Crane height limiting method and device

Abstract

The invention provides a crane height limiting method and device, and relates to the technical field of cranes, and the method comprises the following steps: obtaining rotation state information of a crane roller through a photoelectric rotation coding module, and calculating the lifting height of a crane according to the rotation state information; setting a threshold value of the hoisting height of the crane, and controlling the crane to decelerate or stop hoisting according to the relationship between the hoisting height of the crane and the threshold value; the limiting height of the piezoelectric sensing module is adjusted, and secondary limiting is conducted on the lifting height of the crane through the piezoelectric sensing module; the limiting height of the piezoelectric sensing module is associated with the threshold value, and noise elimination is carried out on limiting signals of the photoelectric rotary coding module and the piezoelectric sensing module; dynamically correcting the rotation state information through a fixed physical reference point where the piezoelectric sensing module is located; the positioning precision and reliability of the limiting device are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cranes, and in particular to a crane height limiting method and device. Background Art

[0002] A crane limiter is an essential safety device for bridge and gantry cranes, used to limit the lifting height of the load-taking device; when the lifting appliance of the crane rises to the upper limit position, the limiter will automatically cut off the power supply to prevent the load-taking devices such as hooks from continuing to rise, so as to avoid causing the crane to overtop and the electric hoist or winch to continue working and break the wire rope, thus triggering a heavy object falling accident.

[0003] However, most of the existing crane lifting height limiting devices are mechanical limiting devices, such as cut-off type, weight type, and pressing plate type limiters. The positioning accuracy of mechanical limiters is insufficient and the reliability is low. Summary of the Invention

[0004] The purpose of the present invention is to provide a crane height limiting method and device to solve the problems of insufficient positioning accuracy and low reliability of the traditional crane lifting height limiting device in the above-mentioned background art in the speech recognition technology.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: The steps of a crane height limiting method include: obtaining the rotation state information of the crane drum through an optoelectronic rotary encoder module, calculating the crane lifting height based on the rotation state information; setting a threshold for the crane lifting height, and controlling the crane to decelerate or stop lifting according to the relationship between the crane lifting height and the threshold; adjusting the limit height of the piezoelectric sensing module, and performing secondary limiting on the crane lifting height through the piezoelectric sensing module; associating the limit height of the piezoelectric sensing module with the threshold, and eliminating noise from the limit signals of the optoelectronic rotary encoder module and the piezoelectric sensing module; dynamically correcting the rotation state information through the fixed physical reference point where the piezoelectric sensing module is located.

[0006] Optionally, the step of calculating the crane lifting height based on the rotation state information specifically includes: calculating the crane lifting height by integrating the speed or through rotation counting based on the rotation speed, rotation count, and rotation direction.

[0007] Optionally, the steps of controlling the crane to lift at a reduced speed or stop lifting according to the relationship between the lifting height of the crane and the threshold specifically include: presetting a first threshold and a second threshold for the lifting height of the crane, where the second threshold is greater than the first threshold; determining whether the lifting height of the crane reaches the first threshold or the second threshold, if it reaches the first threshold, controlling the crane to lift at a reduced speed, and if it reaches the second threshold, controlling the crane to stop lifting.

[0008] Optionally, the step of performing secondary limit on the lifting height of the crane includes: when the lifting height of the crane reaches the limit height of the piezoelectric sensing module, controlling the crane to stop lifting.

[0009] Optionally, the step of dynamically correcting the rotational state information specifically includes: the limit height of the piezoelectric sensing module is higher than the threshold, and when the lifting height of the crane reaches the limit height of the piezoelectric sensing module, correcting the rotational state information.

[0010] Optionally, the step of eliminating noise from the limit signals of the optoelectronic rotation encoding module and the piezoelectric sensing module specifically includes: establishing a linear mapping relationship between the input signal and the output signal by fitting a weight vector; constructing an error variable model, expressing the input limit signal and the output limit signal in a form with noise, and obtaining observation data, where the observation data includes the input limit signal contaminated by noise and the output limit signal contaminated by noise; correcting the observation data by total least squares to obtain real data, where the real data includes the real input limit signal and the real output limit signal; estimating the real data by generalized correlation entropy to handle non-Gaussian noise; constructing a cost function of maximum total generalized synergy, adjusting the weight vector by gradient descent to make the cost function reach the maximum value, and then optimizing the error variable model; iteratively updating the weight vector by a step size adjustment strategy to obtain an optimal weight vector; and establishing an optimal relationship model between the input signal and the output signal by estimating the optimal weight vector.

[0011] Optionally, the step of correcting the observation data by total least squares to obtain real data specifically includes: performing singular value decomposition on the observation matrix to obtain a singular value matrix, setting the smallest singular value to zero for singular value correction to form a new singular value matrix; reconstructing the observation matrix using the corrected singular value matrix, and separating the corrected real input limit signal and real output limit signal from the reconstructed observation matrix to obtain the real data.

[0012] Optionally, the step of estimating the real data by generalized correlation entropy specifically includes: The generalized correlation entropy and the generalized Gaussian density function can be expressed as: Vα，β (X, Y) = E[G α，β (X - Y)]; Wherein, X is the input limit signal random variable, and X = {x1 x2... x i}, Y is the output limit signal random variable, and Y = {y1, y2....y i}, E[·] is the mathematical expectation, Gα,β(X - Y) is the generalized Gaussian density function, α is the shape parameter, and α > 0, β is the scale parameter, and σ is the kernel width; Γ(1 / α) is the Gamma function, and e is the variable; Calculate the average value of the generalized Gaussian density function, and its calculation formula is: Wherein, is the average value of the generalized Gaussian density function, N is the total number of samples, G α，β (X - Y) is the generalized Gaussian density function; Estimate the real data by using the average value of the generalized Gaussian density function.

[0013] Optionally, the step of constructing the cost function of the maximum total generalized synergy, adjusting the weight vector by gradient descent to make the cost function reach the maximum value, and then optimizing the error variable model specifically includes: constructing the cost function of the maximum total generalized synergy, calculating the gradient of the cost function of the maximum total generalized synergy;

[0014] Adjust the weight vector by gradient descent, stop updating the weight vector when the cost function is maximum, and the weight vector at this time is the optimal weight vector, and optimize the error variable model by optimizing the weight vector.

[0015] On the other hand, the present invention also provides a crane height limit device, including: a lifting height calculation module, configured to obtain the rotation state information of the crane drum through the photoelectric rotary encoding module, and calculate the lifting height of the crane according to the rotation state information; a threshold setting module, configured to set the threshold of the lifting height of the crane, and control the crane to decelerate or stop lifting according to the relationship between the lifting height of the crane and the threshold; a secondary limit module, configured to adjust the limit height of the piezoelectric sensing module, and perform secondary limit on the lifting height of the crane through the piezoelectric sensing module; a noise elimination module, configured to associate the limit height of the piezoelectric sensing module with the threshold, and eliminate noise from the limit signals of the photoelectric rotary encoding module and the piezoelectric sensing module; a calibration module, configured to dynamically calibrate the rotation state information through the fixed physical reference point where the piezoelectric sensing module is located.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] This application obtains the rotation status information of the crane drum through an optoelectronic rotary encoding module, and then calculates the lifting height of the crane. A lifting height threshold of the crane is set to limit the lifting height of the crane, and a piezoelectric sensing module in a mechanical limiting structure is used to perform secondary limiting on the lifting height of the crane, which can ensure the reliability of the lifting height limit of the crane. The limiting signals of the optoelectronic rotary encoding module and the piezoelectric sensing module are noise-eliminated, and the rotation status information is dynamically corrected through the fixed physical reference point where the piezoelectric sensing module is located, greatly improving the positioning accuracy of the limiting device and effectively avoiding accidents caused by the crane hitting the top. Brief Description of the Drawings

[0018] Figure 1 It is a schematic diagram of the crane structure of the present invention.

[0019] Figure 2 It is a schematic diagram of the overall detection process of the present invention.

[0020] Figure 3 It is a schematic diagram of the method step process of the present invention.

[0021] Figure 4 It is a schematic diagram of the device structure of the present invention.

[0022] In the figure, 1 - optoelectronic rotary encoder, 2 - piezoelectric sensor, 10 - lifting height calculation module, 20 - threshold setting module, 30 - secondary limiting module, 40 - noise elimination module, 50 - correction module. Detailed Embodiments

[0023] Next, the solutions of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0024] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of this application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so as to implement the embodiments of this application described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0025] Those skilled in the art can understand that, unless specifically stated, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of this application means the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more related listed items.

[0026] Those skilled in the art can understand that, unless otherwise defined, all terms used herein (including technical terms and scientific terms) have the same meaning as the general understanding of those of ordinary skill in the art to which this application belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as here.

[0027] It should be understood that the sequence numbers and magnitudes of the steps in this embodiment do not mean the order of execution. The order of execution of each process is determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0028] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will detail this application with reference to the drawings and in combination with the embodiments.

[0029] Please refer to Figures 1 - 4 , a method for limiting the height of a crane according to the present invention includes the following steps: obtaining the rotation state information of the crane drum through an optoelectronic rotary encoding module, calculating the lifting height of the crane based on the rotation state information; setting a threshold for the lifting height of the crane, and controlling the crane to decelerate or stop lifting according to the relationship between the lifting height of the crane and the threshold; adjusting the limit height of the piezoelectric sensing module, and performing secondary limiting on the lifting height of the crane through the piezoelectric sensing module; associating the limit height of the piezoelectric sensing module with the threshold, and eliminating noise from the limit signals of the optoelectronic rotary encoding module and the piezoelectric sensing module; dynamically correcting the rotation state information through the fixed physical reference point where the piezoelectric sensing module is located.

[0030] Specifically, the present invention overcomes some drawbacks of traditional limiters. For traditional mechanical limit devices, the hoisting mechanism stops only when it reaches a certain specific height to trigger the limit switch. The present invention can control and adjust the limit height through a rotary encoder, and use different heights for limiting under specific circumstances. Moreover, the mechanical limiter provided by the present invention can achieve precise linear motion control due to the meshing of gears and racks, and is applicable to occasions requiring high precision. The motion of the rack and pinion transmission mechanism is stable with less vibration, which can enable the limiter to operate stably for a long time, thus playing a dual protection role.

[0031] It can be understood that the present application obtains the rotation state information of the crane drum through the photoelectric rotary encoder module, and then calculates the hoisting height of the crane. A threshold for the hoisting height of the crane is set to limit the hoisting height of the crane, and the piezoelectric sensing module in the mechanical limiting structure is used to perform secondary limiting on the hoisting height of the crane, which can ensure the reliability of the hoisting height limit of the crane. The noise of the limit signals of the photoelectric rotary encoder module and the piezoelectric sensing module is eliminated, and the rotation state information is dynamically corrected through the fixed physical reference point where the piezoelectric sensing module is located, greatly improving the positioning accuracy of the limiting device and effectively avoiding accidents caused by the crane hitting the top.

[0032] In some embodiments, the step of calculating the hoisting height of the crane based on the rotation state information specifically includes: calculating the hoisting height of the crane by integrating the speed or through rotation counting according to the rotation speed, rotation count, and rotation direction.

[0033] In some embodiments, the step of controlling the crane to decelerate hoisting or stop hoisting according to the relationship between the hoisting height of the crane and the threshold specifically includes: presetting a first threshold and a second threshold for the hoisting height of the crane, where the second threshold is greater than the first threshold; determining whether the hoisting height of the crane reaches the first threshold or the second threshold. If it reaches the first threshold, control the crane to decelerate hoisting. If it reaches the second threshold, control the crane to stop hoisting.

[0034] Specifically, the present application installs the photoelectric rotary encoder 1 on the crane drum through a coupling to obtain the rotation speed, rotation count, forward and reverse rotation conditions, etc. of the motor, integrates the speed or obtains the hoisting height of the crane through rotation counting, and transmits the height signal of the crane at this moment to the crane control system through a wireless collector. The control system receives the signal from the encoder and compares the current height with the preset maximum height limit. If the hoisting height is close to the limit, the system will take deceleration measures and issue an alarm. If the height limit is triggered, the control system will send a control signal to the hoisting drive system of the crane to stop the hoisting motor.

[0035] In some embodiments, the step of performing secondary limit on the hoisting height of the crane includes: when the hoisting height of the crane reaches the limit height of the piezoelectric sensing module, controlling the crane to stop hoisting.

[0036] Specifically, a mechanical limit device is used to perform secondary limit on the limit height. The mechanical limit device is installed on the trolley of the crane and is driven by a stepper motor to drive a gear rack for height adjustment. A piezoelectric sensor 2 is provided at its end. When the encoder fails, double protection is provided against the crane hitting the top. Specifically, the motor drives the gear rack mechanism to lift and lower, and the piezoelectric sensor at the end of the lifting device is used to capture the height signal of the crane. When the crane approaches the limit position, the hook touches the piezoelectric sensor 2, and pressure is applied to the piezoelectric ceramic material due to mechanical movement. These piezoelectric ceramic materials generate charges and send this signal to the hoisting control system. After receiving the signal, the control system can automatically stop or adjust the hoisting movement of the crane to prevent exceeding the limit range.

[0037] In some embodiments, the step of dynamically correcting the rotation state information specifically includes: when the limit height of the piezoelectric sensing module is higher than the threshold, correcting the rotation state information when the hoisting height of the crane reaches the limit height of the piezoelectric sensing module.

[0038] Specifically, piezoelectric ceramic materials are very sensitive to pressure changes and can quickly and accurately detect the approach of the limit position. Compared with traditional mechanical trigger devices, piezoelectric ceramic materials have no vulnerable parts and are therefore more durable and reliable. Piezoelectric materials convert pressure into electrical energy, which can achieve energy recovery and utilization to a certain extent, contributing to energy conservation and environmental protection. By combining the encoder and the mechanical limit device, double monitoring can be carried out at the limit height, improving the reliability and safety of the crane operation. Since the encoder may have inaccurate readings during operation due to reasons such as installation deviation, cumulative error, vibration shock, electromagnetic interference, wear and aging, power supply fluctuation, and environmental condition changes. By correlating the two, the mechanical limiter can dynamically correct these errors by providing a fixed physical reference point, regularly resetting the encoder position to a known reference, and eliminating errors caused by cumulative deviation, vibration, or signal interference. The limiter can also be combined with the control system to calibrate the offset value of the encoder using the trigger position for automated dynamic calibration, eliminating the need for manual intervention. Through the physical characteristics of the mechanical limiter, it can effectively overcome the performance limitations of the encoder under electromagnetic interference and environmental changes, while reducing the maintenance workload of the equipment, improving the fault tolerance of the overall system, expanding the operation range, and ultimately ensuring more stable, efficient, and intelligent operation of the equipment.

[0039] In some embodiments, the step of eliminating noise from the limit signals of the photoelectric rotation encoding module and the piezoelectric sensing module specifically includes: establishing a linear mapping relationship between the input signal and the output signal by fitting a weight vector; constructing an error variable model, expressing the input limit signal and the output limit signal in a form with noise, and obtaining observation data, where the observation data includes the input limit signal contaminated by noise and the output limit signal contaminated by noise; correcting the observation data by total least squares to obtain true data, where the true data includes the true input limit signal and the true output limit signal; estimating the true data by generalized correlation entropy to process non-Gaussian noise; constructing a cost function for maximum total generalized coherence, adjusting the weight vector by gradient descent to make the cost function reach the maximum value, and further optimizing the error variable model; iteratively updating the weight vector through a step size adjustment strategy to obtain an optimal weight vector; and establishing an optimal relationship model between the input signal and the output signal by estimating the optimal weight vector.

[0040] Specifically, in order to accurately sense the state changes during the hoisting process of the crane, the present invention uses an improved maximum total generalized correlation entropy adaptive filtering algorithm to eliminate noise from the limit signals of the photoelectric encoder and the piezoelectric sensor 2 after the signals are collected, improving the quality of the limit signals, thereby preventing the crane from overshooting. Some classic adaptive filtering algorithms can well eliminate the noise in a system where only the output signal is disturbed by noise. However, for the processing of the sensor signals in the present invention, in actual on-site applications, the sensors may be disturbed by environmental noise during signal collection, such as electromagnetic interference, temperature changes, or mechanical vibrations, which may make the input signal impure. At the same time, due to problems such as the measurement error, aging, and non-linear response of the sensors themselves, there may be certain deviations in the input signal itself. During the signal transmission and processing, the output signal may also be disturbed by factors such as system noise, quantization error, and data transmission loss, resulting in the output result not fully reflecting the true situation. Therefore, both the input and the output may be affected by noise, causing distortion of the system signal. This belongs to the task of error variable modeling with noise in both the input and the output. When processing such signals, the performance of existing algorithms such as the least mean square algorithm will decline. In the actual application scenario of the double limit switch of the present invention, the input and output signals are often disturbed by non-Gaussian noise. The present invention proposes a new improved maximum total generalized correlation entropy adaptive filtering algorithm, which combines the generalized maximum correntropy (GMC) criterion and the total least-squared (TLS) strategy to improve the effectiveness of limit signal filtering.

[0041] Specifically, the calculation formula for establishing the linear mapping relationship between the input signal and the output signal by fitting the weight vector is: y i =(w*) T x i , where w* is the weight vector, and (·) T is the transpose operation, y i ∈R 1×1 is the output limit signal at time i, and x i ∈R L×1 is the input limit signal at time i.

[0042] Specifically, the error variable model is constructed to represent the input limit signal and the output limit signal in a noisy form and obtain the observed data. The calculation formula is: where is the actually noisy input limit signal, is the actually noisy output limit signal, x i is the input limit signal at time i, y i is the output limit signal at time i, u i is the input noise, and v i is the output noise.

[0043] It can be understood that and respectively represent the actually noisy limit signals of the input and output contaminated by noise, which can be obtained through actual measurement; the covariance matrix of u i is σ 2 in I L×L (I L×L is the identity matrix), and v i is composed of background noise with variance σ b 2 and impulse noise with variance σ i 2 , and its variance is denoted as σ out 2 The variances of the above noises can be estimated by analyzing the errors and residuals. In this system, u i and v i i are considered to be wide-sense stationary and uncorrelated with each other. To achieve the above purpose, the weight vector w i can be iteratively updated, and the actual system parameter w* can be estimated from the given set .

[0044] In some embodiments, the step of correcting the observation data by total least squares to obtain the true data specifically includes: performing singular value decomposition on the observation matrix to obtain a singular value matrix, setting the smallest singular value to zero for singular value correction to form a new singular value matrix; using the corrected singular value matrix to reconstruct the observation matrix, and separating the corrected true input limit signal and true output limit signal from the reconstructed observation matrix to obtain the true data.

[0045] Specifically, first, it is necessary to fit the true signal from the signal with noise. In the present invention, the observation data is corrected by total least squares to infer the true signal and First, construct an observation matrix: Then perform singular value decomposition: A = U∑V T ; where U is an orthogonal matrix of size, Σ is a diagonal matrix of size, and the elements on its diagonal are called singular values, and V is also an orthogonal matrix; set the singular value corresponding to the smallest singular value to zero to form a new singular value matrix ∑ new ; use the corrected singular value matrix to reconstruct the observation matrix: A new = U∑ new V T ; separate the corrected true signal x i and y i .

[0046] In some embodiments, the step of estimating the true data by generalized correlation entropy specifically includes: The generalized correlation entropy and the generalized Gaussian density function can be expressed as: V α，β (X, Y) = E[G α，β (X - Y)]; where X is the input limit signal random variable, and X = {x1, x2...x i}, Y is the output limit signal random variable, and Y = {y1, y2....y i}, E[·] is the mathematical expectation, G α，β (X - Y) is the generalized Gaussian density function, α is the shape parameter and α > 0, β is the scale parameter and σ is the kernel width; Γ(1 / α) is the Gamma function and e is the variable; calculate the average value of the generalized Gaussian density function, and its calculation formula is: where is the average value of the generalized Gaussian density function, N is the total number of samples, and G α，β (X - Y) is the generalized Gaussian density function; use the average value of the generalized Gaussian density function to estimate the true data.

[0047] In some embodiments, the steps of constructing a cost function for maximizing the total generalized synergy, adjusting the weight vector through gradient descent to maximize the cost function, and then optimizing the error variable model specifically include: constructing a cost function for maximizing the total generalized synergy, calculating the gradient of the cost function for maximizing the total generalized synergy; adjusting the weight vector through gradient descent, and stopping updating the weight vector when the cost function is maximized. At this time, the weight vector is the optimal weight vector, and the error variable model is optimized by optimizing the weight vector.

[0048] Specifically, the cost function for constructing the maximum total generalized synergy is: In the formula, H MTGC (w) is the cost function for the maximum total generalized synergy, ε i is the error normalization value, and e i is the error term, and is the modified augmented weight vector, and γ is the ratio of the output noise variance to the input noise variance, α is the shape parameter, and β is the scale parameter; the gradient of the cost function for the maximum total generalized synergy is:

[0049] In the formula, is the gradient with respect to the weight vector, H MTGC (w) is the cost function for the maximum total generalized synergy, λ is a constant for adjusting the gradient, g(w i ) is a function related to the error, t(w i ) is another function related to the error and the weight, sign(e i ) is a sign function for determining the sign of the error, is the modified augmented weight vector, is the actual input limit signal with noise, is the actual output limit signal with noise; the new gradient-based adaptive filtering algorithm can be expressed as: In the formula, w i+1 is the updated weight vector, w i is the current weight vector, u i is the step size, θ is the enhanced step size, g(w i ) is a function related to the error, t(w i ) is another function related to the error and the weight; stop updating the weight vector w i when the cost function is maximized. At this time, the weight vector w i is the optimal weight vector w *, the error variable model is optimized by optimizing the weight vector.

[0050] In some embodiments, the step of iteratively updating the weight vector through a step size adjustment strategy to obtain an optimal weight vector specifically includes: if the current error is reduced compared to the error at the previous moment, the step size is increased: μ i+1 = aμ i ; μ i+1 = bμ i , where a is the step size increase rate. When the current error is reduced compared to the error at the previous moment, the step size will increase proportionally by a to adjust the weight vector more quickly. Usually, a is greater than 1, for example, 1.1 to 1.2. b is the step size decay rate. When the current error is increased compared to the error at the previous moment, the step size will decrease proportionally by b to avoid instability caused by an overly large step size. Usually, b is less than 1, for example, 0.9 to 0.99.

[0051] On the other hand, the present invention also provides a crane height limit device, including: a lifting height calculation module 10, configured to obtain the rotation state information of the crane drum through an optoelectronic rotary encoding module, and calculate the lifting height of the crane based on the rotation state information; a threshold setting module 20, configured to set a threshold for the lifting height of the crane, and control the crane to decelerate or stop lifting according to the relationship between the lifting height of the crane and the threshold; a secondary limit module 30, configured to adjust the limit height of the piezoelectric sensing module, and perform secondary limiting on the lifting height of the crane through the piezoelectric sensing module; a noise elimination module 40, configured to correlate the limit height of the piezoelectric sensing module with the threshold, and eliminate noise from the limit signals of the optoelectronic rotary encoding module and the piezoelectric sensing module; a calibration module 50, configured to dynamically calibrate the rotation state information through a fixed physical reference point where the piezoelectric sensing module is located.

[0052] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs and other various media that can store program codes.

[0053] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0054] The above are only embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in related technical fields, is equally included in the patent protection scope of the present invention.

Claims

1. A method for limiting the height of a crane, characterized in that the steps Including: Obtain the rotation state information of the crane drum through the optoelectronic rotation encoding module, and calculate the hoisting height of the crane based on the rotation state information; Set the threshold of the hoisting height of the crane, and control the crane to decelerate hoisting or stop hoisting according to the relationship between the hoisting height of the crane and the threshold; Adjust the limit height of the piezoelectric sensing module, and perform secondary limit on the hoisting height of the crane through the piezoelectric sensing module; Associate the limit height of the piezoelectric sensing module with the threshold, and eliminate noise from the limit signals of the optoelectronic rotation encoding module and the piezoelectric sensing module; Dynamically correct the rotation state information through the fixed physical reference point where the piezoelectric sensing module is located.

2. The crane height limit method according to claim 1, characterized in that The step of calculating the hoisting height of the crane based on the rotation state information specifically includes: Calculate the hoisting height of the crane by integrating the speed or through rotation counting based on the rotation speed, rotation count, and rotation direction.

3. The crane height limit method according to claim 1, wherein The step of setting the threshold of the hoisting height of the crane and controlling the crane to decelerate hoisting or stop hoisting according to the relationship between the hoisting height of the crane and the threshold specifically includes: Preset a first threshold and a second threshold for the hoisting height of the crane, and the second threshold is greater than the first threshold; Judge whether the hoisting height of the crane reaches the first threshold or the second threshold. If it reaches the first threshold, control the crane to decelerate hoisting. If it reaches the second threshold, control the crane to stop hoisting.

4. The crane height limit method according to claim 1, wherein, The step of performing secondary limit on the hoisting height of the crane includes: When the hoisting height of the crane reaches the limit height of the piezoelectric sensing module, control the crane to stop hoisting.

5. The crane height limit method according to claim 1, characterized in that, The step of dynamically correcting the rotation state information specifically includes: When the limit height of the piezoelectric sensing module is higher than the threshold, correct the rotation state information when the hoisting height of the crane reaches the limit height of the piezoelectric sensing module.

6. The crane height limit method according to any one of claims 1-5, characterized in that, The step of eliminating noise from the limit signals of the optoelectronic rotation encoding module and the piezoelectric sensing module specifically includes: Establish a linear mapping relationship between the input signal and the output signal by fitting the weight vector: Construct an error variable model, represent the input limit signal and the output limit signal in a form with noise, and obtain the observation data, where the observation data includes the input limit signal contaminated by noise and the output limit signal contaminated by noise; Correct the observation data through total least squares method to obtain the real data, where the real data includes the real input limit signal and the real output limit signal; Estimate the real data through the generalized correlation entropy to process non-Gaussian noise; Construct a cost function of the maximum total generalized synergy, adjust the weight vector through gradient descent to make the cost function reach the maximum value, and then optimize the error variable model; Iteratively update the weight vector through the step size adjustment strategy to obtain the optimal weight vector; Establish an optimal relationship model between the input signal and the output signal by estimating the optimal weight vector.

7. The crane height limit method according to claim 6, wherein The steps of correcting the observed data by total least squares to obtain the true data specifically include: Perform singular value decomposition on the observation matrix to obtain a singular value matrix, set the smallest singular value to zero for singular value correction, and form a new singular value matrix; Use the corrected singular value matrix to reconstruct the observation matrix, and separate the corrected true input limit signal and true output limit signal from the reconstructed observation matrix to obtain the true data.

8. The crane height limit method according to claim 6, characterized in that, The steps of estimating the true data by generalized correlation entropy specifically include: The generalized correlation entropy and the generalized Gaussian density function can be expressed as: V α，β (X, Y) = E[G α，β (X - Y)]; Wherein, X is an input limit signal random variable, and X = {x1 x2... x i}, Y is an output limit signal random variable, and Y = {y1 y2... y i}, E[·] is the mathematical expectation, G α，β (X - Y) is the generalized Gaussian density function, α is the shape parameter, and α > 0, β is the scale parameter, and σ is the kernel width; Γ(1 / α) is the Gamma function, and e is a variable; Calculate the average value of the generalized Gaussian density function, and its calculation formula is: In the formula, is the average value of the generalized Gaussian density function, N is the total number of samples, and G α，β (X - Y) is the generalized Gaussian density function; Estimate the true data using the average value of the generalized Gaussian density function.

9. The crane height limit method according to claim 6, wherein, The steps of constructing the cost function of the maximum total generalized synergy, adjusting the weight vector by gradient descent to make the cost function reach the maximum value, and then optimizing the error variable model specifically include: Construct the cost function of the maximum total generalized synergy and calculate the gradient of the cost function of the maximum total generalized synergy; Adjust the weight vector by gradient descent, stop updating the weight vector when the cost function is maximum, and the weight vector at this time is the optimal weight vector. Optimize the error variable model by optimizing the weight vector.

10. A height limit device for a crane, characterized in that, Include: A lifting height calculation module, which is used to obtain the rotation state information of the crane drum through the photoelectric rotary encoding module and calculate the lifting height of the crane based on the rotation state information; A threshold setting module, which is used to set the threshold of the lifting height of the crane, and control the crane to decelerate or stop lifting according to the relationship between the lifting height of the crane and the threshold; A secondary limit module, which is used to adjust the limit height of the piezoelectric sensing module and perform secondary limit on the lifting height of the crane through the piezoelectric sensing module; A noise elimination module, which is used to associate the limit height of the piezoelectric sensing module with the threshold and eliminate noise from the limit signals of the photoelectric rotary encoding module and the piezoelectric sensing module; A correction module, which is used to dynamically correct the rotation state information through the fixed physical reference point where the piezoelectric sensing module is located.

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