CN117507469B - Automatic servo operation control method and system for oil press - Google Patents
Automatic servo operation control method and system for oil press Download PDFInfo
- Publication number
- CN117507469B CN117507469B CN202410014359.8A CN202410014359A CN117507469B CN 117507469 B CN117507469 B CN 117507469B CN 202410014359 A CN202410014359 A CN 202410014359A CN 117507469 B CN117507469 B CN 117507469B
- Authority
- CN
- China
- Prior art keywords
- task
- error
- data set
- travel
- establishing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000004044 response Effects 0.000 claims abstract description 70
- 230000008859 change Effects 0.000 claims abstract description 35
- 238000004458 analytical method Methods 0.000 claims abstract description 34
- 238000007405 data analysis Methods 0.000 claims description 18
- 238000005457 optimization Methods 0.000 claims description 18
- 238000011156 evaluation Methods 0.000 claims description 15
- 238000004891 communication Methods 0.000 claims description 11
- 238000012795 verification Methods 0.000 claims description 10
- 230000005856 abnormality Effects 0.000 claims description 5
- 230000003993 interaction Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 4
- 230000000737 periodic effect Effects 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 4
- 239000003921 oil Substances 0.000 description 65
- 238000012937 correction Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000000641 cold extrusion Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000003631 expected effect Effects 0.000 description 1
- 238000005242 forging Methods 0.000 description 1
- 239000010720 hydraulic oil Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000009740 moulding (composite fabrication) Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B30—PRESSES
- B30B—PRESSES IN GENERAL
- B30B15/00—Details of, or accessories for, presses; Auxiliary measures in connection with pressing
- B30B15/26—Programme control arrangements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Control Of Presses (AREA)
Abstract
The application provides an automatic servo operation control method and system for an oil press, which relate to the technical field of oil press control, and the method comprises the following steps: acquiring a task data set and an equipment working data set of a servo oil press; establishing task time nodes and establishing task time sequence association; establishing a period compensation window; when the servo oil press executes the task data set, the control feedback receiving of the position sensor is carried out according to the period compensation window, and a response data set of the position sensor is established; acquiring a travel error characteristic and an error change rate characteristic; establishing task update characteristics; generating a fuzzy control response result; the operation control management of the servo oil press is carried out, the technical problem that the accuracy of task execution control of the oil press is poor due to the fact that error feature analysis of task execution is lacking in the prior art is solved, and the technical effect of improving the accuracy of control is achieved by carrying out error analysis compensation of task travel to achieve fine control of the servo oil press.
Description
Technical Field
The application relates to the technical field of oil press control, in particular to an automatic servo operation control method and system for an oil press.
Background
With the development of industrial technology, oil presses are increasingly used in manufacturing industry. However, conventional hydraulic press control is typically manually operated, which is not only inefficient but also prone to error. Therefore, realizing the automatic control of the oil press becomes an important means for improving the production efficiency and the product quality. In the field of automatic control, a servo operation control system is a common technology, and the accurate control of the oil press is realized by accurately controlling the motion of a servo motor. However, the conventional servo operation control system lacks error feature analysis of task execution, so that the accuracy of task execution control of the oil press is poor.
Disclosure of Invention
The application provides an automatic servo operation control method and system for an oil press, which are used for solving the technical problem that in the prior art, due to the lack of error feature analysis of task execution, the accuracy of task execution control of the oil press is poor.
According to a first aspect of the present application, there is provided an automated servo-operated control method for an oil press, comprising: establishing data communication with a servo oil press, and interactively obtaining a task data set and an equipment working data set of the servo oil press; performing task execution fitting on the task data set, establishing task time nodes, and establishing task time sequence association by performing task analysis on the task data set; carrying out data analysis by using the equipment working data set, and establishing a period compensation window according to a data analysis result; when the servo oil press executes the task data set, the control feedback receiving of the position sensor is carried out according to the period compensation window, and a response data set of the position sensor is established; performing error analysis by using the response data set to obtain a travel error characteristic and an error change rate characteristic; time alignment is carried out between the task time node and the acquisition node of the period compensation window, task time sequence association is called based on an alignment result, and task updating characteristics are established; inputting the travel error feature, the error change rate feature and the task update feature into a fuzzy control network to generate a fuzzy control response result; and carrying out operation control management on the servo oil press based on the fuzzy control response result.
According to a second aspect of the present application, there is provided an automated servo-operated control system for an oil press, comprising: the data interaction module is used for establishing data communication with the servo oil press and interactively obtaining a task data set and an equipment working data set of the servo oil press; the task time sequence association module is used for performing task execution fitting on the task data set, establishing task time nodes, and establishing task time sequence association by performing task analysis on the task data set; the compensation window establishment module is used for carrying out data analysis by using the equipment working data set and establishing a periodic compensation window according to a data analysis result; the control feedback receiving module is used for receiving control feedback of the position sensor according to the period compensation window when the servo oil press executes the task data set, and establishing a response data set of the position sensor; the error analysis module is used for carrying out error analysis on the response data set to obtain travel error characteristics and error change rate characteristics; the updating feature establishing module is used for carrying out time alignment with the task time node through the acquisition node of the period compensation window, calling task time sequence association based on an alignment result and establishing task updating features; the fuzzy control response module is used for inputting the travel error characteristic, the error change rate characteristic and the task update characteristic into a fuzzy control network to generate a fuzzy control response result; and the operation control management module is used for carrying out operation control management on the servo oil press based on the fuzzy control response result.
According to one or more technical schemes adopted by the application, the beneficial effects which can be achieved are as follows:
establishing data communication with a servo hydraulic press, interactively obtaining a task data set and an equipment working data set of the servo hydraulic press, performing task execution fitting on the task data set, establishing task time nodes, performing task analysis on the task data set, establishing task time sequence association, performing data analysis on the equipment working data set, establishing a period compensation window according to a data analysis result, performing control feedback receiving of a position sensor according to the period compensation window when the servo hydraulic press executes the task data set, establishing a response data set of the position sensor, performing error analysis on the response data set, obtaining stroke error characteristics and error change rate characteristics, performing time alignment with the task time nodes through acquisition nodes of the period compensation window, calling task time sequence association based on alignment results, establishing task update characteristics, inputting the stroke error characteristics, the error change rate characteristics and the task update characteristics into a fuzzy control network, generating fuzzy control response results, and performing operation control management on the servo hydraulic press based on the fuzzy control response results, thereby realizing refined control on the servo hydraulic press by performing error analysis compensation on the task stroke, and achieving the technical effect of improving control accuracy.
Drawings
In order to more clearly illustrate the technical solutions of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. The accompanying drawings, which form a part hereof, illustrate embodiments of the present application and, together with the description, serve to explain the present application and not to limit the application unduly, and to enable a person skilled in the art to make and use other drawings without the benefit of the present inventive subject matter.
Fig. 1 is a schematic flow chart of an automatic servo operation control method for an oil press according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an automatic servo operation control system for an oil press according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a data interaction module 11, a task time sequence association module 12, a compensation window establishment module 13, a control feedback receiving module 14, an error analysis module 15, an update characteristic establishment module 16, a fuzzy control response module 17 and a control management module 18.
Description of the embodiments
In order to make the objects, technical solutions and advantages of the present application more apparent, exemplary embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
The terminology used in the description is for the purpose of describing embodiments only and is not intended to be limiting of the application. As used in this specification, the singular terms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises" and/or "comprising," when used in this specification, specify the presence of steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other steps, operations, elements, components, and/or groups thereof.
Unless defined otherwise, all terms (including technical and scientific terms) used in this specification should have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. Terms, such as those defined in commonly used dictionaries, should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. Like numbers refer to like elements throughout.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for presentation, analyzed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Examples
As shown in fig. 1, an embodiment of the present application provides an automated servo-operated control method for an oil press, the method comprising:
establishing data communication with a servo oil press, and interactively obtaining a task data set and an equipment working data set of the servo oil press;
establishing data communication with the servo oil press may first incorporate determining the protocol with which the servo oil press is to communicate in conjunction with actually determining the protocol with which the servo oil press is to be connected via an appropriate interface (e.g., serial, ethernet, etc.), and configuring a network or port to communicate with the device. And then, carrying out data interaction acquisition on the servo oil press through established data communication to obtain a task data set and an equipment working data set of the servo oil press. The task data set refers to a data set of task execution conditions of the servo oil press, and includes execution time of a task which is currently executed and needs to be executed subsequently, such as stamping, die forming, forging, cold extrusion, and the like. The equipment working data set is a data set about the running state of the servo oil press, and comprises the pressure and flow of hydraulic oil, the position and speed of an oil cylinder and the like.
Performing task execution fitting on the task data set, establishing task time nodes, and establishing task time sequence association by performing task analysis on the task data set;
extracting the starting and ending of the tasks in the task data set and the time points of the key operation, establishing the starting and ending of each task and the time points of the key operation as task time nodes, further sequencing the tasks according to the sequence of the execution time of the tasks, and establishing a correlation between the corresponding task time nodes and the tasks to obtain task time sequence correlation.
Carrying out data analysis by using the equipment working data set, and establishing a period compensation window according to a data analysis result;
and (3) carrying out data analysis on the equipment working data set, establishing a period compensation window according to a data analysis result, and simply carrying out periodic analysis on the change rule of the equipment working data set, such as periodic analysis on the position of an oil cylinder, so as to obtain the change period of the equipment working data set as the period compensation window, thereby facilitating subsequent task execution error analysis.
When the servo oil press executes the task data set, the control feedback receiving of the position sensor is carried out according to the period compensation window, and a response data set of the position sensor is established;
the position sensor is a conventional sensor for measuring the position of an object, and comprises a potentiometer, an encoder, a grating, a magnetic ruler and the like, and can sense the position change of the object in various modes and convert the position change into an electric signal or other forms of signal output.
Starting a position sensor in a period compensation window, starting to collect position data, continuously receiving the position data collected by the position sensor, including position information and possible acceleration, speed and other information under continuous time in the period compensation window, and storing the data as a response data set.
In a preferred embodiment, further comprising:
acquiring equipment data of a servo oil press, performing execution analysis of equipment tasks according to the equipment data and the task data set, and establishing a sectional travel of the tasks; configuring a position sensor according to the sectional travel, and establishing response association between the position sensor and sectional travel control data; when the execution of the segment travel control data of any segment travel is completed, triggering the position sensor to execute position acquisition based on the response association; the position acquisition data set of all the segment trip trigger position sensors is constructed into a response data set.
Specifically, through carrying out data communication with the servo oil press, equipment data such as state, parameter, operation data of the servo oil press are obtained. And performing execution analysis of equipment tasks according to the equipment data and the task data set, extracting execution flow and steps of the tasks, and performance and state of equipment, and decomposing the tasks into a plurality of sectional strokes based on the execution flow and steps to obtain the sectional strokes. According to the setting of the sectional strokes, a position sensor is configured for each piece of equipment for executing tasks corresponding to each sectional stroke, the position sensor is used for monitoring and collecting position data of each sectional stroke, response association between the position sensor and sectional stroke control data is established, that is, one sectional stroke corresponds to one position sensor, the position sensor is associated with each sectional stroke control data, and the position sensor can be triggered to collect positions after the execution of the control data of each sectional stroke is completed. When the execution of the segment travel control data of any segment travel is completed, the corresponding position sensor is triggered to execute position acquisition based on the response association, the position sensor returns the acquired position data to be stored, and the position acquisition data sets of all segment travel trigger position sensors are constructed into a response data set.
Therefore, the task of the servo oil press equipment is analyzed, the sectional travel of the task is established, and meanwhile, the position acquisition and monitoring of each sectional travel are facilitated by configuring the position sensor and establishing the response association with the sectional travel control data, so that the control accuracy of the servo oil press is ensured.
Performing error analysis by using the response data set to obtain a travel error characteristic and an error change rate characteristic;
and extracting target position data of each sectional travel based on the task data set, and further calculating travel errors between the position data of each sectional travel and the target position data in the response data set, wherein the travel can be understood as travel distance which is required to be completed when the oil cylinder executes the task, and the travel errors can be represented in a difference value, a percentage and the like. Features are extracted from the stroke error data including average error, maximum error, minimum error, error range, etc. as stroke error features. And calculating the change rate of the error by comparing the error data of the adjacent sectional strokes to obtain the characteristic of the change rate of the error.
Time alignment is carried out between the task time node and the acquisition node of the period compensation window, task time sequence association is called based on an alignment result, and task updating characteristics are established;
time alignment is carried out between the acquisition node of the period compensation window and the task time node, and specifically, offset of the acquisition node and the task time node can be compared for time alignment, so that synchronization of a response data set and the task time node is ensured. And invoking task time sequence association based on the alignment result, establishing task update characteristics, wherein the task time sequence association describes states and execution sequences of tasks on different time nodes, and invoking the task time sequence association based on the alignment result, performing comparison of task strokes and extracting the task update characteristics.
In a preferred embodiment, further comprising:
window task extraction is carried out on the period compensation window, and a travel average value of the task is established; determining sequential task strokes through task time sequence association; and taking the ratio of the task journey to the journey average value as a task updating characteristic.
Specifically, window task extraction is carried out on the period compensation window, task strokes of window tasks are obtained, mean value calculation is carried out, and a task stroke mean value is established. According to the task time sequence association, the sequence and the execution sequence of each task are determined, the stroke of each task is compared with the stroke average value, and the proportion of each task is calculated to be used as a task update feature, so that the task update feature can be understood as an error of the task stroke, the subsequent error correction is facilitated, and the control accuracy of the servo oil press is improved.
Inputting the travel error feature, the error change rate feature and the task update feature into a fuzzy control network to generate a fuzzy control response result;
in a preferred embodiment, further comprising:
inputting the error change rate characteristic into a time sequence compensation sub-network to generate a time sequence compensation result; updating network parameters of the error compensation sub-network based on the task updating characteristics, inputting the travel error characteristics into the updated error compensation sub-network, and generating an error compensation result; and generating a fuzzy control response result according to the time sequence compensation result and the error compensation result.
Inputting the travel error feature, the error change rate feature and the task update feature into a fuzzy control network to generate a fuzzy control response result, wherein the fuzzy control network comprises a time sequence compensation sub-network and an error compensation sub-network which are all existing neural network models, and the specific method comprises the following steps of:
the error change rate characteristic is input into a time sequence compensation sub-network to generate a time sequence compensation result, the time sequence compensation sub-network is constructed based on the existing deep learning model, such as a cyclic neural network, and is used for time sequence compensation based on the error change rate characteristic, that is, the error change rate describes the change frequency of the travel error, such as change at regular intervals, and task time sequence compensation is performed based on the change frequency, that is, task time sequence shortening is performed according to the error change rate. Further, based on the task update feature, the network parameters of the error compensation sub-network are updated, the larger the task update feature is, the larger the error possibly occurring in the next task execution is, and at this time, the front step compensation of the travel error by a larger step distance is needed. In particular, different task update feature samples and corresponding pre-step compensation step distances may be obtained by one skilled in the art in combination with actual experience to perform pre-step compensation based on the task update feature matching the corresponding step distance.
And finally, inputting the travel error characteristics into an updated error compensation sub-network, and carrying out error correction on the travel error characteristics through the updated error compensation sub-network to generate an error compensation result, wherein the error compensation result comprises correction or adjustment of the original error after network processing.
And generating a fuzzy control response result according to the time sequence compensation result and the error compensation result, namely generating a fuzzy control response result by adopting a fuzzy logic control method and combining the time sequence compensation and the error compensation result, wherein the fuzzy control response result is an operation control result for the servo oil press obtained after the error compensation and the time sequence compensation. The fuzzy logic control is a control strategy capable of processing inaccurate or fuzzy information, is suitable for processing complex and nonlinear system behaviors, and can specifically construct corresponding fuzzy control rules for time sequence compensation results and error compensation results in advance by a person skilled in the art so as to output fuzzy control response results.
And carrying out operation control management on the servo oil press based on the fuzzy control response result.
And finally, the fuzzy control response result is used as a control parameter for operation of the servo oil press to carry out operation control management of the servo oil press, so that the control accuracy of the servo oil press is improved, and the task travel error is reduced.
In a preferred embodiment, further comprising:
performing supervision window verification on the operation and control management result; if the verification result cannot meet the preset expected threshold, generating an update instruction; and updating the window period of the period compensation window according to the updating instruction.
And (3) performing supervision window verification on the operation and control management result, namely verifying the operation and control management result by combining a time window set by a person skilled in the art, for example, performing operation and control management on the servo oil press by using the fuzzy control response result for 5 minutes. Further setting a preset expected threshold, wherein the preset expected threshold can be understood as an expected allowed travel error, analyzing the task travel error under the supervision window by the method provided by the embodiment, further judging whether the task travel error is within a preset expected threshold range, if not, indicating that the verification result cannot meet the preset expected threshold, namely that the operation control management has deviation or error, generating an update instruction to correct, updating and adjusting the window period of the period compensation window according to the update instruction, namely reducing the period compensation window, and repeatedly performing supervision window verification to ensure that the corrected operation control management result meets the preset expected threshold, and if the verification result still does not meet the requirement, continuing to generate the update instruction and performing window period update of the period compensation window until the expected effect is achieved. Therefore, operation control management supervision of the servo oil press is realized, feedback adjustment is carried out, and control accuracy is improved.
In a preferred embodiment, further comprising:
calculating a travel error value of the segmented travel according to the travel position acquisition result in the response data set; configuring an N-th sectional travel error threshold value through the N+1-th sectional travel length and the stepping distance, and establishing a sectional travel error threshold value set; performing error evaluation of a stroke error value based on the segmented stroke error threshold set; if the error evaluation result is not satisfied, generating control early warning information; and stopping the machine based on the control early warning information and generating a travel abnormality prompt.
And calculating a travel error value of the sectional travel through a travel position acquisition result in the response data set, specifically extracting target position data of each sectional travel based on the task data set, and further calculating a travel error between the position data of each sectional travel and the target position data in the response data set to obtain the travel error value of the sectional travel. The N-th sectional travel error threshold is configured through the N+1-th sectional travel length and the stepping distance, wherein N is an integer larger than 0, namely the sectional travel error threshold of the previous sectional travel is configured for two adjacent sectional travels according to the sectional travel length and the stepping distance of the next sectional travel, and the sectional travel error threshold is an error range which can exist when the servo oil press completes the task of the sectional travel and is required to be determined in combination with reality. The (n+1) th sectional travel length and the stepping distance can be extracted based on the task data set, the sectional travel length can be understood as the moving distance of the oil press, the stepping distance is a single moving distance, and the sectional travel error thresholds corresponding to the sectional travel are combined to obtain a sectional travel error threshold set.
And further performing error evaluation on the stroke error value based on the segmented stroke error threshold set, namely judging whether the stroke error value of the segmented stroke meets the segmented stroke error threshold corresponding to each segmented stroke in the segmented stroke error threshold set or not colloquially, if so, judging that the error evaluation result is a meeting result, and if not, judging that the error evaluation result is a non-meeting result, thereby obtaining the error evaluation result. If the error evaluation result is not satisfied, generating control early warning information, and performing shutdown processing based on the control early warning information, so as to prevent further expansion of errors and protect equipment and personnel safety. And when the machine is stopped, a stroke abnormality prompt is generated, the stroke abnormality prompt can be displayed in an acousto-optic and electric mode, so that workers can be conveniently reminded of abnormal stroke, maintenance and treatment can be carried out, and the safe operation of the servo oil press can be ensured.
In a preferred embodiment, further comprising:
if the error evaluation result is a satisfying result, generating a compensation instruction; adding a stroke error value to the next stroke according to the compensation instruction, and establishing a reconstruction stroke; inputting the reconstruction journey into a parameter optimization model, and executing parameter optimization of the next journey control parameter; and performing error compensation of real-time control according to the parameter optimization result.
If the error evaluation result is a satisfying result, and the stroke error value is within an acceptable range, generating a compensation instruction, adding the stroke error value to the next stroke according to the compensation instruction, namely correcting the previous error, and establishing a reconstruction stroke, namely simply carrying out feedback correction on the sectional stroke according to the stroke error value, wherein the sectional stroke is changed, and the sectional stroke after error correction is used as the reconstruction stroke.
And inputting the reconstructed travel into a parameter optimization model, wherein the parameter optimization model can optimize the control parameters of the next travel according to the error and the compensation instruction of the current travel so as to realize a better control effect, thereby executing the parameter optimization of the control parameters of the next travel. Specifically, the parameter optimization model is a neural network model in the prior art, and the stroke control parameters refer to parameters of the servo oil press for controlling task execution, such as parameters of flow rate, pressure and the like of the oil press. The control relation between the travel and the travel control parameter can be set by a person skilled in the art in combination with the actual situation and stored in the parameter optimization model, and the parameter optimization model obtains the parameter adjustment amplitude when the travel error value is corrected as a parameter optimization result based on the travel error value and the control relation, and further performs error compensation of real-time control according to the parameter optimization result. The stroke control parameters of the servo oil press are adjusted according to the parameter optimization result, so that errors in the actual execution process can be corrected in time, and the accuracy and stability of task execution are ensured.
Based on the above analysis, the one or more technical solutions provided in the present application can achieve the following beneficial effects:
establishing data communication with a servo hydraulic press, interactively obtaining a task data set and an equipment working data set of the servo hydraulic press, performing task execution fitting on the task data set, establishing task time nodes, performing task analysis on the task data set, establishing task time sequence association, performing data analysis on the equipment working data set, establishing a period compensation window according to a data analysis result, performing control feedback receiving of a position sensor according to the period compensation window when the servo hydraulic press executes the task data set, establishing a response data set of the position sensor, performing error analysis on the response data set, obtaining stroke error characteristics and error change rate characteristics, performing time alignment with the task time nodes through acquisition nodes of the period compensation window, calling task time sequence association based on alignment results, establishing task update characteristics, inputting the stroke error characteristics, the error change rate characteristics and the task update characteristics into a fuzzy control network, generating fuzzy control response results, and performing operation control management on the servo hydraulic press based on the fuzzy control response results, thereby realizing refined control on the servo hydraulic press by performing error analysis compensation on the task stroke, and achieving the technical effect of improving control accuracy.
Examples
Based on the same inventive concept as the automated servo-control method for an oil press in the foregoing embodiments, as shown in fig. 2, the present application further provides an automated servo-control system for an oil press, the system comprising:
the data interaction module 11 is used for establishing data communication with the servo oil press and interactively obtaining a task data set and an equipment working data set of the servo oil press;
the task time sequence association module 12, wherein the task time sequence association module 12 is used for performing task execution fitting on the task data set, establishing task time nodes, and establishing task time sequence association by performing task analysis on the task data set;
the compensation window establishment module 13 is used for carrying out data analysis by using the equipment working data set, and establishing a period compensation window according to a data analysis result;
the control feedback receiving module 14 is configured to perform control feedback receiving of the position sensor according to the period compensation window when the servo oil press executes the task data set, and establish a response data set of the position sensor;
the error analysis module 15 is used for carrying out error analysis by using the response data set to obtain a travel error characteristic and an error change rate characteristic;
the update feature establishing module 16, wherein the update feature establishing module 16 is configured to perform time alignment with a task time node through an acquisition node of the period compensation window, and call task time sequence association based on an alignment result to establish a task update feature;
the fuzzy control response module 17 is used for inputting the travel error characteristic, the error change rate characteristic and the task update characteristic into a fuzzy control network to generate a fuzzy control response result;
and the operation control management module 18 is used for carrying out operation control management on the servo oil press based on the fuzzy control response result by the operation control management module 18.
Further, the control feedback receiving module 14 further includes:
acquiring equipment data of a servo oil press, performing execution analysis of equipment tasks according to the equipment data and the task data set, and establishing a sectional travel of the tasks;
configuring a position sensor according to the sectional travel, and establishing response association between the position sensor and sectional travel control data;
when the execution of the segment travel control data of any segment travel is completed, triggering the position sensor to execute position acquisition based on the response association;
the position acquisition data set of all the segment trip trigger position sensors is constructed into a response data set.
Further, the system further comprises a control early warning module, wherein the control early warning module comprises:
calculating a travel error value of the segmented travel according to the travel position acquisition result in the response data set;
configuring an N-th sectional travel error threshold value through the N+1-th sectional travel length and the stepping distance, and establishing a sectional travel error threshold value set;
performing error evaluation of a stroke error value based on the segmented stroke error threshold set;
if the error evaluation result is not satisfied, generating control early warning information;
and stopping the machine based on the control early warning information and generating a travel abnormality prompt.
Further, the system also includes an error compensation module, the error compensation module including:
if the error evaluation result is a satisfying result, generating a compensation instruction;
adding a stroke error value to the next stroke according to the compensation instruction, and establishing a reconstruction stroke;
inputting the reconstruction journey into a parameter optimization model, and executing parameter optimization of the next journey control parameter;
and performing error compensation of real-time control according to the parameter optimization result.
Further, the update feature creation module 16 further includes:
window task extraction is carried out on the period compensation window, and a travel average value of the task is established;
determining sequential task strokes through task time sequence association;
and taking the ratio of the task journey to the journey average value as a task updating characteristic.
Further, the fuzzy control response module 17 further includes:
inputting the error change rate characteristic into a time sequence compensation sub-network to generate a time sequence compensation result;
updating network parameters of the error compensation sub-network based on the task updating characteristics, inputting the travel error characteristics into the updated error compensation sub-network, and generating an error compensation result;
and generating a fuzzy control response result according to the time sequence compensation result and the error compensation result.
Further, the system further includes a window period update module, the window period update module including:
performing supervision window verification on the operation and control management result;
if the verification result cannot meet the preset expected threshold, generating an update instruction;
and updating the window period of the period compensation window according to the updating instruction.
The specific example of the automatic servo control method for an oil press in the first embodiment is also applicable to the automatic servo control system for an oil press in the present embodiment, and those skilled in the art will clearly know the automatic servo control system for an oil press in the present embodiment through the foregoing detailed description of the automatic servo control method for an oil press, so the detailed description thereof will not be repeated here for brevity.
It should be understood that the various forms of flow shown above, reordered, added, or deleted steps may be used, as long as the desired results of the presently disclosed technology are achieved, and are not limited herein.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. Those skilled in the art will appreciate that the present application is not limited to the particular embodiments described herein, but is capable of numerous obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the present application. Therefore, while the present application has been described in connection with the above embodiments, the present application is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the present application, the scope of which is defined by the scope of the appended claims.
Claims (5)
1. An automated servo-operated method for an oil press, the method comprising:
establishing data communication with a servo oil press, and interactively obtaining a task data set and an equipment working data set of the servo oil press;
performing task execution fitting on the task data set, establishing task time nodes, and establishing task time sequence association by performing task analysis on the task data set;
carrying out data analysis by using the equipment working data set, and establishing a period compensation window according to a data analysis result;
when the servo oil press executes the task data set, the control feedback receiving of the position sensor is carried out according to the period compensation window, and a response data set of the position sensor is established;
performing error analysis by using the response data set to obtain a travel error characteristic and an error change rate characteristic;
time alignment is carried out between the task time node and the acquisition node of the period compensation window, task time sequence association is called based on an alignment result, and task updating characteristics are established;
inputting the travel error feature, the error change rate feature and the task update feature into a fuzzy control network to generate a fuzzy control response result;
carrying out operation control management on the servo oil press based on the fuzzy control response result;
wherein the method further comprises:
acquiring equipment data of a servo oil press, performing execution analysis of equipment tasks according to the equipment data and the task data set, and establishing a sectional travel of the tasks;
configuring a position sensor according to the sectional travel, and establishing response association between the position sensor and sectional travel control data;
when the execution of the segment travel control data of any segment travel is completed, triggering the position sensor to execute position acquisition based on the response association;
constructing a response data set from the position acquisition data sets of all the segmented trip trigger position sensors;
wherein the method further comprises:
calculating a travel error value of the segmented travel according to the travel position acquisition result in the response data set;
configuring an N-th sectional travel error threshold value through the N+1-th sectional travel length and the stepping distance, and establishing a sectional travel error threshold value set;
performing error evaluation of a stroke error value based on the segmented stroke error threshold set;
if the error evaluation result is not satisfied, generating control early warning information;
performing shutdown processing based on the control early warning information and generating a travel abnormality prompt;
wherein the method further comprises:
if the error evaluation result is a satisfying result, generating a compensation instruction;
adding a stroke error value to the next stroke according to the compensation instruction, and establishing a reconstruction stroke;
inputting the reconstruction journey into a parameter optimization model, and executing parameter optimization of the next journey control parameter;
and performing error compensation of real-time control according to the parameter optimization result.
2. The method of claim 1, wherein the method further comprises:
window task extraction is carried out on the period compensation window, and a travel average value of the task is established;
determining sequential task strokes through task time sequence association;
and taking the ratio of the task journey to the journey average value as a task updating characteristic.
3. The method of claim 1, wherein said inputting said travel error feature, said error rate of change feature, and said mission update feature into a fuzzy control network further comprises:
inputting the error change rate characteristic into a time sequence compensation sub-network to generate a time sequence compensation result;
updating network parameters of the error compensation sub-network based on the task updating characteristics, inputting the travel error characteristics into the updated error compensation sub-network, and generating an error compensation result;
and generating a fuzzy control response result according to the time sequence compensation result and the error compensation result.
4. The method of claim 1, wherein the method further comprises:
performing supervision window verification on the operation and control management result;
if the verification result cannot meet the preset expected threshold, generating an update instruction;
and updating the window period of the period compensation window according to the updating instruction.
5. An automated servo-control system for an oil press, characterized by the steps for performing the method of any one of claims 1 to 4, said system comprising:
the data interaction module is used for establishing data communication with the servo oil press and interactively obtaining a task data set and an equipment working data set of the servo oil press;
the task time sequence association module is used for performing task execution fitting on the task data set, establishing task time nodes, and establishing task time sequence association by performing task analysis on the task data set;
the compensation window establishment module is used for carrying out data analysis by using the equipment working data set and establishing a periodic compensation window according to a data analysis result;
the control feedback receiving module is used for receiving control feedback of the position sensor according to the period compensation window when the servo oil press executes the task data set, and establishing a response data set of the position sensor;
the error analysis module is used for carrying out error analysis on the response data set to obtain travel error characteristics and error change rate characteristics;
the updating feature establishing module is used for carrying out time alignment with the task time node through the acquisition node of the period compensation window, calling task time sequence association based on an alignment result and establishing task updating features;
the fuzzy control response module is used for inputting the travel error characteristic, the error change rate characteristic and the task update characteristic into a fuzzy control network to generate a fuzzy control response result;
and the operation control management module is used for carrying out operation control management on the servo oil press based on the fuzzy control response result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410014359.8A CN117507469B (en) | 2024-01-05 | 2024-01-05 | Automatic servo operation control method and system for oil press |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410014359.8A CN117507469B (en) | 2024-01-05 | 2024-01-05 | Automatic servo operation control method and system for oil press |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117507469A CN117507469A (en) | 2024-02-06 |
| CN117507469B true CN117507469B (en) | 2024-03-22 |
Family
ID=89755330
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410014359.8A Active CN117507469B (en) | 2024-01-05 | 2024-01-05 | Automatic servo operation control method and system for oil press |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117507469B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118493939B (en) * | 2024-07-18 | 2024-09-27 | 泰安市勇瑞智能装备有限公司 | Control method, equipment and medium for numerical control spinning equipment based on Internet of things |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1038359A (en) * | 1988-03-19 | 1989-12-27 | 亥普沃斯工程有限公司 | The error compensation system of machine tool and so on |
| CN1562563A (en) * | 2004-03-31 | 2005-01-12 | 清华大学 | Method for compensating error of numeric-contrlled machine and system |
| CN114801301A (en) * | 2022-04-28 | 2022-07-29 | 重庆智能机器人研究院 | Control method and device for servo electric cylinder press, electronic equipment and storage medium |
| CN117200638A (en) * | 2023-11-02 | 2023-12-08 | 深圳市知酷信息技术有限公司 | Servo motor control analysis compensation system |
| CN117245872A (en) * | 2023-10-08 | 2023-12-19 | 东莞市坤裕精密模具有限公司 | State compensation model control method and system for batch injection molding process |
-
2024
- 2024-01-05 CN CN202410014359.8A patent/CN117507469B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1038359A (en) * | 1988-03-19 | 1989-12-27 | 亥普沃斯工程有限公司 | The error compensation system of machine tool and so on |
| CN1562563A (en) * | 2004-03-31 | 2005-01-12 | 清华大学 | Method for compensating error of numeric-contrlled machine and system |
| CN114801301A (en) * | 2022-04-28 | 2022-07-29 | 重庆智能机器人研究院 | Control method and device for servo electric cylinder press, electronic equipment and storage medium |
| CN117245872A (en) * | 2023-10-08 | 2023-12-19 | 东莞市坤裕精密模具有限公司 | State compensation model control method and system for batch injection molding process |
| CN117200638A (en) * | 2023-11-02 | 2023-12-08 | 深圳市知酷信息技术有限公司 | Servo motor control analysis compensation system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117507469A (en) | 2024-02-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117507469B (en) | Automatic servo operation control method and system for oil press | |
| JP2019527413A (en) | Computer system and method for performing root cause analysis to build a predictive model of rare event occurrences in plant-wide operations | |
| DE102011102034A1 (en) | Online adjustment of a process-analytical model with effective process operation | |
| JPH07191737A (en) | Apparatus and method for control of factory | |
| CN113094860B (en) | Industrial control network flow modeling method based on attention mechanism | |
| US20210065021A1 (en) | Working condition state modeling and model correcting method | |
| WO2017088208A1 (en) | Data-difference-driven self-learning dynamic optimization method for batch process | |
| CN113779882B (en) | Method, device, equipment and storage medium for predicting residual service life of equipment | |
| DE102020102863A1 (en) | Parameterization of a component in the automation system | |
| CN109634233A (en) | Industrial big data intellectual analysis decision-making technique, readable storage medium storing program for executing and terminal | |
| CN108198268B (en) | Production equipment data calibration method | |
| DE102016117585A1 (en) | Method and apparatus for determining steps for a multi-random variable batch control analysis | |
| CN113534741A (en) | Control method and system for milling thin-walled workpiece | |
| CN111398523B (en) | Sensor data calibration method and system based on distribution | |
| CN109614451A (en) | Industrial big data intellectual analysis decision making device | |
| CN102707617A (en) | Method for realizing fuzzy PID (Proportion Integration Differentiation) ActiveX control | |
| CN106844152B (en) | Bank's background task runs the correlation analysis and device of batch time | |
| US20220044060A1 (en) | Control system and control method | |
| CN116399629A (en) | Fault monitoring and early warning method and system for injection production equipment | |
| CN110083804B (en) | Wind power plant SCADA data missing intelligent repairing method based on condition distribution regression | |
| CN114787735A (en) | Prediction system, information processing device, and information processing program | |
| CN109887253B (en) | Correlation analysis method for petrochemical device alarm | |
| CN110826718A (en) | Naive Bayes-based large-segment unequal-length missing data filling method | |
| CN116435995A (en) | Time series processing method, computer readable storage medium and electronic device | |
| CN116467592A (en) | Production equipment fault intelligent monitoring method and system based on deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |