Intelligent control method for hoisting equipment based on multi-sensor cooperation

Abstract

The invention relates to the technical field of hoisting equipment intelligent control based on multi-sensor cooperation, and discloses a hoisting equipment intelligent control method based on multi-sensor cooperation. A control unit reads and stores parameters such as air density, windward projection area, wind load arm length, arm section rotational inertia, rated output force of a hydraulic cylinder, sampling period and the like, and static self-inspection and dynamic calibration are performed on a wind speed sensor, a rotary encoder and a pressure sensor respectively; multiple paths of wind speeds are collected in parallel during operation, fused wind speeds are obtained through weighted fusion after abnormities are removed, and the wind load torque is calculated and limited by combining the windward area and the force arm length; reading two continuous encoder angle differences, performing amplitude limiting, calculating an angular velocity and kinetic energy, performing adaptive adjustment according to kinetic energy deviation, and performing amplitude limiting on a damping coefficient to generate a damping torque; and the amplitude-limited wind load torque and damping torque are synthesized into total control torque, and after amplitude limiting, the total control torque is converted into amplitude-limiting hydraulic cylinder operation output force which is issued and executed by a control unit.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for lifting equipment based on multi-sensor collaboration, specifically to an intelligent control method for lifting equipment based on multi-sensor collaboration. Background Technology

[0002] With the increasing demands for intelligence, safety, and operational efficiency in lifting equipment from modern ports, construction sites, and heavy manufacturing industries, multi-sensor information fusion and real-time control technology has become a research and application hotspot. Current lifting equipment control mainly relies on single or a few sensors, such as wind speed sensors, encoders, and pressure sensors, which cannot comprehensively and accurately reflect changes in the on-site environment and equipment status, thus presenting shortcomings.

[0003] First, for estimating wind load moment, most solutions only use a single-path sensor or a simple averaging method to obtain wind speed values, ignoring occasional sensor failures or abnormal readings. This leads to misjudgments or delayed responses under complex weather conditions such as strong winds and air surges, potentially causing equipment overload or increased sway amplitude, affecting operational safety and efficiency. Furthermore, existing moment calculations often lack dynamic limiting and redundancy protection mechanisms. Once the wind load moment increases sharply, it can easily exceed the hydraulic system's tolerance limits, posing a hardware damage or safety hazard. Second, vibration suppression of the crane boom mainly relies on passive damping or fixed-gain feedback control, lacking a mechanism for adaptive adjustment based on real-time kinetic energy changes. Once the parameters of the passive damper are determined, it is difficult to effectively suppress both low-frequency and high-frequency oscillations under different operating conditions; fixed-gain control also struggles to simultaneously meet the dual requirements of vibration suppression and system response speed, resulting in insufficient vibration suppression or slow response, and even triggering new oscillation modes. Furthermore, encoders often overlook error sources such as mechanical hysteresis, sensor nonlinearity, and temperature drift in angle measurements. Calibration is mostly manual or offline, failing to provide dynamic verification and correction during operation. Existing encoder calibration relies solely on static testing before shipment, lacking online self-checking and tolerance handling strategies. This leads to accumulated encoder errors, making it impossible to detect and correct angle deviations in a timely manner, thus affecting the accuracy of angular velocity, kinetic energy, and control torque calculations. In addition, the dynamic characteristics of hydraulic valves and pressure sensors are often simplified. Most studies only provide theoretical pressure-force relationships during system design, lacking online self-checking and linear calibration in actual operation. This makes it impossible to promptly identify fault modes such as valve core jamming and pipeline leakage. Without detection, these issues may lead to control failure or execution lag under high loads, posing safety hazards.

[0004] Therefore, this case aims to propose an intelligent control method for lifting equipment based on multi-sensor collaboration. First, during the system startup phase, physical constant parameters are initialized and online self-checks are performed to ensure the accuracy and reliability of readings from key sensors such as wind speed, angle, and pressure. Then, multiple wind speed information is collected in parallel, and abnormal data is eliminated through consistency verification. The current wind speed is accurately obtained through weighted fusion. Next, the kinetic energy of the boom system is calculated by combining the real-time encoder angle change. The damping coefficient is adaptively adjusted based on the kinetic energy deviation to generate the corresponding damping torque. The damping torque and wind load torque are combined into a total control torque and subjected to safety limiting. Finally, the limited torque is mapped into the hydraulic cylinder output force and drives the actuator. Summary of the Invention

[0005] This invention provides an intelligent control method for lifting equipment based on multi-sensor collaboration, which helps to solve the problems mentioned in the background art.

[0006] This invention provides the following technical solution: an intelligent control method for lifting equipment based on multi-sensor collaboration, comprising: When the system starts up, the control unit reads and stores constant parameters such as air density, windward projected area of ​​the boom, wind load resultant boom length, equivalent moment of inertia of boom segment, rated output force of hydraulic cylinder and sensor sampling period, and performs static self-test and dynamic calibration on the multi-channel wind speed sensor, rotary encoder and hydraulic pressure sensor respectively. Wind speed data is collected in parallel from each wind speed sensor that has passed self-testing. Data from abnormal sensors are removed based on preset thresholds and consistency checks, and then a valid sensor set is constructed. The current fused wind speed is obtained by using a weighted fusion algorithm based on data from an effective sensor set, and then the corresponding dynamic pressure is calculated. The wind load moment is calculated by combining the windward projection area of ​​the crane boom and the lever arm length. Finally, the wind load moment is subjected to amplitude limiting processing. Read the encoder angle values ​​of the current and previous samples, calculate the angle difference between the two samples and perform amplitude limiting, then calculate the current angular velocity based on the amplitude-limited angle difference and the sampling period, and calculate the kinetic energy of the crane boom system accordingly. The kinetic energy deviation is calculated based on the preset target kinetic energy and the current kinetic energy, the damping coefficient is dynamically adjusted, and the adjusted damping coefficient is limited. Calculate the corresponding damping torque using the damping coefficient after amplitude limiting and the current angular velocity; The total control torque is obtained by combining the wind load torque and damping torque at the current sampling time, and then the total control torque is subjected to amplitude limiting. The total control torque after limiting is converted into output force according to the output lever arm length of the hydraulic cylinder. After limiting the output force, it is sent to the hydraulic valve by the control unit.

[0007] Optionally, upon system startup, the control unit reads and stores constant parameters such as air density, the windward projected area of ​​the crane boom, the resultant force of the wind load on the boom length, the equivalent moment of inertia of the boom segment, the rated output force of the hydraulic cylinder, and the sensor sampling period. It also performs static self-tests and dynamic calibrations on the multi-channel wind speed sensors, rotary encoders, and hydraulic pressure sensors, specifically including: The control unit reads and stores the constant: air density. , the projected area of ​​the crane boom facing the wind Length of wind load resultant arm equivalent moment of inertia of arm segment Rated maximum angular velocity , radius of hydraulic cylinder output arm Maximum output force of hydraulic cylinder Upper limit of wind speed sensor range Sampling period ; Calculate the derived quantity: , , ;in, Target kinetic energy; The maximum controllable torque; This represents the maximum permissible angle change per cycle. Reading wind speed under static conditions ,verify ; Given the calibrated wind speed Reading ,verify ; If any path If the verification fails, the sensor will be rejected. in, Number the sensor. ; This is a static self-test reading; This is the static verification threshold; Readings were taken under calibrated conditions. For calibration tolerance; Apply calibration angle step Read the encoder's front and rear angles ; verify ;in, To calibrate the encoder tolerance; If the verification fails, switch to the backup encoder; Send three sets of test force step signals Measure the corresponding pressure ;in, Number the test group; For the first Group test force; Verify the linear relationship: ;in, This is the hydraulic pressure-force conversion coefficient; For pressure calibration tolerance; If the verification fails, an alarm will be triggered and the system will enter safe mode.

[0008] Optionally, the step of collecting wind speed data in parallel from each of the self-tested wind speed sensors, removing data from abnormal sensors based on a preset threshold and consistency check, and then constructing a valid sensor set specifically includes: Parallel reading of effective sensor wind speed: ;in, The sampling sequence number; For the first Road wind speed sensor in the first The reading from the next sample; Construct a valid set of sensor indexes: ;in, In the first Set of indexes of valid wind speed sensors for each sample; like If the condition is not met, control will stop and an alarm will be triggered; otherwise, control will continue. For set The number of elements.

[0009] Optionally, the data based on the effective sensor set is used to obtain the current fused wind speed using a weighted fusion algorithm, and then the corresponding dynamic pressure is calculated. The wind load moment is calculated by combining the windward projected area of ​​the crane boom and the lever arm length. Finally, the wind load moment is subjected to amplitude limiting processing, specifically including: Calculate the first Fusion wind speed at the second sampling ; Calculate the first Dynamic pressure during the second sampling ; Calculate the first Wind force at the second sampling ; Calculate the first Wind load moment and amplitude limit during the second sampling: ;in, For the first Secondary wind load moment.

[0010] Optionally, the step of reading the encoder angle values ​​of the current and previous samples, calculating the angle difference between the two samples and performing amplitude limiting, then calculating the current angular velocity based on the amplitude-limited angle difference and the sampling period, and calculating the kinetic energy of the crane boom system accordingly, specifically includes: Read the current and previous encoder angles: , ; Calculate the angle increment and limit the amplitude: , ;in, For the first The change in angle; If it is a sign function, then return ,like return ,like return ; Calculate the first angular velocity of the next sample ; Calculate the first Kinetic energy of the next sample .

[0011] Optionally, the step of calculating the kinetic energy deviation based on the preset target kinetic energy and the current kinetic energy, dynamically adjusting the damping coefficient, and limiting the adjusted damping coefficient specifically includes: Calculate kinetic energy deviation ; Construct the first The original damping coefficient of the second sampling ; Final damping coefficient for limiting amplitude: ;in, For the first Damping coefficient after limiting the amplitude of the second sampling.

[0012] Optionally, the calculation of the corresponding damping torque using the damping coefficient after amplitude limiting and the current angular velocity specifically includes: Generate the first Damping torque of the second sampling .

[0013] Optionally, the step of synthesizing the wind load moment and damping moment at the current sampling time to obtain the total control moment, and then limiting the total control moment, specifically includes: Synthesis of the first The total control torque actually issued this time Specifically: .

[0014] Optionally, the step of converting the total control torque after limiting into an output force according to the length of the hydraulic cylinder's output lever arm, limiting the output force, and then sending it to the hydraulic valve by the control unit specifically includes: torque Force conversion: ;in, For the first The hydraulic cylinder output force of the next sample; Limiting output force: ; The control unit will The signal is sent to the hydraulic valve, and the execution cycle does not exceed [time period missing]. .

[0015] The present invention has the following beneficial effects: 1. The initialization of physical constant parameters is integrated with the static self-testing and dynamic calibration of sensors. Upon system startup, the control unit reads key constants such as air density, windward projected area, wind load arm length, equivalent moment of inertia of the arm segment, and hydraulic cylinder output force in one go. It then performs detailed verification and rejection of multiple anemometers, rotary encoders, and hydraulic pressure sensors under static and preset calibration conditions. Compared to traditional methods that only perform single static calibrations at the factory or during periodic maintenance, this solution can identify faults such as zero-point drift, nonlinear errors, and pipeline leaks at any startup, ensuring that the basic data used in subsequent operation remains in a highly reliable state, fundamentally improving the system's safety margin and fault tolerance.

[0016] 2. By acquiring data from multiple wind speed sensors in parallel and eliminating abnormal channels based on preset thresholds and consistency checks, an effective wind speed ensemble with redundancy and fault tolerance is constructed. Compared to traditional single-sensor wind measurement or simple averaging and fusion, this solution simultaneously considers the range differences, static drift, and external noise of each sensor, and can eliminate short-term abnormal readings or channel faults in real time, improving the reliability and continuity of wind speed information. This effectively avoids the risk of unnecessary shutdown protection or malfunction due to wind load moment estimation errors caused by single-point failures, providing a solid wind speed data guarantee for the control system.

[0017] 3. After obtaining the fused wind speed, the weighted output of multiple wind speed data is used for dynamic pressure conversion. This data, combined with the crane boom's windward area and lever arm length, is then converted into wind load moment, and finally, a safety limiting process is applied. Unlike the traditional, crude method of directly converting data using a single sensor or average value, this solution dynamically allocates weights based on the calibration quality and historical reliability of each sensor, balancing measurement accuracy and stability while reducing the impact of wind speed fluctuations on moment estimation. Simultaneously, by using a limiting strategy that deducts from structural load limits, the contradiction between mechanical overload and accidental activation of the control system's protection mechanisms during sudden wind load increases is avoided, achieving precise compensation and safety protection against wind load impacts.

[0018] 4. After real-time amplitude limiting of the continuously sampled encoder angle differences, the kinetic energy of the crane boom system is calculated based on a reliable rate of angle change. This approach overcomes the limitations of previous methods that relied solely on speed or angle as control inputs, transforming mechanical kinetic energy into quantifiable control parameters, providing a more intuitive and physical basis for subsequent damping coefficient adjustments. Compared to traditional control strategies that struggle to accurately assess the system's dynamic state, this scheme effectively captures oscillation trends and energy accumulation through kinetic energy assessment, achieving early warning and precise suppression of amplitude increases, thus improving response smoothness and vibration resistance.

[0019] 5. Based on the deviation between the current kinetic energy and the preset target kinetic energy, the damping coefficient is adjusted in real time to achieve active energy management of the system's dynamic state. Unlike traditional rigid methods such as fixed damping or manual adjustment based on experience, this scheme continuously tracks the kinetic energy deviation, maps it to damping requirements, and implements safety limits on the adjustment results to ensure that the damping coefficient is always within a reasonable range. This adaptive mechanism takes into account both damping force and response speed, avoiding both sluggish action caused by excessive damping and excessive amplitude caused by insufficient damping, maintaining optimal dynamic performance under different wind loads and operational load conditions.

[0020] 6. Based on the damping coefficient after amplitude limiting and the real-time angular velocity, a corresponding damping torque is generated to resist system oscillations. Unlike the fixed damping generated by traditional mechanical damping elements, this solution achieves precise dissipation of oscillation energy by digitally controlling and outputting adjustable damping force in real time. Furthermore, the damping torque can be flexibly matched at different amplitudes and frequencies, overcoming the shortcomings of traditional dampers that experience performance degradation or failure at low-speed or high-frequency oscillations. This provides lifting equipment with more efficient and reliable vibration suppression capabilities, extends equipment life, and reduces safety hazards.

[0021] 7. By unifying the environmental disturbance torque and adaptive damping torque before applying safety limiting, this innovative approach avoids logical conflicts caused by fragmented processing across multiple stages. Compared to the previous step-by-step method of first compensating for wind load disturbances and then separately superimposing damping forces, this scheme achieves centralized limiting by combining the torques. This satisfies the control objective of resisting wind oscillations while ensuring that the combined torque never exceeds the bearing capacity of the mechanical and hydraulic systems. This strategy simplifies the control logic, improves command consistency, and effectively enhances the robustness and safety of the system under sudden strong wind conditions.

[0022] 8. The total control torque after amplitude limiting is converted into the required output force of the hydraulic cylinder by considering the lever arm, and amplitude limiting is applied again in the digital domain before driving the hydraulic valve and actuator. The entire process of mapping torque to output force is digitally controlled, and safety amplitude limiting is embedded in the signal channel, eliminating the errors and transient overshoot problems caused by traditional reliance on preset mapping curves or manual calibration. This ensures the rapid response and high-precision action of the hydraulic system, improving the reliability and operational safety of the lifting equipment in complex environments. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] Example, refer to Figure 1 A method for intelligent control of lifting equipment based on multi-sensor collaboration, comprising: When the system starts up, the control unit reads and stores constant parameters such as air density, windward projected area of ​​the boom, wind load resultant boom length, equivalent moment of inertia of boom segment, rated output force of hydraulic cylinder and sensor sampling period, and performs static self-test and dynamic calibration on the multi-channel wind speed sensor, rotary encoder and hydraulic pressure sensor respectively. Wind speed data is collected in parallel from each wind speed sensor that has passed self-testing. Data from abnormal sensors are removed based on preset thresholds and consistency checks, and then a valid sensor set is constructed. The current fused wind speed is obtained by using a weighted fusion algorithm based on data from an effective sensor set, and then the corresponding dynamic pressure is calculated. The wind load moment is calculated by combining the windward projection area of ​​the crane boom and the lever arm length. Finally, the wind load moment is subjected to amplitude limiting processing. Read the encoder angle values ​​of the current and previous samples, calculate the angle difference between the two samples and perform amplitude limiting, then calculate the current angular velocity based on the amplitude-limited angle difference and the sampling period, and calculate the kinetic energy of the crane boom system accordingly. The kinetic energy deviation is calculated based on the preset target kinetic energy and the current kinetic energy, the damping coefficient is dynamically adjusted, and the adjusted damping coefficient is limited. Calculate the corresponding damping torque using the damping coefficient after amplitude limiting and the current angular velocity; The total control torque is obtained by combining the wind load torque and damping torque at the current sampling time, and then the total control torque is subjected to amplitude limiting. The total control torque after limiting is converted into output force according to the output lever arm length of the hydraulic cylinder. After limiting the output force, it is sent to the hydraulic valve by the control unit.

[0026] During system startup, the control unit reads and stores key constant parameters, including air density, the windward projected area of ​​the boom, the resultant boom length under wind load, the equivalent moment of inertia of the boom segment, the rated output force of the hydraulic cylinder, and the sensor sampling period. Static self-checks and dynamic calibrations are performed on multiple wind speed sensors, rotary encoders, and hydraulic pressure sensors. During operation, wind speed signals from each channel are collected in parallel, abnormal readings are removed, and a high-reliability wind speed is obtained through weighted fusion. The corresponding wind load torque is then calculated. Subsequently, the system kinetic energy is calculated by limiting the difference in encoder sampling angles at continuous intervals. Based on the kinetic energy deviation, the damping coefficient is adaptively adjusted to generate the corresponding damping torque. Finally, the wind load torque and the damping torque are combined into a total control torque, which is then safely limited, converted into hydraulic cylinder output force, and executed. This method solves the measurement error problem caused by the drift or failure of a single sensor, effectively resists the oscillation caused by the instantaneous impact of wind load, and ensures that the robotic arm and hydraulic system always operate within the safe load range through multi-level amplitude limiting; it improves the wind disturbance resistance and posture stability of the lifting equipment, while reducing operational risks and structural fatigue, extending the service life of the equipment and improving work efficiency and safety.

[0027] The system startup process involves the control unit reading and storing constant parameters such as air density, the windward projected area of ​​the crane boom, the resultant force of the wind load on the boom length, the equivalent moment of inertia of the boom segment, the rated output force of the hydraulic cylinder, and the sensor sampling period. It also performs static self-tests and dynamic calibrations on the multi-channel wind speed sensors, rotary encoders, and hydraulic pressure sensors, specifically including: The control unit reads and stores the constant: air density. , the projected area of ​​the crane boom facing the wind The resultant force arm length of the wind load (distance from the line of action of the wind load to the axis of rotation) was obtained through CAD measurement. The equivalent moment of inertia of the boom segment was obtained through calibration. The rated maximum angular velocity obtained through experiments or simulations The radius of the hydraulic cylinder output lever is given by the equipment specifications. The maximum output force of the hydraulic cylinder was obtained through calibration. The parameters are given, and the upper limit of the wind speed sensor's range is set. Sampling period It is set by the control unit; Initialize system parameters for subsequent calculations; Calculate the derived quantity: , , ;in, Target kinetic energy; The maximum controllable torque; This represents the maximum permissible angle change per cycle. Derived operational boundary conditions, subsequent limits and judgment criteria; Reading wind speed under static conditions ,verify Eliminate zero-point drift; Given the calibrated wind speed Reading ,verify Verify range linearity; If any path If the verification fails, the sensor is removed to ensure the reliability of subsequent data. in, Number the sensor. ; This is a static self-test reading; This is the static verification threshold; Readings were taken under calibrated conditions. For calibration tolerance; like If the value is too small, normal weak airflow or sensor background noise can cause "static drift" to be misjudged as a fault, leading to frequent channel rejection; if If the deviation is too large, the actual zero-point offset (such as sensor damage or pipeline leakage) will be considered "normal," affecting the accuracy of subsequent wind speed calculations. Values ​​are determined based on sensor zero-point noise and resolution specifications, such as... Typical minimum non-faulty wind speed (light breeze) for the installation environment, such as indoors. ; Recommended value: ; like Too small, even if the sensor has a slight linear error at the nominal wind speed (e.g. ), and it will also be mistakenly judged as calibration failure; if Too large an amount masks the sensor's intrinsic nonlinearity and sensitivity drift, leading to systematic deviations in the readings; Value selection criteria: Sensor nominal accuracy, such as... or Uncertainty of wind tunnel or calibration source, such as

[0028] Recommended value: ; Apply calibration angle step Read the encoder's front and rear angles ; If calibrating angle step size If the value is too small, it falls within the encoder resolution or mechanical hysteresis range, and the change cannot be detected; if the calibrated angle step size is too small... If the value is too large, it will be unable to capture subtle nonlinearities or installation errors, thus reducing calibration accuracy. Value selection basis: encoder single-pulse angle resolution, such as ; Repetitive institutional positioning, such as ; Recommended value: ; verify ;in, Calibrate encoder tolerances; ensure position measurement accuracy; like Too small a backlash, even a tiny mechanical backlash or noise, can cause calibration to fail; if If the error is too large, it may mask installation or linearity errors and potentially accumulate to cause positioning deviations. Basis for value selection: Size; repeatability of the mechanism, such as ; Recommended value: ; If the verification fails, switch to the backup encoder to improve system robustness. Send three sets of test force step signals Measure the corresponding pressure ;in, Number the test group; For the first Group test force; Verify the linear relationship: ;in, This is the hydraulic pressure-force conversion coefficient; To check the pressure tolerance; confirm the actuator response; like If the sensor is too small, subtle changes such as temperature drift in the sensor or pressure drop in the ventilation line may be misinterpreted as faults; if... Too large a size can mask major malfunctions such as leakage in the actuator or valve core jamming. Value selection criteria: Pressure sensor accuracy, such as... The stability of the hydraulic system and the pressure drop in the pipeline, such as ; Recommended value: ; Hydraulic pressure to force conversion coefficient Source and value retrieval method: Obtain the output force of the hydraulic cylinder With the hydraulic pressure acting on the piston satisfy: ;in, This represents the effective pressure-bearing area of ​​the piston. Rewrite the above equation as follows: ; Right now In this way, the pressure of reading With output force Through coefficient One-to-one correspondence; Basis for value selection: Get piston diameter The specific value is determined by the specifications of the hydraulic cylinder used. Calculate piston area ;calculate ; Recommended value: In the design, the piston diameter of the hydraulic cylinder used should be determined first; the piston area should be calculated; and then the value should be obtained using the formula above. ; If the verification fails, an alarm will be triggered and the system will enter safe mode to prevent execution failures from causing danger.

[0029] By performing refined self-checks and calibrations on sensors and constant parameters during system initialization, the control unit first reads and stores parameters including air density, windward projected area of ​​the structural surface, distance between the action line and arm, equivalent moment of inertia of the mechanism, rated maximum angular velocity, radius and maximum output force of the hydraulic cylinder output arm, upper limit of the wind speed sensor range, and sampling period. Then, zero-point drift is verified in a static environment, and unqualified channels are eliminated. Linear range is verified in a calibration environment, and channels that failed calibration are eliminated. A preset angular step size is applied to the encoder to verify resolution and linearity error, and a backup channel is switched if necessary. Multiple sets of step test forces are applied to the pressure sensor to verify the sensor pressure-force conversion relationship and enter a safe mode to handle faults. This technical solution solves the problem of the inability to compensate for deviations between factory calibration and field operation in real time, effectively preventing cumulative measurement deviations caused by zero-point drift, linearity error, or actuator leakage. It improves the reliability and robustness of multiple sensors in complex field environments, providing solid and accurate basic data for subsequent real-time control and avoiding the risk of malfunctions and downtime due to sensor failures.

[0030] The process involves collecting wind speed data in parallel from each of the self-tested wind speed sensors, removing data from abnormal sensors based on preset thresholds and consistency checks, and then constructing a valid sensor set. Specifically, this includes: Parallel reading of effective sensor wind speed: ;in, The sampling sequence number; For the first Road wind speed sensor in the first The readings from the next sample; to obtain real-time environmental information; Construct a valid set of sensor indexes: ;in, In the first The set of valid wind speed sensor indexes for each sample; filtering out faulty or over-range sensors; like If the condition is not met, control will stop and an alarm will be triggered; otherwise, control will continue. For set The number of elements; ensure at least two redundant data paths.

[0031] By reading all wind speed channels that have passed preliminary verification in parallel, and eliminating readings that are out of range or significantly deviate based on pre-set validity thresholds and consistency check rules, at least two redundant sets of effective sensors are constructed. When the redundant channels are insufficient, an alarm is automatically issued and control is stopped, ensuring the stability and reliability of the data foundation for subsequent wind load calculations. This step solves the problem of errors in overall wind speed estimation caused by single-point wind measurement failures or occasional abnormal readings; it achieves fault-tolerant redundancy and dynamic elimination capabilities for wind speed measurements, avoiding the impact of misleading data generated by a single faulty channel on control decisions, and improving the accuracy of wind load moment estimation and the overall safety and reliability of the system.

[0032] The data based on the effective sensor set is weighted and fused using a fusion algorithm to obtain the current fused wind speed, then the corresponding dynamic pressure is calculated, and the wind load moment is calculated by combining the windward projected area of ​​the crane boom and the lever arm length. Finally, the wind load moment is subjected to amplitude limiting processing, specifically including: Calculate the first Fusion wind speed at the second sampling Generate a single representative wind speed value to reduce noise; Calculate the first Dynamic pressure during the second sampling ; Calculate the first Wind force at the second sampling ; Convert wind speed into force along the arm segment; Calculate the first Wind load moment and amplitude limit during the second sampling: ;in, For the first Secondary wind load moment; prevent exceeding the torque that the equipment can withstand.

[0033] By employing a dynamic weighted fusion strategy on the effective wind speed channels, a fused wind speed representing the actual on-site operating conditions is generated. This fused wind speed is then converted into dynamic pressure and windward wind load moment. Finally, the wind load moment is safely limited based on the equipment's load-bearing capacity, eliminating the risk of overload. This step solves the problems of low accuracy, large fluctuations, and easy over-limitation associated with traditional simple averaging or single-channel wind measurement. By combining the advantages of multi-channel data, while ensuring calculation accuracy, the limiting mechanism protects the mechanical structure and control system, improving operational stability and safety boundaries under wind load disturbance conditions.

[0034] The process of reading the encoder angle values ​​from the current and previous samples, calculating the angle difference between the two samples and performing amplitude limiting, then calculating the current angular velocity based on the limited angle difference and the sampling period, and calculating the kinetic energy of the crane boom system accordingly, specifically includes: Read the current and previous encoder angles: , Determine the current position and the previous position; Calculate the angle increment and limit the amplitude: , ;in, For the first The change in angle; If it is a sign function, then return ,like return ,like return Suppressing abnormal mutations in measurements; Calculate the first angular velocity of the next sample ; Calculate the first Kinetic energy of the next sample ; Quantify the dynamic state of the arm segment.

[0035] By continuously sampling the encoder to read the current and previous angles, limiting the range of angle changes to filter out abrupt changes, and then combining this with the sampling period to calculate the current angular velocity, the kinetic energy of the crane mechanism is finally obtained, thus quantifying the dynamic state of the robotic arm. This step solves the problem that relying solely on instantaneous velocity or angle as a control reference cannot accurately reflect the system's energy accumulation and oscillation trend; by introducing physical kinetic energy into real-time control, a more intuitive and accurate dynamic scale is provided for subsequent damping parameter adjustments, which helps in early warning of amplitude increases and energy balance management, thus improving the system's vibration resistance.

[0036] The step of calculating the kinetic energy deviation based on the preset target kinetic energy and the current kinetic energy, dynamically adjusting the damping coefficient, and limiting the adjusted damping coefficient specifically includes: Calculate kinetic energy deviation Assess the difference between current momentum and target; Construct the first The original damping coefficient of the second sampling Determine the initial damping requirements based on the kinetic energy difference; Final damping coefficient for limiting amplitude: ;in, For the first The damping coefficient after the amplitude limiting of the second sampling; ensuring that the damping torque is within the allowable range.

[0037] By comparing the real-time calculated system kinetic energy with the pre-set safety target kinetic energy, the damping coefficient is dynamically adjusted and limited within a reasonable range. This ensures that the damping force effectively dissipates excess energy without causing response lag due to over-damping. This step resolves the contradiction between oscillation suppression and response speed under different wind loads and operating conditions when fixed damping settings are insufficient. It achieves adaptive adjustment of oscillation energy, enabling rapid amplitude suppression at high wind speeds while maintaining equipment flexibility at low wind speeds, thus optimizing operational stability and control efficiency.

[0038] The calculation of the corresponding damping torque using the damping coefficient after amplitude limiting and the current angular velocity specifically includes: Generate the first Damping torque of the second sampling ; convert the damping coefficient into a torque that resists oscillation.

[0039] By combining the adaptive damping coefficient after amplitude limiting with the real-time angular velocity, a damping torque matching the current oscillation of the robotic arm is generated to resist oscillation energy and participates in the synthesis of the overall control command in the next control cycle. This step solves the problem that traditional mechanical or hydraulic damping elements cannot flexibly adapt to changes in oscillation frequency and amplitude under multiple operating conditions; the adjustable damping force is output in real time in a digital manner, improving the accuracy of energy dissipation under different oscillation modes, extending the service life of the structure, and reducing maintenance costs.

[0040] The process of synthesizing the wind load moment and damping moment at the current sampling time to obtain the total control moment, and then limiting the total control moment, specifically includes: Synthesis of the first The total control torque actually issued this time Specifically: The damping torque is combined with the environmental disturbance, and the over-limit is prevented.

[0041] By unifying the wind load torque measured on-site with the digitally generated damping torque and applying a safety limit to the combined torque based on equipment limits, compensation and suppression are completed in the same step, avoiding multiple limiting operations or command conflicts and improving execution efficiency. This step solves the logical inconsistencies and safety hazards that may result from traditional step-by-step limiting; it simplifies the control logic, enhances command consistency, and can quickly output a safe and effective control torque when wind load suddenly increases or oscillations intensify, ensuring stable operation of the equipment under extreme conditions.

[0042] The process of converting the total control torque after amplitude limiting into an output force according to the output lever arm length of the hydraulic cylinder, and then limiting the output force before sending it to the hydraulic valve by the control unit, specifically includes: torque Force conversion: ;in, For the first The hydraulic cylinder output force of the next sample; Limiting output force: ; The control unit will The signal is sent to the hydraulic valve, and the execution cycle does not exceed [time period missing]. ; The control torque is converted into hydraulic cylinder force and executed to achieve vibration resistance.

[0043] By converting the total control torque after amplitude limiting into the corresponding hydraulic system execution force based on the hydraulic cylinder output lever arm length, and then limiting it again before sending it to the hydraulic valve by the control unit, the robotic arm can achieve precise and safe motion execution. This step solves the problem of transient overshoot or response delay caused by traditional reliance on preset mapping curves; the combination of full-process digital mapping and amplitude limiting control not only improves the response speed and accuracy of hydraulic execution, but also further ensures the safety and reliability of the actuator and the overall system through secondary amplitude limiting.

[0044] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0045] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent control of lifting equipment based on multi-sensor collaboration, characterized in that, include: When the system starts up, the control unit reads and stores constant parameters such as air density, windward projected area of ​​the boom, wind load resultant boom length, equivalent moment of inertia of boom segment, rated output force of hydraulic cylinder and sensor sampling period, and performs static self-test and dynamic calibration on the multi-channel wind speed sensor, rotary encoder and hydraulic pressure sensor respectively. Wind speed data is collected in parallel from each wind speed sensor that has passed self-testing. Data from abnormal sensors are removed based on preset thresholds and consistency checks, and then a valid sensor set is constructed. The current fused wind speed is obtained by using a weighted fusion algorithm based on data from an effective sensor set, and then the corresponding dynamic pressure is calculated. The wind load moment is calculated by combining the windward projection area of ​​the crane boom and the lever arm length. Finally, the wind load moment is subjected to amplitude limiting processing. Read the encoder angle values ​​of the current and previous samples, calculate the angle difference between the two samples and perform amplitude limiting, then calculate the current angular velocity based on the amplitude-limited angle difference and the sampling period, and calculate the kinetic energy of the crane boom system accordingly. The kinetic energy deviation is calculated based on the preset target kinetic energy and the current kinetic energy, the damping coefficient is dynamically adjusted, and the adjusted damping coefficient is limited. Calculate the corresponding damping torque using the damping coefficient after amplitude limiting and the current angular velocity; The total control torque is obtained by combining the wind load torque and damping torque at the current sampling time, and then the total control torque is subjected to amplitude limiting. The total control torque after limiting is converted into output force according to the output lever arm length of the hydraulic cylinder. After limiting the output force, it is sent to the hydraulic valve by the control unit.

2. The intelligent control method for lifting equipment based on multi-sensor collaboration according to claim 1, characterized in that, The system startup process involves the control unit reading and storing constant parameters such as air density, the windward projected area of ​​the crane boom, the resultant force of the wind load on the boom length, the equivalent moment of inertia of the boom segment, the rated output force of the hydraulic cylinder, and the sensor sampling period. It also performs static self-tests and dynamic calibrations on the multi-channel wind speed sensors, rotary encoders, and hydraulic pressure sensors, specifically including: The control unit reads and stores the constant: air density. , the projected area of ​​the crane boom facing the wind Length of wind load resultant arm equivalent moment of inertia of arm segment Rated maximum angular velocity , radius of hydraulic cylinder output arm Maximum output force of hydraulic cylinder Upper limit of wind speed sensor range Sampling period ; Calculate the derived quantity: , , ;in, Target kinetic energy; The maximum controllable torque; This represents the maximum permissible angle change per cycle. Reading wind speed under static conditions ,verify ; Given the calibrated wind speed Reading ,verify ; If any path If the verification fails, the sensor will be rejected. in, Number the sensor. ; This is a static self-test reading; This is the static verification threshold; Readings were taken under calibrated conditions. For calibration tolerance; Apply calibration angle step Read the encoder's front and rear angles ; verify ;in, To calibrate the encoder tolerance; If the verification fails, switch to the backup encoder; Send three sets of test force step signals Measure the corresponding pressure ;in, Number the test group; For the first Group test force; Verify the linear relationship: ;in, This is the hydraulic pressure-force conversion coefficient; For pressure calibration tolerance; If the verification fails, an alarm will be triggered and the system will enter safe mode.

3. The intelligent control method for lifting equipment based on multi-sensor collaboration according to claim 2, characterized in that, The process involves collecting wind speed data in parallel from each of the self-tested wind speed sensors, removing data from abnormal sensors based on preset thresholds and consistency checks, and then constructing a valid sensor set. Specifically, this includes: Parallel reading of effective sensor wind speed: ;in, The sampling sequence number; For the first Road wind speed sensor in the first The reading from the next sample; Construct a valid set of sensor indexes: ;in, In the first Set of indexes of valid wind speed sensors for each sample; like If the condition is not met, control will stop and an alarm will be triggered; otherwise, control will continue. For set The number of elements.

4. The intelligent control method for lifting equipment based on multi-sensor collaboration according to claim 3, characterized in that, The data based on the effective sensor set is weighted and fused using a fusion algorithm to obtain the current fused wind speed, then the corresponding dynamic pressure is calculated, and the wind load moment is calculated by combining the windward projected area of ​​the crane boom and the lever arm length. Finally, the wind load moment is subjected to amplitude limiting processing, specifically including: Calculate the first Fusion wind speed at the second sampling ; Calculate the first Dynamic pressure during the second sampling ; Calculate the first Wind force at the second sampling ; Calculate the first Wind load moment and amplitude limit during the second sampling: ;in, For the first Secondary wind load moment.

5. The intelligent control method for lifting equipment based on multi-sensor collaboration according to claim 4, characterized in that, The process of reading the encoder angle values ​​from the current and previous samples, calculating the angle difference between the two samples and performing amplitude limiting, then calculating the current angular velocity based on the limited angle difference and the sampling period, and calculating the kinetic energy of the crane boom system accordingly, specifically includes: Read the current and previous encoder angles: , ; Calculate the angle increment and limit the amplitude: , ;in, For the first The change in angle; If it is a sign function, then return ,like return ,like return ; Calculate the first angular velocity of the next sample ; Calculate the first Kinetic energy of the next sample .

6. The intelligent control method for lifting equipment based on multi-sensor collaboration according to claim 5, characterized in that, The step of calculating the kinetic energy deviation based on the preset target kinetic energy and the current kinetic energy, dynamically adjusting the damping coefficient, and limiting the adjusted damping coefficient specifically includes: Calculate kinetic energy deviation ; Construct the first The original damping coefficient of the second sampling ; Final damping coefficient for limiting amplitude: ;in, For the first Damping coefficient after limiting the amplitude of the second sampling.

7. The intelligent control method for lifting equipment based on multi-sensor collaboration according to claim 6, characterized in that, The calculation of the corresponding damping torque using the damping coefficient after amplitude limiting and the current angular velocity specifically includes: Generate the first Damping torque of the second sampling .

8. The intelligent control method for lifting equipment based on multi-sensor collaboration according to claim 7, characterized in that, The process of synthesizing the wind load moment and damping moment at the current sampling time to obtain the total control moment, and then limiting the total control moment, specifically includes: Synthesis of the first The total control torque actually issued this time Specifically: 。 9. The intelligent control method for lifting equipment based on multi-sensor collaboration according to claim 8, characterized in that, The process of converting the total control torque after amplitude limiting into an output force according to the output lever arm length of the hydraulic cylinder, and then limiting the output force before sending it to the hydraulic valve by the control unit, specifically includes: torque Force conversion: ;in, For the first The hydraulic cylinder output force of the next sample; Limiting output force: ; The control unit will The signal is sent to the hydraulic valve, and the execution cycle does not exceed [time period missing]. .

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