CN118295308A - Unmanned control method for monorail crane transport robot - Google Patents
Unmanned control method for monorail crane transport robot Download PDFInfo
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Abstract
The invention relates to the field of unmanned control of a single-rail lifting and conveying robot, in particular to a unmanned control method of a single-rail lifting and conveying robot, which comprises the following steps: acquiring roller vibration data and power speed of the monorail crane, and creating a historical data set; obtaining real-time operation data to calculate similarity values, so as to divide a plurality of similar speed intervals and obtain abnormal degree values of the rollers; calculating the similarity degree of the sequence of the vibration data of the front wheel and the rear wheel in the historical data set and taking the similarity degree as the confidence degree of the vibration data; training a predicted neural network model by using the abnormal degree sequence, calculating a loss value, and predicting the abnormal degree sequence of the idler wheel; and when the abnormality degree of the roller is greater than the abnormality threshold value in the sequence, controlling low-speed operation and completing maintenance. According to the invention, the network model is corrected to improve the model prediction accuracy, so that maintenance personnel can conveniently position the abrasion position and adjust the transportation state of the robot.
Description
Technical Field
The invention relates to the field of unmanned control of single-rail lifting and conveying robots. More particularly, the invention relates to a single-rail overhead-handling robot unmanned control method.
Background
The monorail crane is a transportation device which runs on a suspended monorail and consists of a driving vehicle or a traction vehicle (for wire rope traction), a braking vehicle, a bearing vehicle and the like; the track of the monorail crane is generally arranged on the ceilings of the carriages or the warehouse, so that the track does not occupy the ground space and can flexibly adjust the moving path; the monorail crane is generally applied to the fields of manufacturing industry, warehouse logistics, processing production and the like, and provides a convenient and efficient material handling solution for production operation.
The Chinese patent document with the publication number of CN117078687A discloses a track inspection system and a track inspection method based on machine vision, relates to the technical field of track inspection systems, comprises the steps of shooting image data of a monorail crane in a non-contact manner by a picture shooting module, acquiring track data information based on contour detection, comprehensively calculating track missing degree, track deformation degree, track tread height difference, track suspension bolt looseness degree and track dislocation, generating a track coefficient, and analyzing the overall health state of the monorail crane according to the track coefficient by an evaluation module.
According to the method, the health state of the single hanger rail is analyzed according to the rail coefficient, the single hanger rail vehicle is not analyzed, meanwhile, the rail abrasion position cannot be positioned, the roller of the monorail crane is abraded in the transportation process, so that the roller surface of the roller is cracked, the monorail crane can vibrate in a high amplitude in the high-speed movement process of the monorail crane, the higher the speed is, the higher the vibration frequency is, the larger the vibration amplitude is, the smaller the buffering of the cracking position is, the larger the impact on the severely abraded position is, the transportation of the monorail vehicle is influenced, and if the monorail crane is suspended for operation and maintenance, the production efficiency is reduced.
Disclosure of Invention
In order to solve the problem of how to accurately position the worn position of the wheels and rails of the monorail hoist in order to facilitate maintenance of the worn position, the invention provides the following.
An unmanned control method of a monorail crane transport robot, comprising the following steps: acquiring roller vibration data and power speed of the monorail crane, and creating a historical data set; acquiring roller vibration data and power speed of a real-time running track, calculating similarity values of vibration amplitude of vibration data in the real-time running track and vibration amplitude of vibration data in a historical data set, and dividing a plurality of similar speed intervals; the sequences of the roller vibration data of the real-time running track in the speed interval and the roller vibration data in the historical data set are subjected to difference to obtain a difference sequence, and the ratio of the maximum sequence difference value to the minimum sequence difference value in all the difference sequences is traversed to be accumulated to obtain the roller abnormality degree value of the real-time running track; calculating the similarity degree of the sequences of vibration data corresponding to the front wheel and the rear wheel of the monorail crane in the historical data set to obtain the abrasion degree of the guide rail, and taking the abrasion degree as the confidence coefficient of the vibration data; the method comprises the steps of taking a sequence of the abnormal degree values of the roller of the real-time running track as a training set, inputting the training set into a prediction neural network model for training, calculating a loss value of the prediction neural network model according to the confidence coefficient, obtaining an abnormal value prediction model, and predicting the sequence of the abnormal degree values; and controlling the monorail lifting robot to run at a low speed and completing maintenance within the low-speed running time when the abnormal value in the sequence of the predicted abnormal degree value is greater than the abnormal threshold value.
According to the fact that the vibration amplitude of vibration data is more similar, the fact that similar speed vibration amplitude mapping exists in the change of the historical data is indicated, the similarity degree of the vibration amplitude in the historical data and the vibration amplitude of an actual running track is further analyzed, the roller abnormal degree value is more accurately obtained, the similarity between the front wheel and the rear wheel of the monorail crane is combined to serve as the confidence coefficient, the model output result is enabled to be closer to the true value, the accuracy of prediction is improved, the abrasion position is located through model prediction, maintenance of the abrasion position is facilitated, and the production efficiency is improved.
In one embodiment, the dividing the plurality of similar speed intervals includes:
And continuously collecting vibration data of the real-time running track with the similarity value smaller than or equal to the similarity threshold value until the similarity value is calculated to be larger than the similarity threshold value, dividing the vibration data of the real-time running track and the vibration data in the historical data set into a speed interval, and obtaining similar speed intervals in all the real-time running track and the historical data set.
According to the vibration characteristics that the divided speed interval is closer to the current actual running state, the accuracy of division is improved.
In one embodiment, the speed interval satisfies the following relationship:
；
In the method, in the process of the invention, Representing the similarity value of the real-time running track and the speed interval in the historical data set,Represents the speed interval when running once in real time,Represents the speed interval of one run in the historical dataset,The maximum value is indicated and the maximum value,Representing a minimum value.
In one embodiment, the anomaly level value satisfies the following relationship:
；
In the method, in the process of the invention, An abnormality degree value of the roller representing the real-time running track,Representing the total number of difference sequences for all speed intervals,Indicating the corresponding first speed intervalThe largest sequence difference in the sequence of differences,Indicating the corresponding first speed intervalThe smallest sequence difference in the sequence of differences.
In one embodiment, the confidence of the vibration data satisfies the following relationship:
；
In the method, in the process of the invention, Represent the firstThe degree of confidence in the time of day,The 1 st rollerTime to the firstA vibration sequence of the monorail hoist at a moment in time,The 2 nd rollerTime to the firstA vibration sequence of the monorail hoist at a moment in time,Representing the pearson correlation coefficient.
In one embodiment, the loss value satisfies the following relationship:
；
In the method, in the process of the invention, The loss value is indicated as such,Indicating the 1 st confidence level, the first confidence level,Is the firstThe degree of confidence in the individual data is determined,The 1 st abnormal degree value of the roller in the real-time running track is shown,Indicating that the 1 st abnormality degree value of the wheel is predicted,Representing the occurrence of a roller in a real-time motion trajectoryThe value of the degree of abnormality,Indicating the predicted wheelAnd an abnormality degree value.
In one embodiment, the training process of the predictive neural network model includes:
The prediction neural network model is an LSTM prediction neural network model, the first 80% of the abnormal degree sequence of the idler wheel running in real time is used as a training set, the last 20% is used as a verification set, a deep learning framework is used for constructing a model, the confidence level is used as an adjustment parameter, and the adjustment of the network structure is completed through back propagation until the loss value is smaller than a loss threshold value or reaches the preset training times, so that the abnormal value prediction model is obtained.
By training the abnormality degree of the roller running in real time, the network structure is optimized according to the confidence level, the recognition capability of the network model on abnormal conditions is improved, the prediction error is reduced, and the prediction accuracy is improved.
In one embodiment, the control of the monorail overhead hoist robot to run at a low speed and complete maintenance within the low speed running time comprises the steps of:
And controlling the monorail overhead hoist robot to run at a low speed and simultaneously completing maintenance within the low-speed running time in response to the time interval of the corresponding moment of the abnormal degree value of the first predictive roller which is larger than the abnormal threshold in the sequence of the predictive abnormal degree values as the low-speed running time.
Through carrying out low-speed operation and maintenance at unusual emergence moment, can reduce the unexpected or the risk that causes extra damage of robot in the maintenance process, help the robot to operate more accurately in the maintenance process, improve maintenance efficiency.
The invention has the following effects:
1. According to the invention, through carrying out speed interval matching on the similarity of the vibration amplitude of the real-time data and the historical data set, the abnormal degree value of the roller of the real-time running track is obtained more accurately from the historical running track, and the correction is carried out in the training process of the network model based on the similarity between the front wheel and the rear wheel as the confidence coefficient, so that the output result is closer to the real value, the reliability of the network model and the sensitivity to abnormal conditions are enhanced, and the model prediction accuracy and stability are improved.
2. According to the invention, the abrasion position is positioned by predicting the abnormal value through the abnormal value predicting model, so that the running state of the monorail lifting conveying robot is controlled conveniently, the running state is adjusted to be low-speed running, the monorail lifting conveying robot is adjusted in time at the time interval of transporting the monorail crane, the abrasion position is maintained, and the transportation production efficiency is improved.
Drawings
The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. In the drawings, embodiments of the invention are illustrated by way of example and not by way of limitation, and like reference numerals refer to similar or corresponding parts and in which:
Fig. 1 is a flowchart of a method for controlling steps S1 to S6 in a single-rail overhead-handling robot unmanned control method according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Referring to fig. 1, a method for controlling unmanned monorail crane transport robot includes steps S1 to S6, specifically as follows:
S1: roller vibration data and power speed of the monorail hoist are acquired, creating a historical dataset.
Further, vibration sensors are installed at the center positions of the front wheel and the rear wheel of the monorail crane, and power speed and vibration data corresponding to each moment of the front wheel and the rear wheel are acquired. The acquisition frequency is 50Hz, namely 50 times of acquisition in one second; the power data of the crane can be collected in the control by a PID (pro-oral-integrate-DERIVATIVE CONTROLLER) controller.
The high amplitude vibration is generated by the monorail crane due to the cracking of the wheel surface of the roller, and the higher the speed is, the higher the vibration frequency is.
S2: and acquiring roller vibration data and power speed of the real-time running track, calculating similarity values of vibration amplitude of the vibration data in the real-time running track and vibration amplitude of the vibration data in the historical data set, and dividing a plurality of similar speed intervals.
The velocity interval satisfies the following relationship:
；
In the method, in the process of the invention, Representing the similarity value of the real-time running track and the speed interval in the historical data set,Represents the speed interval when running once in real time,Represents the speed interval of one run in the historical dataset,The maximum value is indicated and the maximum value,Representing a minimum value.
Further, the dividing of the plurality of speed intervals is to find out the mapping corresponding to similar power speeds and vibration amplitudes in the historical data set, otherwise, a large gap exists between the real-time running track and the speed vibration amplitudes in the historical data set, and vibration data of the roller in the real-time running track are further analyzed.
And continuously collecting vibration data of the real-time running track with the similarity value smaller than or equal to the similarity threshold value until the similarity value is calculated to be larger than the similarity threshold value, dividing the vibration data of the real-time running track and the vibration data in the historical data set into a speed interval, and obtaining similar speed intervals in all the real-time running track and the historical data set.
Further, a similarity threshold of 0.7 is set, that is, a speed interval in the historical dataset greater than 0.7 is reserved, and if the speed interval is smaller than the speed interval, the historical dataset is abandoned. And reserving all the speed intervals meeting the similarity threshold in the historical data set, and completing the matching of the speed intervals to obtain one or more speed intervals.
If one power speed corresponds to a plurality of vibration amplitudes, a vibration amplitude average value is calculated as vibration data corresponding to the power speed.
S3: and carrying out difference on the sequences of the roller vibration data of the real-time running track in the speed interval and the roller vibration data in the historical data set to obtain a difference sequence, and traversing the ratio of the maximum sequence difference to the minimum sequence difference in all the difference sequences to accumulate to obtain the roller abnormality degree value of the real-time running track.
The abnormality degree value satisfies the following relation:
，
In the method, in the process of the invention, An abnormality degree value of the roller representing the real-time running track,Representing the total number of difference sequences for all speed intervals,Indicating the corresponding first speed intervalThe largest sequence difference in the sequence of differences,Indicating the corresponding first speed intervalThe smallest sequence difference in the sequence of differences.
Further, a similar speed interval of the historical data set and the real-time running track is obtained, and a difference value of the similar speed interval is obtained, wherein a plurality of vibration data are arranged in one speed interval, one vibration data corresponds to one acquisition time, and the smaller the degree of abnormality is, the more abnormality of the roller is indicated.
Since the present invention is directed to the abrasion of the roller, the abrasion on the rail also affects the vibration, and when the wheel passes the abrasion of the rail, the vibration of a large amplitude occurs, and therefore, the confidence calculation of the vibration data is required.
Abnormal vibration is generated when the monorail crane passes through the damaged guide rail, and the vibration amplitude is larger as the speed is higher; and when adjacent rollers on the monorail crane pass through the same positions, the vibration amplitudes generated by the adjacent rollers are similar, so that the similarity of the vibration amplitudes generated by the adjacent rollers when passing through the damaged guide rail can be calculated, and whether the abrasion of the corresponding guide rail exists on the rail or not is determined.
After one transfer is completed, a sequence of vibration data and a sequence of power speed are generated, wherein the power speed and the moment of vibration data are in a corresponding relationship.
S4: and calculating the similarity degree of the sequences of the vibration data corresponding to the front wheel and the rear wheel of the monorail crane in the historical data set, obtaining the abrasion degree of the guide rail, and taking the abrasion degree as the confidence degree of the vibration data.
Further, when the monorail crane is in operation, the time interval exists when the front wheel and the rear wheel roll to the same position, and the time interval is related to the distance and the speed of one running track and the corresponding running time, so that the time interval when the front wheel and the rear wheel pass through the same position is judged.
The confidence of the vibration data satisfies the following relationship:
；
In the method, in the process of the invention, Represent the firstThe degree of confidence in the time of day,The 1 st rollerTime to the firstA vibration sequence of the monorail hoist at a moment in time,The 2 nd rollerTime to the firstA vibration sequence of the monorail hoist at a moment in time,Representing the pearson correlation coefficient.
Further, when abnormal change of vibration data occurs, the abrasion degree is obviously reduced, and the larger the vibration amplitude of the abnormal change is, the higher the abrasion degree is.
It should be noted that, because the reliability of part of sample data is not high, confidence is added in the training process of the network to train and correct the sample data, that is, the abnormal degree of the roller caused by rail abrasion in the training of the network model is high, the influence weight of the roller on the output result is reduced, so that the output result is closer to a real value.
S5: and taking the sequence of the abnormal degree value of the roller of the real-time running track as a training set, inputting the training set into a prediction neural network model for training, calculating the loss value of the prediction neural network model according to the confidence coefficient, obtaining an abnormal value prediction model, and predicting the sequence of the abnormal degree value.
A training process for predicting a neural network model, comprising:
The prediction neural network model is an LSTM prediction neural network model, the first 80% of the sequence of the abnormal degree of the roller running in real time is used as a training set, the last 20% is used as a verification set, a deep learning framework is used for constructing a model, the confidence level is used as an adjustment parameter, and the adjustment of the network structure is completed through back propagation until the loss value is smaller than a loss threshold value or reaches the preset training times, so that an abnormal value prediction model is obtained;
further, in this embodiment, LSTM (Long Short-Term Memory) is a variant of recurrent neural network (Recurrent Neural Network, RNN) commonly used for processing and predicting time series data, and the loss threshold of the outlier prediction model is 0.005.
The abnormal degree sequence is obtained by matching vibration data continuously acquired according to a real-time running track with historical vibration data, and the abnormal degree sequence is formed according to a time sequence, wherein one time corresponds to one confidence level, and one confidence level corresponds to one abnormal degree value of one roller.
The loss value satisfies the following relation:
；
In the method, in the process of the invention, The loss value is indicated as such,Indicating the 1 st confidence level, the first confidence level,Is the firstThe degree of confidence in the individual data is determined,The 1 st abnormal degree value of the roller in the real-time running track is shown,Indicating that the 1 st abnormality degree value of the wheel is predicted,Representing the occurrence of a roller in a real-time motion trajectoryThe value of the degree of abnormality,Indicating the predicted wheelAnd an abnormality degree value.
S6: and controlling the monorail lifting robot to run at a low speed and completing maintenance within the low-speed running time when the abnormal value in the sequence of the predicted abnormal degree value is greater than the abnormal threshold value.
Further, in this embodiment, the abnormality threshold is 0.2.
And controlling the monorail overhead hoist robot to run at a low speed in response to the time interval of the corresponding moment of the first predicted roller abnormality degree value greater than the abnormality threshold in the sequence of predicted abnormality degree values as the low-speed running time, and completing maintenance in the low-speed running time.
In the description of the present specification, the meaning of "a plurality", "a number" or "a plurality" is at least two, for example, two, three or more, etc., unless explicitly defined otherwise.
While various embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Many modifications, changes, and substitutions will now occur to those skilled in the art without departing from the spirit and scope of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.
Claims (8)
1. The unmanned control method of the monorail crane transport robot is characterized by comprising the following steps of:
acquiring roller vibration data and power speed of the monorail crane, and creating a historical data set;
acquiring roller vibration data and power speed of a real-time running track, calculating similarity values of vibration amplitude of vibration data in the real-time running track and vibration amplitude of vibration data in a historical data set, and dividing a plurality of similar speed intervals;
The sequences of the roller vibration data of the real-time running track in the speed interval and the roller vibration data in the historical data set are subjected to difference to obtain a difference sequence, and the ratio of the maximum sequence difference value to the minimum sequence difference value in all the difference sequences is traversed to be accumulated to obtain the roller abnormality degree value of the real-time running track;
Calculating the similarity degree of the sequences of vibration data corresponding to the front wheel and the rear wheel of the monorail crane in the historical data set to obtain the abrasion degree of the guide rail, and taking the abrasion degree as the confidence coefficient of the vibration data;
The method comprises the steps of taking a sequence of the abnormal degree values of the roller of the real-time running track as a training set, inputting the training set into a prediction neural network model for training, calculating a loss value of the prediction neural network model according to the confidence coefficient, obtaining an abnormal value prediction model, and predicting the sequence of the abnormal degree values;
And controlling the monorail lifting robot to run at a low speed and completing maintenance within the low-speed running time when the abnormal value in the sequence of the predicted abnormal degree value is greater than the abnormal threshold value.
2. The unmanned control method of a monorail crane transport robot of claim 1, wherein the dividing a plurality of similar speed intervals comprises:
And continuously collecting vibration data of the real-time running track with the similarity value smaller than or equal to the similarity threshold value until the similarity value is calculated to be larger than the similarity threshold value, dividing the vibration data of the real-time running track and the vibration data in the historical data set into a speed interval, and obtaining similar speed intervals in all the real-time running track and the historical data set.
3. The unmanned control method of a monorail crane transport robot according to claim 1, wherein the speed interval satisfies the following relation:
；
In the method, in the process of the invention, Representing the similarity value of the real-time running track and the speed interval in the historical data set,Represents the speed interval when running once in real time,Represents the speed interval of one run in the historical dataset,The maximum value is indicated and the maximum value,Representing a minimum value.
4. The unmanned control method of a monorail crane transport robot according to claim 1, wherein the abnormality degree value satisfies the following relation:
；
In the method, in the process of the invention, An abnormality degree value of the roller representing the real-time running track,Representing the total number of difference sequences for all speed intervals,Indicating the corresponding first speed intervalThe largest sequence difference in the sequence of differences,Indicating the corresponding first speed intervalThe smallest sequence difference in the sequence of differences.
5. The unmanned control method of a monorail crane transport robot according to claim 1, wherein the confidence of the vibration data satisfies the following relationship:
；
In the method, in the process of the invention, Represent the firstThe degree of confidence in the time of day,The 1 st rollerTime to the firstA vibration sequence of the monorail hoist at a moment in time,The 2 nd rollerTime to the firstA vibration sequence of the monorail hoist at a moment in time,Representing the pearson correlation coefficient.
6. The unmanned control method of a monorail crane transport robot according to claim 1, wherein the loss value satisfies the following relation:
；
In the method, in the process of the invention, The loss value is indicated as such,Indicating the 1 st confidence level, the first confidence level,Is the firstThe degree of confidence in the individual data is determined,The 1 st abnormal degree value of the roller in the real-time running track is shown,Indicating that the 1 st abnormality degree value of the wheel is predicted,Representing the occurrence of a roller in a real-time motion trajectoryThe value of the degree of abnormality,Indicating the predicted wheelAnd an abnormality degree value.
7. The unmanned control method of a monorail crane transport robot according to claim 1, wherein the training process of the predictive neural network model comprises:
The prediction neural network model is an LSTM prediction neural network model, the first 80% of the abnormal degree sequence of the idler wheel running in real time is used as a training set, the last 20% is used as a verification set, a deep learning framework is used for constructing a model, the confidence level is used as an adjustment parameter, and the adjustment of the network structure is completed through back propagation until the loss value is smaller than a loss threshold value or reaches the preset training times, so that the abnormal value prediction model is obtained.
8. The unmanned control method of a monorail crane transport robot of claim 1, wherein the controlling the monorail crane transport robot to run at a low speed and to complete maintenance within a low speed run time comprises the steps of:
And controlling the monorail overhead hoist robot to run at a low speed and simultaneously completing maintenance within the low-speed running time in response to the time interval of the corresponding moment of the abnormal degree value of the first predictive roller which is larger than the abnormal threshold in the sequence of the predictive abnormal degree values as the low-speed running time.
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