CN114861552A - Method and system for compensating camber deformation of main beam of gantry crane and computer medium - Google Patents

Method and system for compensating camber deformation of main beam of gantry crane and computer medium Download PDF

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CN114861552A
CN114861552A CN202210587929.3A CN202210587929A CN114861552A CN 114861552 A CN114861552 A CN 114861552A CN 202210587929 A CN202210587929 A CN 202210587929A CN 114861552 A CN114861552 A CN 114861552A
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贾凯
张程
杨灿兴
方线伟
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Eurocrane China Co ltd
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Abstract

The invention discloses a method and a system for compensating camber deformation of a main beam of a bridge portal crane and a computer medium, wherein the method comprises the following steps: step S1: calculating an arch distance according to the first preset distance and the second preset distance, and further calculating an actual lifting height according to the arch distance and the lifting height; step S2: training the first preset distance and the second preset distance according to a neural network to generate a first measurement error, generating an error of each segmentation point according to the positions of the segmentation points and the ground position, and further generating a second measurement error according to the errors of the segmentation points; step S3: and generating an upper arch angle after error compensation according to the first measurement error and the upper arch angle of the crane girder, updating the actual lifting height according to the upper arch angle after error compensation, and updating the position of the crane truck according to the second measurement error. The invention can detect, calculate and compensate the camber change of the gantry crane and improve the positioning accuracy of the crane car and lifting.

Description

Method and system for compensating camber deformation of main beam of gantry crane and computer medium
Technical Field
The invention relates to the technical field of crane equipment, in particular to a method and a system for compensating camber deformation of a main beam of a bridge portal crane and a computer medium.
Background
In order to ensure the safety of the bridge-and-portal crane for lifting cargoes, the crane girder is designed with a preset camber, the camber can be reduced after the girder bears, and the change of the camber can interfere with the positioning of a crane truck and lifting.
The existing method for measuring camber change of a main beam of a bridge-type gantry crane comprises the following steps: the measuring method is influenced by various factors such as instruments and environments, and a larger measuring error exists between a measured value and a real value; at present, the positioning detection and calculation only use a linear encoder to reduce the measurement error of the main beam arch to the position of the lift truck, the lifting height is not compensated, the measurement error of the linear encoder is not compensated, and the positioning accuracy of the lift truck and the lifting is limited.
Therefore, a compensation method capable of compensating for changes in camber of a crane and improving positioning accuracy of a lift truck and lifting is needed.
Disclosure of Invention
Therefore, the method, the system and the computer medium for compensating the camber deformation of the main beam of the bridge portal crane overcome the defects of the prior art.
In order to solve the technical problem, the invention provides a main beam camber deformation compensation method of a bridge door type crane, which comprises the following steps:
step S1: calculating an arch distance according to a first preset distance and a second preset distance, and further calculating an actual lifting height according to the arch distance and the lifting height, wherein the first preset distance is the distance from a crane end beam to an absolute value encoder in a crane, and the second preset distance is the distance from the crane end beam to a linear encoder of a 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 dividing point positions according to a linear coding scale laid along a main beam of the crane, generating an error of each dividing point according to the dividing point positions and the ground position, and further generating a second measurement error according to the dividing point errors;
step S3: and generating an upper arch angle after error compensation according to the first measurement error and the upper arch angle of the crane girder, updating the actual lifting height according to the upper arch angle after error compensation, and updating the position of the crane truck according to the second measurement error.
Further, the specific calculation method for calculating the actual hoisting height according to the camber distance and the hoisting height comprises the following steps:
calculating and generating camber distance by pythagorean theorem
Figure BDA0003666591780000021
According to the camber distance delta h and the lifting height h 1 Calculating the actual lifting height h 2 ＝h 1 +Δh；
Wherein c is a first preset distance, and d is a second preset distance.
Further, the method for generating the error-compensated camber height comprises the following steps:
step S30: training preset values of different positions according to a neural network to generate neural network parameters, further generating k values according to the neural network parameters and actually-measured preset values, and calculating a first measurement error according to the k values
Figure BDA0003666591780000022
Wherein the k value is an upper arch angle A of a crane main beam;
step S31: according to a first measurement error e 1 And the actually measured preset value is used for calculating the upper arch height after error compensation
Figure BDA0003666591780000023
Wherein e is 2 Is the second measurement error.
Further, the method for training the preset values of different positions according to the neural network comprises the following steps:
a neural network built according to logistic regression and expressed by c, d, e 2 As input, and y as output, for c, d, e at different positions 2 Training the value of y, and further generating a neural network parameter according to a gradient descent method;
wherein the neural network employs a softmax activation function; y is an n-dimensional column vector, and y i Corresponding to k ═ k i And is a probability of
Figure BDA0003666591780000031
Further, the method for generating the position of the split point comprises the following steps:
dividing the linear coding scale by using the distance of 1/2 mounting pivot as step size to generate position d of each division point i And further according to the position d of each division point i And ground position g i Generating an error e for each division point 2[i] ＝d i -g i Where i ∈ [1, n ]]；
When d ∈ d i According to the error e of each division point 2[i] Generating the second measurement error
Figure BDA0003666591780000032
Further, the method for updating the actual hoisting height comprises the following steps:
according to the upcamber angle after error compensation
Figure BDA0003666591780000033
Updating actual lifting height
Figure BDA0003666591780000034
Wherein h is 1 To raise the height.
Further, the method for updating the position of the crane truck comprises the following steps:
according to the second measurement error e 2 Updating the position of the lift truck
Figure BDA0003666591780000035
The invention also provides a system for compensating camber deformation of a main beam of a bridge portal crane, which comprises:
the distance calculation module is used for calculating an arch distance according to a first preset distance and a second preset distance and further calculating an actual lifting height according to the arch distance and the lifting height;
the 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 length segmentation coding rule, further generates an error of each segmentation point according to the segmentation point positions and the ground position, and generates a second measurement error according to the segmentation point errors;
and the compensation module generates an upper arch angle after error compensation according to the first measurement error and the upper arch angle during manufacturing, updates the actual lifting height according to the upper arch angle after error compensation, and further updates the position of the crane truck according to the second measurement error.
Further, still include:
the detection assembly comprises a distance measurement unit, a linear encoder and an absolute value encoder, wherein the distance measurement unit is installed on an end beam of the gantry crane, the linear encoder is installed on a main beam of the gantry crane, and the absolute value encoder is installed on a crane truck of the gantry crane;
and the control component is respectively connected with the ranging unit, the linear encoder and the absolute value encoder.
The invention also provides a computer medium, characterized in that the computer medium has a computer program stored thereon, which is executed by a processor to implement the method for compensating for camber deformation of a main beam of a bridge and door crane according to any one of claims 1 to 7.
The invention also provides a computer comprising the computer medium.
Compared with the prior art, the technical scheme of the invention has the following advantages:
according to the method, the system and the computer medium for compensating camber deformation of the main beam of the bridge portal crane, after the position and the lifting height of a crane truck are respectively measured, the position error of the trolley and the lifting position error caused by camber change are calculated and compensated, the positioning precision of the direction of the crane truck is further optimized by adopting a geometric method and a machine learning method, and the intelligent and automatic application scenes of the crane are expanded.
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In order that the present disclosure may be more readily and clearly understood, reference will now be made in detail to the present disclosure, examples of which are illustrated in the accompanying drawings.
Fig. 1 is a schematic view of a bridge portal crane of the present invention.
FIG. 2 is a flow chart of the method for compensating camber deformation of a main beam of a portal crane according to the present invention.
FIG. 3 is a flow chart of the method of calculating the error compensated crown height of the present invention.
Fig. 4 is a connection schematic diagram of the main beam camber deformation compensation system of the bridge portal crane of the present invention.
Description reference numbers indicate: 3. the system comprises a control component, 4, a distance calculation module, 5, a first compensation module, 6, a second compensation module, 10, a main beam, 11, an end beam, 12, a crane truck, 20, a distance measurement unit, 21, a linear encoder, 22, an absolute value encoder, 23 and an encoding scale of the linear encoder.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
In the description of the present invention, it should be understood that the term "comprises/comprising" is intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to the listed steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Referring to fig. 1-3, the present invention provides an embodiment of a method for compensating for camber deformation of a main beam 10 of a portal crane, the method comprising the steps of:
step S1: calculating the camber distance according to the first preset distance c and the second preset distance d
Figure BDA0003666591780000051
Step S2: according to the camber distance delta h and the lifting height h 1 Calculating the lifting height h 2 ＝h 1 +Δh；
Step S3: generating a first measurement error e according to a neural network training method 1 Dividing the coding rule according to the step length to generate a plurality of division point positions d i And further according to the position d of the division point i And ground position g i Generating an error e for each segmentation point 2[i] And according to the division point error e 2[i] Generating a second measurement error e 2 ；
Step S4: according to said first measurement error e 1 And an upwarp angle A during manufacturing to generate an upwarp angle after error compensation
Figure BDA0003666591780000061
And according to the upcamber angle after error compensation
Figure BDA0003666591780000062
Updating actual lifting height
Figure BDA0003666591780000063
Step S5: according to the second measurement error e 2 Updating the position of the lift truck 12
Figure BDA0003666591780000064
And is provided with
Figure BDA0003666591780000065
In step S1, referring to fig. 1, the first preset distance c is a 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 a distance from the end beam 11 of the crane to the linear encoder 21 of the crane main beam 10 obtained by ranging, where the first preset distance is longer than the second preset distance due to the upward arching of the main beam 10, and the ranging includes, but is not limited to, laser ranging and ultrasonic ranging, and is set by an operator according to actual production requirements and costs; thus, referring to fig. 1, c, d can be mapped onto triangle ABC, with AB ═ c, AC ═ d, BC ═ Δ h; AB and BC can be measured while the lift truck 12 is in any position, according to the pythagorean theorem:
Figure BDA0003666591780000066
in step S2, the lifting height h 1 For the acquisition of the absolute value encoder 22, according to the camber distance delta h and the lifting height h 1 Then the actual lifting height is h 2 ＝h 1 + Δ h; when the crane is hoisting, the Δ h will decrease due to the effect of gravity, but the above formula is independent of gravity, so that the height compensation value at any hoisting weight can be calculated.
In step S3, what has an effect on the compensation is the measurement errors for AB and AC, the random error for the first preset distance c (i.e. AB) is set as the first measurement error e 1 The random error of the second predetermined distance d (i.e., AC) is a second measurement error e 2 (ii) a At present, the first measurement error e is affected 1 Is generated by training a first preset distance through neural network training 1 (ii) a Because the upwarp angle (namely sinA is sinA) of the crane girder during mechanical design and manufacture is very small, usually 0-sinA-0.0014, the crane girder can be obtained
Figure BDA0003666591780000067
Is provided with
Figure BDA0003666591780000068
Figure BDA0003666591780000069
For a more accurate camber height after error compensation.
In step S4, the second measurement error e 2 Measured by the linear encoder 21, the error of which is only related to the mounting of the encoder scale, and thus e is calculated in a calibrated manner 2 That is, the encoding rule is divided by using the distance of 1/2 installation pivot as step length to obtain the position d of each division point i ，i∈[1,n]At a corresponding ground position g i For reference, wherein corresponding refers to d i Position of (a) and g i Position matching of, e.g. d i Is d 1 Then g is i Is g 1 (ii) a And then measuring the error e of each division point 2[i] ＝d i -g i The error between two points is described linearly, i.e. when d ∈ d i When the utility model is used, the water is discharged,
Figure BDA0003666591780000071
in step S5, when the error compensation is calculated, the arch height is more accurate
Figure BDA0003666591780000072
And then, updating the lifting height as follows:
Figure BDA0003666591780000073
the crane performs more accurate and rapid height positioning according to more accurate lifting height feedback;
when calculating e 2 Then according to e 2 Positioning of the lift truck 12
Figure BDA0003666591780000074
The updating is as follows:
Figure BDA0003666591780000075
according to the position of the lift truck 12
Figure BDA0003666591780000076
The lift truck 12 is driven for more accurate and rapid positioning of the lift truck 12.
Further, at the position
Figure BDA0003666591780000077
Later, it can be seen that 0. ltoreq. k.ltoreq.0.0014, which is smaller for high precision cranes, typically 0. ltoreq. k.ltoreq.0.001; the following steps are provided:
Figure BDA0003666591780000078
wherein, considering the actual span and the positioning requirement, dividing the k value into n grades, k n Is uniformly distributed linearly within 0 to 0.001, and is usually n is within the range of [1,5 ]](ii) a Building a neural network by adopting logistic regression, and adopting softmax as an activation function in order to carry out multi-class classification; neural network with c, d, e 2 As input, with y as output, y being an n-dimensional column vector, y i Corresponding to k ═ k i The probability of (d); and is
Figure BDA0003666591780000079
The neural network is trained by measuring preset values at different positions, and the optimal neural network parameter is found by adopting a gradient descent method; wherein the preset values are a first preset distance c, a second preset distance d and a second measurement error e 2 Outputting y, wherein the operator can increase a new value according to the actual production requirement; wherein the neural network parameters are w (weight), b (deviation); inputting the trained neural network into the measured c, d, e in the operation process of the crane 2 An approximate k value can be obtained, and e is calculated 1 (ii) a C, d, e 1 、e 2 Can calculate more accurate error compensation
Figure BDA0003666591780000081
Namely:
Figure BDA0003666591780000082
in turn according to
Figure BDA0003666591780000083
Updating the lifting height as follows:
Figure BDA0003666591780000084
the crane performs more accurate and rapid height positioning according to more accurate lifting height feedback.
Example two
Referring to fig. 1 and 4, the present invention further provides an embodiment of a system for compensating for camber deformation of a main beam 10 of a gantry crane, including:
the distance calculation module 4 is used for calculating an arch distance according to a first preset distance and a second preset distance and further calculating an actual lifting height according to the arch distance and the lifting height;
the error calculation module 5 generates a first measurement error according to a neural network training method, generates a plurality of segmentation point positions according to a step length segmentation coding rule, 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;
and the compensation module 6 generates an upper arch angle after error compensation according to the first measurement error and the upper arch angle during manufacturing, updates the actual lifting height according to the upper arch angle after error compensation, and further updates the position of the lift truck 12 according to the second measurement error.
The detection assembly comprises a distance measuring unit 20, a linear encoder 21 and an absolute value encoder 22, wherein the distance measuring unit 20 is installed on an end beam 11 of the gantry crane, the linear encoder 21 is installed on a main beam 10 of the gantry crane, and the absolute value encoder 22 is installed on a crane truck 12 of the gantry crane;
and the control component 3 is respectively connected with the ranging unit 20, the linear encoder 21 and the absolute value encoder 22.
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 number of the distance measuring unit 20 are set by an operator according to actual production requirements and cost, and if laser distance measurement is adopted, mirror reflection type laser distance measurement is adopted; referring to fig. 1, 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 for acquiring a lifting height h 1 (ii) a The control component 3 is control equipment of a bridge portal crane, and comprises but not limited to a PLC (programmable logic controller) for position processing, triangulation calculation and positioning calculation, a variable frequency speed control system for driving lifting and accurate positioning of a crane truck 12 and an industrial personal computer for machine learning; in the present embodiment, the network used in the present invention is set by an operator according to actual production requirements and costs, and is referred to as an industrial ethernet network.
EXAMPLE III
The invention also provides a computer medium, wherein a computer program is stored on the computer medium, and the computer program is executed by a processor to realize the camber deformation compensation method for the main beam 10 of the bridge-door crane.
The invention also provides a computer comprising the computer medium.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.

Claims (10)

1. The method for compensating camber deformation of the main beam of the bridge portal crane is characterized by comprising the following steps of:
step S1: calculating an arch distance according to a first preset distance and a second preset distance, and further calculating an actual lifting height according to the arch distance and the lifting height, wherein the first preset distance is the distance from a crane end beam to an absolute value encoder in a crane, and the second preset distance is the distance from the crane end beam to a linear encoder of a 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 dividing point positions according to a linear coding scale laid along a main beam of the crane, generating an error of each dividing point according to the dividing point positions and the ground position, and further generating a second measurement error according to the dividing point errors;
step S3: and generating an upper arch angle after error compensation according to the first measurement error and the upper arch angle of the crane girder, updating the actual lifting height according to the upper arch angle after error compensation, and updating the position of the crane truck according to the second measurement error.
2. The method for compensating for the camber deformation of the main beam of the bridge-and-gate crane according to claim 1, wherein the specific calculation method for calculating the actual hoisting height according to the camber distance and the hoisting height comprises the following steps:
calculating and generating camber distance by pythagorean theorem
Figure FDA0003666591770000011
According to the camber distance delta h and the lifting height h 1 Calculating the actual lifting height h 2 ＝h 1 +Δh；
Wherein c is a first preset distance, and d is a second preset distance.
3. The method for compensating for the camber deformation of the main beam of the bridge-and-gate crane according to claim 1, wherein the method for generating the error-compensated upper camber height comprises:
step S30: training preset values of different positions according to a neural network to generate neural network parameters, and then training the preset values according to the neural network parameters and the actually measured preset valuesGenerating a k value, calculating a first measurement error from said k value
Figure FDA0003666591770000021
Wherein the k value is an upper arch angle A of a crane main beam;
step S31: according to a first measurement error e 1 And the actually measured preset value is used for calculating the upper arch height after error compensation
Figure FDA0003666591770000022
Wherein e is 2 Is the second measurement error.
4. The method for compensating for the camber deformation of the main beam of the bridge-and-gate crane according to claim 3, wherein the method for training according to preset values of the neural network at different positions comprises the following steps:
a neural network built according to logistic regression and expressed by c, d, e 2 As input, and y as output, for c, d, e at different positions 2 Training the value of y, and further generating a neural network parameter according to a gradient descent method;
wherein the neural network employs a softmax activation function; y is an n-dimensional column vector, and y i Corresponding to k ═ k i And is a probability of
Figure FDA0003666591770000023
5. The method for compensating for the camber deformation of the main beam of the bridge-and-gate crane according to claim 1, wherein the generation method of the position of the dividing point is as follows:
dividing the linear coding scale by using the distance of 1/2 mounting pivot as step size to generate position d of each division point i And further according to the position d of each division point i And ground position g i Generating an error e for each division point 2[i] ＝d i -g i Where i ∈ [1, n ]]；
When d ∈ d i According to each division pointError e of 2[i] Generating the second measurement error
Figure FDA0003666591770000024
6. The method for compensating for camber deformation of a main beam of a bridge-and-gate crane according to claim 1, wherein the method for updating the actual hoisting height comprises:
according to the upcamber angle after error compensation
Figure FDA0003666591770000025
Updating actual lifting height
Figure FDA0003666591770000026
Wherein h is 1 To raise the height.
7. A method of compensating for camber deformation of a main beam of a bridge and gate crane according to claim 1, wherein the method of updating the crane position comprises:
according to the second measurement error e 2 Updating the position of the lift truck
Figure FDA0003666591770000031
8. Gantry crane girder camber deformation compensation system, its characterized in that includes:
the distance calculation module is used for calculating an arch distance according to a first preset distance and a second preset distance and further calculating an actual lifting height according to the arch distance and the lifting height;
the error calculation module is used for generating a first measurement error according to a neural network training method, generating a plurality of segmentation point positions according to a step length segmentation coding rule, further generating an error of each segmentation point according to the segmentation point position and the ground position, and generating a second measurement error according to the segmentation point errors;
and the compensation module generates an upper arch angle after error compensation according to the first measurement error and the upper arch angle during manufacturing, updates the actual lifting height according to the upper arch angle after error compensation, and further updates the position of the crane truck according to the second measurement error.
9. The system of claim 8, further comprising:
the detection assembly comprises a distance measurement unit, a linear encoder and an absolute value encoder, wherein the distance measurement unit is installed on an end beam of the gantry crane, the linear encoder is installed on a main beam of the gantry crane, and the absolute value encoder is installed on a crane truck of the gantry crane;
and the control component is respectively connected with the ranging unit, the linear encoder and the absolute value encoder.
10. A computer medium, characterized in that the computer medium has stored thereon a computer program to be executed by a processor for implementing a method for compensating for camber deformation of a main beam of a bridge portal crane according to any one of claims 1 to 7.
CN202210587929.3A 2022-05-27 2022-05-27 Method and system for compensating camber deformation of main beam of gantry crane and computer medium Pending CN114861552A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116839504A (en) * 2023-07-28 2023-10-03 江苏省特种设备安全监督检验研究院 Detection and early warning method and system for camber of crane

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116839504A (en) * 2023-07-28 2023-10-03 江苏省特种设备安全监督检验研究院 Detection and early warning method and system for camber of crane

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