Uncovering processing control method and device for multiple PP rigid-flex boards

Abstract

The embodiment of the invention provides an uncovering processing control method and device for multiple PP rigid-flex boards. The uncovering processing control method comprises the steps of generating topological combination data according to historical flexible board filling characteristic parameters and historical pressing processing parameters; converting the topological combination data into a relation vector; inputting the relation vector into a neural network model for training to obtain a trained neural network model; inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters; the control equipment performs pre-punching operation on the PP sheet to obtain a pre-punched PP sheet; the pre-punched PP sheet and the semi-layer board are subjected to pressing operation according to the pressing processing parameters, and a pressed rigid-flex board is obtained; the uncovering part of PP is burnt through by UV laser in advance before the control equipment is pressed, the PP is cured, the edge sealing reduces the generation of excessive glue, and the uncovering difficulty is reduced; the model training efficiency is improved, and the accurately predicted pressing process parameters are obtained.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and a device for controlling the opening of covers of multiple PP hard-soft combined boards, a computer device and a storage medium. Background Art

[0002] With the extensive application of hard-and-flexible boards, the products are also developing in the trend of high density and high level. With the increase of the number of layers and the complexity of the structure, the products tend to use multiple PP (semi-cured films). Multiple PPs increase the technical difficulty of opening the cover in the flexible folding area. How to improve the efficiency and quality of opening the cover of multiple PPs has become one of the important topics for the breakthrough of hard-and-flexible boards. The overflow of multiple PPs is too large and difficult to control, and it flows into the flexible folding area and adheres to the hard board, making it difficult to open the cover and scrap it. Multiple PPs increase the depth of opening the cover, which can easily lead to unstable UV depth control. If the depth is too deep, it is easy to burn through the soft board. If the depth is too shallow, the PP glass fiber will not be burned off, and the edge of the hard board in the opening area will be uneven and burred. In addition, it is difficult to open the cover and it is not easy to remove the waste. Summary of the invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a device for controlling the opening of covers of multiple PP hard-soft boards, a computer device and a storage medium that overcome the above problems or at least partially solve the above problems.

[0004] In order to solve the above problems, the embodiment of the present invention discloses a method for controlling the cover opening process of multiple PP hard-soft combined boards, comprising:

[0005] Obtain historical soft board filling characteristic parameters and historical pressing processing parameters;

[0006] Generate topological combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters;

[0007] Converting the topological combination data into a relation vector;

[0008] Inputting the relationship vector into a neural network model for training to obtain a trained neural network model;

[0009] Inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters;

[0010] The control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet;

[0011] The pre-punched PP sheet and the half-layer body board are pressed together according to the pressing processing parameters to obtain a pressed hard-soft board.

[0012] Preferably, the historical soft board filling characteristic parameters include filling material characteristic parameters, and the historical pressing processing parameters include pressing high pressure area of ​​the pressing machine, pressing speed of the pressing machine, pressing pressure, pressing temperature, and pressing thickness of the product; generating the topological combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters includes:

[0013] The pressing high pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, the pressing temperature, and the pressing thickness of the product are combined around the characteristic parameters of the filling material to form topological combination data.

[0014] Preferably, converting the topological combination data into a relationship vector comprises:

[0015] Decomposing the topological combination data into a plurality of association relationship data subsets;

[0016] The plurality of associated relationship data subsets are combined into a relationship vector.

[0017] Preferably, the inputting of the new soft board filling characteristic parameters into the trained neural network model to obtain the output pressing processing parameters includes:

[0018] Converting the new soft board filling characteristic parameter into a three-dimensional vector;

[0019] The three-dimensional vector is input into the neural network model to obtain the output of the laminating machine high pressure area, laminating machine laminating speed, laminating pressure and laminating temperature.

[0020] Preferably, the pre-punched PP sheet and the half-layer body sheet are pressed together according to the pressing processing parameters to obtain a pressed hard-flex board, comprising:

[0021] According to the high pressure area of ​​the press, the action area of ​​the press for the PP sheet and the half-layer sheet is adjusted;

[0022] The pressing process operation of the pressing machine is adjusted according to the pressing speed, pressing pressure and pressing temperature of the pressing machine to obtain a pressed hard-flex board.

[0023] Preferably, the control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet, comprising:

[0024] Obtaining a circuit design diagram for the PP sheet;

[0025] Identifying punching positions of the circuit design;

[0026] The UV laser device is controlled to perform a pre-punching operation on the punching position of the PP sheet to obtain a pre-punched PP sheet.

[0027] The embodiment of the present invention discloses a cover opening processing control device for multiple PP hard-soft combined boards, comprising:

[0028] A parameter acquisition module is used to obtain historical soft board filling characteristic parameters and historical pressing processing parameters;

[0029] A topology generation module is used to generate topology combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters;

[0030] A conversion module, used for converting the topological combination data into a relation vector;

[0031] A training module, used for inputting the relationship vector into a neural network model for training to obtain a trained neural network model;

[0032] An input module, used for inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters;

[0033] A pre-punching module is used to control the device to perform a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet;

[0034] The pressing operation module is used to press the pre-punched PP sheet and the half-layer board according to the pressing processing parameters to obtain a pressed soft-hard combination board.

[0035] Preferably, the historical soft board filling characteristic parameters include filling material characteristic parameters, the historical pressing processing parameters include pressing high pressure area of ​​the pressing machine, pressing speed of the pressing machine, pressing pressure, pressing temperature, and product pressing thickness; the generating topology module includes:

[0036] The first component submodule is used to form topological combination data based on the characteristic parameters of the filling material, including the pressing high pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, the pressing temperature, and the pressing thickness of the product.

[0037] The embodiment of the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned steps of controlling the cover opening process of multiple PP hard-soft boards when executing the computer program.

[0038] The embodiment of the present invention further discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of controlling the cover opening process of multiple PP hard-soft boards are implemented.

[0039] The embodiments of the present invention include the following advantages:

[0040] In an embodiment of the present invention, the method for controlling the opening and closing of multiple PP hard-soft boards is characterized in that it includes: obtaining historical soft board filling characteristic parameters and historical pressing processing parameters; generating topological combination data according to the historical soft board filling characteristic parameters and historical pressing processing parameters; converting the topological combination data into a relationship vector; inputting the relationship vector into a neural network model for training to obtain a trained neural network model; inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters; controlling the device to perform a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet; performing a pressing operation on the pre-punched PP sheet and a half-layer sheet according to the pressing processing parameters to obtain to the soft-hard combination board after pressing; the control equipment uses UV laser to burn through the PP opening before pressing, and the PP curing and edge sealing reduce the generation of glue overflow and reduce the difficulty of opening the cover; improve production efficiency; after the PP opening is burned through with UV laser in advance, there is no need to control the UV depth to the next layer of PP, which reduces the difficulty of opening the cover, and the waste is easy to remove. The glass fiber is burned in advance to disconnect the edge of the hard board in the cover area, which is neat and free of burrs and other defects; the characteristic parameters of the filler between the multiple PP sheets and the pressing operation parameters of the pressing machine are converted into topological structure relationships, and then converted into vectors for model training through the divided data subsets, which clearly and concisely express the relationship and connection between the data to be trained, improve the efficiency of model training, and obtain accurately predicted pressing process parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 This is a flowchart of a method for controlling the opening of covers of multiple PP hard-soft boards according to an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of topological combination data in an embodiment of the present invention;

[0044] Figure 3 is a schematic diagram of a subset of association relationship data in an embodiment of the present invention;

[0045] Figure 4 is a schematic diagram of a pre-punched PP sheet in an embodiment of the present invention;

[0046] Figure 5 This is a structural block diagram of an embodiment of a device for controlling the opening of covers of multiple PP hard-soft boards according to an embodiment of the present invention;

[0047] Figure 6 The diagram is a diagram of the internal structure of a computer device according to an embodiment. DETAILED DESCRIPTION

[0048] In order to make the technical problems, technical solutions and beneficial effects solved by the embodiments of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] In a core concept of an embodiment of the present invention, the control device uses UV laser to burn through the PP opening before lamination, and the PP solidification and edge sealing reduce the generation of glue overflow, thereby reducing the difficulty of opening the cover; the production efficiency is improved; after the PP opening is burned through with UV laser in advance, the UV depth does not need to be controlled to the next layer of PP, thereby reducing the difficulty of opening the cover, and waste is easy to remove, and the glass fiber is burned in advance to disconnect the hard board edge of the cover area, which is neat and free of burrs and other undesirable problems; the characteristic parameters of the filler between multiple PP sheets, such as flow rate, solidification rate, and the pressing operation parameters of the pressing machine are collected, which may include the pressing high pressure area, the pressing speed of the pressing machine, the pressing pressure, the pressing temperature, the pressing thickness of the product, etc., the characteristic parameters of the filler between the multiple PP sheets and the pressing operation parameters of the pressing machine are converted into topological structure relationships, and then converted into vectors for model training through the divided data subsets, so as to clearly and concisely express the relationship and connection between the data to be trained, thereby improving the efficiency of model training, being able to process more complex and irregular semi-structured data, effectively process multi-dimensional data, and obtain accurately predicted pressing process parameters.

[0050] Reference Figure 1 , shows a flowchart of a method for controlling the opening of covers of multiple PP hard-soft boards according to an embodiment of the present invention, which may specifically include the following steps:

[0051] Step 101, obtaining historical soft board filling characteristic parameters and historical pressing processing parameters;

[0052] In an embodiment of the present invention, the processing control method can be applied to an integrated semiconductor device, which can be composed of multiple different types of semiconductor devices to realize the functions of multiple semiconductor processing steps, such as material pre-baking equipment, inner layer imaging equipment, drilling equipment, electroplating equipment, browning equipment, lamination and lamination equipment, dry film pasting equipment, UV laser equipment, exposure equipment, developing equipment, etching equipment, etc. The embodiment of the present invention does not impose too many restrictions on this. The above-mentioned multiple different types of semiconductor devices can be used by equipment such as a robotic arm to operate materials. Specifically, the robotic arm can also be equipped with a variety of different devices such as an adsorption device. The embodiment of the present invention does not impose too many restrictions on this; the integrated semiconductor device is also connected to a control terminal or a server, which can be used to control the operation process of the above-mentioned equipment. The terminal can be but not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The server can be implemented with an independent server or a server cluster composed of multiple servers. The terminal is connected to a variety of semiconductor devices, and the terminal's operating system can include Android, Harmony OS, IOS, Windows Phone, Windows, etc. The present invention does not impose too many restrictions on this.

[0053] Specifically applied to the embodiment of the present invention, the historical soft board filling characteristic parameters may include filling material characteristic parameters, and the historical pressing processing parameters include pressing high pressure area of ​​the pressing machine, pressing speed of the pressing machine, pressing pressure, pressing temperature, and product pressing thickness; the embodiment of the present invention does not impose excessive restrictions on the types of the two parameters.

[0054] In practical applications, the filler material characteristic parameters refer to the flow rate parameters and curing rate parameters of the acrylic glue between multiple PP (prepregs); prepregs are one of the main materials in the production of multilayer boards, mainly composed of resin and reinforcing materials. The reinforcing materials are divided into several types such as glass fiber cloth, paper base, and composite materials. The prepregs used in the production of multilayer PCBs mostly use glass fiber cloth as the reinforcing material. Multilayer prepregs, soft boards, and copper-clad boards can be pressed together to obtain hard-flex boards.

[0055] The high-pressure area of ​​the press machine refers to the area of ​​the high-pressure area formed by the downward pressing mechanism of the press machine directly acting on the hard-flexible board; the pressing speed of the press machine refers to the downward pressing feed speed of the press machine for the hard-flexible board; the pressing pressure refers to the downward pressing pressure of the press machine for the hard-flexible board; the pressing temperature refers to the processing temperature of the press machine for the hard-flexible board; the product pressing thickness refers to the thickness of the final product of the hard-flexible board.

[0056] In the embodiment of the present invention, historical soft board filling characteristic parameters and historical pressing processing parameters of the corresponding rigid-flex board can be identified from the raw material database, processing process database, and finished product database of the rigid-flex board.

[0057] Step 102, generating topological combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters;

[0058] Further, after obtaining the historical soft board filling characteristic parameters and the historical pressing processing parameters, the topological combination data can be generated according to the historical soft board filling characteristic parameters and the historical pressing processing parameters; Figure 2 , showing a schematic diagram of a topological combination data of an embodiment of the present invention; wherein a% represents the change in the flow rate parameter or the solidification rate parameter in the characteristic parameters of the filling material corresponding to the unit change in the pressing high pressure area when other factors remain unchanged;

[0059] b% represents the change in the flow rate parameter or the solidification rate parameter in the characteristic parameters of the filling material corresponding to the unit change in the pressing speed of the pressing machine when other factors remain unchanged;

[0060] c% represents the change in the flow rate parameter or the solidification rate parameter in the characteristic parameters of the filling material corresponding to the unit change in the pressing pressure when other factors remain unchanged;

[0061] d% indicates the change in the flow rate parameter or the curing rate parameter in the characteristic parameters of the filling material corresponding to the unit change in the pressing temperature when other factors remain unchanged;

[0062] e% indicates the change in the flow rate parameter or curing rate parameter in the characteristic parameters of the filling material corresponding to the unit change in the pressed thickness of the product when other factors remain unchanged;

[0063] Specifically applied to the embodiment of the present invention, the topological combination data is generated according to the historical soft board filling characteristic parameters and the historical pressing processing parameters, including: the pressing high pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, the pressing temperature, and the pressing thickness of the product are formed into topological combination data around the filling material characteristic parameters, and then the relationship and connection between the data to be trained are clearly and concisely expressed through the associated relationship data subset after division, thereby enhancing the accuracy of the associated relationship of the data training set and improving the efficiency of model training.

[0064] Step 103, converting the topological combination data into a relationship vector;

[0065] Further applied to the embodiment of the present invention, after obtaining the topological combination data, the data can be converted into a relation vector;

[0066] Specifically, converting the topological combination data into a relationship vector includes: decomposing the topological combination data into a plurality of associated relationship data subsets; and composing the plurality of associated relationship data subsets into a relationship vector. Figure 3 As shown, the association relationship data subset 1 is a data subset in which the change amount of the flow rate parameter or the solidification rate parameter in the characteristic parameters of the filling material corresponding to the unit change amount of the pressing high pressure area changes is a % when other factors remain unchanged;

[0067] The associated relationship data subset 2 is a data subset in which the change amount of the flow rate parameter or the solidification rate parameter in the characteristic parameters of the filling material corresponding to the unit change amount of the pressing speed of the pressing machine is b% when other factors remain unchanged;

[0068] The associated relationship data subset 3 is a data subset in which the change amount of the flow rate parameter or the solidification rate parameter in the characteristic parameters of the filling material corresponding to the unit change amount of the pressing pressure is c% when other factors remain unchanged;

[0069] The associated relationship data subset 4 is a data subset in which the change amount of the flow rate parameter or the curing rate parameter in the characteristic parameters of the filling material corresponding to the unit change amount of the pressing temperature is d% when other factors remain unchanged;

[0070] The associated relationship data subset 5 is a data subset in which the change amount of the flow rate parameter or the curing rate parameter in the characteristic parameters of the filling material corresponding to the unit change amount of the product pressing thickness changes is e% when other factors remain unchanged;

[0071] The association relationship data subset refers to the change in the characteristic parameter of the filling material corresponding to the unit change in the first processing parameter when other factors remain unchanged; and the relationship vector refers to a single model training vector transformed from the association relationship data subset. For example, the relationship vector includes the vector β corresponding to the association relationship data subset 1, which can be expressed as

[0072] Step 104, inputting the relationship vector into a neural network model for training to obtain a trained neural network model;

[0073] In actual application to the embodiments of the present invention, the relationship vector can be input into a neural network model for training to obtain a trained neural network model; in a specific application of the embodiments of the present invention, the neural network model may include a supervised neural network model, such as a convolutional neural network, a recursive neural network, etc. The embodiments of the present invention do not impose too many restrictions on the types of supervised neural network models; specifically, the convolutional neural network model may include an input layer, multiple convolutional layers, a maximum pooling layer, a fully connected layer, a hidden layer, a softmax layer and other model units. The embodiments of the present invention do not impose too many restrictions on the specific composition of the convolutional neural network model.

[0074] Among them, the loss function of the neural network model can be: Among them, i represents the number of samples, β i represents the feature vector of the i-th sample, y i represents the label value of the i-th sample, δ represents the mean value of the flow rate parameter, κ represents the mean value of the curing rate parameter, λ represents the number of pressing pressures and pressing temperatures greater than the preset values, and n represents the number of samples. By adding sample-related parameters to iterate the loss function, the accuracy of the loss function can be improved.

[0075] Step 105, inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters;

[0076] After the trained neural network model is obtained, the new soft board filling characteristic parameters can be input into the neural network model to obtain the predicted output pressing processing parameters;

[0077] In a preferred embodiment of the embodiments of the present invention, the new soft board filling characteristic parameters are input into the trained neural network model to obtain the output pressing processing parameters, including: converting the new soft board filling characteristic parameters into a three-dimensional vector; inputting the three-dimensional vector into the neural network model to obtain the output pressing processing parameters such as the pressing high pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, and the pressing temperature.

[0078] Step 106, the control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet;

[0079] Specifically, in the processing control step of the embodiment of the present invention, the PP sheet is first pre-punched. Specifically, the control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet, including: obtaining a circuit design diagram for the PP sheet; identifying a punching position of the circuit design diagram by image recognition; controlling a UV laser device to perform a pre-punching operation on the punching position of the PP sheet to obtain a pre-punched PP sheet, such as Figure 4As shown, Figure 4 a in the figure represents a rigid-flexible board consisting of a rigid board area (100), an opening (200) and a flexible board area (300); Figure 4 b in the figure represents a pre-punched PP sheet (400), wherein the soft board area (300) includes the PP sheet (400); Figure 4 b in the figure indicates the soft board area (300), and 600 indicates the through hole with UV laser controlled depth. In the embodiment of the present invention, the PP cover opening is pre-burned by UV laser before the control device is pressed, and the PP solidification and edge sealing reduce the generation of glue overflow, reduce the difficulty of opening the cover, and improve the production efficiency; after the PP cover opening is pre-burned by UV laser, it is not necessary to control the depth of UV to the next layer of PP, which reduces the difficulty of opening the cover, and the waste is easy to remove, and the glass fiber is burned in advance to disconnect the hard board edge of the cover area, which is neat and free of burrs and other defects.

[0080] It should be noted that the hard-flex board of the embodiment of the present invention also needs to undergo other semiconductor operations. For example, FCCL cutting operation, baking operation, inner layer drilling operation, inner layer image etching operation, browning operation, combination operation, stacking operation, pressing operation, target shooting operation, edge fishing operation, drilling operation, electroplating operation, outer layer image etching operation, solder mask operation, gold plating operation, thermosetting text operation, molding operation, cover opening operation, soft board UV molding operation, electrical measurement operation, etc. can be performed. The embodiment of the present invention does not impose too many restrictions on the operation process steps.

[0081] Step 107 , laminating the pre-punched PP sheet and the half-layer board according to the laminating processing parameters to obtain a laminated rigid-flexible board.

[0082] Further applied to the embodiment of the present invention, the pre-punched PP sheet and the half-layer body board are pressed according to the pressing process parameters to obtain the pressed hard-soft board, including: adjusting the action area of ​​the pressing machine for the PP sheet and the half-layer body board according to the pressing high pressure area of ​​the pressing machine; adjusting the pressing process operation of the pressing machine according to the pressing speed, pressing pressure and pressing temperature of the pressing machine to obtain the pressed hard-soft board.

[0083] After the above-mentioned lamination processing parameters are obtained, the pre-punched PP sheet and the half-layer body sheet are pressed together according to the lamination processing parameters to obtain a pressed hard-flex board.

[0084] In an embodiment of the present invention, the method for controlling the opening and closing of multiple PP hard-soft boards is characterized in that it includes: obtaining historical soft board filling characteristic parameters and historical pressing processing parameters; generating topological combination data according to the historical soft board filling characteristic parameters and historical pressing processing parameters; converting the topological combination data into a relationship vector; inputting the relationship vector into a neural network model for training to obtain a trained neural network model; inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters; controlling the device to perform a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet; performing a pressing operation on the pre-punched PP sheet and a half-layer sheet according to the pressing processing parameters to obtain to the soft-hard combination board after pressing; the control equipment uses UV laser to burn through the PP opening before pressing, and the PP curing and edge sealing reduce the generation of glue overflow and reduce the difficulty of opening the cover; improve production efficiency; after the PP opening is burned through with UV laser in advance, there is no need to control the UV depth to the next layer of PP, which reduces the difficulty of opening the cover, and the waste is easy to remove. The glass fiber is burned in advance to disconnect the edge of the hard board in the cover area, which is neat and free of burrs and other defects; the characteristic parameters of the filler between the multiple PP sheets and the pressing operation parameters of the pressing machine are converted into topological structure relationships, and then converted into vectors for model training through the divided data subsets, which clearly and concisely express the relationship and connection between the data to be trained, improve the efficiency of model training, and obtain accurately predicted pressing process parameters.

[0085] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0086] Reference Figure 5 , shows a structural block diagram of an embodiment of a device for controlling the opening of covers of multiple PP hard-soft boards according to an embodiment of the present invention, which may specifically include the following modules:

[0087] The parameter acquisition module 301 is used to acquire the historical soft board filling characteristic parameters and the historical pressing processing parameters;

[0088] A topology generation module 302 is used to generate topology combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters;

[0089] A conversion module 303, used to convert the topological combination data into a relationship vector;

[0090] A training module 304 is used to input the relationship vector into a neural network model for training to obtain a trained neural network model;

[0091] An input module 305 is used to input the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters;

[0092] The pre-punching module 306 is used to control the device to perform a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet;

[0093] The pressing operation module 307 is used to press the pre-punched PP sheet and the half-layer board according to the pressing processing parameters to obtain a pressed hard-flex board.

[0094] Preferably, the historical soft board filling characteristic parameters include filling material characteristic parameters, the historical pressing processing parameters include pressing high pressure area of ​​the pressing machine, pressing speed of the pressing machine, pressing pressure, pressing temperature, and product pressing thickness; the generating topology module includes:

[0095] The first component submodule is used to form topological combination data based on the characteristic parameters of the filling material, including the pressing high pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, the pressing temperature, and the pressing thickness of the product.

[0096] Preferably, the conversion module comprises:

[0097] A decomposition submodule, used for decomposing the topological combination data into a plurality of association relationship data subsets;

[0098] The second forming submodule is used to form a relationship vector from the plurality of association relationship data subsets.

[0099] Preferably, the input module comprises:

[0100] A conversion submodule, used for converting the new soft board filling characteristic parameter into a three-dimensional vector;

[0101] The output submodule is used to input the three-dimensional vector into the neural network model to obtain the output high-pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, and the pressing temperature.

[0102] Preferably, the pressing operation module includes:

[0103] An area adjustment submodule, used for adjusting the action area of ​​the press machine on the PP sheet and the half-layer sheet material according to the high pressure pressing area of ​​the press machine;

[0104] The pressing submodule is used to adjust the pressing process operation of the pressing machine according to the pressing speed, pressing pressure and pressing temperature of the pressing machine to obtain a pressed hard-flex board.

[0105] Preferably, the pre-punching module comprises:

[0106] An acquisition submodule, used to acquire a circuit design diagram for the PP sheet;

[0107] An identification submodule, used to identify the punching positions of the circuit design diagram;

[0108] The pre-punching operation submodule is used to control the UV laser device to perform a pre-punching operation on the punching position of the PP sheet to obtain a pre-punched PP sheet.

[0109] For the specific definition of the device for controlling the opening of multiple PP hard-soft boards, please refer to the definition of the method for controlling the opening of multiple PP hard-soft boards mentioned above, which will not be repeated here. Each module in the device for controlling the opening of multiple PP hard-soft boards can be implemented in whole or in part by software, hardware and a combination thereof. Each of the modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the modules above.

[0110] The above-mentioned multiple PP soft-hard combination board cover opening processing control device can be used to execute the multiple PP soft-hard combination board cover opening processing control method provided by any of the above-mentioned embodiments, and has corresponding functions and beneficial effects.

[0111] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for simulating daylighting is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0112] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0113] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0114] Obtain historical soft board filling characteristic parameters and historical pressing processing parameters;

[0115] Generate topological combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters;

[0116] Converting the topological combination data into a relation vector;

[0117] Inputting the relationship vector into a neural network model for training to obtain a trained neural network model;

[0118] Inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters;

[0119] The control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet;

[0120] The pre-punched PP sheet and the half-layer body plate are pressed together according to the pressing processing parameters to obtain a pressed hard-soft board.

[0121] Preferably, the historical soft board filling characteristic parameters include filling material characteristic parameters, and the historical pressing processing parameters include pressing high pressure area of ​​the pressing machine, pressing speed of the pressing machine, pressing pressure, pressing temperature, and pressing thickness of the product; generating the topological combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters includes:

[0122] The pressing high pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, the pressing temperature, and the pressing thickness of the product are combined around the characteristic parameters of the filling material to form topological combination data.

[0123] Preferably, converting the topological combination data into a relationship vector comprises:

[0124] Decomposing the topological combination data into a plurality of association relationship data subsets;

[0125] The plurality of associated relationship data subsets are combined into a relationship vector.

[0126] Preferably, the inputting of the new soft board filling characteristic parameters into the trained neural network model to obtain the output pressing processing parameters includes:

[0127] Converting the new soft board filling characteristic parameter into a three-dimensional vector;

[0128] The three-dimensional vector is input into the neural network model to obtain the output of the laminating machine high pressure area, laminating machine laminating speed, laminating pressure and laminating temperature.

[0129] Preferably, the pre-punched PP sheet and the half-layer body sheet are pressed together according to the pressing processing parameters to obtain a pressed hard-flex board, comprising:

[0130] According to the high pressure area of ​​the press, the action area of ​​the press for the PP sheet and the half-layer sheet is adjusted;

[0131] The pressing process operation of the pressing machine is adjusted according to the pressing speed, pressing pressure and pressing temperature of the pressing machine to obtain a pressed hard-flex board.

[0132] Preferably, the control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet, comprising:

[0133] Obtaining a circuit design diagram for the PP sheet;

[0134] Identifying punching positions of the circuit design;

[0135] The UV laser device is controlled to perform a pre-punching operation on the punching position of the PP sheet to obtain a pre-punched PP sheet.

[0136] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0137] Obtain historical soft board filling characteristic parameters and historical pressing processing parameters;

[0138] Generate topological combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters;

[0139] Converting the topological combination data into a relation vector;

[0140] Inputting the relationship vector into a neural network model for training to obtain a trained neural network model;

[0141] Inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters;

[0142] The control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet;

[0143] The pre-punched PP sheet and the half-layer body plate are pressed together according to the pressing processing parameters to obtain a pressed hard-soft board.

[0144] Preferably, the historical soft board filling characteristic parameters include filling material characteristic parameters, and the historical pressing processing parameters include pressing high pressure area of ​​the pressing machine, pressing speed of the pressing machine, pressing pressure, pressing temperature, and pressing thickness of the product; generating the topological combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters includes:

[0145] The pressing high pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, the pressing temperature, and the pressing thickness of the product are combined around the characteristic parameters of the filling material to form topological combination data.

[0146] Preferably, converting the topological combination data into a relationship vector comprises:

[0147] Decomposing the topological combination data into a plurality of association relationship data subsets;

[0148] The plurality of associated relationship data subsets are combined into a relationship vector.

[0149] Preferably, the inputting of the new soft board filling characteristic parameters into the trained neural network model to obtain the output pressing processing parameters includes:

[0150] Converting the new soft board filling characteristic parameter into a three-dimensional vector;

[0151] The three-dimensional vector is input into the neural network model to obtain the output of the laminating machine high pressure area, laminating machine laminating speed, laminating pressure and laminating temperature.

[0152] Preferably, the pre-punched PP sheet and the half-layer body sheet are pressed together according to the pressing processing parameters to obtain a pressed hard-flex board, comprising:

[0153] According to the high pressure area of ​​the press, the action area of ​​the press for the PP sheet and the half-layer sheet is adjusted;

[0154] The pressing process operation of the pressing machine is adjusted according to the pressing speed, pressing pressure and pressing temperature of the pressing machine to obtain a pressed hard-flex board.

[0155] Preferably, the control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet, comprising:

[0156] Obtaining a circuit design diagram for the PP sheet;

[0157] Identifying punching positions of the circuit design;

[0158] The UV laser device is controlled to perform a pre-punching operation on the punching position of the PP sheet to obtain a pre-punched PP sheet.

[0159] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0160] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0161] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0162] The embodiments of the present invention are described with reference to flowcharts and / or block diagrams of apparatuses, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0165] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0166] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, device, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a..." do not exclude the existence of other identical elements in the process, device, article or terminal device including the elements.

[0167] The above describes in detail a method for controlling the opening of covers of multiple PP hard-soft combined boards, a device for controlling the opening of covers of multiple PP hard-soft combined boards, a computer device and a storage medium provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for controlling the opening of covers of multiple PP hard-soft boards, characterized in that: include: Obtain historical soft board filling characteristic parameters and historical pressing processing parameters; Generate topological combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters; Converting the topological combination data into a relation vector; Inputting the relationship vector into a neural network model for training to obtain a trained neural network model; Inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters; The control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet; The pre-punched PP sheet and the half-layer body plate are pressed together according to the pressing processing parameters to obtain a pressed hard-soft board.

2. The method according to claim 1, characterized in that The historical soft board filling characteristic parameters include filling material characteristic parameters, and the historical pressing processing parameters include pressing high pressure area of ​​the pressing machine, pressing speed of the pressing machine, pressing pressure, pressing temperature, and product pressing thickness; the topological combination data is generated according to the historical soft board filling characteristic parameters and the historical pressing processing parameters, including: The pressing high pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, the pressing temperature, and the pressing thickness of the product are combined around the characteristic parameters of the filling material to form topological combination data.

3. The method according to claim 2, characterized in that The converting the topological combination data into a relation vector comprises: Decomposing the topological combination data into a plurality of association relationship data subsets; The plurality of associated relationship data subsets are combined into a relationship vector.

4. The method according to claim 2, characterized in that: The new soft board filling characteristic parameters are input into the trained neural network model to obtain the output pressing processing parameters, including: Converting the new soft board filling characteristic parameter into a three-dimensional vector; The three-dimensional vector is input into the neural network model to obtain the output of the laminating machine high pressure area, laminating machine laminating speed, laminating pressure and laminating temperature.

5. The method according to claim 4, characterized in that The method of laminating the pre-punched PP sheet and the half-layer sheet according to the laminating processing parameters to obtain a laminated hard-soft board comprises: According to the high pressure area of ​​the press, the action area of ​​the press for the PP sheet and the half-layer sheet is adjusted; The pressing process operation of the pressing machine is adjusted according to the pressing speed, pressing pressure and pressing temperature of the pressing machine to obtain a pressed hard-flex board.

6. The method according to claim 4, characterized in that The control device performs a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet, including: Obtaining a circuit design diagram for the PP sheet; Identifying punching positions of the circuit design; The UV laser device is controlled to perform a pre-punching operation on the punching position of the PP sheet to obtain a pre-punched PP sheet.

7. A device for controlling the opening of covers of multiple PP hard-soft boards, characterized in that: include: A parameter acquisition module is used to obtain historical soft board filling characteristic parameters and historical pressing processing parameters; A topology generation module is used to generate topology combination data according to the historical soft board filling characteristic parameters and the historical pressing processing parameters; A conversion module, used for converting the topological combination data into a relation vector; A training module, used for inputting the relationship vector into a neural network model for training to obtain a trained neural network model; An input module, used for inputting the new soft board filling characteristic parameters into the trained neural network model to obtain output pressing processing parameters; A pre-punching module is used to control the device to perform a pre-punching operation on the PP sheet to obtain a pre-punched PP sheet; The pressing operation module is used to press the pre-punched PP sheet and the half-layer board according to the pressing processing parameters to obtain a pressed soft-hard combination board.

8. The device according to claim 7, characterized in that The historical soft board filling characteristic parameters include filling material characteristic parameters, the historical pressing processing parameters include pressing high pressure area of ​​the pressing machine, pressing speed of the pressing machine, pressing pressure, pressing temperature, and product pressing thickness; the generating topology module includes: The first component submodule is used to form topological combination data based on the characteristic parameters of the filling material, including the pressing high pressure area of ​​the pressing machine, the pressing speed of the pressing machine, the pressing pressure, the pressing temperature, and the pressing thickness of the product.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for controlling the cover opening process of multiple PP hard-soft boards according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for controlling the cover opening process of multiple PP hard-soft boards according to any one of claims 1 to 6 are implemented.

Images