CN117434886B - PLC control system and method based on operation digital model - Google Patents
PLC control system and method based on operation digital model Download PDFInfo
- Publication number
- CN117434886B CN117434886B CN202311767821.3A CN202311767821A CN117434886B CN 117434886 B CN117434886 B CN 117434886B CN 202311767821 A CN202311767821 A CN 202311767821A CN 117434886 B CN117434886 B CN 117434886B
- Authority
- CN
- China
- Prior art keywords
- control
- data
- state
- equipment
- production
- 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 38
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 21
- 239000000463 material Substances 0.000 claims description 130
- 238000004519 manufacturing process Methods 0.000 claims description 68
- 238000013507 mapping Methods 0.000 claims description 15
- 238000005457 optimization Methods 0.000 claims description 12
- 238000013506 data mapping Methods 0.000 claims description 8
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000003756 stirring Methods 0.000 abstract description 13
- 230000008569 process Effects 0.000 abstract description 9
- 238000003860 storage Methods 0.000 description 14
- 239000003795 chemical substances by application Substances 0.000 description 11
- 230000009471 action Effects 0.000 description 10
- 238000012986 modification Methods 0.000 description 8
- 230000004048 modification Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 238000004590 computer program Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000002787 reinforcement Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010924 continuous production Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001351 cycling effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008672 reprogramming Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- -1 sensors Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
Classifications
-
- 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
-
- 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
- G05B2219/15008—Identify connected I-O and store in address table
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Programmable Controllers (AREA)
Abstract
The invention discloses a PLC control system and a method based on an operation digital model, and relates to the field of PLC intelligent control. And the invention continuously optimizes the feeding and stirring processes of the control data in the database according to the RL algorithm, so that the system is more intelligent.
Description
Technical Field
The invention relates to the field of PLC intelligent control, in particular to a PLC control system and method based on an operation digital model.
Background
Existing PLC (Programmable Logic Controller) control techniques generally rely on static, pre-programmed logic to handle the need for industrial automation. They play a central role in handling continuous production lines, automated machinery and other industrial processes. PLCs typically use a latched logic program that controls the output device based on an input signal. These programs are fixed and are written in advance by the PLC programmer. In the user program execution phase, the programmable logic controller always scans the user programs (ladder diagrams) sequentially in an order from top to bottom. When each ladder diagram is scanned, the control circuit formed by each contact on the left side of the ladder diagram is always scanned, logic operation is carried out on the control circuit formed by the contacts in the order of left and right, top and bottom, and then the state of the corresponding bit of the logic coil in the storage area of the system RAM is refreshed according to the result of the logic operation; or refreshing the state of the corresponding bit of the output coil in the I/O mapping area; or whether a particular functional instruction specified by the ladder diagram is to be executed. Whereas conventional PLC control logic is not readily adaptable to changes or increases in complexity of the production process, reprogramming may be required whenever changes occur, and in conventional PLC systems, operation is not typically automatically optimized. This means that the system cannot learn from historical operating data, nor can it self-adjust to improve efficiency or adapt to new operating conditions. Therefore, the existing PLC system lacks advanced data processing and analysis capability, and the standard PLC is difficult to implement complex decisions and predictions based on multi-variable inputs, which is a problem to be solved at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a PLC control system and method based on an operation digital model, aiming at the problems that the prior PLC system lacks advanced data processing and analysis capability, the standard PLC is difficult to realize complex decision and prediction based on multivariable input, and the like.
The PLC control system based on the operation digital model comprises an operation end, a control end and an equipment end, wherein the operation end is used for issuing control instructions according to requirements, the control end recognizes and converts the control instructions issued by the operation end into control instructions which can be recognized by the control end through data mapping, and sends driving control instructions to the equipment end through the mapped control instructions, the equipment end drives equipment according to the received driving control instructions and performs data acquisition on the equipment, the acquired equipment state data and material state data are returned to the operation end, and the operation end visualizes the equipment state data;
the control end comprises a database storing production plans, the control instruction comprises a daily production plan, the control end matches the production plan in the database according to the daily production plan, and the control end calls the matched production plan in the database to generate a corresponding driving control instruction;
furthermore, the operation end performs optimization decision on the control data in the database according to the RL algorithm through the acquired equipment state data.
Further, the control end defines rules for mapping the fields or structures of the source data to the fields or structures of the target data, and the source data is subjected to data conversion through the defined mapping rules to generate a target format which can be identified by the control end.
Further, the daily production plan comprises a production material name, a production material time and a total production material amount; the production plan comprises a material table and a control data table, wherein the material table and the control data table are corresponding through a material name material ID of an associated key, and the control data table at least comprises a device state DeviceState, a material required feeding type and a material required quantity feed Amount; and the daily production plan searches the required control data in the corresponding control data table through the material name to generate a control instruction with the control data.
Further, the operation end collects equipment state data and material state data and establishes an equipment state vectorAnd a Material State vector->Wherein, said->Represents mth device status data, said +.>Indicate->And (5) material state data.
Furthermore, the operating end performs combination optimization on the equipment state and the material state through a Q-learning algorithm, and updates the optimized control parameters, and the specific calculation mode is as follows:
；
wherein the saidIndicating the state of the material in the environment at time t, said +.>Indicated in the material state->State vector of time device, said +.>Indicated in the material state->The device status is->Instant rewards obtained at the time, theRepresenting the status of the device->Is the next state vector of said +.>The maximum Q value representing all possible device states in the next state, said +.>Representing a discount factor for calculating the current value of a future reward, said +.>Representing the learning rate.
Furthermore, the operation end also comprises a log database, and the log database stores the past material data, the control data, the equipment state data and the operation parameter updating data.
Further, a PLC control method based on an operation digital model is provided, the method is implemented based on a PLC control system based on an operation digital model as described in any one of the above, the method includes the following steps:
s1, issuing control instructions through an operation end according to requirements, wherein the control instructions comprise daily production plans, and the control end calls the production plans matched in a database to generate corresponding driving control instructions according to the daily production plans matched with the production plans in the database;
s2, through data mapping, the control end recognizes and converts the issued control instruction into a control instruction which can be recognized by the control end; and sending a driving control instruction to the equipment end through the mapped control instruction;
s3, driving the equipment by the equipment end according to the received driving control instruction, and collecting data of the equipment, wherein the collected equipment state data and material state data are transmitted back to the operation end;
in the step S3, the method further includes performing an optimization decision on control data in the database according to the RL algorithm by collecting the acquired equipment state data.
Further, the daily production plan comprises a production material name, a production material time and a total production material amount; the production plan comprises a material table and a control data table, wherein the material table and the control data table are corresponding through a material name material ID of an associated key, and the control data table at least comprises a device state DeviceState, a material required feeding type and a material required quantity feed Amount; and the daily production plan searches the required control data in the corresponding control data table through the material name to generate a control instruction with the control data.
Further, in step S2, the control end identifies and converts the issued control instruction into a control instruction that can be identified by the control end, specifically, defines rules for mapping a field or structure of source data to a field or structure of target data, and the source data performs data conversion through the defined mapping rules to generate a target format that can be identified by the control end.
Further, in the step S3, the optimizing decision of the control data in the database according to the RL algorithm specifically includes the following sub-steps:
s301, the operation end collects equipment state data and material state data and establishes an equipment state vectorAnd a Material State vector->Wherein, said->Represents mth device status data, said +.>Indicate->Individual material status data;
s302, the operation end performs combination optimization on the equipment state and the material state through a Q-learning algorithm, and updates the optimized control parameters, wherein the specific calculation mode is as follows:
；
wherein the saidIndicating the state of the material in the environment at time t, said +.>Indicated in the material state->State vector of time device, said +.>Indicated in the material state->The device status is->Instant rewards obtained at the time, theRepresenting the status of the device->Is the next state vector of said +.>The maximum Q value representing all possible device states in the next state, said +.>Representing a discount factor for calculating the current value of a future reward, said +.>Representing the learning rate.
The beneficial effects of the invention are as follows:
(1) The invention can effectively map the production data such as material names and the like sent by the rear end into related control data, and ensure that the PLC can identify different data so as to control equipment;
(2) According to the invention, the PLC not only adjusts the stirring parameters according to the real-time data, but also learns the characteristics of materials and the change of the production environment according to the learning algorithm, so that the feeding and stirring processes are continuously optimized, and the flexibility of the production process and the stability of the product quality are ensured.
Drawings
FIG. 1 is a system block diagram of a PLC control system based on an operation digital model according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a PLC controlled terminal device based on an operation digital model according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a computer readable storage medium for implementing a PLC control method based on an operation digital model according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the accompanying drawings, but the scope of the present invention is not limited to the following description.
For the purpose of making the technical solution and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention. It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The features and capabilities of the present invention are described in further detail below in connection with the examples.
As shown in fig. 1, the PLC control system based on the operation digital model includes an operation end, a control end and a device end, where the operation end is configured to issue control instructions according to requirements, the control end identifies and converts the control instructions issued by the operation end into control instructions identifiable by the control end through data mapping, and sends a driving control instruction to the device end through the mapped control instructions, the device end drives the device according to the received driving control instruction, performs data acquisition on the device, and returns acquired device state data and material state data to the operation end, where the operation end visualizes the device state data;
the control end comprises a database storing production plans, the control instruction comprises a daily production plan, the control end matches the production plan in the database according to the daily production plan, and the control end calls the matched production plan in the database to generate a corresponding driving control instruction;
furthermore, the operation end performs optimization decision on the control data in the database according to the RL algorithm through the acquired equipment state data.
Further, the control end defines rules for mapping the fields or structures of the source data to the fields or structures of the target data, and the source data is subjected to data conversion through the defined mapping rules to generate a target format which can be identified by the control end. By way of example, a schematic flow of data parsing and data mapping is presented, specifically, for data parsing: checking the data format sent by the back end, and identifying the data format by MIME type, file extension or the structure of the data; reading the data stream through a network, serial port, file system, or the like; parsing the data stream to split it into meaningful units (e.g., JSON key-value pairs, XML elements and attributes); the parsed elements are converted into data structures within the program, such as hash tables, objects, lists, and the like. For data mapping: rules defining mapping the fields or structures of the source data to the fields or structures of the target data, including changes in field names, transformations of data types, changes in units, etc.; the mapping rule is applied and the actual data conversion is performed. For example, converting a character string to a number, or merging values of multiple fields; a new data format is generated according to the requirements of the target system or protocol, and for PLCs, a message format conforming to a particular communication protocol (e.g., modbus) is generated.
Further, the daily production plan comprises a production material name, a production material time and a total production material amount; the production plan comprises a material table and a control data table, wherein the material table and the control data table are corresponding through a material name material ID of an associated key, and the control data table at least comprises a device state DeviceState, a material required feeding type and a material required quantity feed Amount; and the daily production plan searches the required control data in the corresponding control data table through the material name to generate a control instruction with the control data. Specifically, the present embodiment uses a relational database to store control data, in which, by creating tables (tables) and defining columns (columns) to describe data and using rows (rows) to store actual data values, for example, when a PLC control system receives a material name, the search flow is:
1. querying a material table (Materials) to find a material ID corresponding to the material name;
2. querying a control data table (ControlData) for related control parameters by using the found MaterialID; reading DeviceState, feedType and feedcount values from the control data table;
the PLC control system uses these parameter values to set the state of the plant and control the feed process.
As a preferred example, when the control receives a Material name "Material a", it performs the following steps:
1. searching a Material ID (assumed to be 1) corresponding to the Material A in a Material table;
2. querying a row with the materialID of 1 in a control data table;
3. reading the data of the row to obtain equipment state (ON), feeding Type (Type 1) and feeding number (100);
and 4, the PLC performs control operation on the equipment according to the control data.
Further, the operation end collects equipment state data and material state data and establishes an equipment state vectorAnd a Material State vector->Wherein, said->Represents mth device status data, said +.>Indicate->And (5) material state data.
Furthermore, the operating end performs combination optimization on the equipment state and the material state through a Q-learning algorithm, and updates the optimized control parameters, and the specific calculation mode is as follows:
；
wherein the saidIndicating the state of the material in the environment at time t, said +.>Indicated in the material state->State vector of time device, said +.>Indicated in the material state->The device status is->Instant rewards obtained at the time, theRepresenting the status of the device->Is the next state vector of said +.>The maximum Q value representing all possible device states in the next state, said +.>Representing a discount factor for calculating the current value of a future reward, said +.>Representing the learning rate.
Furthermore, for the self-adaptive adjustment scheme, in the continuous production process, a RL algorithm is adopted to enable the system to learn the optimal operation strategy through trial and error; for example, the most efficient charging and stirring strategy for a particular material is learned by the RL algorithm. Preferably, the use of self-learning tuning algorithms in PLC control systems using reinforcement learning (Reinforcement Learning, RL) is proposed, in particular reinforcement learning, which is a machine learning method in which an algorithm (called agent) learns optimal behavior strategies by interacting with the environment to maximize the jackpot. Further, the environment includes a stirred tank, materials, sensors, and any component that can provide system status information; the agent needs to decide how to operate the control system of the stirred tank (such as stirring speed, time and sequence, etc.) to optimize the material mixing.
Further, the agent collects information about the current state of the stirring kettle, such as temperature, pressure, material viscosity, etc., through a sensor, and corresponds to the material state in this embodiment; based on the observed environmental conditions, the agent may select an action, such as adjusting the stirring speed, stirring time, etc., corresponding to the device condition in this embodiment, and it should be further noted that, for the temperature, it is a parameter determining the condition of the material in a part of the solutions, and for the part of the solutions, it is a parameter determining the condition of the device, specifically, selecting according to the solutions. After the action, giving an rewarding signal to the agent according to the stirring result, and if the stirring result approaches or reaches a preset quality standard, obtaining positive rewarding by the agent; if the effect is poor, the prize is negative; by continually cycling through the above process, agents gradually learn and refine their policies until an optimal solution is found or a performance threshold is reached.
By way of example, the application of the Q-learning algorithm in the feed control of a stirred tank is proposed, and the state is assumed to be discretized into three states of low, medium and high based on the current material concentration in the stirred tank; the action taken by the agent is to adjust the feed rate, which is simplified into three types: slow, medium and fast; when the stirring quality after charging reaches the expected one, a positive reward (+1) is given, and when the stirring quality is poor, a negative reward (-1) is given. First, the Q-table is initialized, where each combination of states and actions corresponds to a value, which may initially be 0 or a small random number, the agent begins to interact with the environment, takes action, and observes the results to update the Q-table. Hypothesis learning rate=0.1, discount factor->=0.9, the current state is medium concentration, the agent selects medium feed rate, then obtains a reward (+1) because the stirring quality reaches the expectations, substitutes the value into the formula:the above process is repeated, the Q table is updated after each interaction, and over time, the Q table will more and more accurately reflect the expected return for each action, which the agent will use to select the best action.
Furthermore, the operation end also comprises a log database, and the log database stores the past material data, the control data, the equipment state data and the operation parameter updating data.
Further, a PLC control method based on an operation digital model is provided, the method is implemented based on a PLC control system based on an operation digital model as described in any one of the above, the method includes the following steps:
s1, issuing control instructions through an operation end according to requirements, wherein the control instructions comprise daily production plans, and the control end calls the production plans matched in a database to generate corresponding driving control instructions according to the daily production plans matched with the production plans in the database;
s2, through data mapping, the control end recognizes and converts the issued control instruction into a control instruction which can be recognized by the control end; and sending a driving control instruction to the equipment end through the mapped control instruction;
s3, driving the equipment by the equipment end according to the received driving control instruction, and collecting data of the equipment, wherein the collected equipment state data and material state data are transmitted back to the operation end;
in the step S3, the method further includes performing an optimization decision on control data in the database according to the RL algorithm by collecting the acquired equipment state data.
Further, the daily production plan comprises a production material name, a production material time and a total production material amount; the production plan comprises a material table and a control data table, wherein the material table and the control data table are corresponding through a material name material ID of an associated key, and the control data table at least comprises a device state DeviceState, a material required feeding type and a material required quantity feed Amount; and the daily production plan searches the required control data in the corresponding control data table through the material name to generate a control instruction with the control data.
Further, in step S2, the control end identifies and converts the issued control instruction into a control instruction that can be identified by the control end, specifically, defines rules for mapping a field or structure of source data to a field or structure of target data, and the source data performs data conversion through the defined mapping rules to generate a target format that can be identified by the control end.
In a further step S3, the optimizing decision of the control data in the database according to the RL algorithm specifically includes the following sub-steps:
s301, the operation end collects equipment state data and material state data and establishes an equipment state vectorAnd a Material State vector->Wherein, said->Represents mth device status data, said +.>Indicate->Individual material status data;
s302, the operation end performs combination optimization on the equipment state and the material state through a Q-learning algorithm, and updates the optimized control parameters, wherein the specific calculation mode is as follows:
；
wherein the saidIndicating the state of the material in the environment at time t, said +.>Indicated in the material state->State vector of time device, said +.>Indicated in the material state->The device status is->Instant rewards obtained at the time, theRepresenting the status of the device->Is the next state vector of said +.>The maximum Q value representing all possible device states in the next state, said +.>Representing a discount factor for calculating the current value of a future reward, said +.>Representing the learning rate.
Further, as a preferred implementation of the present example, a terminal device based on PLC control of an operation digital model is proposed, as shown in fig. 2, the terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.
Memory 210 may include readable media in the form of volatile memory, such as Random Access Memory (RAM) 211 and/or cache memory 212, and may further include Read Only Memory (ROM) 213.
The memory 210 further stores a computer program, and the computer program may be executed by the processor 220, so that the processor 220 executes any one of the above-mentioned PLC control methods based on the operation digital model in the embodiments of the present application, and a specific implementation manner of the method is consistent with the implementation manner and the achieved technical effects described in the above-mentioned embodiments, and some contents are not repeated. Memory 210 may also include a program/utility 214 having a set (at least one) of program modules 215 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Accordingly, the processor 220 may execute the computer programs described above, as well as the program/utility 214.
Bus 230 may be a local bus representing one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or using any of a variety of bus architectures.
Terminal device 200 can also communicate with one or more external devices 240, such as a keyboard, pointing device, bluetooth device, etc., as well as one or more devices capable of interacting with the terminal device 200, and/or with any device (e.g., router, modem, etc.) that enables the terminal device 200 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 250. Also, terminal device 200 can communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 260. Network adapter 260 may communicate with other modules of terminal device 200 via bus 230. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with terminal device 200, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, data backup storage platforms, and the like.
Further, as a preferred implementation of the present example, a computer readable storage medium based on a blockchain and a PLC control based on an operational digital model is provided, the computer readable storage medium having stored thereon instructions that when executed by a processor implement any one of the above-described PLC control methods based on an operational digital model. The specific implementation manner of the method is consistent with the implementation manner and the achieved technical effect described in the above embodiments, and some of the details are not repeated.
Fig. 3 shows a program product 300 provided by the present embodiment for implementing the above method, which may employ a portable compact disc read-only memory (CD-ROM) and comprise program code, and may be run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. Program product 300 may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
The present application describes functional improvements and usage elements that are emphasized by the patent laws, and the above description and drawings are merely preferred embodiments of the present application and not limiting the present application, and therefore, all structures, devices, features, etc. that are similar and identical to those of the present application, i.e. all equivalents and modifications made by the patent application are intended to be within the scope of protection of the patent application of the present application.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (7)
1. The PLC control system based on the operation digital model comprises an operation end, a control end and a device end, and is characterized in that the operation end is used for issuing control instructions according to requirements, the control end recognizes and converts the control instructions issued by the operation end into control instructions recognized by the control end through data mapping, and sends driving control instructions to the device end through the mapped control instructions, the device end drives the device according to the received driving control instructions, data acquisition is carried out on the device, the acquired device state data and material state data are returned to the operation end, and the operation end visualizes the device state data;
the control end comprises a database storing production plans, the control instruction comprises a daily production plan, the control end matches the production plan in the database according to the daily production plan, and the control end calls the matched production plan in the database to generate a corresponding driving control instruction;
the operation end performs optimization decision on control data in a database according to an RL algorithm through the acquired equipment state data;
the operation end collects equipment state data and material state data and establishes an equipment state vectorAnd a Material State vector->Wherein, said->Represents mth device status data, said +.>Indicate->Individual material status data;
the operating end performs combination optimization on the equipment state and the material state through a Q-learning algorithm, and updates the optimized control parameters, wherein the specific calculation mode is as follows:
；
wherein the saidIndicating the state of the material in the environment at time t, said +.>Indicated in the material state->State vector of time device, said +.>Indicated in the material state->The device status is->Instant rewards obtained at that time, said +.>Representing the status of the device->Is the next state vector of said +.>The maximum Q value representing all possible device states in the next state, said +.>Representing a discount factor for calculating the current value of a future reward, said +.>Representing the learning rate.
2. The PLC control system according to claim 1, wherein the control terminal defines rules for mapping the fields or structures of the source data to the fields or structures of the target data, and the source data performs data conversion by using the defined mapping rules to generate the target format recognizable by the control terminal.
3. The PLC control system based on the operational digital model of claim 1, wherein the daily production schedule includes a production material name, a production material time, and a total production material amount; the production plan comprises a material table and a control data table, wherein the material table and the control data table are corresponding through a material name material ID of an associated key, and the control data table at least comprises a device state DeviceState, a material required feeding type and a material required quantity feed Amount; and the daily production plan searches the required control data in the corresponding control data table through the material name to generate a control instruction with the control data.
4. The PLC control system based on the operational digital model of claim 1, wherein the operating side further comprises a log database storing past material data, control data, equipment status data, and operating parameter update data.
5. A PLC control method based on an operational digital model, the method being implemented based on a PLC control system based on an operational digital model according to any one of claims 1-4, characterized in that the method comprises the steps of:
s1, issuing control instructions through an operation end according to requirements, wherein the control instructions comprise daily production plans, and the control end calls the production plans matched in a database to generate corresponding driving control instructions according to the daily production plans matched with the production plans in the database;
s2, through data mapping, the control end recognizes and converts the issued control instruction into a control instruction which can be recognized by the control end; and sending a driving control instruction to the equipment end through the mapped control instruction;
s3, driving the equipment by the equipment end according to the received driving control instruction, and collecting data of the equipment, wherein the collected equipment state data and material state data are transmitted back to the operation end;
in the step S3, the method further includes performing an optimization decision on control data in a database according to an RL algorithm by collecting the acquired equipment state data, and specifically includes the following sub-steps:
s301, the operation end collects equipment state data and material state data and establishes an equipment state vectorAnd a Material State vector->Wherein, said->Represents mth device status data, said +.>Indicate->Individual material status data;
s302, the operation end performs combination optimization on the equipment state and the material state through a Q-learning algorithm, and updates the optimized control parameters, wherein the specific calculation mode is as follows:
；
wherein the saidIndicating the state of the material in the environment at time t, said +.>Indicated in the material state->State vector of time device, said +.>Indicated in the material state->The device status is->Instant rewards obtained at that time, said +.>Representing the status of the device->Is the next state vector of said +.>The maximum Q value representing all possible device states in the next state, said +.>Representing a discount factor for calculating the current value of a future reward, said +.>Representing the learning rate.
6. The PLC control method based on the operational digital model of claim 5, wherein the daily production schedule includes a production material name, a production material time, and a total production material amount; the production plan comprises a material table and a control data table, wherein the material table and the control data table are corresponding through a material name material ID of an associated key, and the control data table at least comprises a device state DeviceState, a material required feeding type and a material required quantity feed Amount; and the daily production plan searches the required control data in the corresponding control data table through the material name to generate a control instruction with the control data.
7. The PLC control method based on the running digital model of claim 5, wherein in step S2, the control terminal identifies and converts the issued control command into the control command identifiable by the control terminal, specifically, defines rules for mapping the field or structure of the source data to the field or structure of the target data, and the source data performs data conversion through the defined mapping rules to generate the target format identifiable by the control terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311767821.3A CN117434886B (en) | 2023-12-21 | 2023-12-21 | PLC control system and method based on operation digital model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311767821.3A CN117434886B (en) | 2023-12-21 | 2023-12-21 | PLC control system and method based on operation digital model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117434886A CN117434886A (en) | 2024-01-23 |
| CN117434886B true CN117434886B (en) | 2024-03-08 |
Family
ID=89558682
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311767821.3A Active CN117434886B (en) | 2023-12-21 | 2023-12-21 | PLC control system and method based on operation digital model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117434886B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117666462B (en) * | 2024-01-31 | 2024-05-17 | 成都苔岑智能设备有限公司 | PLC product standardization control system based on independent allocation of multiple parameters |
Citations (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002108600A (en) * | 2000-10-02 | 2002-04-12 | Digital Electronics Corp | Terminal device for control system and recording medium and control system |
| WO2003053035A1 (en) * | 2001-12-18 | 2003-06-26 | Siemens Energy & Automation, Inc. | Plc to plc communications with a modem on the plc i/o bus |
| CN101261695A (en) * | 2007-03-07 | 2008-09-10 | 杭州电子科技大学 | A real time dynamic re-dispatching method for production dispatching system in process industry |
| CN103197634A (en) * | 2013-03-15 | 2013-07-10 | 上海大学 | Generating system and generating method of on-line prediction and on-line processing plan for automatic manufacturing and processing system |
| JP2014032529A (en) * | 2012-08-03 | 2014-02-20 | Hakko Denki Kk | Plc system, drawing editor device thereof, and programmable display |
| CN104057637A (en) * | 2014-05-08 | 2014-09-24 | 浙江工业大学 | Computer numerical control press punch brake curve self-learning method based on support vector machine |
| CN105302096A (en) * | 2015-11-10 | 2016-02-03 | 湖南千盟物联信息技术有限公司 | Factory intelligent scheduling method |
| CN106216079A (en) * | 2016-08-29 | 2016-12-14 | 四川亿欣新材料有限公司 | Vertical Mill intelligent control method based on PLC |
| CN107451165A (en) * | 2016-05-31 | 2017-12-08 | 合肥美亚光电技术股份有限公司 | Based on the HMI formula lookup methods exchanged with PLC online datas and system |
| CN107831720A (en) * | 2017-10-20 | 2018-03-23 | 爱普（福建）科技有限公司 | Optimize the control method and computer equipment of equipment operating mode |
| CN107861478A (en) * | 2017-10-17 | 2018-03-30 | 广东工业大学 | A kind of parallel control method in intelligent workshop and system |
| EP3401801A1 (en) * | 2017-05-12 | 2018-11-14 | BAE SYSTEMS plc | A system for improved data storage and retrieval |
| CN109283895A (en) * | 2017-07-20 | 2019-01-29 | 费希尔-罗斯蒙特系统公司 | General mapping in industrial process plant |
| CN110347564A (en) * | 2019-05-24 | 2019-10-18 | 平安普惠企业管理有限公司 | Data creation method and device, electronic equipment, storage medium |
| KR20200036218A (en) * | 2018-09-28 | 2020-04-07 | 충북대학교 산학협력단 | Method and system for gathering data from heterogeneous programmable logic controller in real time |
| CN111487919A (en) * | 2020-05-09 | 2020-08-04 | 柳州铁道职业技术学院 | P L C industrial automation control system and control method |
| CN112413831A (en) * | 2020-11-25 | 2021-02-26 | 中国电力科学研究院有限公司 | Energy-saving control system and method for central air conditioner |
| CN112596459A (en) * | 2020-12-15 | 2021-04-02 | 江苏久卫智能科技有限公司 | Intelligent bus duct temperature and humidity abnormity early warning system |
| CN113157741A (en) * | 2021-04-26 | 2021-07-23 | 上海淇馥信息技术有限公司 | Service state visualization method and device based on dimension conversion and electronic equipment |
| CN113259486A (en) * | 2021-06-24 | 2021-08-13 | 国网天津市电力公司营销服务中心 | Automatic verification line operation and maintenance system for metering equipment based on digital twins |
| CN113676375A (en) * | 2021-08-13 | 2021-11-19 | 浙江大学 | Industrial control system private protocol structure analysis method |
| CN115858488A (en) * | 2022-10-11 | 2023-03-28 | 厦门市美亚柏科信息股份有限公司 | Parallel migration method and device based on data governance and readable medium |
| CN115997244A (en) * | 2020-06-08 | 2023-04-21 | 英特尔公司 | Collective perception service in intelligent transportation system |
| CN116756837A (en) * | 2023-08-17 | 2023-09-15 | 广东省科学院广州地理研究所 | Digital twin resource data database building management method and device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060074499A1 (en) * | 2004-10-01 | 2006-04-06 | Rafie Hamidpour | System and method for industrial process control |
| US7721019B2 (en) * | 2005-09-30 | 2010-05-18 | Rockwell Automation Technologies, Inc. | Method and apparatus for partitioning industrial control data |
| US11797820B2 (en) * | 2019-12-05 | 2023-10-24 | International Business Machines Corporation | Data augmented training of reinforcement learning software agent |
-
2023
- 2023-12-21 CN CN202311767821.3A patent/CN117434886B/en active Active
Patent Citations (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002108600A (en) * | 2000-10-02 | 2002-04-12 | Digital Electronics Corp | Terminal device for control system and recording medium and control system |
| WO2003053035A1 (en) * | 2001-12-18 | 2003-06-26 | Siemens Energy & Automation, Inc. | Plc to plc communications with a modem on the plc i/o bus |
| CN101261695A (en) * | 2007-03-07 | 2008-09-10 | 杭州电子科技大学 | A real time dynamic re-dispatching method for production dispatching system in process industry |
| JP2014032529A (en) * | 2012-08-03 | 2014-02-20 | Hakko Denki Kk | Plc system, drawing editor device thereof, and programmable display |
| CN103197634A (en) * | 2013-03-15 | 2013-07-10 | 上海大学 | Generating system and generating method of on-line prediction and on-line processing plan for automatic manufacturing and processing system |
| CN104057637A (en) * | 2014-05-08 | 2014-09-24 | 浙江工业大学 | Computer numerical control press punch brake curve self-learning method based on support vector machine |
| CN105302096A (en) * | 2015-11-10 | 2016-02-03 | 湖南千盟物联信息技术有限公司 | Factory intelligent scheduling method |
| CN107451165A (en) * | 2016-05-31 | 2017-12-08 | 合肥美亚光电技术股份有限公司 | Based on the HMI formula lookup methods exchanged with PLC online datas and system |
| CN106216079A (en) * | 2016-08-29 | 2016-12-14 | 四川亿欣新材料有限公司 | Vertical Mill intelligent control method based on PLC |
| EP3401801A1 (en) * | 2017-05-12 | 2018-11-14 | BAE SYSTEMS plc | A system for improved data storage and retrieval |
| CN109283895A (en) * | 2017-07-20 | 2019-01-29 | 费希尔-罗斯蒙特系统公司 | General mapping in industrial process plant |
| CN107861478A (en) * | 2017-10-17 | 2018-03-30 | 广东工业大学 | A kind of parallel control method in intelligent workshop and system |
| CN107831720A (en) * | 2017-10-20 | 2018-03-23 | 爱普（福建）科技有限公司 | Optimize the control method and computer equipment of equipment operating mode |
| KR20200036218A (en) * | 2018-09-28 | 2020-04-07 | 충북대학교 산학협력단 | Method and system for gathering data from heterogeneous programmable logic controller in real time |
| CN110347564A (en) * | 2019-05-24 | 2019-10-18 | 平安普惠企业管理有限公司 | Data creation method and device, electronic equipment, storage medium |
| CN111487919A (en) * | 2020-05-09 | 2020-08-04 | 柳州铁道职业技术学院 | P L C industrial automation control system and control method |
| CN115997244A (en) * | 2020-06-08 | 2023-04-21 | 英特尔公司 | Collective perception service in intelligent transportation system |
| CN112413831A (en) * | 2020-11-25 | 2021-02-26 | 中国电力科学研究院有限公司 | Energy-saving control system and method for central air conditioner |
| CN112596459A (en) * | 2020-12-15 | 2021-04-02 | 江苏久卫智能科技有限公司 | Intelligent bus duct temperature and humidity abnormity early warning system |
| CN113157741A (en) * | 2021-04-26 | 2021-07-23 | 上海淇馥信息技术有限公司 | Service state visualization method and device based on dimension conversion and electronic equipment |
| CN113259486A (en) * | 2021-06-24 | 2021-08-13 | 国网天津市电力公司营销服务中心 | Automatic verification line operation and maintenance system for metering equipment based on digital twins |
| CN113676375A (en) * | 2021-08-13 | 2021-11-19 | 浙江大学 | Industrial control system private protocol structure analysis method |
| CN115858488A (en) * | 2022-10-11 | 2023-03-28 | 厦门市美亚柏科信息股份有限公司 | Parallel migration method and device based on data governance and readable medium |
| CN116756837A (en) * | 2023-08-17 | 2023-09-15 | 广东省科学院广州地理研究所 | Digital twin resource data database building management method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117434886A (en) | 2024-01-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117434886B (en) | PLC control system and method based on operation digital model | |
| EP3462268B1 (en) | Classification modeling for monitoring, diagnostics optimization and control | |
| CN101059695B (en) | Programmatic access to controller construct and variable names | |
| KR102506122B1 (en) | Reinforcement learning model construction method, device, electronic equipment and medium | |
| US11663292B2 (en) | Base analytics engine modeling for monitoring, diagnostics optimization and control | |
| US20100082125A1 (en) | Analytical generator of key performance indicators for pivoting on metrics for comprehensive visualizations | |
| US11644823B2 (en) | Automatic modeling for monitoring, diagnostics, optimization and control | |
| US11567783B2 (en) | Data format transformation for downstream processing in a data pipeline | |
| Genkin et al. | B-SMART: A reference architecture for artificially intelligent autonomic smart buildings | |
| CN115605817A (en) | Control actions of an automatic dimension reduction optimization control system via a mathematical representation of the control system | |
| CN111258984A (en) | Product quality end-edge-cloud collaborative forecasting method under industrial big data environment | |
| CN114721345A (en) | Industrial control method, device and system based on reinforcement learning and electronic equipment | |
| Garcia-Ceja et al. | Towards the automation of a chemical sulphonation process with machine learning | |
| CN115293623A (en) | Training method and device for production scheduling model, electronic equipment and medium | |
| Ma et al. | A data-driven scheduling knowledge management method for smart shop floor | |
| Xiao et al. | Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm | |
| Chen | Big data analytics for semiconductor manufacturing | |
| US11775263B2 (en) | Control code generation and collaboration using active machine learning | |
| US7899559B2 (en) | Language-based organization of controller engine instances | |
| Jianchao et al. | Prediction method of greenhouse environmental factors based on Elman neural network | |
| CN112506164A (en) | Layout planning method, device and platform for reconfigurable manufacturing system | |
| Ellen et al. | Reducing the configuration burden in automation systems-case study in a modern brewery | |
| CN117666448B (en) | Energy saving method, device, equipment and medium based on Internet of things energy consumption prediction | |
| Jiang et al. | Robust explicit solution of multirate predictive control system with external disturbances | |
| WO2016150500A1 (en) | Method for operating an automation system, automation system and automation device |
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 |