Methods, systems, and computer media for compensating for the camber deformation of the main beam of a bridge crane.

Abstract

The present invention discloses a method, system and computer medium for compensating the deformation of the main beam of a gantry crane, comprising: step S1: calculating the camber distance according to a first preset distance and a second preset distance, and then calculating the actual lifting height according to the camber distance and the lifting height; step S2: training the first preset distance and the second preset distance according to a neural network, generating a first measurement error, generating an error of each segmentation point according to a segmentation point position and a ground position, and then generating a second measurement error according to the segmentation point error; step S3: generating an error-compensated upper camber angle according to the first measurement error and the upper camber angle of the crane main beam, and updating the actual lifting height according to the error-compensated upper camber angle, and updating the crane position according to the second measurement error. The present invention can detect, calculate and compensate for the camber change of the gantry crane, and improve the positioning accuracy of the crane and the lifting.

Description

Technical Field

[0001] The present invention relates to the technical field of crane equipment, and in particular to a method, system and computer medium for compensating the camber deformation of a main beam of a gantry crane. Background Art

[0002] In order to ensure the safety of lifting goods with gantry cranes, the main beam of the crane is designed with a preset upward camber. When the main beam is loaded, the camber will decrease. This change in camber will interfere with the positioning of the crane vehicle and the lifting.

[0003] Existing methods for measuring the arch change of the main beam of a gantry crane include: communicating vessels, levels, and wire drawing measurements. However, the above measurement methods are affected by many factors such as the instrument and the environment, and there is a large measurement error between the measured value and the true value. At present, positioning detection and calculation only use linear encoders to reduce the measurement error of the main beam arch to the position of the crane, and there is no compensation for the lifting height or the measurement error of the linear encoder itself, which limits the positioning accuracy of the crane and the lifting.

[0004] Therefore, there is an urgent need for a compensation method that can compensate for the change in crane camber and improve the positioning accuracy of the crane and the lifting. Summary of the invention

[0005] To this end, the present invention provides a method, system and computer medium for compensating for camber deformation of a main beam of a gantry crane, which overcome the defects of the prior art.

[0006] In order to solve the above technical problems, the present invention provides a method for compensating for camber deformation of a main beam of a gantry crane, comprising:

[0007] Step S1: Calculate the camber distance according to the first preset distance and the second preset distance, and then calculate the actual lifting height according to the camber distance and the lifting height, wherein the first preset distance is the distance from the crane end beam to the absolute encoder in the crane, and the second preset distance is the distance from the crane end beam to the linear encoder of the crane main beam;

[0008] Step S2: training the first preset distance and the second preset distance according to a neural network to generate a first measurement error, generating a plurality of segmentation point positions according to a linear encoder laid along the crane main beam, generating an error of each segmentation point according to the segmentation point position and the ground position, and then generating a second measurement error according to the segmentation point error;

[0009] Step S3: Generate an error-compensated arch height according to the first measurement error and the arch angle of the crane main beam, update the actual lifting height according to the error-compensated arch height, and update the crane position according to the second measurement error.

[0010] Furthermore, the specific calculation method for calculating the actual lifting height according to the arch distance and the lifting height is:

[0011] Generate the camber distance by Pythagorean theorem calculation Then calculate the actual lifting height h2=h1+Δh according to the arch distance Δh and the lifting height h1;

[0012] Among them, c is the first preset distance, and d is the second preset distance.

[0013] Furthermore, the method for generating the error-compensated arch height is:

[0014] Step S30: Train the preset values ​​of different positions according to the neural network to generate neural network parameters, and then generate k values ​​according to the neural network parameters and the measured preset values, and calculate the first measurement error through the k value. Among them, the k value is the camber angle ∠A of the crane main beam;

[0015] Step S31: Calculate the error-compensated arch height according to the first measurement error e1 and the measured preset value Wherein, e2 is the second measurement error.

[0016] Furthermore, the method for training preset values ​​of different positions according to the neural network is:

[0017] A neural network built based on logistic regression, with c, d, e2 as input and y as output, trains the values ​​of c, d, e2 and y at different positions, and then generates neural network parameters based on the gradient descent method;

[0018] The neural network adopts the softmax activation function; y is an n-dimensional column vector, and y i Corresponding to k = k i The probability of

[0019] Furthermore, the method for generating the segmentation point position is:

[0020] The linear encoder scale is divided into steps of 1 / 2 the distance of the mounting support, and the position d of each division point is generated. i , and then according to the position d of each segmentation point i and the ground position g i , generating the error e for each segmentation point 2[i] =d i -g i , where i∈[1,n];

[0021] When d∈d i When the error e of each segmentation point is2[i] Generate the second measurement error

[0022]

[0023] Furthermore, the method for updating the actual lifting height is:

[0024] According to the camber angle after error compensation Update actual lifting height Among them, h1 is the lifting height.

[0025] Further, the method for updating the crane position is:

[0026] Update the crane position according to the second measurement error e2

[0027] The present invention also provides a gantry crane main beam camber deformation compensation system, comprising:

[0028] A distance calculation module, which calculates the crown distance according to the first preset distance and the second preset distance, and further calculates the actual lifting height according to the crown distance and the lifting height;

[0029] An error calculation module generates a first measurement error according to a neural network training method, generates a plurality of segmentation point positions according to a step-size segmentation code ruler, further generates an error of each segmentation point according to the segmentation point position and a ground position, and generates a second measurement error according to the segmentation point error;

[0030] The compensation module generates an error-compensated arch height according to the first measurement error and the arch angle during manufacturing, updates the actual lifting height according to the error-compensated arch height, and further updates the crane position according to the second measurement error.

[0031] Furthermore, it also includes:

[0032] A detection component, comprising a distance measuring unit, a linear encoder, and an absolute value encoder, wherein the distance measuring unit is installed on an end beam of a gantry crane, the linear encoder is installed on a main beam of the gantry crane, and the absolute value encoder is installed on a lifting vehicle of the gantry crane;

[0033] The control component is connected to the distance measuring unit, the linear encoder and the absolute value encoder respectively.

[0034] The present invention also provides a computer medium, characterized in that a computer program is stored on the computer medium, and the computer program is executed by a processor to implement the method for compensating for the camber deformation of the main beam of a gantry crane according to any one of claims 1-7.

[0035] The present invention also provides a computer, comprising the computer medium.

[0036] The above technical solution of the present invention has the following advantages compared with the prior art:

[0037] The camber deformation compensation method, system and computer medium of the gantry crane main beam described in the present invention calculate and compensate for the trolley position error and lifting position error caused by the camber change after respectively measuring the crane position and lifting height, and further optimize the positioning accuracy of the crane direction by using geometric methods and machine learning methods, thereby expanding the intelligent and automated application scenarios of the crane. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0039] Figure 1 It is a schematic diagram of the gantry crane of the present invention.

[0040] Figure 2 It is a flow chart of the camber deformation compensation method of the gantry crane main beam of the present invention.

[0041] Figure 3 It is a flow chart of the method for calculating the arch height after error compensation of the present invention.

[0042] Figure 4 It is a connection schematic diagram of the camber deformation compensation system of the main beam of the gantry crane of the present invention.

[0043] Explanation of the reference numerals in the specification: 3. Control component, 4. Distance calculation module, 5. First compensation module, 6. Second compensation module, 10. Main beam, 11. End beam, 12. Crane, 20. Distance measuring unit, 21. Linear encoder, 22. Absolute encoder, 23. Encoding ruler of linear encoder. DETAILED DESCRIPTION

[0044] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0045] In the description of the present invention, it should be understood that the term "comprising" is intended to cover non-exclusive inclusions, such as a process, method, system, product or apparatus comprising a series of steps or units, which is not limited to the listed steps or units but optionally also includes unlisted steps or units, or optionally also includes other steps or units inherent to these processes, methods, products or apparatuses.

[0046] Embodiment 1

[0047] Reference Figure 1-3 As shown, the present invention provides an embodiment of a method for compensating for camber deformation of a main beam 10 of a gantry crane, the method comprising the following steps:

[0048] Step S1: Calculate the camber distance according to the first preset distance c and the second preset distance d

[0049] Step S2: Calculate the lifting height h2=h1+Δh according to the camber distance Δh and the lifting height h1;

[0050] Step S3: Generate a first measurement error e1 according to the neural network training method, and generate a number of segmentation point positions d according to the step-length segmentation code ruler i , and then according to the segmentation point position d i and ground position g i Generate the error e for each segmentation point 2[i] , and according to the segmentation point error e 2[i] Generate a second measurement error e2;

[0051] Step S4: Generate an error-compensated arch height according to the first measurement error e1 and the arch angle ∠A during manufacturing And according to the arch height after error compensation Update actual lifting height

[0052] Step S5: Update the position of the crane 12 according to the second measurement error e2 and

[0053] In step S1, refer to Figure 1 As shown, the first preset distance c is the distance from the end beam 11 of the crane to the absolute value encoder 22 in the crane obtained by ranging, and the second preset distance d is the distance from the end beam 11 of the crane to the linear encoder 21 of the main beam 10 of the crane obtained by ranging, wherein the first preset distance is longer than the second preset distance due to the arch of the main beam 10, and ranging includes but is not limited to laser ranging and ultrasonic ranging, which are set by the operator according to actual production requirements and costs; therefore, reference Figure 1 As shown, c and d can be mapped to triangle ABC, and AB=c, AC=d, BC=Δh; the crane 12 can measure AB and BC at any position. According to the Pythagorean theorem:

[0054]

[0055] In step S2, the lifting height h1 is acquired by the absolute value encoder 22. According to the arch distance Δh and the lifting height h1, the actual lifting height is h2=h1+Δh. When the crane is lifting, Δh will decrease due to the effect of gravity, but the above formula has nothing to do with gravity, so the height compensation value under any lifting weight can be calculated.

[0056] In step S3, the measurement errors of AB and AC affect the compensation effect. The random error of the first preset distance c (i.e., AB) is set as the first measurement error e1, and the random error of the second preset distance d (i.e., AC) is set as the second measurement error e2. At present, there are many factors that affect the first measurement error e1. The first measurement error e1 is generated by training the first preset distance through neural network training. Since the upper arch angle of the crane main beam during mechanical design and manufacturing (i.e., ∠A is sinA) is very small, usually 0≤sinA≤0.0014, it can be obtained set up This is a more accurate arch height after error compensation.

[0057] In step S4, the second measurement error e2 is measured by the linear encoder 21. The error is only related to the installation of the encoder ruler. Therefore, e2 is measured in a calibrated manner, that is, the encoder ruler is divided into steps of 1 / 2 of the distance of the installation support point to obtain the position d of each division point. i , i∈[1,n], with the corresponding ground position g i For reference, the corresponding i The position and g i The position matches, for example, d i is d1, then g i is g1; then measure the error e of each segmentation point 2[i] =d i -g i , the error between two points is described linearly, that is, when d∈d i hour,

[0058]

[0059] In step S5, when the more accurate arch height after error compensation is calculated, Then update the lifting height to:

[0060]

[0061] The crane can perform more precise and faster height positioning based on more accurate lifting height feedback;

[0062] After e2 is calculated, the crane 12 is positioned according to e2. Updated to: Then according to the position of the crane 12 The crane 12 is driven to perform more accurate and faster positioning of the crane 12 .

[0063] Furthermore, in the After that, we can see that 0≤k≤0.0014. If it is a high-precision crane, the value is even smaller, usually 0≤k≤0.001; and:

[0064]

[0065] Among them, considering the actual span and positioning requirements, the k value is divided into n levels, k n is linearly uniformly distributed within 0 to 0.001, usually n∈[1,5]. Logistic regression is used to build a neural network. In order to perform multi-class classification, softmax is used as the activation function. The neural network takes c, d, e2 as input and y as output. y is an n-dimensional column vector, y i Corresponding to k = k i The probability of The neural network is trained by measuring preset values ​​at different positions, and the gradient descent method is used to find the best neural network parameters; wherein the preset values ​​are the first preset distance c, the second preset distance d, the second measurement error e2, and the output y, and the operator can add new values ​​according to actual production needs; wherein the neural network parameters are w (weight) and b (bias); the trained neural network is input with c, d, and e2 measured during the crane operation to obtain an approximate k value, thereby calculating e1; c, d, e1, and e2 can be used to calculate a more accurate value after error compensation. Right now:

[0066]

[0067] Further according to Update the lift height to:

[0068]

[0069] The crane can perform more precise and faster height positioning based on more accurate lifting height feedback.

[0070] Embodiment 2

[0071] Reference Figure 1 , Figure 4 As shown, the present invention also provides an embodiment of a camber deformation compensation system for a gantry crane main beam 10, comprising:

[0072] A distance calculation module 4 calculates the crown distance according to the first preset distance and the second preset distance, and further calculates the actual lifting height according to the crown distance and the lifting height;

[0073] The error calculation module 5 generates a first measurement error according to the neural network training method, generates a plurality of segmentation point positions according to the step-length segmentation code ruler, further generates an error of each segmentation point according to the segmentation point position and the ground position, and generates a second measurement error according to the segmentation point error;

[0074] The compensation module 6 generates an error-compensated arch height according to the first measurement error and the arch angle during manufacturing, updates the actual lifting height according to the error-compensated arch height, and further updates the position of the crane 12 according to the second measurement error.

[0075] The detection component includes a distance measuring unit 20, a linear encoder 21, and an absolute value encoder 22. The distance measuring unit 20 is installed on the end beam 11 of the gantry crane, the linear encoder 21 is installed on the main beam 10 of the gantry crane, and the absolute value encoder 22 is installed on the crane 12 of the gantry crane.

[0076] The control component 3 is connected to the distance measuring unit 20, the linear encoder 21 and the absolute encoder 22 respectively.

[0077] The distance measuring unit 20 includes but is not limited to a laser distance measuring sensor and an ultrasonic distance measuring sensor. The type, model and quantity of the distance measuring unit 20 are set by the operator according to actual production requirements and costs. If laser distance measurement is adopted, it is a mirror reflection type laser distance measurement. Figure 1 As shown, the distance from the end beam 11 to the absolute value encoder 22 is obtained by the distance measuring unit 20, and the distance from the end beam 11 to the linear encoder 21 is obtained by the distance measuring unit 20; the absolute value encoder 22 is a lifting absolute value encoder 22, which is used to collect the lifting height h1; wherein, the control component 3 is a control device of the gantry crane, including but not limited to a PLC controller for position processing, triangulation calculation, and positioning calculation, a variable frequency speed regulation system for driving the lifting and accurate positioning of the crane 12, and an industrial computer for machine learning; wherein, the network used in the present invention is set by the operator according to actual production needs and costs, and in this embodiment, reference is made to industrial Ethernet.

[0078] Embodiment 3

[0079] The present invention also provides a computer medium having a computer program stored thereon, and the computer program is executed by a processor to implement the above-mentioned method for compensating for the camber deformation of the main beam 10 of the gantry crane.

[0080] The present invention also provides a computer, comprising the above-mentioned computer medium.

[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0085] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A method for compensating for camber deformation of a main beam of a gantry crane, characterized in that: The method comprises the following steps: Step S1: Calculate the camber distance according to the first preset distance and the second preset distance, and then calculate the actual lifting height according to the camber distance and the lifting height, wherein the first preset distance is the distance from the crane end beam to the absolute encoder in the crane, and the second preset distance is the distance from the crane end beam to the linear encoder of the crane main beam; Step S2: training the first preset distance and the second preset distance according to a neural network to generate a first measurement error, generating a plurality of segmentation point positions according to a linear encoder laid along the crane main beam, generating an error of each segmentation point according to the segmentation point position and the ground position, and then generating a second measurement error according to the segmentation point error; Step S3: generating an error-compensated camber height according to the first measurement error and the camber angle of the crane main beam, updating the actual lifting height according to the error-compensated camber height, and updating the crane position according to the second measurement error; The method for generating the arch height after error compensation is: Step S30: Train the preset values ​​of different positions according to the neural network to generate neural network parameters, and then generate k values ​​according to the neural network parameters and the measured preset values, and calculate the first measurement error through the k value. Wherein, the value k is the arch angle ∠A of the crane main beam, and c is the first preset distance; Step S31: Calculate the error-compensated arch height according to the first measurement error e1 and the measured preset value Wherein, e2 is the second measurement error, and d is the second preset distance; The method of training the preset values ​​of different positions according to the neural network is: A neural network built based on logistic regression, with c, d, e2 as input and y as output, trains the values ​​of c, d, e2 and y at different positions, and then generates neural network parameters based on the gradient descent method; The neural network adopts the softmax activation function; y is an n-dimensional column vector, and y i Corresponding to k = k i The probability of The method for generating the segmentation point position is: The linear encoder scale is divided into steps of 1 / 2 the distance of the mounting support, and the position d of each division point is generated. i , and then according to the position d of each segmentation point i and ground position g i , generating the error e for each segmentation point 2[i] =d i -g i , where i∈[1,n]; When d∈d i When the error e of each segmentation point is 2[i] Generate the second measurement error 2. The method for compensating for camber deformation of a gantry crane main beam according to claim 1, characterized in that: The specific calculation method for calculating the actual lifting height based on the arch distance and lifting height is: Generate the camber distance by Pythagorean theorem calculation Then calculate the actual lifting height h2=h1+Δh according to the arch distance Δh and the lifting height h1; Among them, c is the first preset distance, and d is the second preset distance.

3. The method for compensating for camber deformation of a gantry crane main beam according to claim 1, characterized in that: The method for updating the actual lifting height is: According to the camber angle after error compensation Update actual lifting height Among them, h1 is the lifting height.

4. The method for compensating for camber deformation of a gantry crane main beam according to claim 1, characterized in that: The method for updating the position of the crane is: Update the crane position according to the second measurement error e2 5. The camber deformation compensation system of the main beam of the gantry crane is characterized by: include: A distance calculation module, which calculates the crown distance according to the first preset distance and the second preset distance, and further calculates the actual lifting height according to the crown distance and the lifting height; An error calculation module generates a first measurement error according to a neural network training method, generates a plurality of segmentation point positions according to a step-size segmentation code ruler, further generates an error of each segmentation point according to the segmentation point position and a ground position, and generates a second measurement error according to the segmentation point error; A compensation module, which generates an error-compensated upper arch height according to the first measurement error and the upper arch angle during manufacturing, updates the actual lifting height according to the error-compensated upper height angle, and further updates the crane position according to the second measurement error; When generating the arch height after error compensation, it includes: The preset values ​​of different positions are trained according to the neural network to generate neural network parameters, and then the k value is generated according to the neural network parameters and the measured preset values, and the first measurement error is calculated by the k value. Wherein, the value k is the arch angle ∠A of the crane main beam, and c is the first preset distance; According to the first measurement error e1 and the measured preset value, the arch height after error compensation is calculated Wherein, e2 is the second measurement error, and d is the second preset distance; When training the neural network with preset values ​​for different positions, including: A neural network built based on logistic regression, with c, d, e2 as input and y as output, trains the values ​​of c, d, e2 and y at different positions, and then generates neural network parameters based on the gradient descent method; The neural network adopts the softmax activation function; y is an n-dimensional column vector, and y i Corresponding to k = k i The probability of The generation of the segmentation point position includes: The linear encoder scale is divided into steps of 1 / 2 the distance of the mounting support, and the position d of each division point is generated. i , and then according to the position d of each segmentation point i and ground position g i , generating the error e for each segmentation point 2[i] =d i -g i , where i∈[1,n]; When d∈d i When the error e of each segmentation point is 2[i] Generate the second measurement error 6. The camber deformation compensation system for the main beam of a gantry crane according to claim 5, characterized in that: Also includes: A detection component, comprising a distance measuring unit, a linear encoder, and an absolute value encoder, wherein the distance measuring unit is installed on an end beam of a gantry crane, the linear encoder is installed on a main beam of the gantry crane, and the absolute value encoder is installed on a lifting vehicle of the gantry crane; The control component is connected to the distance measuring unit, the linear encoder and the absolute value encoder respectively.

7. A computer medium, characterized in that The computer medium stores a computer program, and the computer program is executed by a processor to implement the method for compensating for the camber deformation of the main beam of a gantry crane according to any one of claims 1-4.

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