to achieve more intelligent and adaptive acceleration control in complex dynamic environments. Introducing this motion state coefficient into the operating condition assessment model, working in conjunction with the dynamic inertia coefficient, can more comprehensively reflect the overall operating condition of the overhead crane. This allows the operating acceleration control model to consider not only load characteristics but also the crane's own motion state when deciding on the target operating acceleration. This significantly improves the stability and safety of the crane's operation while ensuring operational efficiency, effectively avoiding conservative operations or potential risks caused by inaccurate motion state assessments.

[0066] A method for intelligent overhead crane operation based on multi-dimensional data includes: a dynamic inertia assessment module, which outputs dynamic inertia coefficients based on load mass and rope length by calling a dynamic inertia assessment model; a motion state assessment module, which outputs motion state coefficients based on the current composite velocity, the remaining distance to the target parking point, and the instantaneous radius of curvature of the path by calling a motion state assessment model; a working condition assessment module, which outputs a working condition matching coefficient based on the load swing angle angular velocity and load swing angle under the dynamic inertia coefficients and motion state coefficients by calling a working condition assessment model; a safety margin assessment module, which outputs a safety margin coefficient based on the shortest expected collision time, the spacing between cooperating overhead cranes, and the distance to the end of the track by calling a safety margin assessment model; and a running acceleration control module, which outputs a target running acceleration and adjusts the current running acceleration to the target running acceleration based on the working condition matching coefficient, real-time lateral wind speed, safety margin coefficient, and reference acceleration by using a running acceleration control model.

[0067] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent operation of a crown block based on multi-dimensional data, characterized in that, The method comprises the following steps: Based on the load mass and the rope length, a dynamic inertia evaluation model is called to output a dynamic inertia coefficient; Based on the current synthetic speed, the remaining distance to the target parking point, and the instantaneous curvature radius of the path, a motion state evaluation model is called to output a motion state coefficient; According to the load swing angular velocity and the load swing angle under the dynamic inertia coefficient and the motion state coefficient, a working condition evaluation model is called to output a working condition coincidence coefficient; Based on the shortest predicted collision time, the cooperative headframe distance, and the distance to the track end, a safety margin evaluation model is called to output a safety margin coefficient; According to the working condition coincidence coefficient, the real-time lateral wind speed, the safety margin coefficient, and the reference acceleration, a running acceleration control model is used to output a target running acceleration and adjust the current running acceleration to the target running acceleration. 2.The intelligent operation method of a crown block based on multi-dimensional data according to claim 1, wherein, The running acceleration control model running process is as follows: According to the relative size relationship between the real-time lateral wind speed and the preset maximum allowed working wind speed, a wind speed influence factor is calculated through a nonlinear attenuation function, wherein the wind speed influence factor decreases with the increase of the real-time wind speed; The reference acceleration is multiplied by the safety margin coefficient, the wind speed influence factor, and an adjustment term determined by the working condition coincidence coefficient weighting to generate a target running acceleration; Based on the difference between the target running acceleration and the current running acceleration, the acceleration adjustment amount of a single control is calculated and output according to the preset adjustment rate proportion. 3.The intelligent operation method of a crown block based on multi-dimensional data according to claim 2, characterized in that, The safety margin evaluation model running process is as follows: The shortest predicted collision time, the cooperative headframe distance, and the distance to the track end are respectively processed by ratio with the collision time threshold value, the cooperative operation safety distance threshold value, and the track end safety distance threshold value, and after the upper limit is limited by the min function, the collision time index, the cooperative headframe distance index, and the end distance index are obtained; The minimum value of the collision time index, the cooperative headframe distance index, and the end distance index is taken as the safety margin coefficient, and the greater the safety margin coefficient, the higher the environmental safety margin, and the greater the upper limit of the running acceleration allowed by the system. 4.The intelligent operation method of a crown block based on multi-dimensional data according to claim 2, wherein, The working condition evaluation model running process is as follows: Based on the dynamic inertia coefficient and the motion state coefficient, the dynamic maximum swing angle and the dynamic maximum angular velocity are obtained; The absolute value of the load swing angle is processed by ratio with the dynamic maximum swing angle to obtain a load swing angle index; The absolute value of the load swing angular velocity is processed by ratio with the dynamic maximum angular velocity to obtain a load swing angular velocity index; According to the larger one of the load swing angle index and the angular velocity index, a monotonically decreasing function with the larger value as the independent variable is used to calculate the working condition coincidence coefficient, and the greater the coefficient value, the more the actual swing state conforms to the ideal state under the current working condition.

5. The intelligent operation method of the crown block based on multi-dimensional data according to claim 4, characterized in that, The specific method for obtaining the dynamic maximum swing angle and the dynamic maximum angular velocity based on the dynamic inertia coefficient and the motion state coefficient is as follows: The dynamic inertia coefficient and the motion state coefficient are linearly weighted and fused to obtain a comprehensive working condition coefficient which comprehensively reflects the severity of the current operation. The preset reference maximum allowable swing angle and the angular velocity are multiplied by the comprehensive working condition coefficient respectively to obtain a maximum allowable swing angle and an angular velocity threshold dynamically adjusted under a current working condition, namely a dynamic maximum swing angle and a dynamic maximum angular velocity. 6.The intelligent operation method of a crown block based on multi-dimensional data according to claim 4, wherein, The dynamic inertia evaluation model running process is: The current load mass and the rope length are subjected to maximum-minimum normalization processing to obtain a load mass index and a rope length index. The load mass index and the rope length index are subjected to weighted summation, and the summation result is subjected to complement operation to obtain a dynamic inertia coefficient. The greater the coefficient value is, the smaller the system inertia is, and the greater the allowed acceleration potential is. 7.The intelligent operation method of a crown block based on multi-dimensional data according to claim 4, wherein, The motion state evaluation model running process is: The synthetic speed and the remaining distance from the target parking point are subjected to normalization processing to obtain a synthetic speed index and a remaining distance index. The path instantaneous curvature radius is subjected to maximum-minimum normalization processing to obtain a path instantaneous curvature radius index. The synthetic speed index, the remaining distance index and the path instantaneous curvature radius index are continuously multiplied to obtain a motion state coefficient. The greater the motion state coefficient is, the more favorable the current motion state is to acceleration operation. 8.The intelligent operation method of a crown block based on multi-dimensional data according to claim 2, wherein, The adjustment term aims to balance the influence of working condition fitting degree on acceleration and avoid complete loss of acceleration ability when the swing state is poor. 9.The intelligent operation method of a crown block based on multi-dimensional data according to claim 7, wherein, The current synthetic speed and the maximum running speed allowed by the system are subjected to ratio processing, and the complement of the ratio is taken as the synthetic speed index. The remaining distance from the target parking point and the sum of the remaining distance from the target parking point and the distance adjustment constant are subjected to ratio processing to obtain the remaining distance index.

10. A multi-dimensional data-based intelligent system for a crown block operation, characterized in that, It comprises: a dynamic inertia evaluation module that outputs a dynamic inertia coefficient based on the load mass and the rope length by calling the dynamic inertia evaluation model; a motion state evaluation module that outputs a motion state coefficient based on the current synthetic speed, the remaining distance from the target parking point and the path instantaneous curvature radius by calling the motion state evaluation model; a working condition evaluation module that outputs a working condition fitting coefficient based on the load swing angle and the load swing angular velocity under the dynamic inertia coefficient and the motion state coefficient by calling the working condition evaluation model; a safety margin evaluation module that outputs a safety margin coefficient based on the shortest predicted collision time, the cooperative distance between the cage and the headstock and the distance from the track end by calling the safety margin evaluation model; an operating acceleration regulation module that outputs a target operating acceleration and adjusts the current operating acceleration to the target operating acceleration by calling the operating acceleration regulation model based on the working condition fitting coefficient, the real-time lateral wind speed, the safety margin coefficient and the reference acceleration.

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