CN112947264A - Control method and device for dispenser, electronic equipment and medium - Google Patents
Control method and device for dispenser, electronic equipment and medium Download PDFInfo
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- CN112947264A CN112947264A CN202110428734.XA CN202110428734A CN112947264A CN 112947264 A CN112947264 A CN 112947264A CN 202110428734 A CN202110428734 A CN 202110428734A CN 112947264 A CN112947264 A CN 112947264A
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- 239000003292 glue Substances 0.000 claims abstract description 207
- 238000012549 training Methods 0.000 claims abstract description 59
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- 238000012360 testing method Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 5
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- 238000002347 injection Methods 0.000 claims description 3
- 239000007924 injection Substances 0.000 claims description 3
- 230000002787 reinforcement Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 10
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- 238000000576 coating method Methods 0.000 description 6
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- 239000004831 Hot glue Substances 0.000 description 1
- 239000000853 adhesive Substances 0.000 description 1
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- 238000013473 artificial intelligence Methods 0.000 description 1
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- 238000010295 mobile communication Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
- B05C—APPARATUS FOR APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
- B05C5/00—Apparatus in which liquid or other fluent material is projected, poured or allowed to flow on to the surface of the work
- B05C5/02—Apparatus in which liquid or other fluent material is projected, poured or allowed to flow on to the surface of the work the liquid or other fluent material being discharged through an outlet orifice by pressure, e.g. from an outlet device in contact or almost in contact, with the work
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25257—Microcontroller
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- Physics & Mathematics (AREA)
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- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Application Of Or Painting With Fluid Materials (AREA)
- Coating Apparatus (AREA)
Abstract
The embodiment of the invention discloses a control method and device of a dispenser, electronic equipment and a medium. The method comprises the following steps: determining the characteristic demand data of the glue lines and the parameter information of a glue dispenser; inputting the glue line characteristic demand data and the glue dispenser parameter information into a control data prediction model obtained by pre-training, and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data; and controlling the dispensing machine to perform dispensing operation according to the dispensing machine control data. By executing the technical scheme, the technical effects of controlling the dispenser to execute the dispensing operation through the control data prediction model, reducing the production cost and improving the production efficiency can be achieved.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a control method and device of a dispenser, electronic equipment and a medium.
Background
The dispensing coating is to control the fluid, and drip, coat and apply the fluid on the surface or inside of the product, and is widely applied in the industries of automobiles, 3C, buildings, food, packaging and the like. The dispensing and coating process parameters determine the coating quality, and the adjustment of the process parameters needs to be manually guided by experienced process engineers, and is time-consuming and labor-consuming. The existing dispensing and coating process parameter debugging model can realize dispensing of a dispensing valve, but has low accuracy, cannot control a dispenser to realize accurate glue width or glue thickness (glue height) data, and has poor applicability, so that a self-adaptive dispenser control method based on artificial intelligence is needed.
Disclosure of Invention
The embodiment of the invention provides a dispenser control method, a dispenser control device, electronic equipment and a medium, which can achieve the technical effects of controlling a dispenser to execute dispensing operation by controlling a data prediction model, reducing production cost and improving production efficiency.
In a first aspect, an embodiment of the present invention provides a control method for a dispenser, including:
determining the characteristic demand data of the glue lines and the parameter information of a glue dispenser;
inputting the glue line characteristic demand data and the glue dispenser parameter information into a control data prediction model obtained by pre-training, and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data;
controlling a dispenser to perform dispensing operation according to the dispensing control data
In a second aspect, an embodiment of the present invention further provides a control device for a dispenser, including:
the information determining module is used for determining the characteristic demand data of the glue line and the parameter information of the glue dispenser;
the data determination module is used for inputting the glue line characteristic demand data and the glue dispenser parameter information into a control data prediction model obtained by pre-training and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data;
and the glue dispensing execution module is used for controlling the glue dispenser to execute glue dispensing operation according to the glue dispenser control data.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
one or more processors;
storage means for storing one or more programs;
the one or more programs are executed by the one or more processors, so that the one or more processors implement the dispenser control method as provided in any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the dispenser control method as provided in any of the embodiments of the present invention.
The embodiment of the invention provides a control method of a dispenser, which comprises the steps of determining characteristic demand data of a glue line and parameter information of the dispenser; inputting the glue line characteristic demand data and the glue dispenser parameter information into a control data prediction model obtained by pre-training, and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data; and controlling the dispensing machine to perform dispensing operation according to the dispensing machine control data.
By adopting the technical scheme, firstly, the control data of the dispensing machine is determined by determining the characteristic demand data of the glue line and the parameter information of the dispensing machine, and then the characteristic demand data of the glue line and the parameter information of the dispensing machine are input into a control data prediction model obtained by pre-training; the control data prediction model is obtained by training based on preset number of dispenser training sample data, and finally the dispenser is controlled to perform dispensing operation according to dispenser control data to complete automatic dispensing, so that the technical effects of controlling the dispenser to perform dispensing operation through the control data prediction model, reducing the production cost and improving the production efficiency can be achieved.
The above summary of the present invention is merely an overview of the technical solutions of the present invention, and the present invention can be implemented in accordance with the content of the description in order to make the technical means of the present invention more clearly understood, and the above and other objects, features, and advantages of the present invention will be more clearly understood.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart of a control method of a dispenser according to an embodiment of the present application;
FIG. 2 is a schematic diagram of input and output of a control data prediction model according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a dispenser control device according to a second embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present application.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1 is a flowchart of a control method of a dispenser according to an embodiment of the present invention, where the method is applicable to a case where a dispensing operation is performed by a model-controlled dispenser, and the method is executed by a dispenser control device, where the device is implemented by software and/or hardware and can be integrated in an electronic device. As shown in fig. 1, the control method of the dispenser in the present embodiment includes the following steps:
and S110, determining the characteristic demand data of the glue lines and the parameter information of the glue dispenser.
The glue line characteristic demand data can be glue line form data of a glue dispensing effect which the glue dispensing control system wants to achieve, and the parameter information of the glue dispenser can be various physical and chemical parameters of the glue dispenser and can be adjusted through the glue dispenser.
In an alternative of the present embodiment, it may be combined with one or more of the alternatives of the present embodiment. Glue line characteristic demand data and include: glue width, glue height and dimensional tolerance; the glue width is used for representing the glue dispensing width; the glue height is used for representing the glue dispensing thickness; and the dimensional tolerance is used for representing the maximum value of the deviation of the dispensing width and the dispensing thickness.
The glue width and the glue height refer to the width and the thickness of the glue line, and the dimensional tolerance refers to the maximum error of the glue width and the glue height allowed by the glue dispensing control system.
In an alternative of the present embodiment, it may be combined with one or more of the alternatives of the present embodiment. The dispenser parameter information includes: the automatic glue dispensing device comprises machine parameters and environment parameters, wherein the machine parameters comprise a glue dispensing height, a glue dispensing speed and a glue valve nozzle diameter, and the environment parameters comprise an environment temperature and an environment humidity.
The machine parameters refer to adjustable parameters of the dispensing machine, the dispensing height refers to the distance between a glue head of the dispensing machine and a position to be dispensed, the dispensing speed refers to the moving speed of a glue outlet, and the diameter of a nozzle of the glue valve can be adjusted. The environmental parameters refer to the temperature and humidity of the current operating environment of the dispenser. These parameters are all factors that affect the glue discharging effect of the glue dispenser, so that the model needs to be further trained by referring to the setting environment parameters.
S120, inputting the glue line characteristic demand data and the glue dispenser parameter information into a control data prediction model obtained through pre-training, and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data.
The preset number of dispenser training sample data can be dispensing result data and dispenser parameter data with good quality, which are completed by a dispensing process master operating a dispenser within a long period of time. As shown in fig. 2, the input of the control model may include glue parameters, machine parameters, environmental parameters, product information, and glue valve parameters, and the output of the control model may include machine control data of the dispenser, glue valve control data of the glue outlet, and environmental adjustment data according to the glue barrel temperature, etc.
By adopting the technical scheme, the thinking process and the intelligent behavior of a process engineer are realized by constructing the control data prediction model of the dispensing machine, and the automatic dispensing and coating control is completed.
In an alternative of the present embodiment, it may be combined with one or more of the alternatives of the present embodiment. The control data of the glue dispenser is used for controlling the glue dispenser to obtain the desired glue line characteristics through glue dispensing operation, and the control data of the glue dispenser comprises machine control data, glue valve control data and environment data.
Wherein, machine control data can be the functioning speed of the mouth of gluing of point gum machine play, supplies to glue atmospheric pressure etc. and environmental data can be the bucket heating temperature of gluing of current glue, glues first heating temperature etc..
In an alternative of the present embodiment, it may be combined with one or more of the alternatives of the present embodiment. The glue valve control data is used to control the glue injection operation including the opening time, closing time, striker travel and delay time.
In an alternative of the present embodiment, it may be combined with one or more of the alternatives of the present embodiment. The training process of the control data prediction model can comprise the steps A1-A3:
step A1, obtaining the training sample data of the glue dispenser, wherein the training sample data of the glue dispenser comprise the morphological characteristic information samples of the artificial glue lines, and the morphological characteristic information samples of the artificial glue lines are marked with control data of the glue dispenser.
And A2, performing parameter training on the initial model according to the manual glue line morphological characteristic information sample marked with the control data of the glue dispenser to obtain a training result.
Step A3, if the training result is tested by the test sample data of the dispenser, determining that a control data prediction model is obtained through reinforcement learning training.
The manual glue line morphological characteristic information sample refers to glue line information finished by a glue dispensing process master through manual control of a glue dispenser, and control parameter data for controlling the glue dispenser to perform glue dispensing are marked on the manual glue line morphological characteristic information sample. The test that the training result passes through the test sample data of the dispenser can mean whether the control data output by the test model is consistent with or close to the control data in the test sample data by inputting the morphological characteristic information of the sample.
The control data prediction model is an expandable model, is suitable for various dispensing coating controls, supports various glue valves, such as piezoelectric valves, screw valves and the like, and also supports various types of glue, such as hot melt adhesives, UV adhesives and the like.
And S130, controlling the dispenser to perform dispensing operation according to the dispensing machine control data.
In an alternative of the present embodiment, it may be combined with one or more of the alternatives of the present embodiment. After controlling the dispenser to perform dispensing operation according to the dispenser control data, steps B1-B3 may be included:
and step B1, detecting the morphological characteristic information of the glue line.
And step B2, if the deviation between the glue line morphological characteristic information and the glue line characteristic demand data is larger than a preset deviation range, feeding back the input and output data of the current control data prediction model to the control data prediction model.
And step B3, iteratively updating the control data prediction model, and re-determining the control data of the glue dispenser to execute the glue dispensing operation.
The glue line morphological characteristic information may refer to morphological characteristic data of the glue line after the glue dispensing operation is actually completed by the glue dispenser. The glue line morphological characteristic information also comprises glue line quality information, which can comprise results of whether glue breaking, glue shortage, glue line waving and the like occur. The preset deviation range may be ± 0.2 mm. And when the deviation between the glue line morphological characteristic information and the glue line characteristic demand data is larger than a preset deviation range, performing iterative processing on the control data prediction model, updating the control data prediction model, and re-determining the control data of the glue dispenser according to the updated control data prediction model to execute glue dispensing operation.
According to the technical scheme of the embodiment, the required data of the glue line characteristics and the parameter information of the glue dispenser are determined; inputting the glue line characteristic demand data and the parameter information of the glue dispenser into a control data prediction model obtained by pre-training, and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of dispenser training sample data; the dispenser is controlled to perform dispensing operation according to the dispensing machine control data, so that the technical effects of controlling the dispenser to perform dispensing operation through the control data prediction model, reducing the production cost and improving the production efficiency are achieved.
Example two
Fig. 3 is a schematic structural diagram of a control device of a dispenser according to a second embodiment of the present invention. The device can be suitable for the condition of carrying out dispensing operation by a model control dispenser, can be realized by software and/or hardware, and is integrated in electronic equipment. The device is used for realizing the control method of the dispenser provided by the embodiment. As shown in fig. 3, the dispenser control device provided in this embodiment includes:
an information determining module 310, configured to determine the glue line characteristic demand data and the glue dispenser parameter information;
the data determining module 320 is configured to input the glue line characteristic demand data and the dispenser parameter information into a control data prediction model obtained through pre-training, and determine dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data;
on the basis of the foregoing embodiment, optionally, the data determining module 320 is configured to:
the training process of the control data prediction model comprises the following steps:
obtaining training sample data of a dispenser, wherein the training sample data of the dispenser comprise an artificial glue line morphological characteristic information sample, and the artificial glue line morphological characteristic information sample is marked with control data of the dispenser;
performing parameter training on the initial model according to the manual glue line morphological characteristic information sample marked with the control data of the glue dispenser to obtain a training result;
and if the training result is tested by the sample data of the dispenser test, determining that a control data prediction model is obtained through reinforcement learning training.
And the dispensing executing module 330 is configured to control the dispenser to execute dispensing operation according to the dispenser control data.
On the basis of the foregoing embodiment, optionally, the dispensing executing module 330 is configured to:
detecting morphological characteristic information of the glue lines;
if the deviation between the glue line morphological characteristic information and the glue line characteristic demand data is larger than a preset deviation range, feeding back input and output data of the current control data prediction model to the control data prediction model;
and iteratively updating the control data prediction model, and re-determining the control data of the glue dispenser to execute the glue dispensing operation.
On the basis of the above embodiment, optionally, the information determining module 310 is configured to:
the glue line characteristic demand data comprises: glue width, glue height and dimensional tolerance;
the glue width is used for representing the glue dispensing width;
the glue height is used for representing the glue dispensing thickness;
and the dimensional tolerance is used for representing the maximum value of the deviation of the dispensing width and the dispensing thickness.
On the basis of the foregoing embodiment, optionally, the information determining module 310 is further configured to:
the dispenser parameter information includes: the automatic glue dispensing device comprises machine parameters and environment parameters, wherein the machine parameters comprise a glue dispensing height, a glue dispensing speed and a glue valve nozzle diameter, and the environment parameters comprise an environment temperature and an environment humidity.
On the basis of the foregoing embodiment, optionally, the data determining module 320 is further configured to:
the control data of the glue dispenser is used for controlling the glue dispenser to obtain the desired glue line characteristics through glue dispensing operation, and the control data of the glue dispenser comprises machine control data, glue valve control data and environment data.
On the basis of the foregoing embodiment, optionally, the data determining module 320 is further configured to:
the glue valve control data is used to control the glue injection operation including the opening time, closing time, striker travel and delay time.
The dispenser control device provided in the embodiment of the present invention can execute the dispenser control method provided in any embodiment of the present invention, and has corresponding functions and beneficial effects for executing the dispenser control method, and the detailed process refers to the related operations of the dispenser control method in the foregoing embodiments.
EXAMPLE III
Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present application. The embodiment of the application provides electronic equipment, and the glue dispenser control device provided by the embodiment of the application can be integrated in the electronic equipment. As shown in fig. 4, the present embodiment provides an electronic device 400, which includes: one or more processors 420; the storage device 410 is used for storing one or more programs, and when the one or more programs are executed by the one or more processors 420, the one or more processors 420 are enabled to implement the dispenser control method provided by the embodiment of the application, the method includes:
determining the characteristic demand data of the glue lines and the parameter information of a glue dispenser;
inputting the glue line characteristic demand data and the glue dispenser parameter information into a control data prediction model obtained by pre-training, and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data;
and controlling the dispensing machine to perform dispensing operation according to the dispensing machine control data.
Of course, those skilled in the art can understand that the processor 420 also implements the technical solution of the dispenser control method provided in any embodiment of the present application.
The electronic device 400 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 4, the electronic device 400 includes a processor 420, a storage device 410, an input device 430, and an output device 440; the number of the processors 420 in the electronic device may be one or more, and one processor 420 is taken as an example in fig. 4; the processor 420, the storage device 410, the input device 430, and the output device 440 in the electronic apparatus may be connected by a bus or other means, and are exemplified by a bus 450 in fig. 4.
The storage device 410 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and module units, such as program instructions corresponding to the dispenser control method in the embodiments of the present application.
The storage device 410 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the storage 410 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, storage 410 may further include memory located remotely from processor 420, which may be connected via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 430 may be used to receive input numbers, character information, or voice information, and to generate key signal inputs related to user settings and function control of the electronic device. The output device 440 may include a display screen, speakers, or other electronic equipment.
The electronic equipment provided by the embodiment of the application can achieve the technical effects of controlling the dispenser to execute dispensing operation through the control data prediction model, reducing the production cost and improving the production efficiency.
Example four
A fourth embodiment of the present invention provides a computer-readable medium having a computer program stored thereon, where the computer program is used to execute a dispenser control method when executed by a processor, and the method includes:
determining the characteristic demand data of the glue lines and the parameter information of a glue dispenser;
inputting the glue line characteristic demand data and the glue dispenser parameter information into a control data prediction model obtained by pre-training, and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data;
and controlling the dispensing machine to perform dispensing operation according to the dispensing machine control data.
Optionally, the program, when executed by the processor, may be further configured to perform the dispenser control method provided in any embodiment of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a flash Memory, an optical fiber, a portable CD-ROM, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. A computer 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take a variety of forms, including, but not limited to: an electromagnetic signal, an optical signal, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and 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 computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects 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, Smalltalk, 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., 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 invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A control method of a dispenser is characterized by comprising the following steps:
determining the characteristic demand data of the glue lines and the parameter information of a glue dispenser;
inputting the glue line characteristic demand data and the glue dispenser parameter information into a control data prediction model obtained by pre-training, and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data;
and controlling the dispensing machine to perform dispensing operation according to the dispensing machine control data.
2. The method of claim 1, wherein controlling the training process of the data prediction model comprises:
obtaining training sample data of a dispenser, wherein the training sample data of the dispenser comprise an artificial glue line morphological characteristic information sample, and the artificial glue line morphological characteristic information sample is marked with control data of the dispenser;
performing parameter training on the initial model according to the manual glue line morphological characteristic information sample marked with the control data of the glue dispenser to obtain a training result;
and if the training result is tested by the sample data of the dispenser test, determining that a control data prediction model is obtained through reinforcement learning training.
3. The method according to claim 1, after controlling the dispenser to perform the dispensing operation according to the dispenser control data, comprising:
detecting morphological characteristic information of the glue lines;
if the deviation between the glue line morphological characteristic information and the glue line characteristic demand data is larger than a preset deviation range, feeding back input and output data of the current control data prediction model to the control data prediction model;
and iteratively updating the control data prediction model, and re-determining the control data of the glue dispenser to execute the glue dispensing operation.
4. The method of claim 1, wherein the glue line characteristic demand data comprises: glue width, glue height and dimensional tolerance;
the glue width is used for representing the glue dispensing width;
the glue height is used for representing the glue dispensing thickness;
and the dimensional tolerance is used for representing the maximum value of the deviation of the dispensing width and the dispensing thickness.
5. The method of claim 1, wherein the dispenser parameter information comprises: the automatic glue dispensing device comprises machine parameters and environment parameters, wherein the machine parameters comprise a glue dispensing height, a glue dispensing speed and a glue valve nozzle diameter, and the environment parameters comprise an environment temperature and an environment humidity.
6. The method of claim 1, wherein the dispenser control data is used to control the dispenser to obtain desired glue line characteristics through a dispensing operation, including machine control data, glue valve control data, and environmental data.
7. The method of claim 6, wherein said glue valve control data is used to control glue injection operations including open time, close time, striker travel and delay time.
8. A control device of a dispenser, characterized in that the device comprises:
the information determining module is used for determining the characteristic demand data of the glue line and the parameter information of the glue dispenser;
the data determination module is used for inputting the glue line characteristic demand data and the glue dispenser parameter information into a control data prediction model obtained by pre-training and determining glue dispenser control data; the control data prediction model is obtained by training based on preset number of glue dispenser training sample data;
and the glue dispensing execution module is used for controlling the glue dispenser to execute glue dispensing operation according to the glue dispenser control data.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the dispenser control method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the dispenser control method of any one of claims 1 to 7.
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