Method and system for controlling accurate amplitude variation of main arm of mast crane

Abstract

The invention relates to the technical field of mast crane equipment control, and provides a precise amplitude variation control method and system for a main arm of a mast crane. The method comprises the steps of obtaining structural parameters, and calibrating an arm angle sensor and a steel wire rope tension sensor; sensor data of an arm angle sensor and a steel wire rope tension sensor are collected and preprocessed in real time, and angle and tension feedback signals are generated; judging an adjustment strategy corresponding to the current working condition according to the angle and tension feedback signal, and generating a winch traction rate parameter corresponding to the adjustment strategy; a synchronous control instruction is sent to the two sets of variable frequency winches, the rotating angle and the hoisting state are adjusted, corresponding feedback data are obtained, closed-loop correction is conducted based on the feedback data, and control parameters are dynamically adjusted; wherein in the micro-amplitude adjustment stage, a segmented low-speed control and tension balance strategy is adopted, and high-precision positioning is ensured; and after the conditions of angle stability and stress balance are met, adjustment is ended. The method realizes control of high-precision adjustment of the angle of the main arm.

Description

Technical Field

[0001] This application relates to the field of mast crane equipment control technology, and in particular to a method and system for precise luffing control of the main boom of a mast crane. Background Technology

[0002] In the field of large equipment hoisting, mast cranes are widely used in scenarios such as wind turbine tower installation and reactor hoisting due to their large lifting capacity and wide operating range. However, existing mast crane boom luffing control technology has the following significant drawbacks: 1. Limited control strategy and insufficient precision in fine-tuning: Traditional methods often use PID control logic with fixed parameters, which can only meet the needs of conventional angle adjustment. When entering the fine-tuning stage (such as when the angle deviation is less than 1.5 degrees), due to the lack of a refined control strategy designed for small-amplitude movements, over-adjustment or response lag is likely to occur, making it difficult to break through the 0.5-degree deviation in the final positioning accuracy. This fails to meet the millimeter-level positioning requirements of scenarios such as wind turbine tower docking and high-precision equipment installation.

[0003] 2. Disconnect between sensor feedback and control logic: Although the existing system collects arm angle and tension data, it only serves as a safety monitoring signal and has not established an effective closed-loop control link. For example, the tension change rate is not combined with the angle adjustment range to determine the working condition, resulting in poor coordination between the lifting winch and the angle control winch. During the adjustment process, uneven force can easily cause the cargo to sway, and there is even a risk of off-center loading.

[0004] 3. Poor adaptability to complex operating conditions: In scenarios such as offshore platform swaying and unstable winds in mountainous areas, traditional control methods cannot dynamically adjust control parameters. For example, they fail to intelligently compensate for winch speed based on real-time tension fluctuations, or fail to automatically reduce the adjustment rate when the angle approaches the target value, leading to increased system vibration, prolonged adjustment time, and in severe cases, requiring manual intervention. This results in insufficient intelligence and reliability.

[0005] Therefore, a method is urgently needed to solve at least one of the above problems. Summary of the Invention

[0006] This application provides a method and system for precise luffing control of the main boom of a mast crane, which aims to solve the problems of existing mast crane main boom luffing control technology, such as single control strategy, insufficient accuracy of micro-adjustment, disconnect between sensor feedback and control logic, and poor adaptability to complex working conditions.

[0007] In a first aspect, embodiments of this application provide a method for precise luffing control of the main boom of a mast crane, the method comprising: Obtain structural parameters, including at least the length of the rotating beam, the maximum lifting capacity, and the angle range; calibrate the arm angle sensor and the wire rope tension sensor; The sensor data from the arm angle sensor and the wire rope tension sensor are collected and preprocessed in real time to generate angle and tension feedback signals. Based on the angle and tension feedback signals, determine the adjustment strategy corresponding to the current working condition, and generate the winch traction rate parameters corresponding to the adjustment strategy. Synchronous control commands are sent to two sets of variable frequency winches to adjust the rotation angle and lifting status and obtain corresponding feedback data. Closed-loop correction is performed based on the feedback data, and control parameters are dynamically adjusted. In the micro-adjustment stage, segmented low-speed control and tension balance strategies are adopted to ensure high-precision positioning. The adjustment ends after the conditions of angle stability and force balance are met.

[0008] In some embodiments, calibrating the arm angle sensor and the wire rope tension sensor includes: confirming that the mechanical connection between the arm angle sensor and the angle monitoring position of the rotating beam is stable, and that the connection between the wire rope tension sensor and the force-bearing ends of the two sets of traction cables is reliable; setting the sensor data acquisition frequency to not less than 100Hz, and establishing a digital communication protocol between the sensor and the central controller; performing initial zero-point calibration on the arm angle sensor, using the fixed angle of the support beam as a reference value, and performing no-load zero-point calibration and full-scale verification on the wire rope tension sensor.

[0009] In some embodiments, obtaining structural parameters includes: loading the length of the rotating beam, the maximum lifting capacity, the lifting radius, the fixed angle value of the support beam, and the rotation angle range of the rotating beam from a preset database; initializing the control parameters of two sets of variable frequency winches; confirming the connection status of each set of two winches with the corresponding traction cable; and establishing a linear mapping relationship between the winch speed and the winding and unwinding length of the traction cable.

[0010] In some embodiments, the real-time acquisition and preprocessing of sensor data from the arm angle sensor and the wire rope tension sensor includes: real-time reading of the analog signal of the current angle of the rotating beam output by the arm angle sensor, and the two sets of real-time tension analog signals of the traction cable output by the wire rope tension sensor; after performing analog-to-digital conversion on the analog signals, using a moving average filtering method to remove abnormal fluctuation values ​​with a duration of less than 20ms, and generating continuous and smooth angle digital signals and tension digital signals.

[0011] In some embodiments, generating angle and tension feedback signals includes: calculating the difference between the filtered angle digital signal and the target angle value of the rotating beam to obtain an angle deviation signal; calculating the difference between the filtered tension digital signal and the theoretical tension value calculated based on the weight of the cargo to obtain a tension deviation signal; and merging the angle deviation signal and the tension deviation signal into a feedback input parameter group, which is then input to the working condition identification algorithm module.

[0012] In some embodiments, determining the adjustment strategy corresponding to the current working condition based on the angle and tension feedback signals includes: calculating the current adjustment range of the rotating beam angle, i.e., the absolute value of the difference between the current angle and the angle of the previous control cycle; calculating the wire rope tension change rate, i.e., the ratio of the absolute value of the difference between the current tension and the tension of the previous control cycle to the rated tension corresponding to the current cargo weight; if the angle adjustment range is ≤1.5 degrees and the tension change rate is ≤5%, it is determined to be a micro-adjustment working condition and fuzzy control logic is invoked; if the angle adjustment range is >1.5 degrees or the tension change rate is >5%, it is determined to be a normal adjustment working condition and PID control logic is invoked.

[0013] In some embodiments, generating the winch traction rate parameters corresponding to the adjustment strategy includes: under normal adjustment conditions, calculating the initial value of the traction rate of the winch controlled by the rotation angle based on the proportional, integral, and differential components of the angle deviation signal, and adjusting the rate compensation value of the hoisting control winch based on the tension deviation signal; under micro-adjustment conditions, using the angle deviation, the rate of change of the angle deviation, and the tension fluctuation value as fuzzy control input variables, and generating a refined rate adjustment amount through a preset fuzzy rule base, wherein the minimum resolution of the rate adjustment amount is not less than 0.1 m / min.

[0014] In some embodiments, sending synchronous control commands to two sets of variable frequency winches, adjusting the rotation angle and lifting status and obtaining corresponding feedback data, performing closed-loop correction based on the feedback data, and dynamically adjusting control parameters include: the control commands include the target speed and steering signal of the winch for rotation angle control, and the synchronous rate compensation signal of the winch for lifting control, with the time synchronization error between the two sets of signals not exceeding 10 milliseconds; after each command is executed, wait 20-50ms for the system mechanical response to stabilize before collecting new boom angle and tension data; if the angle deviation exceeds 0.1 degrees or the tension fluctuation exceeds 10% of the rated load, dynamically adjust the traction rate parameter of the next cycle according to 10%-30% of the current deviation, and if the correction fails to meet the target after three consecutive corrections, force a switch to a micro-adjustment control strategy.

[0015] In some embodiments, the segmented low-speed control and tension balancing strategy adopted in the micro-adjustment stage to ensure high-precision positioning includes: shortening the data acquisition cycle to 200Hz, and reducing the traction rate of the rotation angle control winch to 10%-20% of the rated rate; when the deviation between the rotation beam angle and the target value is ≤0.5 degrees, the segmented micro-adjustment mode is activated: after each 0.1 degree angle adjustment, the winch is paused for 2-3 seconds, and the adjustment continues only after the fluctuation amplitude of the tension sensor data is ≤3%; the tension of the two sets of wire ropes is monitored in real time, and if the tension of one set exceeds 15% of the tension of the other set, a rate compensation command is automatically sent to the winch on the side with the smaller tension, and the compensation rate does not exceed 20% of the main adjustment rate until the tension difference is reduced to within 5%.

[0016] Secondly, this application provides a mast crane main boom precision luffing control system, the system comprising: A parameter acquisition unit is used to acquire structural parameters, which include at least the length of the rotating beam, the maximum lifting capacity, and the angle range; and to calibrate the arm angle sensor and the wire rope tension sensor. The data acquisition unit is used to collect and preprocess sensor data from the arm angle sensor and the wire rope tension sensor in real time, and generate angle and tension feedback signals. The parameter generation unit is used to determine the adjustment strategy corresponding to the current working condition based on the angle and tension feedback signals, and generate the winch traction rate parameters corresponding to the adjustment strategy. The adjustment completion unit is used to send synchronous control commands to the two sets of variable frequency winches, adjust the rotation angle and lifting status and obtain corresponding feedback data, perform closed-loop correction based on the feedback data, and dynamically adjust the control parameters. In the micro-adjustment stage, segmented low-speed control and tension balance strategy are adopted to ensure high-precision positioning. The adjustment ends after the angle stability and force balance conditions are met.

[0017] This application provides a method and system for precise luffing control of a mast crane's main boom. The invention distinguishes between micro-adjustment and regular adjustment conditions using a working condition identification algorithm. In the micro-adjustment stage, segmented low-speed control and a pause-and-fine-adjustment mechanism are employed, combined with a tension balancing strategy, to improve angle adjustment accuracy and meet the high-precision requirements of scenarios such as wind turbine tower flange connections and precision equipment installation. By constructing a closed-loop feedback link for sensor data, the control logic is automatically switched in real-time based on angle deviation and tension change rate. Regular working conditions ensure adjustment efficiency, while micro-adjustment conditions activate a fuzzy rule base to generate refined rate adjustment values, adapting to complex loads and environmental changes without manual intervention, thus enhancing system autonomy. Through synchronous control of two sets of variable frequency winches and real-time tension monitoring, rate compensation is automatically triggered when the tension difference of a single cable exceeds 15%, avoiding the risk of off-center loading. A closed-loop correction mechanism forcibly switches the control strategy when three consecutive adjustments fail to meet the target, eliminating the risk of runaway due to mechanical vibration or sensor noise, making it particularly suitable for harsh working conditions such as offshore and mountainous areas. By automatically loading preset structural parameters during the initialization phase, the calibration process is standardized, eliminating the need to redevelop control algorithms for different devices. Combined with the design of a movable central control room and winch, it significantly shortens on-site commissioning time, combining flexibility and versatility.

[0018] In summary, this invention breaks through the bottlenecks of traditional mast crane luffing control in terms of accuracy, intelligence, and adaptability to working conditions. Through multi-strategy integration and refined control, it provides a reliable technical solution for the hoisting of large equipment.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart illustrating the steps of a method for precise luffing control of the main boom of a mast crane, provided in one embodiment of this application. Figure 2 This is a schematic diagram illustrating the principle of a precise luffing control method for the main boom of a mast crane provided in an embodiment of this application; Figure 3 This is a schematic block diagram of a mast crane main boom precision luffing control system provided in one embodiment of this application; Figure 4 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0025] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0026] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0028] In the field of large equipment hoisting, mast cranes are widely used in scenarios such as wind turbine tower installation and reactor hoisting due to their large lifting capacity and wide operating range. However, existing mast crane boom luffing control technology has the following significant drawbacks: 1. Limited control strategy and insufficient precision in fine-tuning: Traditional methods often use PID control logic with fixed parameters, which can only meet the needs of conventional angle adjustment. When entering the fine-tuning stage (such as when the angle deviation is less than 1.5 degrees), due to the lack of a refined control strategy designed for small-amplitude movements, over-adjustment or response lag is likely to occur, making it difficult to break through the 0.5-degree deviation in the final positioning accuracy. This fails to meet the millimeter-level positioning requirements of scenarios such as wind turbine tower docking and high-precision equipment installation.

[0029] 2. Disconnect between sensor feedback and control logic: Although the existing system collects arm angle and tension data, it only serves as a safety monitoring signal and has not established an effective closed-loop control link. For example, the tension change rate is not combined with the angle adjustment range to determine the working condition, resulting in poor coordination between the lifting winch and the angle control winch. During the adjustment process, uneven force can easily cause the cargo to sway, and there is even a risk of off-center loading.

[0030] 3. Poor adaptability to complex operating conditions: In scenarios such as offshore platform swaying and unstable winds in mountainous areas, traditional control methods cannot dynamically adjust control parameters. For example, they fail to intelligently compensate for winch speed based on real-time tension fluctuations, or fail to automatically reduce the adjustment rate when the angle approaches the target value, leading to increased system vibration, prolonged adjustment time, and in severe cases, requiring manual intervention. This results in insufficient intelligence and reliability.

[0031] Currently, there is no technical solution that combines working condition recognition algorithms, fuzzy control logic, and segmented low-speed adjustment strategies, especially lacking tension balance control and high-precision stabilization mechanisms for the micro-amplitude adjustment phase. Therefore, there is an urgent need for a precise luffing control method for mast crane main booms that can dynamically switch control strategies based on real-time working conditions and balance adjustment efficiency with positioning accuracy.

[0032] To solve the above problem, please refer to Figure 1 and Figure 2This application provides a method for precise luffing control of the main boom of a mast crane. The computer equipment can be deployed on a single server or a server cluster. It can also be deployed on a handheld terminal, laptop, wearable device, or robot, etc.

[0033] The provided method for precise luffing control of the mast crane's main boom includes steps S101 to S104. Details are as follows: Step S101. Obtain structural parameters, which include at least the length of the rotating beam, the maximum lifting capacity, and the angle range; calibrate the arm angle sensor and the wire rope tension sensor.

[0034] Specifically, this step establishes the basic data model of the control algorithm by initializing the basic parameters of the equipment and calibrating the sensors, thereby ensuring the accuracy and reliability of subsequent control logic.

[0035] The structural parameters are obtained by loading the mast crane's mechanical structural parameters from the preset database of the central controller, including: the physical length of the rotating beam (e.g., 100 meters), the maximum lifting capacity (e.g., 400 tons), the angle range of the rotating beam (e.g., 49.9 degrees to 85.4 degrees), the lifting radius (e.g., 68 meters), the fixed angle of the support beam, and other key parameters. The control parameters of the two sets of variable frequency winches (rated speed, cable winding and unwinding coefficient) are initialized, the connection status of each set of two winches with the traction cable is confirmed, and a linear mapping relationship between the winch speed and the cable winding and unwinding length is established (e.g., 1 revolution / minute corresponds to a winding and unwinding rate of 0.5 meters).

[0036] Sensor calibration includes: Mechanical connection confirmation: Check the installation stability of the arm angle sensor and the hinge point of the rotating beam to ensure that the sensor shaft is coaxial with the rotating beam shaft; The wire rope tension sensor is installed at the force-bearing end of the traction cable (such as the end of the fixed pulley shaft), using a pin-type sensor and applying preload to eliminate gaps.

[0037] Electrical parameter configuration: Set the sensor data acquisition frequency to no less than 100Hz, establish a Modbus RTU or CAN bus communication protocol between the sensor and the central controller, and ensure that the data transmission delay is ≤5ms.

[0038] Zero point and range calibration: The arm angle sensor uses the horizontal state of the support beam as a reference. By manually fixing and rotating the beam to the 0-degree reference position, 10 sets of data are collected and the average value is taken as the zero point. The wire rope tension sensor is calibrated at zero point under no-load conditions (no load and loose cable). The rated load (such as 400 tons) is applied for full-range calibration. If the error exceeds 1%, a calibration alarm is triggered.

[0039] By loading structural parameters from a pre-set database, errors from manual input are avoided, and rapid parameter switching is adapted to different models of mast cranes; the effects of mechanical installation deviations and electrical noise are eliminated, providing reliable input for subsequent control algorithms; a mathematical model of winch speed and cable winding and unwinding is established, providing a physical layer mapping basis for subsequent rate parameter generation, and eliminating the risk of control failure due to equipment parameter mismatch.

[0040] Step S102. Collect and preprocess sensor data from the arm angle sensor and wire rope tension sensor in real time to generate angle and tension feedback signals.

[0041] Specifically, by acquiring the raw signals of arm angle and tension in real time, and generating stable feedback signals through filtering and noise reduction, high-quality data is provided for condition judgment and control strategies.

[0042] Real-time signal acquisition synchronously reads two types of sensor signals at a frequency of 100Hz: the arm angle sensor outputs a 0-10V analog voltage signal (corresponding to an angle range of 0-90 degrees), which is converted into a digital signal by a 24-bit ADC; the two sets of wire rope tension sensors output 4-20mA current signals (corresponding to a tension range of 0-500 tons), which are converted into digital signals by a signal conditioning module.

[0043] The preprocessing algorithm uses a 5-point moving average filtering algorithm to process the original signal, eliminating abnormal fluctuations with a duration of <20ms (such as instantaneous spike signals caused by mechanical vibration); temperature drift compensation is performed on the filtered angle signal (corrected based on the temperature chip data built into the sensor), and cable self-weight compensation is performed on the tension signal (calculated based on the influence of cable sag based on the current angle).

[0044] Feedback signal generation is achieved by calculating the angle deviation signal: the difference between the current angle and the target angle (Δθ = θ_current - θ_target); calculating the tension deviation signal by calculating the difference between the actual tension and the theoretical tension (ΔF = F_current - F_theory, where F_theory is calculated based on the geometric relationship between cargo weight and angle); and merging Δθ and ΔF into a two-dimensional feedback vector [Δθ, ΔF], which is then input into the working condition recognition module.

[0045] By calculating the deviation, the original signal is converted into a feedback quantity that can be directly used by the control algorithm, establishing a direct mapping relationship between sensor data and control strategy; at the same time, angle and tension signals are collected to provide dual-parameter basis for subsequent working condition judgment, solving the one-sided problem of single signal monitoring in the existing technology.

[0046] Step S103. Determine the adjustment strategy corresponding to the current working condition based on the angle and tension feedback signals, and generate the winch traction rate parameters corresponding to the adjustment strategy.

[0047] Specifically, based on real-time feedback signals, the current operating condition (normal adjustment / micro-adjustment) is identified, and the corresponding control algorithm is called to generate winch speed parameters, thereby realizing dynamic switching of control strategies.

[0048] Operating condition judgment logic: Calculate the angle adjustment range: |Δθ_current - Δθ_prev| (the absolute value of the angle deviation between the current cycle and the previous cycle); Calculate the tension change rate: |F_current - F_prev| / F_rated × 100% (the ratio of the tension difference between the current cycle and the previous cycle to the rated tension); Operating condition classification: If the angle adjustment range is ≤1.5 degrees and the tension change rate is ≤5%, it is judged as a micro-adjustment operating condition, triggering fuzzy control logic; otherwise, it is judged as a normal adjustment operating condition, and PID control logic is enabled. Adjustment strategy generation: Normal operating condition (PID control): Rotation angle controls the winch speed: Calculate the base speed based on the proportional (P), integral (I), and derivative (D) components of the angle deviation (e.g., v_θ = Kp×Δθ + Ki×∫Δθdt + Kd×dΔθ / dt); Lifting controls the winch speed: Dynamically compensate for the tension deviation (e.g., v_F = Kf×ΔF, to avoid sudden load changes caused by angle adjustment). Micro-amplitude operation (fuzzy control): Input variables: angle deviation (Δθ), angle deviation change rate (Δθ_rate), tension fluctuation value (ΔF_fluct); Fuzzy rule base: 25 preset fuzzy rules (such as "if Δθ is small and Δθ_rate is negative and small, then the speed adjustment amount is small"), which generate refined speed adjustment amount through Mamdani inference (resolution ≤ 0.1 m / min); Output constraint: Forcefully limit the winch speed in the micro-amplitude stage to ≤ 20% of the rated speed (such as 1 m / min) to avoid overshoot caused by high-speed adjustment.

[0049] By using dual-parameter thresholds (angle amplitude + tension change rate) to distinguish the operation stage, the system avoids misjudgment of the working condition caused by a single parameter (such as only angle deviation). For example, when the tension changes suddenly under sea wind interference, the system prioritizes triggering the safety compensation strategy. At the same time, the system considers the dynamic coupling relationship between angle and tension (such as angle change inevitably causing change in tension distribution). Through rate compensation, the system achieves coordinated action of the two sets of winches, avoiding the problem of uneven force caused by independent control in traditional methods.

[0050] Step S104. Send synchronous control commands to the two sets of variable frequency winches, adjust the rotation angle and lifting status and obtain the corresponding feedback data, perform closed-loop correction based on the feedback data, and dynamically adjust the control parameters; wherein, in the micro-adjustment stage, a segmented low-speed control and tension balance strategy is adopted to ensure high-precision positioning; the adjustment ends after the angle stability and force balance conditions are met.

[0051] Specifically, by sending high-precision synchronous control commands to the variable frequency winch, the control parameters are dynamically corrected based on real-time feedback data. Especially in the micro-adjustment stage, millimeter-level positioning accuracy is achieved through a segmented low-speed and tension balance strategy.

[0052] Synchronous control command generation: The command includes two sets of winch control parameters: rotation angle control winch: target speed (rpm), direction signal (rope take-up / unwinding), accuracy ≤0.5rpm; lifting control winch: synchronous speed compensation signal (speed correction based on tension deviation, range ±20% of main speed); command synchronization mechanism: adopts hardware clock synchronization technology to ensure that the time difference between the two sets of command transmissions is ≤10ms, avoiding mechanical shock caused by control asynchrony.

[0053] Closed-loop correction process: After each command is executed, wait 20-50ms (mechanical system response delay time) before collecting new arm angle and tension data; if the angle deviation is >0.1 degrees or the tension fluctuation is >10% of the rated load, correct the next cycle rate parameter = current parameter ±(10%-30%) × deviation; if the correction fails to meet the standard after three consecutive corrections (the deviation continues to exceed the threshold), force switch to micro-adjustment strategy and reduce the rate to 10% of the rated value, triggering a system warning.

[0054] Micro-adjustment enhancement strategy: Segmented low-speed control: When the angle deviation is ≤0.5 degrees, enter the segmented micro-adjustment mode: After adjusting the angle by 0.1 degrees, control the winch to pause for 2-3 seconds, and continue adjusting after the tension sensor data fluctuation is ≤3% to eliminate the influence of mechanical inertia; Tension balance strategy: Monitor the tension of the two sets of wire ropes in real time. If the tension difference of a single set is >15%, send a compensation rate (≤20% of the main adjustment rate) to the side with smaller tension until the tension difference is reduced to within 5% to prevent the risk of the main boom tilting due to uneven load.

[0055] Termination condition judgment: If the angle deviation is ≤0.1 degrees and the tension difference is ≤5% for 5 consecutive control cycles (50ms), it is judged as "angle stable and force balanced", and the adjustment process ends.

[0056] A 10ms-level command synchronization error ensures consistent operation of the two sets of winches, avoiding torsional stress on the main boom caused by traditional asynchronous control and extending the mechanical life; a dynamic parameter adjustment mechanism adapts to time-varying factors such as mechanical wear and cable elastic deformation, ensuring long-term reliability of the control algorithm; a segmented low-speed and pause fine-tuning strategy improves angle adjustment accuracy from 0.5 degrees in the existing technology to within 0.1 degrees, combined with tension balance control; a forced strategy switching and early warning mechanism responds quickly to sensor malfunctions or mechanical jamming, preventing accidents from escalating and meeting the safety control standards for special equipment.

[0057] This invention constructs a complete closed loop of "parameter initialization → data processing → strategy decision → execution correction" through a four-step control process. It addresses the shortcomings of existing technologies, such as single control strategy, disconnected feedback, and poor adaptability. It innovatively introduces core mechanisms such as working condition recognition, fuzzy control, and segmented fine-tuning, achieving a triple breakthrough in the mast crane main boom luffing control in terms of accuracy, safety, and intelligence (fully automatic working condition switching). It is especially suitable for harsh scenarios such as high-precision hoisting and complex environment operations.

[0058] In some embodiments, calibrating the arm angle sensor and the wire rope tension sensor includes: confirming that the mechanical connection between the arm angle sensor and the angle monitoring position of the rotating beam is stable, and that the connection between the wire rope tension sensor and the force-bearing ends of the two sets of traction cables is reliable; setting the sensor data acquisition frequency to not less than 100Hz, and establishing a digital communication protocol between the sensor and the central controller; performing initial zero-point calibration on the arm angle sensor, using the fixed angle of the support beam as a reference value, and performing no-load zero-point calibration and full-scale verification on the wire rope tension sensor.

[0059] The hardware connection stability, data communication protocol, and initial calibration of the arm angle sensor and the wire rope tension sensor are standardized to ensure the reliability and accuracy of the sensor signals.

[0060] Mechanical connection confirmation was achieved by checking the fixing bolts at the hinge point between the arm angle sensor and the rotating beam using a torque wrench (torque value ≥ 20N). m), ensuring that the coaxiality error between the sensor shaft and the rotating beam shaft is ≤0.2mm; the wire rope tension sensor is rigidly connected to the fixed pulley shaft end of the traction cable through a pin shaft and fixed with an anti-loosening nut. After connection, a pull-out test is performed (apply 10% of the rated tension, displacement ≤0.1mm).

[0061] The communication parameters are configured by forcibly setting the sensor data acquisition frequency to 100Hz (by triggering an interrupt via a hardware timer). The communication protocol uses a CAN bus with strong anti-interference capability (baud rate 1Mbps) and is configured with a CRC check mechanism (bit error rate ≤ 10^-6).

[0062] Zero-point and range calibration includes: Arm angle sensor calibration: Manually fix the rotating beam to the horizontal position of the support beam (mechanical limit), continuously collect 100 cycles of data and take the average value as the 0-degree reference value (error ±0.05 degrees); Tension sensor calibration: Under no-load conditions (no cable load), collect 50 cycles of data and take the average value as the zero point, apply 120% of the rated load (e.g., 480 tons) for full-scale calibration, and trigger a calibration alarm when the linearity error of the output signal is >1.5%. Through torque calibration and pull-out testing, eliminate measurement deviations caused by mechanical loosening and ensure rigid coupling between the sensor and the mechanical structure.

[0063] In some embodiments, obtaining structural parameters includes: loading the length of the rotating beam, the maximum lifting capacity, the lifting radius, the fixed angle value of the support beam, and the rotation angle range of the rotating beam from a preset database; initializing the control parameters of two sets of variable frequency winches; confirming the connection status of each set of two winches with the corresponding traction cable; and establishing a linear mapping relationship between the winch speed and the winding and unwinding length of the traction cable.

[0064] By obtaining the mechanical structure parameters of the mast crane from a pre-set database, initializing the control parameters of the winch, and establishing a physical motion model, the basic data framework for the control algorithm is constructed.

[0065] The structural parameters are loaded via a central controller that queries a preset database using SQL, automatically retrieving the following parameters: Geometric parameters of the rotating beam: length L = 100m, moment of inertia I = 0.8m. 4 Mass M = 50 tons; Performance parameters: maximum lifting capacity F_max = 400 tons, lifting radius R = 68m, angle range θ ∈ [49.9°, 85.4°]; Support beam parameters: fixed angle θ_support = 30°, support point spacing D = 20m.

[0066] The winch initialization process involves confirming the cable connections of two sets of variable frequency winches (two in parallel per set): Set A controls the rotation angle (retracting and extending the main boom traction cable), and Set B controls the lifting height (retracting and extending the cargo traction cable). A speed-retracting / extending length mapping relationship is established: through actual measurement using a pulse encoder, the relationship between the winch speed n (rpm) and the cable retracting / extending speed v (m / min) is v = 0.01 × n (error ±0.5%), which is stored as the conversion formula for the control algorithm.

[0067] In some embodiments, the real-time acquisition and preprocessing of sensor data from the arm angle sensor and the wire rope tension sensor includes: real-time reading of the analog signal of the current angle of the rotating beam output by the arm angle sensor, and the two sets of real-time tension analog signals of the traction cable output by the wire rope tension sensor; after performing analog-to-digital conversion on the analog signals, using a moving average filtering method to remove abnormal fluctuation values ​​with a duration of less than 20ms, and generating continuous and smooth angle digital signals and tension digital signals.

[0068] By real-time acquisition, analog-to-digital conversion, and filtering of the analog signals output by the sensors, smooth and continuous digital signals are generated, providing high-quality input for subsequent control algorithms.

[0069] Analog signal acquisition: Arm angle sensor: 0-10V voltage signal corresponds to 0-90° angle, which is converted into digital quantity (resolution 0.009° / LSB) through a 24-bit ADC module (accuracy 0.001V); Tension sensor: 4-20mA current signal corresponds to 0-500 tons of tension, which is connected to a 16-bit ADC module (resolution 0.048 tons / LSB) after passing through an I / V conversion circuit (accuracy 0.1%FS).

[0070] The noise filtering process uses a 5-point moving average filtering algorithm: current period signal value = (previous two periods + current period + next two periods signal) / 5, filtering out spike noise with a duration of <20ms (such as instantaneous interference caused by mechanical vibration); the filtered signal is further suppressed by first-order hysteresis correction (time constant τ = 50ms), and the output signal fluctuation amplitude is ≤0.1° (angle) or 1%FS (tension).

[0071] In some embodiments, generating angle and tension feedback signals includes: calculating the difference between the filtered angle digital signal and the target angle value of the rotating beam to obtain an angle deviation signal; calculating the difference between the filtered tension digital signal and the theoretical tension value calculated based on the weight of the cargo to obtain a tension deviation signal; and merging the angle deviation signal and the tension deviation signal into a feedback input parameter group, which is then input to the working condition identification algorithm module.

[0072] By converting the filtered sensor signals into deviation signals that can be directly used by the control algorithm, a quantitative difference between the actual state and the target state is established, providing core input parameters for condition judgment.

[0073] The angle deviation is calculated by inputting the target angle θ_target from the host computer interface or by generating it automatically through a planning algorithm (such as the design angle for wind turbine tower installation); the angle deviation Δθ = θ_current - θ_target, the absolute value |Δθ| is used to determine the adjustment range, and the sign is used to distinguish the adjustment direction (positive deviation requires a decrease in angle, and negative deviation requires an increase in angle).

[0074] Tension deviation is calculated using theoretical tension F_theory based on the geometric relationship between cargo weight G and angle θ_current: F_theory = G × cos(θ_current - θ_support) (considering the mechanical decomposition of the fixed angle of the support beam); tension deviation ΔF = F_current - F_theory is used to determine the force balance state of the two sets of winches.

[0075] The feedback parameter set is generated by encapsulating Δθ and ΔF into a two-dimensional array [Δθ, ΔF], which serves as the input vector for the working condition identification algorithm. At the same time, a timestamp (accuracy 1ms) is attached to calculate the rate of change.

[0076] In some embodiments, determining the adjustment strategy corresponding to the current working condition based on the angle and tension feedback signals includes: calculating the current adjustment range of the rotating beam angle, i.e., the absolute value of the difference between the current angle and the angle of the previous control cycle; calculating the wire rope tension change rate, i.e., the ratio of the absolute value of the difference between the current tension and the tension of the previous control cycle to the rated tension corresponding to the current cargo weight; if the angle adjustment range is ≤1.5 degrees and the tension change rate is ≤5%, it is determined to be a micro-adjustment working condition and fuzzy control logic is invoked; if the angle adjustment range is >1.5 degrees or the tension change rate is >5%, it is determined to be a normal adjustment working condition and PID control logic is invoked.

[0077] The current working condition is determined by a dual-parameter threshold of angle adjustment amplitude and tension change rate, and the control strategy (conventional adjustment / PID control and micro-adjustment / fuzzy control) is dynamically switched to achieve strategy adaptation.

[0078] The calculation of operating condition characteristic parameters includes: angle adjustment range Δθ_amp=|θ_current - θ_prev| (absolute change in angle between the current cycle and the previous cycle); tension change rate ΔF_rate=|F_current-F_prev| / F_rated×100% (F_rated is the rated tension corresponding to the current cargo weight, i.e., F_rated=G).

[0079] The operating condition judgment logic includes: micro-adjustment operating condition: triggered when Δθ_amp≤1.5° and ΔF_rate≤5%, indicating that the system has entered the fine positioning stage and a high-precision control strategy needs to be activated; regular adjustment operating condition: triggered when Δθ_amp>1.5° or ΔF_rate>5%, indicating that the system is in the coarse adjustment stage and priority is given to ensuring adjustment efficiency.

[0080] The strategy invocation mechanism uses a state machine to switch strategies. In low-amplitude operation, the PID integral component is disabled (to avoid integral saturation), and in normal operation, the low-speed limit of fuzzy control is disabled.

[0081] In some embodiments, generating the winch traction rate parameters corresponding to the adjustment strategy includes: under normal adjustment conditions, calculating the initial value of the traction rate of the winch controlled by the rotation angle based on the proportional, integral, and differential components of the angle deviation signal, and adjusting the rate compensation value of the hoisting control winch based on the tension deviation signal; under micro-adjustment conditions, using the angle deviation, the rate of change of the angle deviation, and the tension fluctuation value as fuzzy control input variables, and generating a refined rate adjustment amount through a preset fuzzy rule base, wherein the minimum resolution of the rate adjustment amount is not less than 0.1 m / min.

[0082] Differentiated winch speed control parameters are generated based on the type of operating condition: PID algorithm is used to ensure adjustment efficiency in normal operating conditions, while fuzzy control is used to achieve fine speed adjustment in micro-amplitude operating conditions, meeting the control requirements of different stages.

[0083] Normal operating conditions (PID control): Expression for winch speed control by rotation angle: ; The proportionality coefficient Kp = 0.5 (° / min) (degrees), integral coefficient Ki = 0.1 (° / minute) Spend (seconds), differential coefficient Kd = 2 (° / minute) Spend (seconds), pre-set using the critical proportional method; hoisting control winch speed compensation, real-time compensation for tension changes caused by angle adjustment, to avoid sudden lifting and lowering of goods causing swaying.

[0084] The input variables are fuzzified using the following methods: angle deviation Δθ: universe of discourse [-1.5°, +1.5°], fuzzy subset {NB, NM, NS, ZO, PS, PM, PB} (negative large, negative medium, negative small, zero, positive small, positive medium, positive large); angle deviation change rate Δθ_rate: universe of discourse [-0.5° / s, +0.5° / s], fuzzy subset {NB, NS, ZO, PS, PB}; tension fluctuation ΔF_fluct: universe of discourse [-10%F_rated, +10%F_rated], fuzzy subset {NG, NS, ZO, PS, PG} (negative large fluctuation, negative small fluctuation, zero, positive small fluctuation, positive large fluctuation). The fuzzy rule base has 25 preset rules (e.g., "If Δθ=PS and Δθ_rate=ZO and ΔF_fluct=ZO, then the rate adjustment amount = SM"). The fuzzy variables are defuzzified using the centroid method, and the output rate adjustment amount (resolution 0.1 m / min) is calculated. Output constraints are imposed by forcibly limiting the micro-amplitude stage rate to ≤20% of the rated value (e.g., when the rated rate is 5 m / min, the micro-amplitude rate is ≤1 m / min).

[0085] In some embodiments, sending synchronous control commands to two sets of variable frequency winches, adjusting the rotation angle and lifting status and obtaining corresponding feedback data, performing closed-loop correction based on the feedback data, and dynamically adjusting control parameters include: the control commands include the target speed and steering signal of the winch for rotation angle control, and the synchronous rate compensation signal of the winch for lifting control, with the time synchronization error between the two sets of signals not exceeding 10 milliseconds; after each command is executed, wait 20-50ms for the system mechanical response to stabilize before collecting new boom angle and tension data; if the angle deviation exceeds 0.1 degrees or the tension fluctuation exceeds 10% of the rated load, dynamically adjust the traction rate parameter of the next cycle according to 10%-30% of the current deviation, and if the correction fails to meet the target after three consecutive corrections, force a switch to a micro-adjustment control strategy.

[0086] By sending high-precision synchronous control commands to the variable frequency winch and dynamically correcting control parameters based on feedback data, a closed-loop control-execution-feedback circuit is constructed to solve the problems of mechanical system delay and time-varying parameters.

[0087] Synchronous control command design: Command format: {timestamp, Group A speed, Group A direction, Group B rate compensation, check code}, the hardware synchronization clock (accuracy 1μs) ensures that the time difference between the two sets of commands is ≤10ms; the steering signal adopts double redundancy encoding (e.g. 00=stop, 01=reel in, 10=release, 11=error) to prevent steering errors caused by communication errors.

[0088] Closed-loop correction process: After the command is sent, a 20-50ms waiting period is entered (covering the winch inverter response time of 15ms + mechanical transmission delay of 25ms); after collecting new data, the correction amount is calculated: if |Δθ|>0.1° or |ΔF|>10%F_rated, then the next cycle rate = current rate × (1±α) Δx), where α = 0.1~0.3 (the larger the deviation, the larger the correction coefficient); if the correction fails to meet the standard for three consecutive times (Δθ continuously > 0.1°), the strategy is downgraded: the system is forced to switch to micro-amplitude operation, the speed is reduced to 10% of the rated value, and an alarm is triggered through the HMI interface (red flashing prompt).

[0089] In some embodiments, the segmented low-speed control and tension balancing strategy adopted in the micro-adjustment stage to ensure high-precision positioning includes: shortening the data acquisition cycle to 200Hz, and reducing the traction rate of the rotation angle control winch to 10%-20% of the rated rate; when the deviation between the rotation beam angle and the target value is ≤0.5 degrees, the segmented micro-adjustment mode is activated: after each 0.1 degree angle adjustment, the winch is paused for 2-3 seconds, and the adjustment continues only after the fluctuation amplitude of the tension sensor data is ≤3%; the tension of the two sets of wire ropes is monitored in real time, and if the tension of one set exceeds 15% of the tension of the other set, a rate compensation command is automatically sent to the winch on the side with the smaller tension, and the compensation rate does not exceed 20% of the main adjustment rate until the tension difference is reduced to within 5%.

[0090] By shortening the acquisition cycle, reducing the adjustment rate, employing segmented fine-tuning, and using tension balancing strategies during the micro-adjustment phase, the accuracy bottleneck of traditional technologies is overcome, ensuring high-precision positioning and force balance.

[0091] Ultra-high frequency acquisition and low-speed control: The data acquisition cycle is shortened from the conventional 10ms to 5ms (200Hz), capturing high-frequency signal changes during micro-adjustments (such as 100Hz harmonics of mechanical vibration); the rotation angle control reduces the winch speed to 10%-20% of the rated value (such as rated 5 m / min → 0.5-1 m / min), reducing the influence of mechanical inertia (inertial displacement ≤ 0.02 m / cycle).

[0092] Segmented fine-tuning mode: Starts when |Δθ|≤0.5°. After each 0.1° angle adjustment, a "stop" command is sent and held for 2-3 seconds, during which tension fluctuations are monitored: If ΔF_fluct≤3%F_rated, the adjustment continues; if the fluctuation exceeds the limit, a 0.05° backoff compensation is triggered.

[0093] Tension balancing strategy: Calculate the tension difference between the two groups ΔF_group=|F_A-F_B| in real time. When ΔF_group>15%F_rated, send a compensation rate v_comp=main rate×10%-20% to the group with smaller tension (e.g., main rate 1 m / min → compensation rate 0.1-0.2 m / min) until ΔF_group≤5%F_rated.

[0094] In some embodiments, by constructing a knowledge graph of mast crane structural parameters, parameter correlation analysis and anomaly detection are achieved through graph neural networks (GNNs), solving the problem of working condition matching error caused by isolated parameter storage in traditional databases, and supporting intelligent parameter retrieval under complex working conditions.

[0095] Knowledge graph construction: Entity definition: includes 12 types of entities such as "equipment model", "rotating beam parameters", "winch model", "operating environment", and "historical faults". Relationship definition: 8 semantic relationships such as "belongs to", "associates with", and "affects". The graph is stored using the Neo4j graph database. Node attributes include parameter thresholds (such as the design value, measured value, and safety boundary value of the rotating beam angle range). Edge weights are determined by training with historical fault data (such as the "lifting radius - wire rope tension" associated edge weight reflecting overload risk).

[0096] Intelligent parameter retrieval includes: working condition input: when the central controller receives a work instruction (such as "lifting 300 tons of goods in an environment with a wind speed of 12m / s and a temperature of 35℃"), it generates a parameter matching vector through the GNN inference module; anomaly detection: comparing the path distance between "current structural parameters + environmental parameters" and "the best parameters for similar historical working conditions" in the graph, if it exceeds the safety threshold (such as the deviation between the design value and the measured value of the lifting radius > 5%), it automatically triggers the parameter verification process (retrieving three-dimensional laser scanning data to verify the deformation of the rotating beam).

[0097] After each operation, the actual control parameters (such as winch speed and tension fluctuation peak) are compared with the "standard parameter set" in the graph. The edge weights are updated through graph embedding technology to achieve the self-evolution of the knowledge graph (update cycle ≤ 1 hour).

[0098] In some embodiments, an end-to-end GAN denoising model is designed to address non-Gaussian noise (such as inverter harmonic interference and multi-source aliasing noise) that is difficult to handle by traditional filtering algorithms. This model enables nonlinear feature extraction and noise suppression of angle and tension signals, thereby improving the signal-to-noise ratio.

[0099] The GAN model architecture includes: Generator G: a 3-layer one-dimensional convolutional neural network (CNN) that takes a noisy signal sequence as input (100 points in length, corresponding to 1 second of data) and outputs a denoised signal sequence. The activation function is a combination of ReLU and LeakyReLU. Discriminator D: a dual-channel structure that simultaneously takes a clean real signal and the generator output signal as input. The difference is measured by Wasserstein distance, and the optimization objective is: .

[0100] The data training strategy includes: training data: collecting 300 hours of field noise data, generating analog signals with different noise levels (noise types include impulse noise, white noise, and inverter harmonics (50-500Hz)) through a hardware signal injection station, and clean signals are provided by a high-precision calibration device (error ±0.01° / 0.1%FS); dynamic adversarial: updating the discriminator weights every 10 training cycles to avoid mode collapse, and finally achieving a denoised SNR ≥ 25dB on a noisy signal with an SNR of 10dB.

[0101] Online deployment solution: Lightweight model: The number of parameters is compressed to 1.2MB through knowledge distillation (Teacher-Student model) and deployed on an embedded controller (ARM Cortex-A72), with a single frame processing time of <1ms; Real-time noise reduction: The GAN model is immediately connected after analog-to-digital conversion, and the output signal is synchronously input into the traditional moving average filter (as a double guarantee); Anomaly detection mechanism: When the difference between the GAN output and the filter output is >3%, a hardware self-test is triggered.

[0102] In some embodiments, to address the problem that traditional PID and fuzzy control parameters rely on manual tuning and are difficult to adapt to time-varying operating conditions, a feedback parameter self-tuning algorithm based on DRL is designed. Through real-time interaction with the controlled system, the combination of control parameters is dynamically optimized to achieve full-condition adaptation.

[0103] The DRL system modeling includes: State space S: containing 8 dimensions such as angle deviation Δθ, tension deviation ΔF, deviation change rate Δθ_rate, ΔF_rate, and current control strategy type (normal / micro); Action space A: discrete action set {adjusting PID Kp, Ki, Kd; adjusting quantization factor and scaling factor of fuzzy control}, with a total of 12 adjustable parameters, each adjusted in ±5% steps; Reward function R is designed with a multi-objective reward R=−0.8|Δθ|−0.2|ΔF|+0.1vefficiency−10δovershoot, where vefficiency is the normalized value of the adjustment rate, and δovershoot is the overshoot penalty (assigned a value of 1 when overshoot occurs).

[0104] The network architecture uses an improved version of DQN (DDQN + priority experience replay). The Actor network is a 3-layer fully connected network (8-dimensional input, 12-dimensional action value output), and the Critic network evaluates the state-action value. The training mode is divided into two stages. In the offline stage, 100,000 episodes are trained in a virtual simulation environment (based on the multibody dynamics software RecurDyn to build a mast crane dynamics model). In the online stage, real-time fine-tuning is performed with an "exploration rate ε=0.1". Parameter change boundaries are set (e.g., Kp∈[0.3,0.7] to avoid control parameter divergence). When the reward value decreases for 5 consecutive cycles, the parameter fallback mechanism is triggered (restoring to the historical optimal parameters).

[0105] Please see Figure 3 As shown, Figure 3 This is a schematic diagram of the mast crane main boom precise luffing control system 200 provided in this application embodiment. The mast crane main boom precise luffing control system 200 is used to execute the steps of the mast crane main boom precise luffing control method shown in the above embodiments. The mast crane main boom precise luffing control system 200 can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

[0106] like Figure 3 As shown, the mast crane main boom precision luffing control system 200 includes: The parameter acquisition unit 201 is used to acquire structural parameters, which include at least the length of the rotating beam, the maximum lifting capacity, and the angle range; and to calibrate the arm angle sensor and the wire rope tension sensor. The data acquisition unit 202 is used to collect and preprocess the sensor data of the arm angle sensor and the wire rope tension sensor in real time, and generate angle and tension feedback signals. The parameter generation unit 203 is used to determine the adjustment strategy corresponding to the current working condition based on the angle and tension feedback signals, and generate the winch traction rate parameters corresponding to the adjustment strategy. The adjustment completion unit 204 is used to send synchronous control commands to the two sets of variable frequency winches, adjust the rotation angle and lifting status and obtain corresponding feedback data, perform closed-loop correction based on the feedback data, and dynamically adjust the control parameters; in the micro-adjustment stage, segmented low-speed control and tension balance strategy are adopted to ensure high-precision positioning; the adjustment ends after the angle stability and force balance conditions are met.

[0107] In some embodiments, calibrating the arm angle sensor and the wire rope tension sensor includes: confirming that the mechanical connection between the arm angle sensor and the angle monitoring position of the rotating beam is stable, and that the connection between the wire rope tension sensor and the force-bearing ends of the two sets of traction cables is reliable; setting the sensor data acquisition frequency to not less than 100Hz, and establishing a digital communication protocol between the sensor and the central controller; performing initial zero-point calibration on the arm angle sensor, using the fixed angle of the support beam as a reference value, and performing no-load zero-point calibration and full-scale verification on the wire rope tension sensor.

[0108] In some embodiments, obtaining structural parameters includes: loading the length of the rotating beam, the maximum lifting capacity, the lifting radius, the fixed angle value of the support beam, and the rotation angle range of the rotating beam from a preset database; initializing the control parameters of two sets of variable frequency winches; confirming the connection status of each set of two winches with the corresponding traction cable; and establishing a linear mapping relationship between the winch speed and the winding and unwinding length of the traction cable.

[0109] In some embodiments, the real-time acquisition and preprocessing of sensor data from the arm angle sensor and the wire rope tension sensor includes: real-time reading of the analog signal of the current angle of the rotating beam output by the arm angle sensor, and the two sets of real-time tension analog signals of the traction cable output by the wire rope tension sensor; after performing analog-to-digital conversion on the analog signals, using a moving average filtering method to remove abnormal fluctuation values ​​with a duration of less than 20ms, and generating continuous and smooth angle digital signals and tension digital signals.

[0110] In some embodiments, generating angle and tension feedback signals includes: calculating the difference between the filtered angle digital signal and the target angle value of the rotating beam to obtain an angle deviation signal; calculating the difference between the filtered tension digital signal and the theoretical tension value calculated based on the weight of the cargo to obtain a tension deviation signal; and merging the angle deviation signal and the tension deviation signal into a feedback input parameter group, which is then input to the working condition identification algorithm module.

[0111] In some embodiments, determining the adjustment strategy corresponding to the current working condition based on the angle and tension feedback signals includes: calculating the current adjustment range of the rotating beam angle, i.e., the absolute value of the difference between the current angle and the angle of the previous control cycle; calculating the wire rope tension change rate, i.e., the ratio of the absolute value of the difference between the current tension and the tension of the previous control cycle to the rated tension corresponding to the current cargo weight; if the angle adjustment range is ≤1.5 degrees and the tension change rate is ≤5%, it is determined to be a micro-adjustment working condition and fuzzy control logic is invoked; if the angle adjustment range is >1.5 degrees or the tension change rate is >5%, it is determined to be a normal adjustment working condition and PID control logic is invoked.

[0112] In some embodiments, generating the winch traction rate parameters corresponding to the adjustment strategy includes: under normal adjustment conditions, calculating the initial value of the traction rate of the winch controlled by the rotation angle based on the proportional, integral, and differential components of the angle deviation signal, and adjusting the rate compensation value of the hoisting control winch based on the tension deviation signal; under micro-adjustment conditions, using the angle deviation, the rate of change of the angle deviation, and the tension fluctuation value as fuzzy control input variables, and generating a refined rate adjustment amount through a preset fuzzy rule base, wherein the minimum resolution of the rate adjustment amount is not less than 0.1 m / min.

[0113] In some embodiments, sending synchronous control commands to two sets of variable frequency winches, adjusting the rotation angle and lifting status and obtaining corresponding feedback data, performing closed-loop correction based on the feedback data, and dynamically adjusting control parameters include: the control commands include the target speed and steering signal of the winch for rotation angle control, and the synchronous rate compensation signal of the winch for lifting control, with the time synchronization error between the two sets of signals not exceeding 10 milliseconds; after each command is executed, wait 20-50ms for the system mechanical response to stabilize before collecting new boom angle and tension data; if the angle deviation exceeds 0.1 degrees or the tension fluctuation exceeds 10% of the rated load, dynamically adjust the traction rate parameter of the next cycle according to 10%-30% of the current deviation, and if the correction fails to meet the target after three consecutive corrections, force a switch to a micro-adjustment control strategy.

[0114] In some embodiments, the segmented low-speed control and tension balancing strategy adopted in the micro-adjustment stage to ensure high-precision positioning includes: shortening the data acquisition cycle to 200Hz, and reducing the traction rate of the rotation angle control winch to 10%-20% of the rated rate; when the deviation between the rotation beam angle and the target value is ≤0.5 degrees, the segmented micro-adjustment mode is activated: after each 0.1 degree angle adjustment, the winch is paused for 2-3 seconds, and the adjustment continues only after the fluctuation amplitude of the tension sensor data is ≤3%; the tension of the two sets of wire ropes is monitored in real time, and if the tension of one set exceeds 15% of the tension of the other set, a rate compensation command is automatically sent to the winch on the side with the smaller tension, and the compensation rate does not exceed 20% of the main adjustment rate until the tension difference is reduced to within 5%.

[0115] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the mast crane main boom precision luffing control system and its modules described above can be found in the corresponding contents of the various embodiments of the mast crane main boom precision luffing control method, and will not be repeated here.

[0116] The aforementioned method for precise luffing control of the mast crane's main boom can be implemented as a computer program, which can be used in various applications such as... Figure 3 It runs on the device shown.

[0117] Please see Figure 4 , Figure 4 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0118] The storage medium can store operating equipment and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any method of precise luffing control of the mast crane's main boom.

[0119] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0120] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any precise luffing control method for the mast crane boom.

[0121] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0122] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0123] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: Obtain structural parameters, including at least the length of the rotating beam, the maximum lifting capacity, and the angle range; calibrate the arm angle sensor and the wire rope tension sensor; The sensor data from the arm angle sensor and the wire rope tension sensor are collected and preprocessed in real time to generate angle and tension feedback signals. Based on the angle and tension feedback signals, determine the adjustment strategy corresponding to the current working condition, and generate the winch traction rate parameters corresponding to the adjustment strategy. Synchronous control commands are sent to two sets of variable frequency winches to adjust the rotation angle and lifting status and obtain corresponding feedback data. Closed-loop correction is performed based on the feedback data, and control parameters are dynamically adjusted. In the micro-adjustment stage, segmented low-speed control and tension balance strategies are adopted to ensure high-precision positioning. The adjustment ends after the conditions of angle stability and force balance are met.

[0124] In some embodiments, calibrating the arm angle sensor and the wire rope tension sensor includes: confirming that the mechanical connection between the arm angle sensor and the angle monitoring position of the rotating beam is stable, and that the connection between the wire rope tension sensor and the force-bearing ends of the two sets of traction cables is reliable; setting the sensor data acquisition frequency to not less than 100Hz, and establishing a digital communication protocol between the sensor and the central controller; performing initial zero-point calibration on the arm angle sensor, using the fixed angle of the support beam as a reference value, and performing no-load zero-point calibration and full-scale verification on the wire rope tension sensor.

[0125] In some embodiments, obtaining structural parameters includes: loading the length of the rotating beam, the maximum lifting capacity, the lifting radius, the fixed angle value of the support beam, and the rotation angle range of the rotating beam from a preset database; initializing the control parameters of two sets of variable frequency winches; confirming the connection status of each set of two winches with the corresponding traction cable; and establishing a linear mapping relationship between the winch speed and the winding and unwinding length of the traction cable.

[0126] In some embodiments, the real-time acquisition and preprocessing of sensor data from the arm angle sensor and the wire rope tension sensor includes: real-time reading of the analog signal of the current angle of the rotating beam output by the arm angle sensor, and the two sets of real-time tension analog signals of the traction cable output by the wire rope tension sensor; after performing analog-to-digital conversion on the analog signals, using a moving average filtering method to remove abnormal fluctuation values ​​with a duration of less than 20ms, and generating continuous and smooth angle digital signals and tension digital signals.

[0127] In some embodiments, generating angle and tension feedback signals includes: calculating the difference between the filtered angle digital signal and the target angle value of the rotating beam to obtain an angle deviation signal; calculating the difference between the filtered tension digital signal and the theoretical tension value calculated based on the weight of the cargo to obtain a tension deviation signal; and merging the angle deviation signal and the tension deviation signal into a feedback input parameter group, which is then input to the working condition identification algorithm module.

[0128] In some embodiments, determining the adjustment strategy corresponding to the current working condition based on the angle and tension feedback signals includes: calculating the current adjustment range of the rotating beam angle, i.e., the absolute value of the difference between the current angle and the angle of the previous control cycle; calculating the wire rope tension change rate, i.e., the ratio of the absolute value of the difference between the current tension and the tension of the previous control cycle to the rated tension corresponding to the current cargo weight; if the angle adjustment range is ≤1.5 degrees and the tension change rate is ≤5%, it is determined to be a micro-adjustment working condition and fuzzy control logic is invoked; if the angle adjustment range is >1.5 degrees or the tension change rate is >5%, it is determined to be a normal adjustment working condition and PID control logic is invoked.

[0129] In some embodiments, generating the winch traction rate parameters corresponding to the adjustment strategy includes: under normal adjustment conditions, calculating the initial value of the traction rate of the winch controlled by the rotation angle based on the proportional, integral, and differential components of the angle deviation signal, and adjusting the rate compensation value of the hoisting control winch based on the tension deviation signal; under micro-adjustment conditions, using the angle deviation, the rate of change of the angle deviation, and the tension fluctuation value as fuzzy control input variables, and generating a refined rate adjustment amount through a preset fuzzy rule base, wherein the minimum resolution of the rate adjustment amount is not less than 0.1 m / min.

[0130] In some embodiments, sending synchronous control commands to two sets of variable frequency winches, adjusting the rotation angle and lifting status and obtaining corresponding feedback data, performing closed-loop correction based on the feedback data, and dynamically adjusting control parameters include: the control commands include the target speed and steering signal of the winch for rotation angle control, and the synchronous rate compensation signal of the winch for lifting control, with the time synchronization error between the two sets of signals not exceeding 10 milliseconds; after each command is executed, wait 20-50ms for the system mechanical response to stabilize before collecting new boom angle and tension data; if the angle deviation exceeds 0.1 degrees or the tension fluctuation exceeds 10% of the rated load, dynamically adjust the traction rate parameter of the next cycle according to 10%-30% of the current deviation, and if the correction fails to meet the target after three consecutive corrections, force a switch to a micro-adjustment control strategy.

[0131] In some embodiments, the segmented low-speed control and tension balancing strategy adopted in the micro-adjustment stage to ensure high-precision positioning includes: shortening the data acquisition cycle to 200Hz, and reducing the traction rate of the rotation angle control winch to 10%-20% of the rated rate; when the deviation between the rotation beam angle and the target value is ≤0.5 degrees, the segmented micro-adjustment mode is activated: after each 0.1 degree angle adjustment, the winch is paused for 2-3 seconds, and the adjustment continues only after the fluctuation amplitude of the tension sensor data is ≤3%; the tension of the two sets of wire ropes is monitored in real time, and if the tension of one set exceeds 15% of the tension of the other set, a rate compensation command is automatically sent to the winch on the side with the smaller tension, and the compensation rate does not exceed 20% of the main adjustment rate until the tension difference is reduced to within 5%.

[0132] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the mast crane main boom precise luffing control method provided in any embodiment of this application.

[0133] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0134] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for precise luffing control of the main boom of a mast crane, characterized in that, The method includes: Obtain structural parameters, including at least the length of the rotating beam, the maximum lifting capacity, and the angle range; calibrate the arm angle sensor and the wire rope tension sensor; The sensor data from the arm angle sensor and the wire rope tension sensor are collected and preprocessed in real time to generate angle and tension feedback signals. Based on the angle and tension feedback signals, determine the adjustment strategy corresponding to the current working condition, and generate the winch traction rate parameters corresponding to the adjustment strategy. Synchronous control commands are sent to two sets of variable frequency winches to adjust the rotation angle and lifting status and obtain corresponding feedback data. Closed-loop correction is performed based on the feedback data, and control parameters are dynamically adjusted. In the micro-adjustment stage, segmented low-speed control and tension balance strategies are adopted to ensure high-precision positioning. The adjustment ends after the conditions of angle stability and force balance are met.

2. The method according to claim 1, characterized in that, The calibration arm angle sensor and wire rope tension sensor include: Confirm that the mechanical connection between the arm angle sensor and the rotating beam angle monitoring position is secure, and that the connection between the wire rope tension sensor and the force-bearing ends of the two sets of traction cables is reliable. Set the sensor data acquisition frequency to no less than 100Hz and establish a digital communication protocol between the sensor and the central controller. The arm angle sensor was initially zero-point calibrated, and the fixed angle of the support beam was used as the reference value. The wire rope tension sensor was then calibrated for no-load zero-point calibration and full-scale verification.

3. The method according to claim 1, characterized in that, The acquisition of structural parameters includes: Load the length of the rotating beam, the maximum lifting capacity, the lifting radius, the fixed angle of the support beam, and the rotation angle range of the rotating beam from the preset database; Initialize the control parameters of the two sets of variable frequency winches, confirm the connection status of each set of two winches and the corresponding traction cable, and establish a linear mapping relationship between the winch speed and the winding and unwinding length of the traction cable.

4. The method according to claim 1, characterized in that, The real-time acquisition and preprocessing of sensor data from the arm angle sensor and the wire rope tension sensor includes: The system reads the simulated current angle signal of the rotating beam output by the arm angle sensor and the simulated real-time tension signals of the two sets of traction cables output by the wire rope tension sensor in real time. After analog-to-digital conversion of the analog signal, the moving average filtering method is used to remove abnormal fluctuation values ​​with a duration of less than 20ms, generating continuous and smooth angle digital signals and tension digital signals.

5. The method according to claim 4, characterized in that, The generated angle and tension feedback signals include: The difference between the filtered digital angle signal and the target angle value of the rotating beam is calculated to obtain the angle deviation signal. The tension deviation signal is obtained by calculating the difference between the filtered digital tension signal and the theoretical tension value calculated based on the weight of the goods. The angle deviation signal and the tension deviation signal are combined into a feedback input parameter group and input to the working condition identification algorithm module.

6. The method according to claim 1, characterized in that, The adjustment strategy for determining the current working condition based on the angle and tension feedback signal includes: Calculate the current adjustment range of the rotating beam angle, which is the absolute value of the difference between the current angle and the angle of the previous control cycle; Calculate the wire rope tension change rate, which is the ratio of the absolute value of the difference between the current tension and the tension in the previous control cycle to the rated tension corresponding to the current cargo weight. If the angle adjustment range is ≤1.5 degrees and the tension change rate is ≤5%, it is judged as a micro-adjustment condition and the fuzzy control logic is invoked; If the angle adjustment range is greater than 1.5 degrees or the tension change rate is greater than 5%, it is determined to be a normal adjustment condition and the PID control logic is invoked.

7. The method according to claim 6, characterized in that, The hoist traction rate parameters corresponding to the generation and adjustment strategy include: Under normal adjustment conditions, the initial value of the traction rate of the winch for rotation angle control is calculated based on the proportional, integral, and differential components of the angle deviation signal, and the rate compensation value of the hoisting control winch is adjusted based on the tension deviation signal. Under micro-adjustment conditions, the angle deviation, the rate of change of the angle deviation, and the tension fluctuation value are used as fuzzy control input variables. A refined rate adjustment amount is generated through a preset fuzzy rule base, and the minimum resolution of the rate adjustment amount is not less than 0.1 m / min.

8. The method according to claim 1, characterized in that, The process involves sending synchronous control commands to two sets of variable frequency winches, adjusting the rotation angle and lifting status, acquiring corresponding feedback data, performing closed-loop correction based on the feedback data, and dynamically adjusting control parameters, including: The control commands include the target speed and direction signals for the winch to control the rotation angle, and the synchronous speed compensation signal for the winch to control the lifting. The time synchronization error between the two sets of signals shall not exceed 10 milliseconds. After each command is executed, wait 20-50ms for the system's mechanical response to stabilize before collecting new arm angle and tension data; If the angle deviation exceeds 0.1 degrees or the tension fluctuation exceeds 10% of the rated load, the traction rate parameter for the next cycle will be dynamically adjusted according to 10%-30% of the current deviation. If the target is not met after three consecutive corrections, the system will be forced to switch to a micro-adjustment control strategy.

9. The method according to claim 1, characterized in that, The segmented low-speed control and tension balancing strategy employed during the micro-adjustment phase ensures high-precision positioning, including: The data acquisition cycle was shortened to 200Hz, and the traction speed of the rotation angle control winch was reduced to 10%-20% of the rated speed. When the deviation between the rotating beam angle and the target value is ≤0.5 degrees, the segmented fine-tuning mode is activated: after each 0.1 degree adjustment, the winch is paused for 2-3 seconds, and the adjustment is continued only after the fluctuation of the tension sensor data is ≤3%. The tension of the two sets of wire ropes is monitored in real time. If the tension of one set exceeds the tension of the other set by 15%, a speed compensation command is automatically sent to the winch on the side with the smaller tension. The compensation rate does not exceed 20% of the main adjustment rate until the tension difference is reduced to within 5%.

10. A precision luffing control system for the main boom of a mast crane, characterized in that, The system includes: A parameter acquisition unit is used to acquire structural parameters, which include at least the length of the rotating beam, the maximum lifting capacity, and the angle range; and to calibrate the arm angle sensor and the wire rope tension sensor. The data acquisition unit is used to collect and preprocess sensor data from the arm angle sensor and the wire rope tension sensor in real time, and generate angle and tension feedback signals. The parameter generation unit is used to determine the adjustment strategy corresponding to the current working condition based on the angle and tension feedback signals, and generate the winch traction rate parameters corresponding to the adjustment strategy. The adjustment completion unit is used to send synchronous control commands to the two sets of variable frequency winches, adjust the rotation angle and lifting status and obtain corresponding feedback data, perform closed-loop correction based on the feedback data, and dynamically adjust the control parameters. In the micro-adjustment stage, segmented low-speed control and tension balance strategy are adopted to ensure high-precision positioning. The adjustment ends after the angle stability and force balance conditions are met.

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