CN117684243B - Intelligent electroplating control system and control method - Google Patents
Intelligent electroplating control system and control method Download PDFInfo
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Abstract
The invention discloses an intelligent electroplating control system and a control method, which belong to the field of general control systems.
Description
Technical Field
The invention belongs to the technical field of general control systems, and particularly relates to an intelligent electroplating control system and an intelligent electroplating control method.
Background
The intelligent electroplating is an electroplating production mode which realizes automation, high efficiency and intelligence by utilizing advanced technology. The method can realize comprehensive control on the electroplating production process through means of data acquisition, production modeling, intelligent control, optimization adjustment, fault diagnosis, prevention and the like, improves the production efficiency and quality, and reduces the energy consumption and the cost. Compared with the traditional electroplating, the intelligent electroplating has higher production efficiency and quality stability, and simultaneously can reduce the manual intervention and error rate and reduce the production cost and environmental load. In the prior art, the plating quality is abnormal due to the change of the environment in the process of plating, and the plating cannot be quickly adapted to the external environment;
for example, in chinese patent publication No. CN114355856a, the field of a control system for an electroplating production line is proposed, and in particular, an intelligent data collection and analysis system applied to an electroplating production line is related, which includes a PLC overall control module, an industrial computer program module, an industrial computer database, an android terminal program module, a cloud database module, a water treatment device module and a water treatment sensor module, where the PLC overall control module conveys a control instruction to the industrial computer program module in a data communication manner, and the industrial computer program module realizes a data storage function by means of the industrial computer database, and the industrial computer database provides a data query service for the android terminal program module. The invention realizes the direct connection of the industrial personal computer and the PLC to collect information, classifies and groups the information after the information is collected, and optimizes data storage after the cloud receives data; the batch storage and inquiry of the data are optimized, the information acquisition speed is improved, and the inquiry time of the historical information is reduced;
meanwhile, an intelligent control system of an electroplating production line is disclosed in Chinese patent publication No. CN102156460A, for example. The system is characterized by comprising an industrial control computer, a printer, an Ethernet switch, a programmable terminal, a programmable controller, a power supply and a temperature controller, wherein the printer is connected with the industrial control computer. The industrial control computer is connected with an Ethernet switch, the Ethernet switch is provided with at least a first interface, a second interface and a third interface, the programmable controller is provided with at least a first programmable controller and a second programmable controller, and the power supply is provided with at least a first power supply and a second power supply. The temperature controller has at least a first and a second temperature controller. The first interface is connected with the programmable terminal, the second interface is connected with the first programmable controller, and the first programmable controller is respectively connected with the first temperature controller and the first temperature controller. The third interface is connected with a second programmable controller, and the second programmable controller is respectively connected with the communication interfaces of the second temperature controller and the second temperature controller. The invention has low manufacturing cost and is convenient to operate.
The problems proposed in the background art exist in the above patents: in the prior art, in the process of electroplating, the electroplating device cannot be quickly adapted to the external environment, the situation that the electroplating quality is abnormal along with the change of the environment can often occur in the electroplating process, the problems exist in the prior art, and in order to solve the problems, the application designs an intelligent electroplating control system and an intelligent electroplating control method.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent electroplating control system and an intelligent electroplating control method.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent electroplating control method comprises the following specific steps:
s1, collecting historical environment data and equipment operation data in an electroplating process, and constructing an electroplating production model based on the historical environment data and the equipment operation data;
s2, collecting environmental data and equipment operation data in the electroplating process in real time, inputting an electroplating production model, and outputting production data;
s3, judging whether the production data is in a safe production data range, if not, performing S4, and if so, directly performing electroplating operation;
s4, importing the acquired real-time environment data and the required production data standard value into a required data output strategy to output required equipment operation data;
s5, adjusting the data of the equipment to the required equipment operation data, and normally operating the electroplating operation.
Specifically, the step S1 includes the following specific steps:
s11, setting an environment sensing module and an equipment operation sensing module on the surface of electroplating equipment, wherein the environment sensing module collects environment data in the operation process of the electroplating equipment, the environment data comprise temperature and humidity data, the equipment operation sensing module collects voltage, current, flow and liquid drop time data in the operation process of the electroplating equipment, and simultaneously collects quality and rejection condition data of electroplating workpieces operated by historical environment data and equipment operation data equipment, and the quality and rejection condition data of the electroplating workpieces are the probability of judging whether products of the electroplating equipment operated by corresponding historical environment data and equipment operation data are quality and rejection;
s12, collecting historical environment data, historical equipment operation data and corresponding electroplating workpiece excellent and useless condition data, constructing an electroplating production model which is input into the historical environment data and the historical equipment operation data and output into the electroplating workpiece excellent and useless condition;
s13, dividing the acquired historical environment data, historical equipment operation data and corresponding electroplating workpiece excellent and reject condition data into a 70% parameter training set and a 30% parameter testing set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial electroplating production model; testing the initial electroplating production model by using a 30% parameter test set, and outputting the initial electroplating production model meeting the preset accuracy of predicting the excellent and defective conditions of the electroplated workpiece as an electroplating production model, wherein the electroplating production model formula is as follows:wherein->For the set optimal reject condition standard value of the electroplated workpiece, exp () is the exponent power of e, n is the data type in the environment data, m is the data type in the equipment operation data, < + >>Duty factor for the ith environmental data, +.>For the ith environmental data in the real-time data, < >>Is the ith environmental data standard value, +.>Duty factor of operating data for jth device,/->Running data for the j-th device in the real-time data, a program for executing the data>The data standard value is run for the j-th device.
Specifically, the specific content in S2 is as follows:
s21, collecting environmental data and equipment operation data in the electroplating process in real time;
s22, substituting the environmental data and the equipment operation data which are collected in real time into the constructed electroplating production model, and outputting the excellent and waste condition data of the electroplated workpiece in real time.
Specifically, the specific contents of the data output policy required in S4 are:
s41, acquiring real-time environment data and required production data standard values, and importing the acquired real-time environment data and the required production data standard values into an electroplating production model to output required equipment operation data;
s42, extracting output required equipment operation data, selecting data in which the required equipment operation data are in a safety range, marking the data as a required equipment operation data standard group, acquiring the required equipment operation data standard group, and importing the equipment operation data in the required equipment operation data standard group into an adjustment value calculation formula to calculate an adjustment value, wherein the adjustment value calculation formula is as follows:wherein->Running data for the j-th device in real time, +.>The j-th item of equipment operation data in the equipment operation data standard group is needed;
s43, acquiring a required equipment operation data standard group corresponding to the minimum regulating value as required equipment operation data, and outputting the required equipment operation data.
Specifically, the step S5 includes the following specific steps:
s51, acquiring specific values of all the equipment data in the output needed equipment operation data, and adjusting the corresponding data types of the actual equipment to the specific values of all the equipment data;
and S52, after the adjustment is completed, running all the equipment to perform electroplating operation.
Specifically, an intelligent electroplating control system is realized based on the intelligent electroplating control method, and specifically comprises the following steps: the electroplating production system comprises a data acquisition module, an electroplating production model construction module, a production data judgment module, an equipment operation data output module, an equipment operation data adjustment module, an electroplating module and a control module, wherein the data acquisition module is used for acquiring historical environment data and equipment operation data in an electroplating process, the electroplating production model construction module is used for constructing an electroplating production model based on the historical environment data and the equipment operation data, the production data judgment module is used for collecting the environment data and the equipment operation data in the electroplating process in real time, inputting the electroplating production model to output the production data, and judging whether the production data is in a safe production data range.
Specifically, the equipment operation data output module is used for importing the acquired real-time environment data and the required production data standard value into a required data output strategy to output required equipment operation data, the equipment operation data adjustment module is used for adjusting the equipment data to the required equipment operation data, and the electroplating module is used for carrying out electroplating operation of the equipment.
Specifically, the control module is used for controlling the operation of the data acquisition module, the electroplating production model construction module, the production data judgment module, the equipment operation data output module, the equipment operation data adjustment module and the electroplating module.
Specifically, an electronic device includes: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes an intelligent electroplating control method by calling a computer program stored in the memory.
Specifically, a computer readable storage medium stores instructions that, when executed on a computer, cause the computer to perform an intelligent plating control method as described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, historical environment data and equipment operation data in the electroplating process are collected, an electroplating production model is constructed based on the historical environment data and the equipment operation data, the environment data and the equipment operation data in the electroplating process are collected in real time, the electroplating production model is input to output production data, whether the production data is in a safe production data range or not is judged, the acquired real-time environment data and a required production data standard value are imported into a required data output strategy to output required equipment operation data, the data of the equipment are regulated to the required equipment operation data, then the electroplating operation is normally operated, intelligent control on the electroplating production process can be realized, the production efficiency and quality are improved, and the influence of environmental change on the electroplating production process is neutralized by accurately controlling the data of the equipment.
Drawings
FIG. 1 is a schematic flow chart of an intelligent electroplating control method according to the present invention;
FIG. 2 is a schematic diagram showing a specific flow of the step S1 of the intelligent electroplating control method of the present invention;
FIG. 3 is a schematic diagram showing a specific flow of step S4 of the intelligent electroplating control method according to the present invention;
FIG. 4 is a schematic diagram of an intelligent plating control system architecture according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1-3, an embodiment of the present invention is provided: an intelligent electroplating control method comprises the following specific steps:
s1, collecting historical environment data and equipment operation data in an electroplating process, and constructing an electroplating production model based on the historical environment data and the equipment operation data;
in this embodiment, S1 includes the following specific steps:
s11, setting an environment sensing module and an equipment operation sensing module on the surface of electroplating equipment, wherein the environment sensing module collects environment data in the operation process of the electroplating equipment, the environment data comprise temperature and humidity data, the equipment operation sensing module collects voltage, current, flow and liquid drop time data in the operation process of the electroplating equipment, and simultaneously collects quality and rejection condition data of electroplating workpieces operated by historical environment data and equipment operation data equipment, and the quality and rejection condition data of the electroplating workpieces are the probability of judging whether products of the electroplating equipment operated by corresponding historical environment data and equipment operation data are quality and rejection;
s12, collecting historical environment data, historical equipment operation data and corresponding electroplating workpiece excellent and useless condition data, constructing an electroplating production model which is input into the historical environment data and the historical equipment operation data and output into the electroplating workpiece excellent and useless condition;
s13, dividing the acquired historical environment data, historical equipment operation data and corresponding electroplating workpiece excellent and reject condition data into a 70% parameter training set and a 30% parameter testing set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial electroplating production model; testing the initial electroplating production model by using a 30% parameter test set, and outputting the initial electroplating production model meeting the preset accuracy of predicting the excellent and defective conditions of the electroplated workpiece as an electroplating production model, wherein the electroplating production model formula is as follows:wherein->For the set optimal reject condition standard value of the electroplated workpiece, exp () is the exponent power of e, n is the data type in the environment data, m is the data type in the equipment operation data, < + >>Duty factor for the ith environmental data, +.>For the ith environmental data in the real-time data, < >>Is the ith environmental data standard value, +.>Duty factor of operating data for jth device,/->Running data for the j-th device in the real-time data, a program for executing the data>Running a data standard value for the j-th device;
the following is an example code showing how to collect historical environmental data, historical equipment operation data and corresponding data of the best and best rejection conditions of the electroplated workpiece, train the deep learning neural network model by using 70% of the data, and finally output the Python code of the electroplating production model obtained by training:
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
collecting historical environment data, historical equipment operation data and corresponding electroplating workpiece excellent and waste condition data
Let # assume that each piece of data has 6 environmental features and 2 equipment operating features, and the last is the best reject condition of electroplated workpieces
data = [
[0.5, 0.2, 0.3, 0.4, 0.5, 0.1, 1.0, 0],
[0.4, 0.1, 0.2, 0.6, 0.3, 0.2, 0.8, 1],
[0.2, 0.3, 0.1, 0.7, 0.6, 0.3, 0.9, 1],
]
# convert data to NumPy array
data = np.array(data)
# dividing data set
X=data [: 1] # features of all samples (historical environmental data and historical device operational data)
y=data [: -1] # labels of all samples (good rejection of electroplated workpieces)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# creation of neural network model
model = MLPClassifier(hidden_layer_sizes=(100, ), max_iter=1000)
Training model #
model.fit(X_train, y_train)
# output model parameters
print('weights:', model.coefs_)
print('biases:', model.intercepts_)
Note that it needs to be replaced by actually collected historical data, and in addition, the structure and super parameters of the neural network model can be adjusted according to the need to obtain better results;
s2, collecting environmental data and equipment operation data in the electroplating process in real time, inputting an electroplating production model, and outputting production data;
in this embodiment, the specific content in S2 is as follows:
s21, collecting environmental data and equipment operation data in the electroplating process in real time;
s22, substituting the environmental data and the equipment operation data which are collected in real time into a constructed electroplating production model, and outputting the excellent and waste condition data of the electroplated workpiece in real time;
s3, judging whether the production data is in a safe production data range, if not, performing S4, and if so, directly performing electroplating operation;
s4, importing the acquired real-time environment data and the required production data standard value into a required data output strategy to output required equipment operation data;
in this embodiment, the specific contents of the data output policy required in S4 are:
s41, acquiring real-time environment data and required production data standard values, and importing the acquired real-time environment data and the required production data standard values into an electroplating production model to output required equipment operation data;
s42, extracting output required equipment operation data, selecting data in which the required equipment operation data are in a safety range, marking the data as a required equipment operation data standard group, acquiring the required equipment operation data standard group, and importing the equipment operation data in the required equipment operation data standard group into an adjustment value calculation formula to calculate an adjustment value, wherein the adjustment value calculation formula is as follows:wherein->Running data for the j-th device in real time, +.>The j-th item of equipment operation data in the equipment operation data standard group is needed;
s43, acquiring a required equipment operation data standard group corresponding to the minimum regulating value as required equipment operation data, and outputting the required equipment operation data;
s5, adjusting the data of the equipment to the required equipment operation data, and normally operating the electroplating operation;
in this embodiment, S5 includes the following specific steps:
s51, acquiring specific values of all the equipment data in the output needed equipment operation data, and adjusting the corresponding data types of the actual equipment to the specific values of all the equipment data;
and S52, after the adjustment is completed, running all the equipment to perform electroplating operation.
The implementation of the embodiment can be realized: the method comprises the steps of collecting historical environment data and equipment operation data in an electroplating process, constructing an electroplating production model based on the historical environment data and the equipment operation data, collecting the environment data and the equipment operation data in the electroplating process in real time, inputting the electroplating production model to output production data, judging whether the production data is in a safe production data range, importing the acquired real-time environment data and required production data standard values into a required data output strategy to output required equipment operation data, adjusting the data of the equipment to the required equipment operation data, and then normally operating the electroplating operation, so that intelligent control on the electroplating production process can be realized, the production efficiency and quality are improved, and the influence of environmental changes on the electroplating production process is neutralized by accurately controlling the data of the equipment.
Example 2
As shown in fig. 4, an intelligent electroplating control system is implemented based on the above-mentioned intelligent electroplating control method, and specifically includes: the electroplating production system comprises a data acquisition module, an electroplating production model construction module, a production data judgment module, an equipment operation data output module, an equipment operation data adjustment module, an electroplating module and a control module, wherein the data acquisition module is used for acquiring historical environment data and equipment operation data in the electroplating process; the equipment operation data output module is used for importing the acquired real-time environment data and the required production data standard value into a required data output strategy to output required equipment operation data, the equipment operation data adjustment module is used for adjusting the equipment data to the required equipment operation data, and the electroplating module is used for carrying out electroplating operation of the equipment; the control module is used for controlling the operation of the data acquisition module, the electroplating production model construction module, the production data judgment module, the equipment operation data output module, the equipment operation data adjustment module and the electroplating module.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes an intelligent plating control method as described above by calling a computer program stored in the memory.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, where at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to implement an intelligent plating control method provided in the foregoing method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
the computer program, when run on a computer device, causes the computer device to perform a smart plating control method as described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one, and there may be additional partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The intelligent electroplating control method is characterized by comprising the following specific steps of:
s1, collecting historical environment data and equipment operation data in an electroplating process, and constructing an electroplating production model based on the historical environment data and the equipment operation data;
s2, collecting environmental data and equipment operation data in the electroplating process in real time, inputting an electroplating production model, and outputting production data;
s3, judging whether the production data is in a safe production data range, if not, performing S4, and if so, directly performing electroplating operation;
s4, importing the acquired real-time environment data and the required production data standard value into a required data output strategy to output required equipment operation data;
s5, adjusting the data of the equipment to the required equipment operation data, and normally operating the electroplating operation; the S1 comprises the following specific steps:
s11, setting an environment sensing module and an equipment operation sensing module on the surface of the electroplating equipment, wherein the environment sensing module collects environment data in the operation process of the electroplating equipment, the environment data comprise temperature and humidity data, and the equipment operation sensing module collects voltage, current, flow and drop time data in the operation process of the electroplating equipment and collects excellent waste condition data of the electroplating workpiece operated by historical environment data and equipment operation data equipment;
s12, collecting historical environment data, historical equipment operation data and corresponding electroplating workpiece excellent and useless condition data, constructing an electroplating production model which is input into the historical environment data and the historical equipment operation data and output into the electroplating workpiece excellent and useless condition;
s13, dividing the acquired historical environment data, historical equipment operation data and corresponding electroplating workpiece excellent and reject condition data into a 70% parameter training set and a 30% parameter testing set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial electroplating production model; testing the initial electroplating production model by using a 30% parameter test set, and outputting the initial electroplating production model meeting the preset accuracy of predicting the excellent and defective conditions of the electroplated workpiece as an electroplating production model, wherein the electroplating production model formula is as follows:wherein->For the set optimal reject condition standard value of the electroplated workpiece, exp () is the exponent power of e, n is the data type in the environment data, m is the data type in the equipment operation data, < + >>Duty factor for the ith environmental data, +.>For the ith environmental data in the real-time data, < >>Is the ith environmental data standard value, +.>Duty factor of operating data for jth device,/->Is real-time numberAccording to the j-th device operation data, < >>Running a data standard value for the j-th device; the specific content of the data output strategy required in the step S4 is as follows:
s41, acquiring real-time environment data and required production data standard values, and importing the acquired real-time environment data and the required production data standard values into an electroplating production model to output required equipment operation data;
s42, extracting output required equipment operation data, selecting data in which the required equipment operation data are in a safety range, marking the data as a required equipment operation data standard group, acquiring the required equipment operation data standard group, and importing the equipment operation data in the required equipment operation data standard group into an adjustment value calculation formula to calculate an adjustment value, wherein the adjustment value calculation formula is as follows:wherein->Running data for the j-th device in real time, +.>The j-th item of equipment operation data in the equipment operation data standard group is needed;
s43, acquiring a required equipment operation data standard group corresponding to the minimum regulating value as required equipment operation data, and outputting the required equipment operation data.
2. The intelligent electroplating control method as set forth in claim 1, wherein: the specific content in the S2 is as follows:
s21, collecting environmental data and equipment operation data in the electroplating process in real time;
s22, substituting the environmental data and the equipment operation data which are collected in real time into the constructed electroplating production model, and outputting the excellent and waste condition data of the electroplated workpiece in real time.
3. The intelligent electroplating control method as set forth in claim 2, wherein S5 comprises the following steps:
s51, acquiring specific values of all the equipment data in the output needed equipment operation data, and adjusting the corresponding data types of the actual equipment to the specific values of all the equipment data;
and S52, after the adjustment is completed, running all the equipment to perform electroplating operation.
4. An intelligent plating control system, which is realized based on the intelligent plating control method according to any one of claims 1 to 3, characterized in that it specifically comprises: the electroplating production system comprises a data acquisition module, an electroplating production model construction module, a production data judgment module, an equipment operation data output module, an equipment operation data adjustment module, an electroplating module and a control module, wherein the data acquisition module is used for acquiring historical environment data and equipment operation data in an electroplating process, the electroplating production model construction module is used for constructing an electroplating production model based on the historical environment data and the equipment operation data, the production data judgment module is used for collecting the environment data and the equipment operation data in the electroplating process in real time, inputting the electroplating production model to output the production data, and judging whether the production data is in a safe production data range.
5. An intelligent plating control system, as recited in claim 4, wherein said equipment operation data output module is configured to import the acquired real-time environmental data and the required production data standard values into a required data output strategy for outputting the required equipment operation data, wherein said equipment operation data adjustment module is configured to adjust the equipment data to the required equipment operation data, and wherein said plating module is configured to perform a plating operation of the equipment.
6. The intelligent plating control system of claim 5, wherein the control module is configured to control operation of the data acquisition module, the plating production model construction module, the production data determination module, the equipment operation data output module, the equipment operation data adjustment module, and the plating module.
7. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes an intelligent plating control method according to any one of claims 1 to 3 by calling a computer program stored in the memory.
8. A computer-readable storage medium, characterized by: instructions stored which, when executed on a computer, cause the computer to perform a smart plating control method according to any of claims 1-3.
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