Crane lifting capacity monitoring and limiting method and system based on multi-sensor cooperation

Abstract

The invention provides a crane lifting capacity monitoring and limiting method and system based on multi-sensor collaboration, and relates to the technical field of engineering machinery, the method comprises the following steps: collecting operation condition data of a crane; based on the winch output shaft torque, the winch rotating speed and the part relative displacement data, a first-order dynamics diagnosis model is constructed, so that the first-order dynamics diagnosis model outputs winch output pulling force, the object lifting speed and the accelerated speed; calculating the real-time mass of the object lifted by the crane according to the output tension of the winch, the speed of the lifted object, the acceleration and the dynamic balance equation; and judging whether the real-time mass is within a preset lifting capacity permissible range or not, and if the real-time mass is out of the preset lifting capacity permissible range, sending a regulation and control signal to a hydraulic damper of the crane so as to regulate the operation state of the object lifted by the crane. According to the invention, the response timeliness and the operation stability of crane control can be improved.

Description

Technical Field

[0001] This invention relates to the field of engineering machinery technology, and in particular to a method and system for monitoring and limiting the lifting capacity of a crane based on multi-sensor collaboration. Background Technology

[0002] Lifting equipment, as a crucial tool for material handling and heavy lifting in modern industrial production, directly impacts a company's production efficiency and the safety of personnel on-site. Among them, bridge cranes, due to their stable structure, high load-bearing capacity, and flexible operation, are widely used in manufacturing, metallurgy, power, warehousing, and logistics industries, serving as core equipment for achieving production automation and heavy-duty handling. However, during lifting operations, without effective monitoring of the load condition, overload, operational errors, or equipment aging can easily lead to lifting accidents, causing mechanical damage, personal injury, and even significant property loss. The risk of overloading is particularly pronounced for bridge cranes operating under high-frequency, heavy-load conditions.

[0003] Chinese Patent CN111960279B discloses a crane control method, device, crane, processor, and storage medium. The crane control method includes: generating first and second working condition information based on externally input working condition demand information and a set of crane lifting capacity tables, wherein the set of lifting capacity tables includes lifting capacity tables for multiple outriggers of arbitrary length combinations; controlling each outrigger to extend to support the crane based on the first working condition information; and controlling the crane's operation based on externally input control information and the second working condition information. However, the above solution only generates working information based on externally input working condition demand information and a preset set of lifting capacity tables, lacking the ability to dynamically perceive and adaptively adjust to changes in the crane's real-time operating status and working environment. This results in insufficient response timeliness and operational stability of the crane under complex or variable working conditions. Therefore, it is essential to provide a crane lifting capacity monitoring and limiting method and system based on multi-sensor collaboration to improve the response timeliness and operational stability of crane control. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for monitoring and limiting the lifting capacity of a crane based on multi-sensor collaboration.

[0005] This invention provides a method for monitoring and limiting the lifting capacity of a crane based on multi-sensor collaboration, the method comprising: Collect crane operating condition data, including winch output shaft torque, winch speed, and relative displacement data of parts; Based on the output shaft torque of the winch, the speed of the winch, and the relative displacement data of the parts, a first-order dynamic diagnostic model is constructed so that the first-order dynamic diagnostic model can output the output pulling force of the winch, the speed of the hoisted object, and the acceleration. The real-time mass of the object being lifted by the crane is calculated based on the output pulling force of the winch, the speed and acceleration of the object being lifted, and the dynamic equilibrium equation. If the real-time mass is outside the preset lifting capacity range, a control signal is sent to the hydraulic damper of the crane to adjust the operating state of the object being lifted by the crane.

[0006] Based on the above technical solutions, preferably, the process of acquiring the relative displacement data of the parts includes: The echo signal is acquired by high-frequency sampling, and the baseline of the echo signal is estimated by using the average value of multiple sampling points to obtain a calibration echo signal; The calibration echo signal is denoised using median filtering and wavelet transform, and then normalized to obtain a normalized echo signal. The echo components of the normalized echo signal are fitted based on the Gaussian mixture model. The peak time of each echo component corresponding to the normalized echo signal is obtained by the expectation-maximization algorithm in order to calculate the preliminary distance estimate of each echo component. The relative displacement data of the parts are calculated based on Bayesian filtering, Kalman filtering, and the preliminary distance estimate.

[0007] Based on the above technical solutions, preferably, the step of calculating the relative displacement data of the parts based on Bayesian filtering, Kalman filtering, and the preliminary distance estimate specifically includes: The update probability of each echo component corresponding to the normalized echo signal is calculated based on Bayesian filtering, and the preliminary distance estimate is weighted and fused based on the update probability to obtain a weighted distance estimate. The weighted distance estimate of the current frame is fused with the preliminary distance estimate of the previous frame using Kalman filtering to obtain the relative displacement data of the parts.

[0008] More preferably, the construction of the first-order dynamic diagnostic model, so that the first-order dynamic diagnostic model outputs the winch's output pulling force, the speed of the hoisted object, and the acceleration, specifically includes: The hoist, wire rope, smart hook, and hoisted object of the crane can be equivalently represented as a first-order dynamic system consisting of the mass of the hoisted object, spring elements, and damping elements. Force analysis is performed on the hoisted object in the hoisting direction to establish a dynamic equilibrium relationship, and the output pulling force of the winch is calculated based on the output shaft torque, drum radius and transmission efficiency parameters of the winch. The relative displacement data of the parts is used as the displacement in the dynamic equilibrium relationship, and the relative displacement is differentiated to obtain the speed of the hoisted object; The theoretical rising speed of the wire rope is calculated based on the speed of the winch, and a slippage compensation coefficient is introduced to correct the theoretical rising speed of the wire rope. The actual speed of the hoisted object is obtained, and the actual speed is differentiated to obtain the acceleration of the hoisted object in the hoisting direction.

[0009] More preferably, the spring element inside the smart hook is equivalent to a first spring stiffness parameter, the elastic deformation of the crane structural component is equivalent to a second spring stiffness parameter, the hydraulic damper inside the smart hook is equivalent to a first damping coefficient parameter, and the friction between the wire rope and the transmission mechanism is equivalent to a second damping coefficient parameter. The first spring stiffness parameter, the second spring stiffness parameter, the first damping coefficient parameter, and the second damping coefficient parameter are then incorporated into the dynamic equilibrium relationship.

[0010] More preferably, the expression for the preliminary distance estimate is:

[0011]

[0012]

[0013]

[0014]

[0015] in, S fit ( t ) indicates the first t The echo component model after time-fitting. α i,j Indicates the first i The first echo j The amplitude of each echo component, μ i,j Indicates the first i The first echo j The center time of each echo component N This indicates the total number of echo components. σ i,j Indicates the width of the echo. Indicates the first tThe residuals of the time-time echo fitting, This indicates the time corresponding to the peak position of each echo. j * This represents the principal value of the argument corresponding to the amplitude of the maximum echo component. Indicates the time of laser signal emission. μ i,j* Indicates the first i The center time of the maximum echo component of each echo Δt i Indicates the propagation time of the laser signal. D i This represents the initial distance estimate, and c represents the speed of light.

[0016] More preferably, the method further includes: The system receives the operating condition data and the control signal via wireless communication, performs historical data storage, operating condition analysis and fault recording on the operating condition data and the control signal, and displays the processing results corresponding to the control signal in a graphical manner on the data visualization terminal. The lifting capacity limit strategy is optimized based on the collected historical operating condition data and historical control signals to generate an updated lifting capacity allowable range, which is then sent to the hydraulic damper of the crane via the network.

[0017] A second aspect of this application provides a crane lifting capacity monitoring and limiting system based on multi-sensor collaboration. The crane lifting capacity monitoring and limiting system includes a data acquisition module, a data processing module, and an operation control module, wherein... The data acquisition module is used to collect the operating condition data of the crane, including the output shaft torque of the winch, the speed of the winch, and the relative displacement data of the parts. The data processing module is used to construct a first-order dynamic diagnostic model based on the output shaft torque of the winch, the speed of the winch, and the relative displacement data of the parts, so that the first-order dynamic diagnostic model outputs the output tension of the winch, the speed of the hoisted object, and the acceleration. Based on the output tension of the winch, the speed of the hoisted object, the acceleration, and the dynamic balance equation, the real-time mass of the object hoisted by the crane is calculated. The operation control module is used to determine whether the real-time quality is within the preset lifting weight permissible range. If the real-time quality is outside the preset lifting weight permissible range, a control signal is sent to the hydraulic damper of the crane to adjust the operating state of the object being lifted by the crane.

[0018] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory.

[0019] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of a crane lifting capacity monitoring and limiting method based on multi-sensor collaboration.

[0020] The crane lifting capacity monitoring and limiting method and system based on multi-sensor collaboration provided by this invention has the following advantages over the prior art: (1) A first-order dynamic diagnostic model is constructed using multi-source data consisting of the output shaft torque of the winch, the speed of the winch, and the relative displacement of the parts. This avoids the measurement errors and uncertainties caused by relying on a single sensor. Furthermore, the real-time mass of the hoisted object is calculated using the dynamic equilibrium equation. Compared with the traditional static weighing or single force sensor scheme, the mass estimation is more accurate and robust. At the same time, the mass of the hoisted object is calculated in real time using the tension, velocity, and acceleration output by the first-order dynamic diagnostic model. This allows for continuous monitoring of load changes throughout the entire operation of the crane, rather than just detection under a single working condition. The trend of exceeding the lifting weight limit can be detected in advance, which is conducive to early intervention and improves operational safety. The collaborative modeling of multiple physical quantities ensures that even when the accuracy of some sensors decreases or noise is present, the mass estimation accuracy can still be maintained through the first-order dynamic diagnostic model and other sensor data, thereby improving the response timeliness and operational stability of the crane control.

[0021] (2) High-frequency sampling can capture subtle changes in the echo signal more precisely, providing higher accuracy for subsequent time / distance resolution. Using the average value of multiple sampling points for baseline estimation and echo signal calibration can effectively eliminate slow-varying errors such as hardware bias and DC drift, making the extracted displacement benchmark more stable. Furthermore, median filtering has good suppression capabilities for impulse noise and isolated spikes, which can reduce the impact of electromagnetic interference and instantaneous interference on the echo signal. Wavelet transform has good time-frequency localization characteristics, which can suppress broadband noise and structural noise while preserving effective echo details, thereby improving the signal-to-noise ratio. Normalizing the denoised signal can unify echo signals of different working conditions and intensities to a comparable scale, which is beneficial for the stable operation of subsequent models and reduces the impact of amplitude changes on peak detection accuracy. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating the crane lifting capacity monitoring and limiting method based on multi-sensor collaboration provided by the present invention; Figure 2 A schematic diagram of the crane lifting capacity limiter provided by the present invention; Figure 3 Internal structure diagram of the smart hook provided by the present invention; Figure 4 This is a schematic diagram of the crane lifting capacity monitoring and limiting system provided by the present invention; Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0024] Explanation of reference numerals in the attached diagram: 1. Crane lifting capacity limiter; 101. First data acquisition card; 102. First wireless transmission device; 103. Drive rod; 104. Torque sensor; 105. First battery; 106. Winch; 107. Speed ​​sensor; 108. Second data acquisition card; 109. Wireless sensor; 110. Second battery; 111. Wire rope; 112. Second wireless transmission device; 113. Smart hook base; 114. Smart hook top cover; 115. Third battery; 116. First hydraulic... 1. Dampers; 117. First spring damper; 118. Second hydraulic damper; 119. PLC control board; 120. Third hydraulic damper; 121. Second spring damper; 122. Fourth hydraulic damper; 123. Laser rangefinder; 2. Crane lifting capacity monitoring and limiting system; 21. Data acquisition module; 22. Data processing module; 23. Operation control module; 3. Electronic equipment; 31. Processor; 32. Communication bus; 33. User interface; 34. Network interface; 35. Memory. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0026] This invention discloses a method for monitoring and limiting the lifting capacity of a crane based on multi-sensor collaboration, with reference to... Figure 1 The steps of this method include S1 to S4.

[0027] Step S1: Collect the crane's operating condition data, which includes the winch output shaft torque, winch speed, and relative displacement data of the parts.

[0028] In this step, while the crane is in operation, sensors placed at key locations on the crane collect real-time operating condition data. This operating condition data includes at least the following: A torque sensor is installed on the output shaft of the winch to detect the torque applied to the shaft in real time. The electrical signal output by the torque sensor is amplified and filtered by a signal conditioning circuit before being input to a data acquisition module or acquisition card to obtain the real-time torque data of the winch output shaft. This torque data reflects the change in tension applied to the wire rope by the winch at different points in time, and is the basis for subsequent calculations of the lifting force and mass.

[0029] A speed sensor (such as an encoder or Hall effect speed sensor) is installed on the output shaft of the winch to detect the shaft's rotational speed. The pulse or voltage signal output by the speed sensor is processed and then counted or measured by a data acquisition module to obtain the real-time angular velocity of the winch's output shaft. This angular velocity data, combined with the drum radius, is used to calculate the linear velocity of the wire rope, thereby obtaining the theoretical upward speed of the winch's winding cable.

[0030] This step also includes steps S11 to S14.

[0031] Step S11: Acquire the echo signal using high-frequency sampling, and use the average value of multiple sampling points to perform baseline estimation on the echo signal to obtain the calibration echo signal.

[0032] In this step, echo signal acquisition is performed first. During this stage, the laser ranging system samples the echo signal in real time using a high-speed analog-to-digital converter (ADC), recording the voltage waveform after the target reflects the laser. To ensure complete acquisition of the target's echo process, the sampling frequency and time window width need to be appropriately set. The echo waveform is defined as:

[0033]

[0034]

[0035]

[0036] in, S i ( t ) indicates the firsti The time series of the echo signal S n-1 Indicates the first n -1 echo signal, f s This indicates the sampling frequency, which determines the number of samples collected per second, typically 1 GHz or higher; T win This indicates the length of the time window, typically set as the laser pulse's flight time plus the system delay. This time window must cover the entire time period from laser emission to echo reception; T s This indicates the time required to complete a full cycle.

[0037] In practical laser ranging systems, factors such as changes in the sensor front-end circuitry and ambient temperature can cause a DC offset or baseline drift in the received echo signal. This offset not only affects the efficiency of utilizing the signal's dynamic range but also reduces the accuracy of subsequent peak detection and waveform fitting. Therefore, before proceeding with any form of echo modeling or peak extraction, baseline calibration of the original signal must be performed first, by calculating the signal front-end... k The baseline value is estimated by averaging the values ​​of each sampling point. And subtract this baseline value from the signal.

[0038]

[0039] in, Indicates the first i The baseline value of each echo signal is obtained by analyzing the previous... k The average of the sampling points represents the initial offset of the signal; k This represents the number of baseline estimation points, typically the length of the blank data at the beginning of the sampled waveform, used to estimate baseline drift. This indicates the calibration echo signal after baseline calibration.

[0040] Step S12: Denoise the calibration echo signal according to median filtering and wavelet transform, and normalize the denoised calibration echo signal to obtain a normalized echo signal.

[0041] In this step, since the echo signal may be affected by environmental noise (such as electronic noise), median filtering is used for denoising. The median value within the window surrounding each point is taken to eliminate noise and preserve the core structure of the signal. Simultaneously, wavelet transform is performed on the signal to smooth it by removing high-frequency noise while retaining its main characteristics. Therefore, the denoised calibration echo signal can be expressed as:

[0042]

[0043] in, This represents the final signal obtained by using median filtering and wavelet transform. InverseWaveletTransform() represents the inverse wavelet transform function, WaveletTransform() represents the wavelet transform function, and median() represents the median filtering function.

[0044] Step S13: Fit the echo components of the normalized echo signal based on the Gaussian mixture model, and obtain the peak time of each echo component corresponding to the normalized echo signal through the expectation-maximization algorithm, so as to calculate the preliminary distance estimate of each echo component.

[0045] In this step, because the signal amplitude is affected by the reflecting object, each echo signal is normalized to ensure that the amplitude range of all signals is consistent. The normalized echo signal can be expressed as:

[0046]

[0047] in, Indicates the first t The normalized echo signal at time 10:00. This represents a small constant to prevent division by zero, usually taken as 10. -6 To prevent calculation errors during normalization, This indicates the maximum amplitude of the waveform and the maximum intensity of the signal.

[0048] Furthermore, the expression for the preliminary distance estimate is:

[0049]

[0050]

[0051]

[0052]

[0053] in, S fit Indicates the first t The echo component model after time-fitting; α i,j Indicates the first i The first echo j The amplitude of each echo component, i.e., the intensity of the echo; μ i,j Indicates the first i The first echo j The center time of each echo component, i.e., the position of the echo peak; NThis indicates the total number of echo components. σ i,j It represents the width of the echo, controls the range of the Gaussian curve, and is usually related to the physical characteristics of the echo signal; Indicates the first t The residual of the time-time echo fitting, i.e. the difference between the signal and the fitted model; This indicates the time corresponding to the peak position of each echo. j * This represents the principal value of the argument corresponding to the amplitude of the maximum echo component. Indicates the time of laser signal emission. μ i,j* Indicates the first i The center time of the maximum echo component of each echo Δt i Indicates the propagation time of the laser signal. D i This represents the initial distance estimate, and c represents the speed of light.

[0054] Step S14: Calculate the relative displacement data of the parts based on Bayesian filtering, Kalman filtering, and preliminary distance estimates.

[0055] Furthermore, the update probability of each echo component corresponding to the normalized echo signal is calculated based on Bayesian filtering, and the preliminary distance estimate is weighted and fused based on the update probability to obtain the weighted distance estimate; the weighted distance estimate of the current frame is fused with the preliminary distance estimate of the previous frame based on Kalman filtering to obtain the relative displacement data of the parts.

[0056] In this step, to further improve the accuracy and robustness of distance measurement, Bayesian filtering and Kalman filtering are used to fuse the ranging results from multiple echo signals. Bayesian filtering improves stability by updating the prior probability of each ranging result, while Kalman filtering fuses the ranging results from the previous frame with the observation results from the current frame to obtain a more accurate estimate. Bayesian filtering updates the current distance estimate by combining prior information (the ranging result from the previous frame) and the current observation results.

[0057]

[0058] in, This represents the updated probability. Represents the prior probability. Represents the likelihood probability of the current observation. This represents the probability of evidence.

[0059] Updated probability of Bayesian filtering This will be used as a weighting factor in the weighted fusion of echo signals, with each echo signal having a weight... It can be represented as:

[0060] Then calculate the distance estimate of the weighted echo signal. :

[0061] in, Indicates the first i Preliminary distance estimate for each echo signal, This is represented as the distance estimation result of the echo signal after Bayesian filtering and weighting. Indicates the first i The probability of each echo signal after updating.

[0062] Kalman filtering updates the target distance estimate by weighted fusion of the previous time step's prediction and the current echo signal measurement. Kalman gain ( The weighting factor is a key factor in Kalman filtering, and its calculation formula is as follows:

[0063]

[0064] in, This represents the prediction covariance at the previous moment, i.e., the uncertainty of the predicted value; R represents the update probability calculated by Bayesian filtering, i.e., the reliability of the current echo signal; R represents the measurement noise covariance, i.e., the error of the measurement signal.

[0065] The state update formula for Kalman filtering is:

[0066] in, This represents the distance estimate of the echo signal after Bayesian filtering and weighting. This represents the predicted value from the previous moment, i.e., the estimated distance from the previous moment. Represents the measurement matrix. This represents the updated distance estimate.

[0067] The updated probability represents the confidence level of the current ranging result. In multi-echo ranging, each echo has an updated probability calculated using Bayesian filtering. and Kalman gain This reflects the reliability of the echo range estimate. In the final range fusion, this probability and gain are used as weighting factors in the calculation.

[0068]

[0069] in, Indicates the first i The combined weighting factor for each echo signal, Indicates the first i The weight of each echo signal, Indicates the first i Kalman gain of each echo signal.

[0070] The final distance estimation formula is as follows:

[0071] Here, 's' represents the relative displacement data of the part. In this way, echoes with higher probability account for a larger proportion of the final result, while the influence of weak or noise signals is automatically reduced.

[0072] In this embodiment, high-frequency sampling can capture subtle changes in the echo signal more precisely, providing higher accuracy for subsequent time / distance resolution. Using the average value of multiple sampling points for baseline estimation and echo signal calibration can effectively eliminate slow-varying errors such as hardware bias and DC drift, making the subsequently extracted displacement benchmark more stable. Furthermore, median filtering has good suppression capabilities for impulse noise and isolated spikes, reducing the impact of electromagnetic interference and transient interference on the echo signal. Wavelet transform has good time-frequency localization characteristics, which can suppress broadband noise and structural noise while preserving effective echo details, thereby improving the signal-to-noise ratio. Normalization processing of the denoised signal unifies echo signals of different operating conditions and intensities to a comparable scale, which is beneficial for the stable operation of subsequent models and reduces the impact of amplitude changes on peak detection accuracy.

[0073] Step S2: Based on the output shaft torque of the winch, the speed of the winch, and the relative displacement data of the parts, a first-order dynamic diagnostic model is constructed so that the first-order dynamic diagnostic model can output the output pulling force of the winch, the speed of the hoisted object, and the acceleration.

[0074] This step also includes steps S21 to S24.

[0075] Step S21: The crane's winch, wire rope, smart hook, and the object being lifted are equivalent to a first-order dynamic system consisting of the mass of the object being lifted, spring elements, and damping elements.

[0076] Step S22: Perform force analysis on the hoisted object in the hoisting direction, establish dynamic equilibrium relationship, and calculate the output pulling force of the winch based on the output shaft torque, drum radius and transmission efficiency parameters of the winch.

[0077] In this step, the spring element inside the smart hook is equivalent to the first spring stiffness parameter, the elastic deformation of the crane structural components is equivalent to the second spring stiffness parameter, the hydraulic damper inside the smart hook is equivalent to the first damping coefficient parameter, and the friction between the wire rope and the transmission mechanism is equivalent to the second damping coefficient parameter. The first spring stiffness parameter, the first damping coefficient parameter, and the second damping coefficient parameter are then introduced into the dynamic equilibrium relationship.

[0078] Furthermore, the first-order dynamic diagnostic system is based on the drive response characteristics of lifting equipment. It utilizes sensors for real-time data acquisition, such as torque, speed, and displacement, and employs a first-order system model for dynamic modeling. By analyzing changes in these parameters, the system calculates information such as load, lifting speed, and acceleration during the hoisting process, thereby monitoring the equipment's operating status in real time. When the system detects deviations from normal operating conditions, it automatically triggers fault diagnosis and response mechanisms, promptly issuing alarm signals or making control adjustments to ensure safe equipment operation and avoid malfunctions caused by overload or impact loads. The system collects the winch output shaft torque M, speed ω, and relative displacement s, with units of N·m, rad / s, and m, respectively.

[0079] The output pulling force F of the winch can be obtained by the following formula:

[0080] Where r represents the output shaft radius of the winch in meters, and η represents the transmission efficiency, where 0 < η ≤ 1.

[0081] The dynamic equilibrium equations can be expressed as:

[0082] in, k 1 represents the first spring stiffness parameter. k 2 represents the second spring stiffness parameter. C 1 represents the first damping coefficient parameter. C 2 represents the second damping coefficient parameter. Let represent the first derivative of displacement, i.e., relative velocity, and 'a' represent the acceleration of the suspended object, which can be obtained from the velocity difference. Let m be the acceleration due to gravity, and m represent the mass of the object being hoisted.

[0083] Step S23: Use the relative displacement data of the parts as the displacement quantity in the dynamic equilibrium relationship, and perform a differential operation on the relative displacement to obtain the speed of the hoisted object.

[0084] In this step, the hoisting speed of the winch rope is expressed as:

[0085] The actual speed of the suspended load is expressed as:

[0086] in, v t This indicates the theoretical rising speed of the winch wire rope. ω This indicates the angular velocity of the winch drum. δ This represents the slippage compensation coefficient. r This indicates the equivalent radius of the winch drum. v This indicates the actual speed of the suspended load.

[0087] Step S24: Calculate the theoretical rising speed of the wire rope based on the winch rotation speed, and introduce a slippage compensation coefficient to correct the theoretical rising speed of the wire rope. Obtain the actual speed of the hoisted object, and perform a differential operation on the actual speed to obtain the acceleration of the hoisted object in the hoisting direction.

[0088] In this step, the acceleration is estimated using the numerical difference method:

[0089] in, Δt This indicates the time difference in hoisting / lifting operations.

[0090] Step S3: Calculate the real-time mass of the object being lifted by the crane based on the output pulling force of the winch, the speed and acceleration of the object being lifted, and the dynamic equilibrium equation.

[0091] In this step, the above model is rearranged to derive the mass calculation formula:

[0092] Among them, the first spring stiffness parameter k 1. Stiffness parameters of the second spring k 2. First damping coefficient parameter C 1. Second damping coefficient parameter C 2. Transmission efficiency η and slippage compensation coefficient δ It can be obtained through experimental calibration or model fitting.

[0093] Step S4: Determine whether the real-time mass is within the preset lifting capacity allowable range. If the real-time mass is outside the preset lifting capacity allowable range, send a control signal to the hydraulic damper of the crane to adjust the operating state of the object being lifted by the crane.

[0094] In this step, operating condition data and control signals are received via wireless communication. The operating condition data and control signals are stored historically, analyzed for their operation, and recorded for faults. The processing results corresponding to the control signals are displayed graphically on the data visualization terminal. The lifting capacity limit strategy is optimized based on the collected historical operating condition data and historical control signals to generate an updated lifting capacity permissible range, which is then sent to the hydraulic damper of the crane via the network.

[0095] Furthermore, the crane control system compares the real-time mass of the object being lifted, calculated in the aforementioned steps, with a pre-set permissible lifting capacity range. When the real-time mass exceeds the pre-set permissible lifting capacity range, the control system generates a corresponding control signal and sends it to the hydraulic damper installed on the crane to adjust the damping force of the hydraulic damper, thereby changing the operating state of the object being lifted. For example, it may reduce the lifting speed of the object, limit further lifting, or control its descent at a set deceleration, thus limiting the lifting capacity and ensuring the safety of the crane and the object being lifted.

[0096] In this step, the crane control system also receives operating condition data and feedback control signals from the data acquisition module at the crane end via wireless communication. The operating condition data includes at least the winch output shaft torque, winch speed, and relative displacement data of components. The control signals include control commands for the hydraulic damper and their execution feedback information. The control system stores the received operating condition data and control signals as historical data and performs condition analysis and fault recording based on this historical data. This is used to identify and trace overload conditions, impact load conditions, and abnormal hydraulic damper operation. The control system displays the processing results corresponding to the control signals graphically on a data visualization terminal. The processing results include at least real-time quality curves, permissible lifting capacity ranges, hydraulic damper damping force change curves, and overload alarm status, allowing operators to intuitively understand the current operating status of the crane and the effectiveness of the control strategy.

[0097] The control system also optimizes the lifting capacity limit strategy based on collected historical operating data and control signals. It employs statistical analysis or machine learning algorithms to evaluate overload conditions and control effectiveness under different operating conditions, generating an updated permissible lifting capacity range. This updated range is then distributed to the crane-end control unit via the network. The crane-end control unit adjusts the control parameters of the hydraulic damper accordingly, achieving adaptive optimization and dynamic updating of the lifting capacity limit strategy. This improves the crane's safety and adaptability under different operating conditions.

[0098] In this embodiment, a first-order dynamic diagnostic model is constructed using multi-source data consisting of winch output shaft torque, winch speed, and relative displacement of parts. This avoids measurement errors and uncertainties caused by relying solely on a single sensor. Furthermore, the real-time mass of the hoisted object is calculated comprehensively using dynamic equilibrium equations. Compared to traditional static weighing or single force sensor solutions, the mass estimation is more accurate and robust. Simultaneously, the mass of the hoisted object is calculated in real time using the tension, velocity, and acceleration output by the first-order dynamic diagnostic model. This allows for continuous monitoring of load changes throughout the crane's operation, rather than just detection under a single working condition. It enables early detection of lifting weight over-limit trends, facilitating early intervention and improving operational safety. The collaborative modeling of multiple physical quantities ensures that even when the accuracy of some sensors decreases or noise is present, the first-order dynamic diagnostic model and other sensor data can still maintain high mass estimation accuracy, thereby improving the responsiveness and operational stability of crane control.

[0099] like Figure 2 As shown, this application also provides a crane lifting capacity limiter 1, which includes an upper measuring component installed at the end of the winch 106 and an intelligent hook component installed at the lower end of the wire rope 111. The two are connected by the wire rope 111 to form a complete lifting monitoring system.

[0100] The upper measuring components are installed on both sides of the winch 106 to acquire mechanical parameters during the operation of the winch 106 and to perform data acquisition and wireless transmission. The output end of the winch 106 is connected to the drum via a transmission rod 103. A torque sensor 104 is mounted on the transmission rod 103 to detect the torque signal output by the winch 106 in real time. A speed sensor 107 is also provided on one side of the winch 106 to detect the speed signal of the winch 106.

[0101] Torque sensor 104 and speed sensor 107 are electrically connected to a first data acquisition card 101 and a second data acquisition card 108 respectively located on both sides of the winch 106. Both the first data acquisition card 101 and the second data acquisition card 108 are used to sample, quantize, and perform preliminary processing on the torque and speed signals. The first data acquisition card 101 and the second data acquisition card 108 are also connected to the corresponding first wireless transmission device 102 and wireless sensor 109 respectively, for transmitting the collected data wirelessly to a host computer or remote monitoring terminal, thereby realizing remote monitoring of the operating status and lifting capacity of the winch 106.

[0102] To ensure independent power supply, the data acquisition card and its corresponding wireless transmission equipment are powered by the first battery 105 and the second battery 110, respectively. The first battery 105 and the second battery 110 are fixedly mounted on mounting bases on both sides of the winch 106 for easy replacement and maintenance. With this structure, the torque and speed information of the winch 106 can be acquired in real time without changing the original transmission method of the winch 106, and transmitted wirelessly, providing basic data for subsequent load calculation and load limiting control.

[0103] Please continue reading. Figure 2 The winch 106 has multiple strands of wire rope 111 wound on its drum. The lower ends of the wire ropes 111 are connected to the intelligent hook assembly for lifting the object being hoisted. The intelligent hook assembly includes an intelligent hook base 113 and an intelligent hook cover 114. The intelligent hook base 113 is suspended below the winch 106 by several wire ropes 111. The lower part of the base is provided with a hook structure for connecting to the object being hoisted, and the upper part is fixedly connected to the wire ropes 111. The intelligent hook cover 114 is installed above the intelligent hook base 113. The two together form a closed or semi-closed mounting cavity for accommodating the sensors, control circuits, power supply modules, and other components inside the intelligent hook.

[0104] The intelligent hook assembly is equipped with a second wireless transmission device 112, which is electrically connected to various sensors and control units inside the intelligent hook. This device transmits displacement, acceleration, or other load-related information collected at the intelligent hook to a host computer or works in conjunction with the wireless transmission device at the winch 106 end to achieve comprehensive monitoring of the entire lifting system's operating status. Through this structural arrangement, the intelligent hook assembly not only performs traditional lifting functions but also serves as a lower-level measurement and control node, working in conjunction with the upper measurement components at the winch 106 end to perform multi-point, multi-parameter monitoring of lifting weight, stress state, and operating conditions.

[0105] During operation, the winch 106 drives the wire rope 111 to wind, causing the intelligent hook base 113 and the suspended object connected to it to rise and fall. The torque sensor 104 and speed sensor 107 at the end of the winch 106 output torque and speed signals respectively. After being processed by the first acquisition card 101 and the second acquisition card 108, they are transmitted to the host computer or control center through the first wireless transmission device 102 and the wireless sensor 109.

[0106] Meanwhile, the intelligent hook assembly collects information such as force, displacement, or acceleration at the hook point through sensing units installed inside the intelligent hook base 113 and the intelligent hook cover 114, and uploads this information wirelessly. The host computer or control center comprehensively analyzes the multi-source data from the winch 106 and the intelligent hook, calculates and judges the actual lifting capacity, and sends deceleration, stop, or alarm commands to the crane control system when overload or abnormal working conditions are detected, thereby realizing intelligent limitation and safety protection of the lifting capacity.

[0107] Through the above structural arrangement, the crane lifting capacity limiter 1 shown in this embodiment can realize the coordinated measurement and wireless communication between the winch 106 end and the hook end without significantly changing the existing crane overall structure, thereby improving the accuracy of lifting capacity detection and the safety of system operation.

[0108] Please see Figure 3 The intelligent hook assembly in this embodiment mainly includes a third battery 115, a first hydraulic damper 116, a second hydraulic damper 118, a third hydraulic damper 120, a fourth hydraulic damper 122, a first spring damper 117, a second spring damper 121, a PLC control board 119, and a laser rangefinder 123. An annular mounting cavity is formed between the upper cover of the intelligent hook assembly and the intelligent hook base 113, and all the aforementioned components are arranged within this cavity.

[0109] The third battery 115 is fixedly installed on one side of the inner cavity of the smart hook, and is used to provide independent power to the PLC control board 119, the laser rangefinder 123 and other electronic components inside the hook, so that the smart hook has self-powered capability and does not rely on the power supply of the crane body.

[0110] The first hydraulic damper 116, the second hydraulic damper 118, the third hydraulic damper 120, and the fourth hydraulic damper 122 are arranged circumferentially along the inner cavity of the intelligent hook. One end is fixedly connected to the intelligent hook base 113, and the other end is connected to the intelligent hook upper cover 114. During hoisting, the hydraulic dampers provide axial damping and buffering for the relative movement between the upper cover and the base. When the hoisted object experiences impact loads or vibrations, the hydraulic dampers absorb and dissipate the impact energy, reducing the impact on the wire rope 111 and the hoisting mechanism, which helps to improve the safety and stability of the system.

[0111] The first spring damper 117 and the second spring damper 121 are disposed between the partial hydraulic dampers. One end of each damper is hinged to the intelligent hook base 113 or its inner wall, and the other end is connected to the intelligent hook cover 114. Through the cooperation of the bending structure and the damping unit, the spring damper can buffer and limit the relative displacement between the cover and the base in both radial and axial directions, further improving the stress condition of the hook under conditions such as off-center loading and oscillation.

[0112] The PLC control board 119 is installed inside the intelligent hook near the side wall and is electrically connected to the third battery 115, the laser rangefinder 123, and an external wireless transmission device via wires. The PLC control board 119 is used to collect and process laser ranging signals, acceleration or displacement signals, and upload the processing results to the host computer or crane control system. It can also control the adjustable damping element as needed to realize local logic judgment and control of the intelligent hook.

[0113] A laser rangefinder 123 is installed at the center of the smart hook, with its transmitting / receiving optical axis arranged along the lifting direction and aligned with the corresponding reflective surface or structural component. It is used to measure the axial displacement between the smart hook's upper cover 114 and the base. By analyzing the change of this displacement over time, the force and vibration state of the hook end can be deduced, providing displacement input data for the subsequent first-order dynamic diagnostic model.

[0114] Through the above structural arrangement, the hydraulic damper and the bending arm damper inside the intelligent hook form a multi-directional buffer system. The laser rangefinder 123 accurately measures the buffer stroke, the PLC control board 119 is responsible for data processing and control logic, and the third battery 115 provides a stable power supply. Thus, the intelligent hook has both good mechanical buffer performance and the ability to monitor and intelligently judge the hoisting status in real time.

[0115] Based on the above method, this application discloses a crane lifting capacity monitoring and limiting system based on multi-sensor collaboration, referencing... Figure 4 The crane lifting capacity monitoring and limiting system 2 includes a data acquisition module 21, a data processing module 22, and an operation control module 23, wherein... The data acquisition module 21 is used to collect the operating condition data of the crane, including the output shaft torque of the winch, the speed of the winch, and the relative displacement data of the parts. The data processing module 22 is used to construct a first-order dynamic diagnostic model based on the output shaft torque of the winch, the speed of the winch, and the relative displacement data of the parts, so that the first-order dynamic diagnostic model outputs the output tension of the winch, the speed of the hoisted object, and the acceleration. Based on the output tension of the winch, the speed of the hoisted object, the acceleration, and the dynamic balance equation, the real-time mass of the object hoisted by the crane is calculated. The operation control module 23 is used to determine whether the real-time quality is within the preset lifting weight permissible range. If the real-time quality is outside the preset lifting weight permissible range, it sends a control signal to the hydraulic damper of the crane to adjust the operating state of the object being lifted by the crane.

[0116] In one example, the data acquisition module 21 is used to acquire echo signals with high-frequency sampling and to perform baseline estimation on the echo signals using the average value of multiple sampling points to obtain calibrated echo signals; to denoise the calibrated echo signals according to median filtering and wavelet transform, and to normalize the denoised calibrated echo signals to obtain normalized echo signals; to fit the echo components of the normalized echo signals based on a Gaussian mixture model, and to obtain the peak time of each echo component corresponding to the normalized echo signals through the expectation-maximization algorithm, so as to calculate the preliminary distance estimate of each echo component; and to calculate the relative displacement data of the parts based on Bayesian filtering, Kalman filtering, and the preliminary distance estimate.

[0117] In one example, the data acquisition module 21 is used to calculate the update probability of each echo component corresponding to the normalized echo signal based on Bayesian filtering, and to perform weighted fusion on the preliminary distance estimate based on the update probability to obtain the weighted distance estimate; and to fuse the weighted distance estimate of the current frame with the preliminary distance estimate of the previous frame based on Kalman filtering to obtain the relative displacement data of the parts.

[0118] In one example, the data processing module 22 is used to equate the crane's winch, wire rope, smart hook, and the object being lifted as a first-order dynamic system consisting of the object's mass, spring elements, and damping elements; perform force analysis on the object being lifted in the lifting direction, establish a dynamic equilibrium relationship, and calculate the winch's output tension based on the winch's output shaft torque, drum radius, and transmission efficiency parameters; use the relative displacement data of the parts as the displacement quantity in the dynamic equilibrium relationship, and perform a differential operation on the relative displacement to obtain the velocity of the object being lifted; calculate the theoretical rising speed of the wire rope based on the winch's rotational speed, and introduce a slippage compensation coefficient to correct the theoretical rising speed of the wire rope, obtain the actual velocity of the object being lifted, and perform a differential operation on the actual velocity to obtain the acceleration of the object being lifted in the lifting direction.

[0119] In one example, the spring element inside the smart hook is equivalent to the first spring stiffness parameter, the elastic deformation of the crane structure is equivalent to the second spring stiffness parameter, the hydraulic damper inside the smart hook is equivalent to the first damping coefficient parameter, and the friction between the wire rope and the transmission mechanism is equivalent to the second damping coefficient parameter. The first spring stiffness parameter, the second spring stiffness parameter, the first damping coefficient parameter, and the second damping coefficient parameter are then introduced into the dynamic equilibrium relationship.

[0120] In one example, the expression for the initial distance estimate is:

[0121]

[0122]

[0123]

[0124]

[0125] in, S fit t Indicates the first t The echo component model after time-fitting. α i,j Indicates the first i The first echo j The amplitude of each echo component, μ i,j Indicates the first i The first echo j The center time of each echo component N This indicates the total number of echo components. σ i,j Indicates the width of the echo. Indicates the first t The residuals of the time-time echo fitting, This indicates the time corresponding to the peak position of each echo. j * This represents the principal value of the argument corresponding to the amplitude of the maximum echo component. Indicates the time of laser signal emission. μ i,j* Indicates the first i The center time of the maximum echo component of each echo Δt i Indicates the propagation time of the laser signal. D i This represents the initial distance estimate, and c represents the speed of light.

[0126] In one example, the method also includes: The system receives operating condition data and control signals via wireless communication, performs historical data storage, operating condition analysis, and fault recording on the operating condition data and control signals, and displays the processing results corresponding to the control signals graphically on the data visualization terminal. The lifting capacity limit strategy is optimized based on the collected historical operating condition data and historical control signals to generate an updated lifting capacity allowable range, which is then distributed to the hydraulic damper of the crane via the network.

[0127] Please see Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device 3 may include: at least one processor 31, at least one network interface 34, user interface 33, memory 35, and at least one communication bus 32.

[0128] The communication bus 32 is used to enable communication between these components.

[0129] The user interface 33 may include a display screen and a camera. Optionally, the user interface 33 may also include a standard wired interface and a wireless interface.

[0130] The network interface 34 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0131] The processor 31 may include one or more processing cores. The processor 31 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 35, and by calling data stored in the memory 35. Optionally, the processor 31 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 31 and may be implemented as a separate chip.

[0132] The memory 35 may include random access memory (RAM) or read-only memory. Optionally, the memory 35 may include non-transitory computer-readable storage medium. The memory 35 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 35 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 35 may also be at least one storage device located remotely from the aforementioned processor 31. Figure 5 As shown, the memory 35, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a crane lifting weight monitoring and limiting method based on multi-sensor collaboration.

[0133] exist Figure 5 In the electronic device 3 shown, the user interface 33 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 31 can be used to call an application stored in the memory 35 for a crane lifting weight monitoring and limiting method based on multi-sensor collaboration. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0134] A computer-readable storage medium storing instructions that, when executed by one or more processors, cause a computer to perform one or more methods as described in the embodiments above.

[0135] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0136] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0141] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for crane load monitoring and limiting based on multi-sensor collaboration, characterized by, The method comprises: Collecting operating condition data of the crane, wherein the operating condition data comprises hoist output shaft torque, hoist rotating speed and part relative displacement data; Based on the hoist output shaft torque, the hoist rotating speed and the part relative displacement data, a first-order dynamics diagnosis model is constructed, so that the first-order dynamics diagnosis model outputs hoist output tension, hoisted object speed and acceleration; According to the hoist output tension, the hoisted object speed, acceleration and dynamics balance equation, the real-time mass of the hoisted object of the crane is calculated; It is judged whether the real-time mass is within the preset allowable range of hoisting weight, and if the real-time mass is outside the preset allowable range of hoisting weight, a control signal is sent to the hydraulic damper of the crane to adjust the operating state of the hoisted object of the crane.

2. The multi-sensor collaboration based crane load monitoring and limiting method according to claim 1, characterized in that, The process of obtaining the part relative displacement data comprises: High-frequency sampling is used to obtain echo signals, and baseline estimation is performed on the echo signals by using the average values of multiple sampling points to obtain calibrated echo signals; The calibrated echo signals are denoised according to median filtering and wavelet transform, and the denoised calibrated echo signals are normalized to obtain normalized echo signals; Based on a Gaussian mixture model, the normalized echo signals are fitted with echo components, and the peak time of each echo component corresponding to the normalized echo signals is obtained by an expectation maximization algorithm to calculate a preliminary distance estimation value of each echo component; The part relative displacement data is calculated according to Bayesian filtering, Kalman filtering and the preliminary distance estimation value.

3. The hoist load monitoring and limiting method based on multi-sensor collaboration according to claim 2, characterized in that, The part relative displacement data is calculated according to Bayesian filtering, Kalman filtering and the preliminary distance estimation value, specifically comprising: Based on Bayesian filtering, the update probability of each echo component corresponding to the normalized echo signals is calculated, and the preliminary distance estimation value is weighted and fused based on the update probability to obtain a weighted distance estimation value; Based on Kalman filtering, the weighted distance estimation value of the current frame is fused with the preliminary distance estimation value of the previous frame to obtain the part relative displacement data.

4. The multi-sensor collaboration based crane load monitoring and limiting method according to claim 1, characterized in that, The first-order dynamics diagnosis model is constructed to make the first-order dynamics diagnosis model output hoist output tension, hoisted object speed and acceleration, specifically comprising: The hoist, steel wire rope, intelligent hook and hoisted object of the crane are equivalent to a first-order dynamics system composed of hoisted object mass, spring element and damping element; Force analysis is performed on the hoisted object in the hoisting direction, a dynamics balance relationship is established, and hoist output tension is calculated based on the hoist output shaft torque, hoist drum radius and transmission efficiency parameters; The part relative displacement data is used as the displacement in the dynamics balance relationship, and the relative displacement is differentiated to obtain the hoisted object speed; The theoretical ascending speed of the steel wire rope is calculated according to the hoist rotating speed, a slip compensation coefficient is introduced to correct the theoretical ascending speed of the steel wire rope to obtain the actual speed of the hoisted object, and the actual speed is differentiated to obtain the acceleration of the hoisted object in the hoisting direction.

5. The multi-sensor collaboration based crane load monitoring and limiting method according to claim 4, characterized in that, The spring element inside the intelligent hook is equivalent to a first spring stiffness parameter, the elastic deformation of the crane structure is equivalent to a second spring stiffness parameter, the hydraulic damper inside the intelligent hook is equivalent to a first damping coefficient parameter, and the steel wire rope and the transmission mechanism are equivalent to a second damping coefficient parameter, and the first spring stiffness parameter, the second spring stiffness parameter, the first damping coefficient parameter and the second damping coefficient parameter are introduced into the dynamic equilibrium relationship.

6. The hoist load monitoring and limiting method based on multi-sensor collaboration of claim 2, wherein, The expression of the preliminary distance estimation value is: ； ； ； ； ； wherein, S fit ( t ) denotes the echo component model fitted at the t time instant, α i,j denotes the amplitude of the i th echo component of the j th echo, μ i,j denotes the center time of the i th echo component of the j th echo, N denotes the total number of echo components, σ i,j denotes the width of the echo, denotes the residual of the echo fitting at the t time instant, denotes the time corresponding to the peak position of each echo, j * denotes the principal value of the argument of the maximum echo component amplitude, denotes the time of the laser signal emission, μ i,j* denotes the center time of the maximum echo component of the i th echo, Δt i denotes the propagation time of the laser signal, D i denotes the preliminary range estimate, c denotes the speed of light.

7. The multi-sensor collaboration based crane load monitoring and limiting method according to claim 1, characterized in that, The method further comprises: The operation condition data and the control signal are received through wireless communication, and the operation condition data and the control signal are stored, analyzed and recorded, and the processing result corresponding to the control signal is displayed in a graphical manner on a data visualization terminal; The lifting weight limit strategy is optimized according to the collected historical operation condition data and historical control signal to generate an updated lifting weight permission range, and the updated lifting weight permission range is issued to the hydraulic damper of the crane through the network.

8. A crane load monitoring and limiting system based on multi-sensor collaboration, characterized by The crane lifting weight monitoring and limiting system (2) comprises a data acquisition module (21), a data processing module (22) and an operation control module (23), wherein The data acquisition module (21) is used for acquiring operation condition data of the crane, wherein the operation condition data comprises winch output shaft torque, winch speed and part relative displacement data; The data processing module (22) is used for constructing a first-order dynamic diagnosis model based on the winch output shaft torque, the winch speed and the part relative displacement data, so that the first-order dynamic diagnosis model outputs winch output tension, hoisted object speed and acceleration, and the real-time mass of the hoisted object of the crane is calculated according to the winch output tension, the hoisted object speed, acceleration and the dynamic equilibrium equation; The operation control module (23) is used for judging whether the real-time mass is within a preset lifting weight permission range, and if the real-time mass is outside the preset lifting weight permission range, a control signal is sent to the hydraulic damper of the crane to adjust the operation state of the hoisted object of the crane.

9. An electronic device, comprising: The electronic device (3) comprises a processor (31), a memory (35), a user interface (33) and a network interface (34), the memory (35) is used for storing instructions, the user interface (33) and the network interface (34) are used for communicating with other devices, and the processor (31) is used for executing the instructions stored in the memory (35) to enable the electronic device (3) to perform the method of any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1-7. The computer program is executed by the processor to implement the steps of the method of any one of claims 1-7.

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