CN115562141A - Industrial equipment remote control method and device based on PLC - Google Patents
Industrial equipment remote control method and device based on PLC Download PDFInfo
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- CN115562141A CN115562141A CN202211027100.4A CN202211027100A CN115562141A CN 115562141 A CN115562141 A CN 115562141A CN 202211027100 A CN202211027100 A CN 202211027100A CN 115562141 A CN115562141 A CN 115562141A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/05—Programmable logic controllers, e.g. simulating logic interconnections of signals according to ladder diagrams or function charts
- G05B19/054—Input/output
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/10—Plc systems
- G05B2219/15—Plc structure of the system
- G05B2219/15028—Controller and device have several formats and protocols, select common one
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- 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]
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Abstract
The invention relates to the technical field of PLC control, and discloses a PLC-based industrial equipment remote control method, which comprises the following steps: step 1, inputting an analog quantity or digital quantity signal into the interior of the PLC by the PLC input end, and step 2, transmitting the signal input of the input end to a remote cloud server by the PLC through a 5G communication module. The PLC control industrial equipment operation analysis is more accurate through a PLC module, a 5G communication module, a remote cloud server and a deep learning module, the deep learning module defines problems and collects data, a model prediction performance index is defined, a model evaluation mode is determined, data preprocessing, a model is built, cross verification and a test set verification step are carried out, the model built in real time can ensure that the control operation process of industrial equipment control signal input received by the PLC is calculated by the current optimal model, the adoption of a traditional fixed operation control model is avoided, and therefore the PLC control industrial equipment is enabled to be remotely, intelligently and accurately controlled.
Description
Technical Field
The invention relates to the technical field of PLC control, in particular to a PLC-based industrial equipment remote control method and device.
Background
The PLC control system is a novel industrial control device of a generation formed by introducing a microelectronic technology, a computer technology, an automatic control technology and a communication technology on the basis of a traditional sequence controller, and aims to replace sequential control functions of a relay, execution logic, timing, counting and the like and establish a flexible remote control system.
The PLC controls the industrial equipment automatically, the automation of the industrial equipment realized by the PLC needs to realize automation and also needs to realize intellectualization along with the production requirements of high precision, high speed, environmental protection and intelligence of industrial production, and the remote control of the current PLC controlled industrial equipment cannot realize the intellectualization of a control operation process.
Disclosure of Invention
In order to solve the production requirements of high precision, high speed, environmental protection and intelligence along with industrial production, the automation of industrial equipment realized by PLC not only needs to realize automation, but also needs to realize intelligence, and the problem that the remote control of the current PLC-controlled industrial equipment cannot realize the intelligence of the control operation process is solved, and the purpose of high precision and intelligent control of the control operation process is realized.
The invention is realized by the following technical scheme: the PLC-based industrial equipment remote control method comprises the following steps:
step 1, inputting an analog quantity or digital quantity signal into a PLC by a PLC input end;
step 2, the PLC inputs the signals of the input end and transmits the signals to a remote cloud server through a 5G communication module;
step 3, the deep learning module converts the signal input transmitted to the interior of the remote cloud server into a problem point and conducts machine learning to build an optimal calculation model;
step 4, defining problems and collecting data of signal input received by a remote server, defining model prediction performance indexes, determining a model evaluation mode, preprocessing data, building a model, performing cross validation, verifying a test set and building an optimal model;
step 5, providing the model built by the deep learning module for a remote cloud server to perform operation analysis on the PLC input signal transmitted by the 5G communication module;
step 6, after the operation and analysis of the remote cloud server, a PLC control instruction is formed and is transmitted to the interior of the PLC through the 5G communication module;
step 7, the PLC receives a formed instruction after the operation and analysis of the remote cloud server and inputs the instruction into the control execution mechanism to act by analog quantity or switching value;
the remote cloud server management and maintenance realizes man-machine interaction through a management control end;
the specific operation flow of the deep learning module in the PLC industrial equipment remote control comprises the following processes:
the method comprises the following steps that 1, signals transmitted to the interior of a remote communication module through a 5G communication module are converted into a problem which is defined as machine readable, and meanwhile, a large amount of relevant data are collected;
determining common calculation precision of the balance classification analysis definition problem, the area under the operating characteristic curve of a receiver, common precision and recall rate of the imbalance classification analysis definition problem and common average absolute error of scalar regression, determining the accurate index of the model required by the definition problem, and ensuring that the definition problem calculated by the screened model meets the set calculation performance index;
3, evaluating model indexes and precision of the calculation definition problem by adopting a leave-out method, K-fold cross validation and disorder repeated K-fold cross validation, thereby evaluating whether the validation model meets the set calculation performance index;
the method comprises the following steps of (1) preprocessing collected data related to a definition problem, wherein a preprocessing target needs to meet four aspects that an eigenvalue is tensor data, the eigenvalue is small (zero to one interval or positive and negative one interval), the characteristic non-heterogeneous data and the characteristic missing processing is zero;
and 5, determining an activation function and a loss function, generally selecting an LeakReLU function by a hidden layer to obtain a relatively ideal effect, gradually enlarging the model scale from a simple structure, and considering regularization and dropout.
The process 6 is that training is carried out on the verification set for multiple times to find the model structure of the calculation definition problem of the optimal performance index;
and 7, the difference between the performance of the test set and the performance of the verification set is large, and a more complex verification method is considered, such as out-of-order repeated K-fold cross verification, until a model of a calculation definition problem of a calculation performance index is met and is provided for a remote cloud server to carry out operational analysis on an input signal.
The model quantity of the PLC input end is divided into pressure quantity, temperature quantity, hydraulic pressure quantity and flow quantity.
Further, the analog quantity of the PLC output end is divided into a voltage quantity and a current quantity.
Further, the switching value of the output end is divided into the output value of the transistor and the output of the relay.
Further, the 5G communication module and the PLC carry out data communication through a PROFIBUS, a PROFINET, a DH-data protocol, MECHANOLINK, an MPI-multipoint interface, an intelligent distributed protocol and a HART-high speed addressable remote sensor protocol communication protocol.
The device suitable for the PLC-based industrial equipment remote control method comprises the following steps:
each industrial device is correspondingly matched with the corresponding PLC controller, and the PLC controller receives output signals of the corresponding industrial devices and transmits the output signals to the 5G communication module;
the 5G signal controller corresponds to the 5G communication module, and the 5G signal controller receives a signal sent by the PLC and transmits the signal to the inside of the remote cloud server;
the remote service system comprises a remote service host, a remote cloud server, a PLC controller, a management control end software, a deep learning software, a filtering calculation model, a 5G signal controller and a PLC trigger instruction, wherein the management control end software carried by the remote service host corresponding to the remote cloud server carries out remote maintenance and upgrading on the remote cloud service host by a user, the deep learning software carried by the remote cloud service host establishes the filtering calculation model to carry out calculation analysis on input signals transmitted by the PLC controller in the remote service host, data after calculation analysis are transmitted to the PLC controller through the 5G signal controller, and the PLC controller triggers the instruction to control corresponding industrial equipment to act.
Furthermore, the PLC controller and the remote service host are in communication connection with the PLC through a PROFIBUS, PROFINET, a DH-data protocol, MECHANOPOLINK, MPI-multi-point interface, an intelligent distributed protocol and a HART-high speed addressable remote sensor protocol communication protocol by adopting a 5G communication module through a 5G signal controller.
The invention provides a PLC-based industrial equipment remote control method and device. The method has the following beneficial effects:
the PLC control industrial equipment operation analysis is more accurate through a PLC module, a 5G communication module, a remote cloud server and a deep learning module, the deep learning module defines problems and collects data, a model prediction performance index is defined, a model evaluation mode is determined, data preprocessing, a model is built, cross verification and a test set verification step are carried out, the model built in real time can ensure that the control operation process of industrial equipment control signal input received by the PLC is calculated by the current optimal model, the adoption of a traditional fixed operation control model is avoided, and therefore the PLC control industrial equipment is enabled to be remotely, intelligently and accurately controlled.
Drawings
FIG. 1 is a diagram illustrating a method for remotely controlling industrial equipment according to the present invention;
FIG. 2 is a diagram of the communication protocol between the PLC and the 5G communication module according to the present invention;
FIG. 3 is a system diagram of the deep learning module operating process of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the PLC-based industrial equipment remote control method and device is as follows:
example (b):
referring to fig. 1 to 3, a PLC-based industrial equipment remote control method includes the steps of:
step 1, inputting an analog quantity or digital quantity signal into a PLC by a PLC input end;
step 2, the PLC inputs signals at the input end and transmits the signals to a remote cloud server through a 5G communication module;
step 3, the deep learning module converts the signal input transmitted to the interior of the remote cloud server into a problem point and conducts machine learning to build an optimal calculation model;
step 4, defining problems and collecting data of signal input received by a remote server, defining model prediction performance indexes, determining a model evaluation mode, preprocessing data, building a model, performing cross validation, verifying a test set and building an optimal model;
step 5, providing the model built by the deep learning module for a remote cloud server to perform operation analysis on the PLC input signal transmitted by the 5G communication module;
step 6, after the operation and analysis of the remote cloud server, a PLC control instruction is formed and is transmitted to the interior of the PLC through the 5G communication module;
step 7, the PLC receives a formed instruction after the operation and analysis of the remote cloud server and inputs the instruction into the control execution mechanism to act by analog quantity or switching value;
the remote cloud server management and maintenance realizes man-machine interaction through a management control end;
the specific operation flow of the deep learning module in the PLC industrial equipment remote control comprises the following processes:
the method comprises the following steps that 1, signals transmitted to the interior of a remote communication module through a 5G communication module are converted into a problem which is defined as machine readable, and meanwhile, a large amount of relevant data are collected;
determining common calculation precision of the balanced classification analysis definition problem, the area under an operation characteristic curve of a receiver, common precision and recall rate of the unbalanced classification analysis definition problem and common average absolute error of scalar regression, determining an accurate index of a model required by the definition problem, and ensuring that the definition problem calculated by the screened model meets a set calculation performance index;
3, evaluating model indexes and precision evaluation of the calculation definition problem by adopting a leave-out method, K-turn cross validation and disorder repeated K-turn cross validation, so as to evaluate whether the validation model meets the set calculation performance index;
the method comprises the following steps of 4, preprocessing collected data related to a definition problem, wherein a preprocessing target needs to meet the four aspects that an eigenvalue is tensor data, the eigenvalue is small (from zero to one interval or positive and negative one intervals), the characteristic is non-heterogeneous data and the characteristic missing processing is zero;
and 5, determining an activation function and a loss function, wherein a LeakReLU function is selected by a common hidden layer to obtain a relatively ideal effect, and gradually enlarging the model scale from a simple structure by considering regularization and dropout.
The process 6 is that training is carried out on the verification set for multiple times to find the model structure of the calculation definition problem of the optimal performance index;
and 7, the difference between the test set performance and the verification set performance is large, and a more complex verification method is considered, such as disorder repeated K-fold cross verification, until the model of the calculation definition problem of the calculation performance index is met and is provided for a remote cloud server to carry out operational analysis on the input signal.
The model quantity of the PLC input end is divided into pressure quantity, temperature quantity, hydraulic pressure quantity and flow quantity.
The analog quantity at the output end of the PLC is divided into a voltage quantity and a current quantity.
The switching value of the output end is divided into the output quantity of the transistor and the output of the relay.
The 5G communication module and the PLC carry out data communication through PROFIBUS, PROFINET, DH-data protocol, MECHANTOPOLINK, MPI-multipoint interface, intelligent distributed protocol and HART-high speed addressable remote sensor protocol communication protocol.
The device suitable for the PLC-based industrial equipment remote control method comprises the following steps:
each industrial device is correspondingly matched with the corresponding PLC controller, and the PLC controllers receive output signals of the corresponding industrial devices and transmit the output signals to the 5G communication module;
the 5G signal controller corresponds to the 5G communication module, and the 5G signal controller receives a signal sent by the PLC controller and transmits the signal to the interior of the remote cloud server;
the remote cloud service system comprises a remote cloud server, a remote service host, a PLC (programmable logic controller) and a management control end software, wherein the management control end software carried by the remote service host corresponding to the remote cloud server carries out remote maintenance and upgrade of the remote cloud service host, a deep learning software carried by the remote cloud service host establishes a screening calculation model to carry out calculation and analysis on input signals transmitted by the PLC in the remote service host, data after calculation and analysis are transmitted to the PLC through a 5G signal controller, and a PLC trigger instruction controls corresponding industrial equipment to act.
The PLC controller and the remote service host are in communication connection with the PLC through a PROFIBUS, a PROFINET, a DH-data protocol, a MECHANOPOLINK, an MPI-multi-point interface, an intelligent distributed protocol and a HART-high-speed addressable remote sensor protocol communication protocol by adopting a 5G communication module through a 5G signal controller.
Through the PLC module, the 5G communication module, long-range cloud ware, the degree of deep learning module makes PLC control industrial equipment's operational analysis more accurate, the degree of deep learning module is through defining the problem and collecting data, define model prediction performance index, confirm the model aassessment mode, data preprocessing, build the model, cross verification, the model that test set verification step was built in real time can guarantee that the control operation process of PLC received industrial equipment control signal input adopts current optimal model calculation, avoid adopting traditional fixed operation control model, thereby make PLC control industrial equipment realize long-range, intelligence, accurate control.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. The PLC-based industrial equipment remote control method is characterized by comprising the following steps:
step 1, inputting an analog quantity or digital quantity signal into a PLC by a PLC input end;
step 2, the PLC inputs signals at the input end and transmits the signals to a remote cloud server through a 5G communication module;
step 3, the deep learning module converts the signal input transmitted to the interior of the remote cloud server into a problem point and conducts machine learning to build an optimal calculation model;
step 4, defining problems and collecting data of signal input received by a remote server, defining model prediction performance indexes, determining a model evaluation mode, preprocessing data, building a model, performing cross validation, verifying a test set and building an optimal model;
step 5, providing the model built by the deep learning module for a remote cloud server to perform operation analysis on the PLC input signal transmitted by the 5G communication module;
step 6, after the operation and analysis of the remote cloud server, a PLC control instruction is formed and is transmitted to the interior of the PLC through the 5G communication module;
step 7, the PLC receives a formed instruction after the operation and analysis of the remote cloud server and inputs the instruction into the control execution mechanism to act by analog quantity or switching value;
the remote cloud server management and maintenance realizes man-machine interaction through a management control end;
the specific operation flow of the deep learning module in the PLC industrial equipment remote control comprises the following processes:
the method comprises the following steps that 1, signals transmitted to the interior of a remote communication module through a 5G communication module are converted into a problem which is defined as machine readable, and meanwhile, a large amount of relevant data are collected;
determining common calculation precision of the balanced classification analysis definition problem, the area under an operation characteristic curve of a receiver, common precision and recall rate of the unbalanced classification analysis definition problem and common average absolute error of scalar regression, determining an accurate index of a model required by the definition problem, and ensuring that the definition problem calculated by the screened model meets a set calculation performance index;
3, evaluating model indexes and precision of the calculation definition problem by adopting a leave-out method, K-fold cross validation and disorder repeated K-fold cross validation, thereby evaluating whether the validation model meets the set calculation performance index;
the method comprises the following steps of 4, preprocessing collected data related to a definition problem, wherein a preprocessing target needs to meet the four aspects that an eigenvalue is tensor data, the eigenvalue is small (from zero to one interval or positive and negative one intervals), the characteristic is non-heterogeneous data and the characteristic missing processing is zero;
and 5, determining an activation function and a loss function, generally selecting an LeakReLU function by a hidden layer to obtain a relatively ideal effect, gradually enlarging the model scale from a simple structure, and considering regularization and dropout.
The process 6 is that training is carried out on the verification set for multiple times to find the model structure of the calculation definition problem of the optimal performance index;
and 7, the difference between the performance of the test set and the performance of the verification set is large, and a more complex verification method is considered, such as out-of-order repeated K-fold cross verification, until a model of a calculation definition problem of a calculation performance index is met and is provided for a remote cloud server to carry out operational analysis on an input signal.
2. The PLC-based industrial device remote control method according to claim 1, wherein: the model quantity of the PLC input end is divided into pressure quantity, temperature quantity, hydraulic pressure quantity and flow quantity.
3. The PLC-based industrial device remote control method according to claim 1, wherein: the analog quantity of the PLC output end is divided into a voltage quantity and a current quantity.
4. The PLC-based industrial device remote control method according to claim 1, wherein: and the switching value of the output end is divided into the output value of the transistor and the output of the relay.
5. The PLC-based industrial device remote control method according to claim 1, wherein: the 5G communication module and the PLC carry out data communication through a PROFIBUS, a PROFINET, a DH-data protocol, a MECHATPOLINK, an MPI-multipoint interface, an intelligent distributed protocol and a HART-high speed addressable remote sensor protocol communication protocol.
6. The apparatus for the PLC-based industrial device remote control method according to claim 1, comprising:
each industrial device is correspondingly matched with the corresponding PLC controller, and the PLC controllers receive output signals of the corresponding industrial devices and transmit the output signals to the 5G communication module;
the 5G signal controller corresponds to the 5G communication module, and the 5G signal controller receives a signal sent by the PLC controller and transmits the signal to the inside of the remote cloud server;
the remote service system comprises a remote service host, a remote cloud server, a PLC controller, a management control end software, a deep learning software, a filtering calculation model, a 5G signal controller and a PLC trigger instruction, wherein the management control end software carried by the remote service host corresponding to the remote cloud server carries out remote maintenance and upgrading on the remote cloud service host by a user, the deep learning software carried by the remote cloud service host establishes the filtering calculation model to carry out calculation analysis on input signals transmitted by the PLC controller in the remote service host, data after calculation analysis are transmitted to the PLC controller through the 5G signal controller, and the PLC controller triggers the instruction to control corresponding industrial equipment to act.
7. The PLC-based industrial device remote control apparatus according to claim 6, wherein: the PLC controller is in communication connection with the remote service host through a 5G signal controller, a 5G communication module is adopted by the PLC controller, and the PLC controller is in communication connection with a PROFIBUS, a PROFINET, a DH-data protocol, a MECHANOPOLINK, an MPI-multi-point interface, an intelligent distributed protocol and a HART-high-speed addressable remote sensor protocol.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117471982A (en) * | 2023-11-07 | 2024-01-30 | 广东知业科技有限公司 | Method for remotely controlling PLC (programmable logic controller) through edge calculation |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117471982A (en) * | 2023-11-07 | 2024-01-30 | 广东知业科技有限公司 | Method for remotely controlling PLC (programmable logic controller) through edge calculation |
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